Powder Characterization for Additive

Manufacturing Processes

Lisa Markusson

Materials Engineering, masters level

2017

Luleå University of Technology

Department of Engineering Sciences and Mathematics

i

Preface

This master thesis work has been carried out at GKN Aerospace Engine Systems Sweden at

the Department of Process Engineering in Trollhättan, Sweden. This is a degree project in

engineering materials performed as the final part of my Masters of Materials Sciences and

Engineering degree.

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To my grandparents

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Abstract

The aim of this master thesis project was to statistically correlate various powder

characteristics to the quality of additively manufactured parts. An additional goal of this

project was to find a potential second source supplier of powder for GKN Aerospace Sweden

in Trollhättan. Five Inconel® alloy 718 powders from four individual powder suppliers have

been analyzed in this project regarding powder characteristics such as: morphology, porosity,

size distribution, flowability and bulk properties. One powder out of the five, Powder C, is

currently used in production at GKN and functions as a reference. The five powders were

additively manufactured by the process of laser metal deposition according to a pre-

programmed model utilized at GKN Aerospace Sweden in Trollhättan. Five plates were

produced per powder and each cut to obtain three area sections to analyze, giving a total of

fifteen area sections per powder. The quality of deposited parts was assessed by means of

their porosity content, powder efficiency, geometry and microstructure. The final step was to

statistically evaluate the results through the analysis methods of Analysis of Variance

(ANOVA) and simple linear regression with the software Minitab.

The method of ANOVA found a statistical significant difference between the five powders

regarding their experimental results. This made it possible to compare the five powders

against each other. Statistical correlations by simple linear regression analysis were found

between various powder characteristics and quality of deposited part. This led to the

conclusion that GKN should consider additions to current powder material specification by

powder characteristics such as: particle morphology, powder porosity and flowability

measurements by a rheometer.

One powder was found to have the potential of becoming a second source supplier to GKN,

namely Powder A. Powder A had overall good powder properties such as smooth and

spherical particles, high particle density at 99,94% and good flowability. The deposited parts

with Powder A also showed the lowest amount of pores compared to Powder C, a total of 78

in all five plates, and sufficient powder efficiency at 81,6%.

Keywords: Powder Characteristics, Inconel 718, Additive Manufacturing, Laser Metal

Deposition, ANOVA, Regression Analysis

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Sammanfattning

Syftet med detta examensarbete var att statistiskt korrelera olika pulveregenskaper med

kvaliteten på additivt tillverkade delar. Ett vidare syfte med projektet var att finna en

potentiell andrahandsleverantör av pulver för GKN Aerospace Sweden i Trollhättan. Fem

pulver av materialet Inconel® 718 från fyra individuella pulverleverantörer har analyserats i

detta projekt gällande pulveregenskaper såsom: morfologi, porositet, storleksfördelning,

flytbarhet och bulkegenskaper. Ett pulver av de fem, Pulver C, används för nuvarande

produktion på GKN och fungerar som en referens. De fem pulvren har additivt tillverkats

enligt en förprogrammerad modell, framtagen på GKN Aerospace Sweden i Trollhättan,

genom processen ’laser metal deposition’. Fem provplåtar producerades per pulver och

provbereddes för att erhålla tre ytor för vidare analys, totalt femton ytor per pulver. Kvaliteten

på deponerat material bedömdes utifrån dess porositet, verkningsgrad av pulver, geometri och

mikrostruktur. Det slutliga steget var en statistisk analys av resultaten genom metoderna

Analysis of Variance (ANOVA) och enkel linjär regression med mjukvaran Minitab.

Metoden enligt ANOVA fann en statistisk signifikant skillnad mellan de fem pulvren

gällande dess egenskaper och experimentella resultat. Detta gjorde det möjligt att kunna

jämföra de fem pulvren mot varandra. Statistiska samband utifrån en enkel linjär

regressionsanalys erhölls mellan olika pulveregenskaper och kvalitativa resultat av deponerat

material. Detta ledde till slutsatsen att GKN bör överväga tillägg till nuvarande

pulvermaterialspecifikation med pulveregenskaper såsom: partikelmorfologi, pulverporositet

och flytbarhet genom mätningar av en reometer.

Ett pulver bedöms ha potential att bli en andrahandsleverantör till GKN Aerospace, nämligen

Powder A. Pulver A hade övergripande goda pulveregenskaper såsom jämna och sfäriska

partiklar, hög partikeltäthet på 99,94% och god flytbarhet. De deponerade proverna med

Pulver A uppvisade även lägst antal porer i jämförelse med Powder C, totalt 78 porer i alla

fem provplåtar, och godkänd pulververkningsgrad på 81,6%.

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Acknowledgements

I would like to give my deepest gratitude to my supervisors during this master thesis project;

Lars Östergren at GKN Aerospace Sweden in Trollhättan and Marta-Lena Antti at Luleå

University of Technology. Their great knowledge and guidance have brought this project to

its best. Special thanks also goes to Jimmy Johansson at GKN Aerospace Sweden for sharing

his expertise throughout this master thesis project. The operators at GKN who produced the

experimental samples must not be forgotten and I thank you for the help and support. A final

acknowledgement for the staff at the Department of Process Engineering at GKN for making

these past months filled with laughter and new experience. Thank you.

Trollhättan, March 2017

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Table of Contents

1 Introduction..................................................................................................................... 1

1.1 Collaborate Company Presentation .......................................................................... 1

1.2 Background .............................................................................................................. 1

1.3 Goal .......................................................................................................................... 2

1.4 Scope ........................................................................................................................ 2

2 Literature Review ........................................................................................................... 3

2.1 Additive Manufacturing Processes .......................................................................... 3

2.1.1 Process Classifications ........................................................................................ 3

2.1.2 Advantages & Disadvantages .............................................................................. 4

2.2 Laser Metal Deposition with Powder ....................................................................... 6

2.2.1 Basis of Deposition Process ................................................................................ 6

2.2.2 Powder Nozzles & Feeder System ....................................................................... 7

2.2.3 Basic Deposition Geometry ................................................................................. 9

2.2.4 Process Parameters & Their Effect on Deposited Geometry ............................ 10

2.2.5 Heat Transfer, Solidification & Microstructure Characteristics ...................... 10

2.3 Nickel Based Superalloys ...................................................................................... 12

2.3.1 Inconel 718 ........................................................................................................ 13

2.4 Powder Manufacturing Processes .......................................................................... 14

2.4.1 Gas Atomization ................................................................................................ 15

2.4.2 Plasma Atomization ........................................................................................... 16

2.4.3 Plasma Rotation Electrode Process .................................................................. 16

2.5 Powder Characteristics ........................................................................................... 16

2.5.1 Morphology ....................................................................................................... 17

2.5.2 Porosity .............................................................................................................. 17

2.5.3 Size & Size Distribution ..................................................................................... 18

2.5.4 Rheology ............................................................................................................ 19

2.5.5 Bulk Properties .................................................................................................. 21

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2.5.6 Quality Assessment of Powder ........................................................................... 22

2.6 Statistical Significance ........................................................................................... 22

2.6.1 Analysis of Variance .......................................................................................... 22

2.6.2 Regression Analysis ........................................................................................... 24

3 Materials & Methods .................................................................................................... 25

3.1 Powder & Sheet Material ....................................................................................... 25

3.2 Powder Characterization ........................................................................................ 26

3.2.1 Morphology ....................................................................................................... 26

3.2.2 Porosity .............................................................................................................. 27

3.2.3 Particle Size Distribution .................................................................................. 29

3.2.4 Rheology ............................................................................................................ 30

3.2.5 Bulk Properties .................................................................................................. 32

3.3 Laser Metal Deposition .......................................................................................... 33

3.4 Deposit Evaluation ................................................................................................. 34

3.4.1 Sample Preparation ........................................................................................... 34

3.4.2 Defects ............................................................................................................... 34

3.4.3 Geometry ........................................................................................................... 34

3.4.4 Microstructure ................................................................................................... 35

4 Results & Discussions ................................................................................................... 36

4.1 Powder Characterization ........................................................................................ 36

4.1.1 Morphology ....................................................................................................... 36

4.1.2 Porosity .............................................................................................................. 42

4.1.3 Particle Size Distribution .................................................................................. 44

4.1.4 Rheology ............................................................................................................ 49

4.1.5 Bulk Properties .................................................................................................. 53

4.2 Deposit Evaluation ................................................................................................. 54

4.2.1 Powder Efficiency .............................................................................................. 54

4.2.2 Geometry ........................................................................................................... 56

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4.2.3 Defects ............................................................................................................... 59

4.2.4 Microstructure ................................................................................................... 62

4.3 Statistical Evaluation.............................................................................................. 68

4.4 Statistical Correlation ............................................................................................. 76

4.4.1 Median Particle Size – Basic Flow Energy ....................................................... 76

4.4.2 Median Particle Size – Powder Efficiency ........................................................ 79

4.4.3 ShapeFactor – Basic Flow Energy .................................................................... 81

4.4.4 ShapeFactor – Powder Efficiency ..................................................................... 83

4.4.5 Basic Flow Energy – Powder Efficiency ........................................................... 85

4.4.6 Basic Flow Energy – Multi-bead Height ........................................................... 87

4.4.7 Particle Pore Frequency – Deposit Pore Frequency ........................................ 89

4.4.8 Particle Pore Size – Deposit Pore Size .............................................................. 91

5 Conclusions .................................................................................................................... 98

6 Future Work................................................................................................................ 100

7 Bibliography ................................................................................................................ 101

8 Appendix ...................................................................................................................... 106

8.1 Particle Pixel Area Fraction ................................................................................. 106

8.2 Particle Pore Diameter ......................................................................................... 107

8.3 Summary of Pore Data in Powder & Deposited Part ........................................... 108

8.4 Morphology .......................................................................................................... 109

8.5 Rheometer ............................................................................................................ 109

8.6 Part Geometry ...................................................................................................... 110

8.7 Statistical Evaluation............................................................................................ 111

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Abbreviations & Nomenclature

Name Abbreviation

Additive manufacturing AM

Aluminum Al

American society for testing and materials ASTM

Analysis of variance ANOVA

Chromium Cr

Electrode induction melting gas atomization EIGA

Face-centered cubic FCC

Gas atomization GA

General electric GE

Hot isostatic pressing HIP

Iron Fe

Laser metal deposition LMD

Laser metal deposition with powder LMD-p

Nickel Ni

Niobium Nb

Optical microscope OM

Particle size distribution PSD

Plasma Atomization PA

Plasma rotation electrode process PREP

Scanning Electron Microscopy SEM

Tantalum Ta

Three-dimensional 3D

Titanium Ti

Vacuum Inert Gas Atomization VIGA

x

Parameter Symbol Unit

Apparent density g/cm3

Aeration energy mJ

Aeration ratio -

Basic flowability energy mJ

Bead area mm2

Conditioned bulk density g/cm3

Consolidation energy mJ

Consolidation index -

Depth of penetration mm

Deposition height mm

Deposition width mm

Flow rate index FRI -

Hall Flow rate s/50g

Heat flux W/m2

Laser power W

Powder efficiency %

Powder mass delivered g

Powder mass deposited g

Powder mass flow rate g/min

Root angle α °

Scanning speed mm/min

Specific energy mJ/g

Stability index SI -

Tap density g/cm3

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1 Introduction

The worldwide success of the emerging technology additive manufacturing (AM) is due to

the exceptional opportunity to produce complex near-net-shapes in a single process. By

adding material layer upon layer, a preprogrammed three-dimensional (3D) model is formed

without extensive subtracting methods associated with conventional production. Additive

manufacturing has great future promises and comprehensive research is put in to the

technology. The key to the success of AM will be to understand the relationship between

process variables, material properties and final structure.

1.1 Collaborate Company Presentation

GKN Aerospace is one out of four divisions of GKN PLC, a British company in the forefront

of global technology. GKN has a long industrial heritage which can be traced back to late 18th

century to a small iron work on the hillside of Welsh. For the last two-and-a-half centuries

they have been one of the leaders in industrial evolution.

GKN Aerospace is one of the world’s largest independent suppliers to the aviation industry,

both commercial and military. With three main product areas; Aerostructures, Engine

components/sub-systems and Special products, they are the global market frontier in all three

areas. GKN Aerospace Engine Systems is a subdivision to GKN Aerospace and is located

over four continents with its headquarter in Trollhättan, Sweden. Today, 90% of all

commercial flights take off every day with technology supplied from the division of Engine

Systems (GKN Aerospace, n.d.).

1.2 Background

Additive manufacturing is a suitable process to restore and strengthen components where the

technology of laser metal deposition (LMD) is used in the facilities at GKN Aerospace

Engine Systems Sweden in Trollhättan. In order to produce structures by laser metal

deposition of sufficient standard, the initial powder need to meet proper quality. It is therefore

of importance to understand the impact of various powder characteristics on the final

deposited structure. The knowledge of powder characteristics and their correlation to

deposited structures is of importance to GKN’s further development of the technology in

order to enhance future production capabilities. Nickel based superalloys are commonly used

materials for high-performance aerospace applications which will be the material of interest

in this project.

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1.3 Goal

The goal of this project is to find a powder characteristics guideline, based on statistical

evaluation, to review on powder batches to appraise an output of certain quality. The goal is

also to find a potential second source powder supplier. The main two questions to answer

during this project are:

Do some powder characteristics have a statistical significant impact on part

quality?

Is there a powder supplier that shows the potential of becoming a second source to

GKN?

1.4 Scope

The initial scope of this project is to distinguish and evaluate powder characteristics of

interest. This follows by laser metal deposition of standardized structures and evaluation of

their final quality. The aim is to identify and discuss any statistical evaluation and correlation

between the characteristics of powder and final part quality.

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2 Literature Review

This chapter will provide the theory necessary to set a foundation to the project. An

introduction to additive manufacturing processes is the first area in subject during this

literature review. This follows by a deeper look into the specific additive manufacturing

process for this project, laser metal deposition with powder (LMD-p). A section on the

material used in this project, a nickel based superalloy, is also presented. A review on powder

characteristics and quality assessment of powders with the LMD process in mind follows. The

statistical importance and appropriate means of assessment is presented as a final part.

2.1 Additive Manufacturing Processes

Additive manufacturing is a collective term for various fabrication techniques whereby

material is joined with a layer-on-layer approach to produce a preprogrammed 3D data model.

Other well-known terms to the common mass are 3D printing, additive layer manufacturing,

rapid prototyping and freeform fabrication. The term of 3D printing is more common in

commercial contexts whereas additive manufacturing is more referred to in industry. This is a

technology that has attained a lot of research and development within numerous sectors such

as aerospace, automotive, medical and consumer goods (EPMA, 2015).

2.1.1 Process Classifications

The standard definition of additive manufacturing established by the American society for

testing and materials (ASTM) F42 Technical Committee on Additive Manufacturing

Technologies is the ‘process of joining materials to make objects from 3D model data, usually

layer upon layer, as opposed to subtractive manufacturing methodologies’. The ASTM F42

committee has also classified these methods by means of the process baseline where seven

major processes have been identified. Three of the seven categories are applicable to construct

parts by metal in a single-step AM process which is mainly of interest in this project, see

Figure 1. Level one in the diagram shows the three process categories: Powder bed fusion,

Direct energy deposition and Sheet lamination. Level two specifies the material distribution

type followed by the source of fusion in level three. The fourth and final level specifies the

type of material used as feedstock (ASTM International, 2015).

The boxes in blue highlights the process classification that is of interest in this project; Direct

energy deposition also called laser metal deposition (LMD). The process of laser metal

deposition with powder (LMD-p) will be further explained in section 2.2. The two processes

that implement powder as a raw material are Powder bed fusion and Direct energy deposition.

The fundamental difference between these two methods is the way in which the powder is

introduced. For Powder bed fusion a powder bed is pre-laid over the substrate whereby the

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laser beam moves over the surface and fuses the two together. With Direct energy deposition

the powder is fed directly onto the surface whereby the laser beam simultaneously melts the

powder and surface layer and creates a metallurgical bond. Both methods create thin tracks of

rapidly solidified material, also referred to as beads, resulting in high-density structures close

to 100% (Gibson, Rosen & Stucker, 2010).

Figure 1. An overview of the various single-step AM processes for metallic materials (modified from

ASTM International, 2015).

2.1.2 Advantages & Disadvantages

The many advantages of additive manufacturing are the reasons for this growing technology

within industrial sectors worldwide. Some general advantages of AM are the following:

Less Material Waste

This is a great advantage for the aerospace industry where the volumes of high-cost

materials, such as titanium and superalloys, will drastically be reduced. This is simply

due to the AM approach of bottom-up manufacturing rather than a production of

subtracting nature from large billets, which reduces material use and waste

substantially (Royal Academy of Engineering, 2013). Up to 60% can be saved by

LMD by reduced material waste (Ford & Despeisse, 2016). For parts with a high buy

to fly ratio, i.e. the weight ratio between initial billet and finished part, AM will play a

major role in reducing material uses and costs (Allen, 2006).

Shorter Lead-Times

Another ground for cost savings compared to conventional manufacturing is the basis

of the single–step process and its resulting shorter production lead-time, from design

5

to finished product (Ford & Despeisse, 2016). The lead-time is believed to be reduced

by up to 80% compared to conventional production methods within the aerospace

industry (SmarTech Market Publishing, 2014).

Low-Volume Customized Production

For customized metal parts in small or medium size batches, AM is a suitable

production method. Production of complex shaped parts that requires extensive and

difficult machining will profit from AM (Baumers, 2012). The initial investment cost

is large in terms of the actual machinery, but the lack of moulds and additional tooling

is a large beneficiary. Due to the simplicity of the AM process, it is also easier to

make design changes or produce different products with the same equipment, simply

change the input 3D data model. This may boost a more product innovative business

environment within a company. With conventional methods a change in product

design might require new moulds and/or tooling, an expensive adjustment (Beiker

Kair & Sofos, 2014).

According to General Electric (GE) Aviation, the production of fuel nozzles by AM will

reduce production cost with up to 75%. This cost reduction is thanks to the advantage of non

extensive assembly, i.e. a single-step process with less material used. There are big

expectations and believes that additive manufacturing will reach a more standardized product

market with an economy of scale that can compete against conventional manufacturing

(D’Aveni, 2015). But the challenges today are many such as:

Low Deposition Rates

When it comes to the production rate for high-volume production, AM does not

challenge conventional methods today. To allure industry sectors with larger

production volumes, deposition rates of AM need to increase (Baumers, Dickens,

Tuck & Hague, 2016).

Feedstock Material

The building materials available on the market are still somewhat limited and research

need to be put in to develop and standardize new materials of sufficient quality (Ford

& Despeisse, 2016). Even though less material is used, the price of raw material is still

high. But as the technology enhances and the market for AM grows, both machinery

and raw material prices will hopefully drop (SmarTech Market Publishing, 2014).

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Reliability & Quality

AM is still a novel and emerging process where the reliability and applicability to

produce parts in high-demanding industry sectors needs research. Research is needed to

understand material behavior, process liability, structure properties and the relationship

between these areas (Chen, He, Yang, Niu & Ren, 2016). This also goes hand in hand

by standardizing materials and various technologies to ensure the quality and reliability

of AM. This is important for already investing business sectors and to appeal others to

invest and implement (Royal Academy of Engineering, 2013).

2.2 Laser Metal Deposition with Powder

2.2.1 Basis of Deposition Process

The process of laser metal deposition creates surface layers or near-net-shaped 3D structures

by laser fusion of powder and substrate. The powder material is fed onto the surface through a

nozzle by an inert carrier gas, normally argon or nitrogen, and is completely melted by the

laser beam, see Figure 2. The laser beam simultaneously melts a thin surface layer and a

metallurgical fused bond is formed between the two (Fraunhofer ILT, 2012). One of the great

advantages of laser metal deposition is a small heat affected zone due to the low heat input.

The system of feeding powder to the laser beam focus is either through a coaxial or off-axis

nozzle (Hauser, 2014). The complete system of powder nozzle, laser system and inert gas

tubes are referred to as the deposition head. The relative movement of the deposition head and

work piece is performed by a multi-axis robot or gantry system. How the work piece and

deposition head moves relatively to each other can vary; the substrate and deposition head

may move simultaneously or the substrate moves and tilts with the deposition head at a more

fixed position and vise versa (Gibson, Rosen & Stucker, 2010).

Figure 2. Schematic illustration of LMD-p with a coaxial powder nozzle (Frank, 2016).

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2.2.2 Powder Nozzles & Feeder System

With a coaxial powder nozzle the powder flows directly into the laser beam assisted and

protected by the inert carrier gas. The powder stream flows through a conically shaped outlet

with an annular gap, Figure 3, or multiple outlets, Figure 4. A conically shaped nozzle is

constructed by a concentrically mounted inner and outer cone creating a defined offset

between the two. In case of multiple outlets, the powder flows through channels inside the

nozzle.

Figure 3. Coaxial powder nozzle with a conformed

annular gap (Fraunhofer ILT, 2014a).

Figure 4. Coaxial powder nozzle with multiple

outlets (Fraunhofer ILT, 2014b).

The off-axis system feeds the powder in a lateral position to the laser beam. The powder

efficiency of the system highly depends on the angle and distance between the nozzle and

work piece as well as the relative movement of powder stream and work piece. This nozzle is

therefore more suitable for surface cladding and not high precision printing (Poprawe, 2011).

A simple comparison of the presented nozzle systems are seen in Figure 5. A higher precision

is achieved with an annular gap but this comes with the cost of a lower deposition rate.

Figure 5. Comparison of the different powder nozzle systems (modified from Hauser, 2014).

A common powder feeding system is based on the principal of a rotating feed disk with a

rectangular annular groove, see Figure 6 for illustration. The powder is contained in the so

called hopper with an incorporated stirrer which rotates during feeding to ensure a continuous

flow. The powder is delivered from the hopper down to underlying container onto the rotating

8

powder disk. The annular groove is filled with powder in a controlled and consistent manner

by the spreader unit under simultaneous rotation of the disk. Under rotation the powder is

transported from the spreader to the opposite side of the disk where a suction unit is placed.

This nozzle-shaped suction unit is attached to a hose that enables the delivery of powder from

the feeding unit to the deposition nozzle. The powder is emptied from the annular groove, and

its incorporated suction unit, by suction with carrier gas. The carrier gas is introduced to the

feeding system from the bottom whereby the container is slightly pressurized during

operation. This slight pressure forces the carrier gas to be exhausted from the suction unit,

with powder, to the delivery hose. During one full disk rotation the filled groove is emptied of

powder. The powder mass flow rate is then consequently controlled by the number of

rotations per minute of the disk. A volumetric method is used to control an accurate and

stable amount of delivered powder to the system. This means that a scale is incorporated with

the powder hopper to measure the continuous weight loss of powder (GTV, n.d.).

Figure 6. Illustrations of a powder feeding system with a rotating feed disk (top) as well as a closer

view of its suction and spreader units (bottom) (Oerlikon Metco, 2016).

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2.2.3 Basic Deposition Geometry

The deposition of a single track, also referred to as a bead, is seen in Figure 7. The bead

height (h), width (w), root angle (α) and bead area (A) is illustrated (Zhang, Li & Deceuster,

2011). When depositing multi-bead tracks the beads can have varying degrees of overlap, see

Figure 8 for illustration. The centre distance between adjacent beads (d), and consequently its

degree of overlap, is of importance when considering the area of valley generated between the

beads and layers. An increase in centre distance between two beads will increase the area of

valley and thereby generate a less smooth and flat surface and potential lack of fusion.

Deposition of a multi-bead and multi-layer structure can also be deposited with various

degree of overlap between individual beads and subsequent layers. An example of a multi-

layer deposit with subsequent layers positioned with an overlap close to 0% is seen in Figure

9. The subsequent layers can also be deposited with a 50% overlap as seen in Figure 10

(Ding, Pan, Cuiuri & Li, 2015).

Figure 7. Illustration of single-bead

geometry (Zhang, Li & Deceuster, 2011).

Figure 8. Schematic illustration of basic overlap between

multi-bead deposits (Ding, Pan, Cuiuri & Li, 2015).

Figure 9. Experimental result of multi-bead and multi-layer wire deposition (Ding, Pan, Cuiuri & Li,

2015).

Figure 10. 3D model (left) and illustration (right) of a multi-layered structure with a 50% longitudinal

and 50% transverse overlap (Zhang, Li & Deceuster, 2011).

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2.2.4 Process Parameters & Their Effect on Deposited Geometry

The final geometry of a deposited structure is highly dependent on various input process

parameters such as:

Laser power ( )

Scanning speed (

Powder mass flow rate ( )

Zhong, Biermann, Gasser and Poprawe (2015) have shown that an increase in scanning speed

decreases the single track width and height, due to the smaller amount of powder fed per unit

length of deposit. The same result in track height and width is seen if only the laser power is

increased. On the other hand, if powder mass flow is increased the track height instead

increases, along with the root angle. Consequently, by increasing powder mass flow, a higher

amount of laser is absorbed and a smaller melt pool is created. This dependency of powder

mass flow on track width and height was also showed by Ahsan, Pinkerton, Moat and

Shackleton (2011) but with a more pronounced decrease in track width, i.e. more increased

root angle.

Powder efficiency ( , i.e. the percentage of blown powder actually deposited, has been seen

to increase with increasing laser power. If more power is put into the system, more powder is

able to melt. However, if the laser power is too large, the amount of energy will melt deeper

into the substrate causing dilution of material and thereby lower the deposition height, with

respect to a constant powder mass flow (Mahamood, Akinlabi, Shukla & Pitvana, 2013).

2.2.5 Heat Transfer, Solidification & Microstructure Characteristics

The molten pool created during deposition conveys its heat to the substrate, the build material

and through the shield gas. The kinetics of heat transfer of the material determines the grain

growth morphology and orientation. The solidification kinetics is dependent on the melt pool

geometry which is influenced by the speed and power of the laser beam (Sames, List,

Pannala, Dehoff & Babu, 2016). The heat flux of the molten pool ( ) is the resultant of the

horizontal ( ) and vertical heat ( ) fluxes, see Figure 11. The vertical heat flux is generated

by the heat loss to the substrate whereby the horizontal flux is generated by the moving laser

beam. Grain growth is then evidently in the opposite direction of the resultant heat flux ( ).

This generally generates epitaxial columnar grains grown from the substrate parallel to the

scanning direction. The primary dendritic spacing is also proportional to the cooling rate

where a finer microstructure is obtained with a higher cooling rate (Zhong et al., 2016).

11

Figure 11. Illustration of longitudinal section of a deposited track and the resultant heat fluxes (Zhong

et al., 2016).

The grain orientation is also dependent on the scanning path of a multi-layer deposit. Typical

scanning paths are unidirectional and bi-directional as seen in Figure 12. The section where

the beads starts and ends is referred to as start-and-stop in the process. These deposition paths

may create different amounts of time for cooling between the layers. A forced time for

cooling occurs with a unidirectional scanning path when the laser focus moves back to the

starting point before depositing the following layer. This forced cooling time is lost for bi-

directional paths as the deposition head continuously build the next layer. Parimi,

AswathanarayanaSwamy, Clark and Attallah (2014) studied the influence on grain orientation

of the two different deposition strategies. It was clearly shown that the grain orientation aligns

with the moving energy input, giving an alternating orientation whit a bi-directional, see

Figure 13(a,b). The unidirectional layers were oriented at an angle of 50-60° with respect to

the substrate. In case of bi-directional layers, they were oriented at an angle of 45-50° with

respect to the substrate. In both cases the grains re-nucleate from previous layers. This shows

that the orientation is not only dependent on the vertical and horizontal heat fluxes but also on

the orientation of previous layer. Similar grain orientation and angles between adjacent layers

were found in the work by Wei, Mazumder & DebRoy (2015) with 60° and 45° between

layers with respect to the horizontal plane. Parimi et al. (2014) also showed the presence of a

banded structure, indicated by white arrows in Figure 13(a,b), where a finer equiaxed grain

zone is found between the layers. This banded structure is more evident for unidirectional

scanning than bi-directional due to a lower temperature between the layers, as explained with

the forced cooling time. These fine grained zones also minimize and finally disappear when

moving further away from the substrate, i.e. the heat sink effect is subsequently lost. The

grain morphology development is also greatly affected by laser power. Parimi et al. (2014)

also increased the laser power, from 390 W to 910 W, with a bi-directional scanning. This

resulted in long columnar grains continuously growing from previous layer with an angle of

80°, see Figure 13c. The higher energy input gave no inter-layer fine grained zones due to

insufficient cooling rates, but a clear demarcation was found at the top of the deposit with

small columnar grains.

12

Figure 12. Typical scanning paths a) unidirectional and b) bi-directional (Parimi,

AswathanarayanaSwamy, Clark & Attallah, 2014).

Figure 13. OM images showing the grain orientation between layers for a) unidirectional b) bi-

directional and c) bi-directional scanning with high laser power (Parimi, AswathanarayanaSwamy,

Clark & Attallah, 2014).

2.3 Nickel Based Superalloys

Due to the demanding environment of many aerospace components, mainly the gas turbine

engine, the aerospace sector is the main market for these nickel based superalloys. Aerospace

engines have high demands on mechanical properties at elevated temperatures, generally

speaking above 70% of a materials melting temperature. In the hottest regions of the jet

engine, an operating temperature up to 1300°C can be reached. Nickel (Ni) based superalloys

13

are rare materials which can match these demands with their high-temperature creep strength

as well as a high oxidation and corrosion resistance during long time periods. The main

contribution to the hot corrosion resistance is the high levels of chromium (Cr) added to

superalloys with help by titanium (Ti). Due to the high market price of these alloys, the

possibility to lower material usage by AM is of particular interest. The parts of a jet engine

which are exposed to the highest temperatures such as the combustion chamber, turbine

blades and exhaust nozzle, are dependent on these high-performing superalloys.

The microstructure of a general Ni-based superalloy is mainly constituted by two phases, a

face-centered cubic (FCC) -nickel matrix and ordered distributed phase, i.e

with a FCC crystal structure, see Figure 14. Formation of these precipitates are the key

strengthening mechanism for many superalloys and the addition of tantalum (Ta), titanium

and niobium (Nb) promotes this phase. A Ni-based superalloy generally has a composition

of 40-50 wt% Ni along with other various alloying elements (Mouritz, 2012).

Figure 14. Schematic illustrations of the two main crystal structures in a nickel based super alloy: FCC

γ-nickel (left) and (right) (modified from Aveson, 2011).

2.3.1 Inconel 718

One of the most commercially known superalloys is Inconel 718 which is mainly used at

lower service temperatures up to 750°C. Niobium is added whereby the formation of body-

centered tetragonal ordered precipitates are the main strengthening mechanism.

Strengthening by formation of is also of importance. However, the

strengthening phase is only stable up to 649°C and a longtime exposure above this

temperature may transform the phase to a more unfavorable -phase, orthorhombic , as

a result of overaging. A smaller amount of this phase is beneficial due to grain refinement

effect and control. Carbides are also important secondary phases in terms of strengthening

(Donachie & Donachie, 2002). The so called intermetallic Laves phases may also form which

are hexagonally close-packed phases with a general form of

and is detrimental in larger amounts to the structure. These intermetallic Laves phases form

Ni

Ni

Al, (Al,Ti)

14

by segregation of Nb in the material and is reported to be visual as bright inter-dendritic

phases (Radhakrishna & Rao Prasad, 1997).

The microstructure of a 718 alloy deposited by laser differs compared to conventionally

casted parts with subsequent heat treatment due to different thermal history of the material. A

casted alloy 718 with subsequent hot isostatic pressing treatment (HIP) is viewed in Figure 15

(Lee, Chang, Tang, Ho & Chen, 2006). The microstructure of a laser metal deposited alloy

718 is seen in Figure 16. This image shows a resultant dendritic structure for a single track

with long columnar grains. The red marks in the image displays how the geometry of a

single-bead is measured (Zhong et al., 2016).

Figure 15. OM image (left) and SEM image (right) of a casted and HIP treated alloy 718 viewing (Lee,

Chang, Tang, Ho & Chen, 2006).

Figure 16. Viewed etched microstructure of a single track deposited alloy 718 (Zhong et al., 2016).

2.4 Powder Manufacturing Processes

How the powder will behave and spread under deposition is very much influenced by the

manufacturing process of the raw powder. This is influenced by the quality and size

distribution of the powder. There are various techniques to produce metal powder, but the

most common methods to produce high quality powders suitable for LMD are gas

15

atomization (GA), plasma atomization (PA) and plasma rotating electrode process (PREP). In

common for all these methods is that the particle size distribution (PSD) is usually under

control by the manufacturer through process control and sieving as a final part of the process

(Dawes, Bowerman & Trepleton, 2015).

2.4.1 Gas Atomization

Gas atomization is a common and well established method for producing desired spherical

particles. A raw material is placed in a top chamber, a furnace, where the material is melted.

Molten metal then enters the chamber below, either through a tundish or directly, where a

high-pressure jet stream of inert gas atomizes the melt. The metal droplets solidify and are

collected in a bottom chamber, see Figure 17. Due to the high demands on powder purity in

aerospace, melting is usually performed by vacuum induction or electrode induction melting

furnaces, see Figure 18 and Figure 19. Vacuum inert gas atomization (VIGA) produces

refined and degassed melts where the melt pours through a tundish nozzle to the atomization

chamber. Electrode induction melting gas atomization (EIGA) uses raw materials in terms of

rods. The rod is fed, rotated and melted by an induction coil. The melt directly enters the

atomization chamber with no use of a tundish. This process reinsures no contact with a

melting crucible minimizing the risk of contamination for reactive alloys (ALD Vacuum

Technologies, n.d.). This results in spherical particles with a wide particle size distribution of

0-500 µm. This PSD can be in a more narrow range by controlling the gas flow. However,

producing powder with GA normally generates so called satellites. Theses satellites are

surface irregularities consisting of smaller particles adhered to the surface of larger ones, a

less attractive characteristic (Dawes, Bowerman & Trepleton, 2015).

Figure 17. Basic illustration of the gas atomization process (Dawes, Bowerman & Trepleton, 2015).

16

Figure 18. Illustration of a tilting crucible (left) and a

bottom pouring crucible (right) used for a vacuum

induction melting furnace (ALD Vacuum Technologies,

n.d.).

Figure 19. Illustration of the induction

coil used for an electrode induction

melting furnace (ALD Vacuum

Technologies, n.d.).

2.4.2 Plasma Atomization

Plasma atomization is a process that produces highly spherical particles of good quality by

raw material with a high melting point. A feedstock in terms of wire is fed into a chamber

whereby it is melted by a plasma torch. The melt simultaneously atomizes in a low vacuum,

inert gas environment inside the chamber. The metal droplets then solidify when freely falling

in the chamber. This process also minimizes potential impurities of the material since the

atomized droplets never comes in contact with any solid surface during solidification. The

range of particle size distribution produced is between 0-250 µm with the greater part of

particles ranging between 0-106 µm (AP&C, 2015).

2.4.3 Plasma Rotation Electrode Process

This process is similar to plasma atomization and shows comparable powder quality. The

difference between them two is that PREP uses a feedstock bar whereby the material rotates

as it is melted. This leads to that the molten material atomizes and rapidly solidifies by

influence of centrifugal forces. This process is also performed in an inert gas environment

inside the chamber minimizing contamination and oxidation. The range of particle sizes

produced is between 0-100 µm (Dawes, Bowerman & Trepleton, 2015).

2.5 Powder Characteristics

There is a need to understand and assess more knowledge about powder characteristics and its

impact on the AM process. Powder characteristics such as morphology, particle size and

distribution, flowability, bulk properties and porosity are important to assess in order to

ensure powder performance in the machine and final structures. Evenly spreading of powder

during deposition is crucial to deposit uniform layers (Zlotwinski & Garboczi, 2015). ASTM

17

International has provided a technical standard guide to characterize metal powder in purpose

for additive manufacturing (ASTM International, 2014).

2.5.1 Morphology

The particle morphology is an important characteristic that influence laser metal deposition in

terms of flowability and packing density. This characteristic is dependent on the

manufacturing process of powders where gas atomization, plasma atomization and plasma

rotation electrode process are common methods as described in previous section 2.4. These

processes produce mainly spherically shaped particles. In general PA and PREP

manufactured powders show a higher quality in terms of a highly spherical shape and fine

surfaces with less shape irregularities. These surface irregularities are referred to as satellites

which are more widely seen for GA manufactured particles (Ahsan, Pinkerton, Moat &

Shackleton, 2011). These satellites are formed due to a difference in solidification rate

between smaller molten particles that adheres to partially molten particles of a larger size

(Zhong, Biermann, Gasser & Poprawe, 2015).

Particle morphology is easily studied through investigation by scanning electron microscopy

(SEM). A clear comparison between GA and PREP manufactured powders are seen in Figure

20.

Figure 20. SEM images showing comparison in particle morphology of GA (left) and PREP (right)

produced Ti6A4V powders (Ahsan, Pinkerton, Moat & Shackleton, 2011).

2.5.2 Porosity

The porosity of powders, both internal and on particle surfaces is an unwanted characteristic

that will affect the degree of porosity on deposited structures. This is greatly influenced by the

chosen manufacturing method. GA produced powders have been seen to have a higher

porosity than equivalent powder manufactured by PREP. This higher degree of porosity can

be explained by entrapped gas inside the particle due to the nature of the GA process. The

18

surface pores possibly generated by GA, seen in Figure 20, are usually non-existing for the

PREP powder seen in the same figure. The internal porosity can be viewed by optical

microscopy (OM) on samples with a polished cross-section, see Figure 21, where it is

apparent that GA powders experience a lower quality (Zhong et al., 2016).

Figure 21. OM image of polished cross-sections of GA (left) and PREP (right) produced Inconel 718

powders viewing internal porosity (Zhong et al., 2016).

A non-destructive imaging technique available for studying internal porosities is micro

computed tomography which 3D scans the sample by means of x-ray to detect any pores. This

method can be implemented to study both raw powder (Ahsan, Bradley & Pinkerton, 2011)

and deposited structures (Khademzadeh, Carmignato, Parvin, Zanini & Bariani, 2015).

2.5.3 Size & Size Distribution

Produced powder will have various size ranges dependent on the method of production as

mentioned in section 2.4. The desired powder size distribution depends on the process of

application whereby the general desired range for LMD processes is between 50-150 µm i.e.

particle diameter. The particle size will affect the powders ability for flow, spread and

packing during deposition (EPMA, 2015).

Powder size distribution can also affect the powder efficiency as showed by Kong, Carroll,

Brown & Scudamore (2007). They deposited Inconel 625 and found the highest powder

efficiency, and evidently highest layer deposition, with a size range of 44-88 µm and median

value around 74 µm. The use of smaller particles showed a lower efficiency most likely due

to coagulation inside the nozzle, disturbing the mass flow. A decrease in efficiency was also

shown for too large particles due to a resulting powder beam focus larger than laser beam

focus, consequentially not able to melt all powder.

There are several techniques to find the size distribution such as dynamic image analysis,

laser diffraction and dry sieving. The shape of the particles to investigate may affect the

19

choice of method. Many of the available techniques operate with the physical assumption of

spherical particles, such as laser diffraction and sieving. The particle size distribution is often

viewed as a histogram with the particle diameter on the x-axis, given in bin ranges, with the

frequency and cumulative percentages of the sizes on the y-axis (Horiba Instruments, 2016).

2.5.3.1 Laser Diffraction

Laser diffraction results are often viewed as a volume distribution where the distribution

width on the x-axis shows the values, Dv10, Dv50 and Dv90, where the lowered v simply

stands for volume distribution. Dv50 is the particle diameter where 50% of the population lies

below this value and the other 50% above, i.e. the median of a distribution. Dv10 and Dv90 is

the particle diameter under which 10 and 90% of the population lies. In general the median

value of a volume distribution, i.e. Dv50, is the most reported value when a single value of the

size distribution is described. A measure of the distribution width is often given by the span

and is defined as (Horiba Instruments, 2016):

2.5.3.2 Image Analysis

Image analysis is a tool that measures each individual particle giving a number distribution of

the sample, which can be performed on a static or dynamic basis. Values of Dn10, Dn50 and

Dn90 along with the mean are also often reported with image analysis, where the lowered n

stands for number distribution. This number distribution can often be converted to a volume

distribution in order to compare with other techniques, which is recommended if possible. In

general the median value of a number distribution, i.e. Dn50, is the most reported single value

of a size distribution (Horiba Instruments, 2016).

2.5.4 Rheology

A powder’s ability to flow is an important property for additive manufacturing since it affects

powder supply through the powder feeder and nozzle and ultimately production rate,

spreading and packing ability. Flowability is very much affected by the particle size and

distribution since the basis of powder flow is due to surface friction between particles. This

gives that a finer powder has more apparent surface area and resultantly higher interparticle

friction and lower flow characteristics. This also includes the presence of satellites; a higher

degree of satellites may have an effect on the flow due to mechanical interlocking between

surface irregularities. Another aspect that affects this interparticle friction is the moisture

content of the powder; higher moisture content increases friction and may result in larger

particle agglomerates and lowers the flowability (Slotwinski & Garboczi, 2015).

20

2.5.4.1 Hall Flowmeter

One standardized method of measuring powder flowability is based on the time it takes for a

particular mass to freely flow through a so called Hall Flowmeter funnel. A mass of 50 g is

placed in the funnel, a cone-shaped tool, whereby the powder is released, timed and captured

in a container below. The Hall Flow rate ( ) is given in the units of time per sample mass,

whereby the release of powder can be on a static or dynamic basis (ASTM International,

2013a). The level of sensitivity of this method is low, but it might give an indication of

flowability if powders are compared to each other.

2.5.4.2 Rheometer

Another method to describe the flowability is by a FT4 Freeman rheometer. It can be used to

measure dynamic flow and shear properties giving valuable result of internal particle friction

of powder in motion.

It has been shown that the flowability of powders from the same supplier with similar size

distribution and Hall Flowmeter results, may result in various degrees of resistance to flow

measured by a rheometer. Freeman (2007) showed that the internal friction and cohesive

forces between particles are not simply explained by one method alone and that the Hall Flow

measurement may not be sensitive enough to detect differences among various powders.

Clayton, Millington-Smith & Armstrong (2015) also demonstrated the variation in flowability

of powders with similar size distributions from various suppliers and manufacturing

processes. The use of a rheometer detected the impact of manufacturing method and also the

variance between suppliers that deliver powder with similar specifications.

Flowability of a powder is measured by the energy required to establish flow when a rotating

blade moves either downwards or upwards through the powder, as seen in Figure 22. The

definition of a confined test, i.e. downward motion, is basic flowability energy (BFE) in units

of mJ. This motion generates a state of a relatively high stress mode and compression in the

powder. The basic flowability energy is affected by many factors such as, morphology, size

distribution, texture and cohesivity. The factor that influences the flowability to a larger

degree is the particle size distribution. Therefore, if various powders with similar size

distributions result in different BFE values one can suspect that physical factors such as

surface area, texture and morphology are more likely to be the reason for variations. The

definition of an unconfined test, i.e. upward clockwise motion, is specific energy (SE) and is

normalized by the powder mass given in unit mJ/g (Freeman, 2007).

21

Figure 22. Illustration of the confined, i.e. downward (left) and unconfined, i.e. upward (right) testing

by a FT4 Freeman rheometer (Freeman Technology, n.d.).

The flowability can also be measured by influence of external factors such as air or

consolidation. This is of interest due to external influence by carrier and protection gas as

well as potential vibrations that might pack the powder in feeder. An aeration test is used to

measure the influence of air to the resistance to flow defined as aeration energy (AE). This

aeration test gives an indirect measure of the cohesive strength between particles. Low

cohesive strength will result in well aerated powders and lower flow energies. The reduction

in flow energy from a non-aerated state, i.e. simply the BFE value, may be given as the ratio

between the two measurements defined as aeration ratio (AR) = BFE/AE. A consolidation test

may be performed to examine how the powder will flow after a powder mass has been

subjected to a number of taps to the vessel, defined as consolidation energy (CE). The flow

energy increase between the un-consolidated and consolidated state can now be given as the

ratio between the two, defined as the consolidation index (CI) = CE/BFE (Freeman

Technology, n.d.).

2.5.5 Bulk Properties

Apparent density ( ), also known as bulk density, is a measured physical characteristic

related to a powder’s ability to pack. This is a property of importance concerning die packing

and powder feeding during the deposition process. The apparent density of a powder shows

the correlation of a powders mass to freely fill a hollow space. This property may be

measured by the use of a Hall Flowmeter funnel where the powder flows freely through an

orifice and fills a container placed below (ASTM International, 2013b).

The apparent density may also be measured using a rheometer, also referred to as conditioned

bulk density (CBD). The principal method is similar with the addition of an initial

conditioning sequence with the rheometer where a rotating blade goes through the powder

22

filled container to remove any possible air pockets and thus homogenizing the powder mass

(Freeman Technology, n.d.).

Tap density ( ) is a measure of a powder’s mass ability to fill a vessel after a defined

number of taps to the vessel. This will consolidate the powder giving an increased density

compared to a powder’s bulk density. This test will simulate vibrations that may occur during

the process, handling and transporting. The difference between the two values will indicate

how sensitive a powder is to possible vibrations (Freeman Technology, n.d.).

2.5.6 Quality Assessment of Powder

To summarize, the powder characteristics that on hand indicates a powder of high quality for

laser metal deposition are:

Highly spherical particles

Fine surfaces

Few satellites

Low internal porosity

Few surface pores

Narrow size distribution

High purity

The various techniques to identify and verify these characteristics are summarized in Table 1.

Table 1. Summary of powder characteristics and their assessment techniques.

Characteristic Techniques

Morphology SEM/OM

Porosity Particle polishing+OM/Micro computed tomopgraphy

Particle size distribution Dry sieving/laser diffraction/dynamic image analysis

Flowability Hall Flowmeter/rheometer

Bulk properties Hall Flowmeter/rheometer

2.6 Statistical Significance

2.6.1 Analysis of Variance

It is of value to use basic statistics to compare sample data in order to assess whether group

populations differ from one another. One can compare data between two or multiple groups,

independently or dependently. A simple test to analyze data from multiple independent

23

groups, three or more, is analysis of variance (ANOVA). To clarify further reading in this

section, the independent groups of analysis in this project are the various powder suppliers.

ANOVA is based on a so called F-test which is the ratio of two variances with accounts for

the number of degrees of freedom. The variance (σ2) of sample data is the square of the

standard deviation and describes the dispersion of data from the population mean. The

variance is given by:

where is the observed value, is the mean value for the data series and is the number of

observations. The F-value of the F-test is given as:

The numerator of Variation between sample mean is calculated by the variation of each

individual group mean against the overall mean of the groups. This means that if the

individual group means are close to the overall mean, the variance is low. The denominator of

Variation within the samples is calculated as the sum of each observation variance from its

own group mean divided by the error degrees of freedom. This means that if the group

populations are substantially different and one can compare them against each other, the F-

value need to be high. ANOVA tests the groups against a null hypothesis which states that the

group population means are equal. If the null hypothesis is not statistically true, the mean of

two or more populations are different. It is important to statistically prove the difference

between group data to be able to compare the groups against each other. If the ANOVA test

would be repeated by picking random data from each population and plot the given F-values,

a probability distribution would result known as the F-distribution, see Figure 23 as an

example. This F-distribution is plotted with the assumption of a true null hypothesis. If the F-

value from a single ANOVA test, for example 3,3 in Figure 23, is placed in the F-distribution

plot, the probability of receiving a value at least as high as the value of 3,3 from the sample

data is 3,1%. The probability of receiving a certain F-value from the distribution is known as

the p-value. This p-value is compared to a pre set significance level of the analysis which in

most cases is 0,05 or 5%. This consequently means that if the p-value is lower or equal to

0,05 the null hypothesis can be rejected. This p-value is what determines the statistical

significance against the null hypothesis (Frost, 2016). However, this p-value should not be

interpreted as the probability of mistakenly rejecting the null hypothesis. It is simply a

probability value of observing an F-value due to sampling error, not a proof that the null

hypothesis is false (Frost, 2014).

24

Figure 23. Example of an F-distribution and resulting probability of an F-value, i.e. the p-value (Frost,

2016).

2.6.2 Regression Analysis

A regression analysis is a statistical approach to describe the relationship between input data,

known as the predictors, to an output data, known as response. The regression can be

performed by a linear or nonlinear model to describe this relationship. In this project a simple

linear regression analysis is performed whereby one predictor is linearly described by one

response. The regression analysis will results in a linear model which best explain the

relationship between predictor and response, commonly noted as:

where is the predicted response, is the intercept value, is the slope coefficient and

is the predictor value. This linear model is based on the least-squares approach, i.e. the

analysis finds a line that makes the sum of squared prediction errors as small as possible. This

also means to find the values of and that minimizes the sum of squared prediction

errors, noted Q in this example as (Pennsylvania State University, 2017):

The analysis also gives an R-sq value which is a statistical measurement of how well the

regression model describes the data. This value is given as a percentage of the response

variation that is described by the linear model. The higher the R-sq value, the more data fits

the regression model. The regression analysis can visually be presented by a fitted line plot as

seen in Figure 24 with the resultant regression model and corresponding coefficients at the

top of the graph. However, a high R-sq value does not always indicate a good fit to the model

25

which can be viewed by a residual plot. The residual is simply the difference between the

observed and modeled value, i.e. a positive or negative response error to the model. The

residual plot reveals how the residuals are scattered in reference to the fitted values and

should be in a random and unpredicted pattern. This means that if the residuals show a pattern

of negative or positive residuals, the error to the model is biased, i.e. the error is predictable.

For a biased pattern the interpretation of a high and good R-sq value may be false (Frost,

2013a).

Figure 24. Example of a fitted line plot viewing the resulting regression model and R-sq value (Frost,

2013b).

3 Materials & Methods

In this section the powders in subject of investigation in this project are presented and their

characterization techniques. The methodology of laser metal deposition is explained, followed

by the quality assessment methods for deposited structures.

3.1 Powder & Sheet Material

The materials used in this project are Inconel 718 alloy powders aimed for additive

manufacturing processes. The powders are dry with no flow additives. The substrate material

available for deposition was three mm thick Inconel 718 plates. The standard composition of

alloy 718 is seen in Table 2 (Special Metals Corporation, 2007). Studies have been made on

five different powders from four individual commercial powder suppliers. These powders

have been named A, B, C, D and E and will be referred to as so from now on forward. Each

powder and their main applicable process and respective manufacturing method is seen in

Table 3. The current in-house material specification at GKN of Inconel 718 powder for laser

26

metal deposition specifies the required composition, manufacturing method, Hall Flow rate

and particle size distribution.

The powder used at GKN in Trollhättan today is Powder C. Powder C is therefore included in

this project as a reference powder. The obtained results in terms of powder characteristics and

quality in the final part will be evaluated with Powder C in mind as the reference.

Table 2. Standard composition of Inconel alloy 718 given in wt%.

INCONEL ® Alloy 718 Standard Composition

Ni Cr Fe Nb+Ta Mo Ti Al Co

50,0-55,0 17,0-21,0 Bal. 4,75-5,50 2,80-3,30 0,65-1,15 0,20-0,80 1,00 max

C Mn Si P S B Cu

0,08 max 0,35 max 0,35 max 0,015 max 0,015 max 0,006 max 0,30 max

Table 3. Information of powders in subject of investigation in this project.

Investigated Powders

Powder Applicable process Manufacturing method

A Electron beam melting PA

B Plasma spray GA

C - EIGA

D Powder bed fusion/selective laser melting VIGA

E Laser Sintering VIGA

3.2 Powder Characterization

3.2.1 Morphology

The shape of powder particles was qualitatively analyzed by SEM, a Hitachi TM3000. Each

powder was studied in order to visually evaluate the quality in terms of satellite content and

sphericity. Evaluations were made with aims to rank the powders in comparison to each other.

The powder morphology was also quantitatively analyzed by image analysis. A very thin

layer of powder was adhered to a strip of crystal clear tape. The powder covered tape was

attached onto a sturdy plastic slide and placed in the filmstrip holder. The film scanner, a

CanoScan FS4000US Film Scanner, works with a fluorescent light source which illuminates

and scans the tape with a scanning resolution of 4000 dpi. Three plastic slides were prepared

for each powder. The scanned images were analyzed in the image analysis software

PowderShape. Random areas were selected over the three slides resulting in five

measurements per powder. A detailed statistical analysis was done per area giving a

parameter denoted as ShapeFactor which is related to an ideal shape, in this case a circle. This

27

shape factor compares the measured surface of the object (PO) to a surface equivalent circle

(PC) by the convex perimeter as:

A ShapeFactor of 1 gives that the analyzed particle is an ideal circle.

3.2.2 Porosity

3.2.2.1 Sample Preparation

In order to qualitatively and quantitatively establish the porosity of the powders, they were

hot mounted and further grinded and polished to obtain a cross-section. The machine used for

hot mounting was a Buehler SIMPLIMET™ 2000. The machines used for grinding and

polishing were a Buehler MOTOPOL™ 2000 and an AUTOMET™ 300 respectively.

A thin layer of powder was placed onto the bottom flat surface of the hot mounting cylinder

where high-density bakelite was added to enclose the metal powder sufficiently. Once the

powder was mounted the specimen was gently grinded with a 1200 and further 2500 grit size

SiC paper. This grinding scheme of each specimen was done in small steps, with monitoring

in OM in between the steps, in order to assess when an approximate cross-section of the

particles was reached. As a final step the specimen was polished with 3 µm diamond paste

slurry.

3.2.2.2 Image Analysis

There is no available standard to quantitatively measure the porosity of powder. In this

project, a free image analysis software has been used, ImageJ, to quantitatively measure the

area fraction of pores.

Numerous optical microscope images of random areas with overall representation of the

specimen were taken using Olympus BX60M with a magnification of 50x. These images

were processed and analyzed in ImageJ by first transforming the image to a grayscale 8-bit

image followed by making the image binary, i.e. in only a black and white scale. The

software was then set to measure the area fraction of black in the image. The apparent pores

were then colored and filled manually with white, see Figure 25. The area fraction was

measured once again and a difference in the amount of black was obtained. This difference

gives a pixel area fraction of pores in each image. The average area fraction was taken over

all images analyzed.

28

Figure 25. Binary image viewing apparent pores (left) and manually filled pores (right).

For a highly spherical powder with low satellite content, internal and closed pores are easy to

identify. Difficulty arises for judgment of surface connected pores, mainly for particles with a

more irregular shape and a lot of satellites. The identification is very much based on

individual judgment. To ensure that equal judgment is made for each image and powder, own

decision grounds are set. As a general rule the pore need to be closed of their

circumferential to be considered as a pore. These pores are then manually drawn and closed to

the most likable shape and filled in. Those particles who do not meet these grounds are

simply considered to be of an abnormal shape. Some examples of both cases of judgment are

given below. A good example of a clear open pore is viewed in Figure 26. A case not as

obvious is seen in Figure 27, but where the hollowness is considered as a pore due to its

closed nature and dept into the particle. Two judgment calls for non-pores are seen in Figure

28.

Figure 26. Clear example of a surface connected

pore.

Figure 27. Example of an open pore (red arrow)

with grounds of judgment.

29

Figure 28. Example of a two cavities (red arrows) that do not count as pores.

The pore sizes were also of interest and measured on ten frames taken with a magnification of

50x in the optical microscope, Olympus BX60M, for each powder. Measurements were done

from the same images taken for pixel area fraction measurement in ImageJ. For pores that

were non-spherical it was the largest axis that was measured. All pores from the size of 5 µm

were noted and averaged giving a mean pore size of the powder. Limitations to this step were

the resolution of the image when small particles were measured. This gives that pore sizes

measuring around 5-8 µm should be viewed with some uncertainty.

3.2.3 Particle Size Distribution

3.2.3.1 Image Analysis

The particle size distribution of the powders was measured by image analysis. Plastics slides

with powder were prepared and scanned as described above in section 3.2.1. Three plastic

slides were prepared for each powder. The scanned images were analyzed in the image

analysis software PowderShape. Three areas were randomly selected and averaged per slide,

giving three measurements per powder. A statistical analysis was done per image giving a

volume distribution of the particle size for each powder.

3.2.3.2 Laser Diffraction

The particle size distribution of the powders was measured by laser diffraction using a

Mastersizer 2000. A laser beam illuminated a dry powder sample whereby detectors

measured the intensity of light scattered. A volume based particle size distribution of the

sample sphere diameter is obtained. Each powder was tested three times with an equal

powder mass for each new batch. The sample mass ranged from 15-18 g for the five powders.

30

3.2.4 Rheology

3.2.4.1 Hall Flow Rate

The Hall Flow rate was measured by a static flow method. A schematic illustration of the test

setup is seen in Figure 29. The bottom of the orifice was blocked by a finger whereby powder

was poured into the center of the funnel. The orifice was then unblocked and the timing

device was manually and simultaneously started. The powder mass of 50 g flowed unaided

through the funnel into a bottom container. Three measurements of each powder,

continuously fresh samples batches, were recorded and averaged according to ASTM

standard (ASTM International, 2013a). All measurements were performed by one operator

only due to human errors. Protective gloves were also used to avoid moisture from the finger

blocking the orifice. These tests were conducted in order for the same operator to obtain a

value for all powders.

Figure 29. Schematic illustration of Hall Flowmeter setup according to ASTM standard. Setup stand

and bottom scale (left) and funnel (right) are the setup basis of the method. Modified from: (ASTM

International, 2013a)

3.2.4.2 Rheometer

A FT4 Freeman rheometer was used when measuring various rheological parameters. The

equipment consisted of a test vessel with a defined volume placed onto a bottom scale with a

precision blade able to move downward and upward the vessel. The test vessel had a

removable top part making it possible to perform a splitting action to level of excessive

powder giving a precise volume, see Figure 30 for illustration.

Funnel

holder

Scale

Funnel

31

Figure 30. Illustration of the splitting action of test vessel (Freeman Technology, n.d.).

All powders were dried at 70°C over night prior testing to ensure the same starting condition

concerning any disturbing moisture content. The powders were then cooled down to room

temperature in a desiccator before testing.

A stability and variable flow rate test method was performed on each powder twice. The

stability test sequence is a general basic flowability energy test performed seven times. The

BFE test sequence consisted of a conditioning cycle, splitting action and test cycle. The vessel

was firstly excessively filled with powder followed by a conditioning cycle. Conditioning

gently displaces the powder by a clockwise rotating action of the precision blade downward

through the vessel. This will remove possible air pocket, consolidation during filling and

create a homogenized powder sample that is slightly aerated. After conditioning of powder,

the vessel was split to give a precise powder volume. The precision blade then rotated anti-

clockwise downwards through the powder with a blade tip speed of 100 mm/s. During this

cycle the force, torque and height were measured parameters giving resulting flow energy

diagram from where the flow energy was calculated, see Figure 31. This test cycle was

repeated seven times where conditioning was performed before each new test. The variable

flow rate was performed in the same manner but with a decreasing blade tip speed starting

from 100 mm/s decreasing to 70, 40 and finally 10 mm/s. The stability and variable flow rate

test methods were performed in one combined program. Each powder was tested two times

with a new powder lot for the second test. If one value is to be reported as the BFE it is the

seventh test cycle from the stability test that is noted according to manufacturer’s guidance.

32

Figure 31. Illustration of test vessel viewing with measured parameters (left) and the resulting energy

gradient diagram (Freeman Technology, n.d.).

The influence of external factors was also tested. An aeration test sequence measured the

change in flow energy with increasing air supply from the bottom of the vessel. The flow

energy was initially measured with no air supply, a general BFE test but with two

conditioning cycles prior to test. The flow energy was further measured with air evenly

supplied through a grid net at a speed of 1 mm/s, also with two conditioning cycles prior to

test. The flow energy was then measured with a stepwise increase of air supply of 2, 3, 4, 5, 6,

8, and finally 10 mm/s. One conditioning cycle was performed in between these following

test cycles. One aeration test sequence was performed per powder.

A consolidation test was also performed whereby the vessel is manually tapped fifty times

prior to testing. A conditioning cycle was performed before tapping and the splitting action

occurs after tapping. The test cycle performed was a general anti-clockwise downward

motion of the precision blade. Each powder was tested one time.

3.2.5 Bulk Properties

3.2.5.1 Apparent Density

Apparent density ( ) is measured using a density cup with a volume of 25,01 cm3

accompanied by the Hall Flowmeter setup. The cup was weighed empty and placed below the

funnel whereby a volume of powder was poured into the center of the funnel. The powder

flowed freely into the cup below until powder overflowed the cup. Excessive powder on top

was leveled off by using a nonmagnetic blade. The density cup was then weighed once again

and the difference between weights was recorded as the powder mass. This powder mass was

divided by the cup volume and reported as g/cm3. One measurement for each powder was

done according to ASTM standard (ASTM International, 2013a).

33

3.2.5.2 Conditioned Bulk Density and Tap Density

The conditioned bulk density (CBD) was measured with the use of Freeman rheometer

equipment. The cup with a defined volume was placed onto the bottom scale and excessively

filled. The powder underwent a conditioning sequence whereby the powder sample gently

displaced into a more homogenized state. The filled vessel was then split giving a precise

volume in the cup. The mass was now measured and a resulting conditioned bulk density was

reported. One measurement was performed for each powder.

The tap density was performed with the same equipment and manner but with the addition of

fifty manual taps to the vessel before splitting. The powder mass was measured and the tap

density recorded ( ). One measurement was performed for each powder.

3.3 Laser Metal Deposition

Inconel 718 powder was deposited by a TRUMPF TruLaser Cell 7020 with a 4 kW disc laser.

Scanning movements were controlled by a 5-axis gantry system. The powder was fed by a

rotating disc feeder system, GTV PF 2/2, carried with argon gas. The powder was deposited

onto the substrate with a coaxial annular nozzle from Fraunhofer ILT. An argon gas shielding

was also introduced through the central passage on the nozzle in order to protect the molten

pool from oxidation as well as the laser optics. Each powder was deposited by a pre-defined

standard test utilized at GKN facilities in Trollhättan. The standard test consists of a single-

bead and multi-bead build-up on Inconel 718 plates deposited with a bi-directional scanning

path. The first layer onto the substrate of the multi-bead deposit is sixteen beads wide with a

50% overlap between beads. Following layer decreases by one bead in width giving a 50%

overlap between beads of subsequent layer. Five layers were deposited for each multi-bead

build-up giving a width of twelve beads in the top layer, see Figure 32. Five plates were

produced per powder. Each plate was weighed previous to and after deposition to be able to

calculate the powder efficiency.

The process parameters used were fixed and equal for all powders. The parameters are

however optimized for the reference powder, Powder C, and should be kept in mind.

Figure 32. Illustration of a standard deposit cross-section (not to scale).

34

3.4 Deposit Evaluation

3.4.1 Sample Preparation

All deposits were equally prepared for subsequent evaluation of their geometry,

microstructure and porosity content, both for multi and single-bead. The deposited parts were

sliced at four positions along the transverse direction creating three area sections for

evaluation. Cutting was performed by a Struers Secotom-10 using aluminum oxide cut wafers

to obtain a narrow cut and fine surfaces. The three surfaces of interest for each multi and

single-bead, see Figure 33, were indicated by arrows and named A, B and C. The cut surfaces

were hot mounted, grinded and polished. The machine used for hot mounting was Buehler

SIMPLIMET™ 2000. Grinding and polishing were performed using Buehler MOTOPOL™

2000 and AUTOMET™ 300 respectively. The specimen was grinded with a 600 and 1200

grit size SiC paper. As a final step the specimen was polished subsequently with 9 and 3 µm

diamond paste slurry and finalized with a Mastermet solution. Each mounted surface was

labeled according to the example shown below.

Example: A1A

A(B,C,D,E) = Powder supplier 1(2,3,4,5) = Plate No. A(B,C) = Area section

Figure 33. Image of a deposited standard test viewing marked cutting lines, cross-sections of interest

(arrows) and labeling.

3.4.2 Defects

Each polished section was inspected in an optical microscope, an Olympus BX60M, for

defects such as pores, micro-cracks and potential lack of fusion. All detected pores above the

size of 10 µm were measured and noted.

3.4.3 Geometry

In order to evaluate the deposited geometry of the cross-sections, the polished surfaces were

electrolytic etched using Oxalic acid 3,0 V for approximately five seconds. A

35

stereomicroscope, an Olympus SZX9, was used to measure the deposits. The measured

dimensions were width (w), height (h) as well as the minimum and maximum depth of

penetration ( ) into the substrate. The multi and single-bead width and height was measured

as seen in Figure 34. The width was firstly measured from edge to edge in line with the

substrate, which also makes a guideline for further measurement of height and penetration.

The height was measured approximately at the center if the deposit. The chosen minimum

and maximum penetration depth measured for the multi-bead was by visual estimation. The

measured penetration and height of the single-bead was made approximately in the middle,

see Figure 34.

Figure 34. Image showing how the geometry of a multi- and single-bead is measured.

3.4.4 Microstructure

The microstructure of deposits was evaluated in an optical microscope, an Olympus BX60M.

Etching of the samples was done with Kalling’s agent for approximately eight seconds. Plate

number 3 and corresponding area section B were chosen for each powder to be observed.

36

4 Results & Discussions

The results from various powder characterization techniques are presented, followed by the

assessed quality of deposited structures. The statistical evaluation of powder characteristics

to quality of deposited part is presented as a final part in this section.

4.1 Powder Characterization

4.1.1 Morphology

The morphology of all five samples was evaluated with the ambition to rank them against one

another according to apparent shape abnormalities. The results are given in Table 4 where the

powders are valued from 1-7, where 1 indicates the lowest level of irregularities and 7 the

highest. Grounds of judgment were from observations and images taken in SEM. Images that

are overall representative for each powder are seen in Figure 35 to Figure 39 below.

The powder with least satellite content and shape irregularities is Powder A, given a value of

1. Powder B is on the opposite side of the scale with a high satellite content and shape

irregularities, given a value of 7. In between these two ends of the scale are three powders

that are somewhat similar. Powder D and E are given a value of 5 with a quality far lower

than Powder A but still not as much irregularities as Powder B. Powder C is judged to have a

slightly higher quality than D and E, given a value of 4. This result was somewhat expected

considering the different manufacturing techniques. Powder A is produced by plasma spray

which is known to deliver spherical particles with a low amount of satellites, as presented in

section 2.5.1. The remaining powders are all produced by the same technique, gas

atomization, which in general generates more satellites than plasma spray. This also shows

that powder quality, in terms of satellite content, is greatly influenced by powder supplier

alone.

Table 4. Qualitative ranking of powders according to their morphology.

Powder Morphology

Value Powder

1 A

2 -

3 -

4 C

5 D,E

6 -

7 B

37

Powder A

Figure 35. SEM images of Powder A with 250x (top), 500x (bottom left) and 1000x magnifications

(bottom right).

38

Powder B

Figure 36. SEM images of Powder B with 250x (top), 500x (bottom left) and 1000x magnifications

(bottom right).

39

Powder C

Figure 37. SEM images of Powder C with 250x (top), 500x (bottom left) and 1000x magnifications

(bottom right).

40

Powder D

Figure 38. SEM images of Powder D with 250x (top), 500x (bottom left) and 1000x magnifications

(bottom right).

41

Powder E

Figure 39. SEM images of Powder E with 250x (top), 500x (bottom left) and 1000x magnifications

(bottom right).

The quantitative results from image analysis measurements are seen in Table 5 below. A

ShapeFactor of 1 gives a particle mean shape equivalent to the ideal circle. The reported

values are average of minimum 600 particles. The total number of particles analyzed varied

for each powder, from 5200 to 18600 particles.

The powder with ShapeFactor closest to 1 is A followed by Powder C. The powder with

highest values is B. The descending order of ShapeFactor values corresponds to the

observations in SEM and final qualitative ranking. What can be thought to be a small

42

difference between the values of Powder A and B show a big difference in qualitative

observations.

Table 5. Average particle ShapeFactor for all five powders obtained from image analysis.

Particle ShapeFactor

A B C D E

Mean StDev Mean StDev Mean StDev Mean StDev Mean StDev

1,055 0,001 1,072 0,002 1,059 0,001 1,064 0,001 1,070 0,001

4.1.2 Porosity

The porosity content given in pixel area fraction is plotted for each analyzed image taken for

each powder. The pixel area fraction is averaged over all images as values are added. This

enables to see how many images are needed to reach a stable average, i.e. when an additional

measurement is added the average does not fluctuate. This gives that the number of

measurements, or images, are not equal for each powder since the average is stabilized at

different levels. The measured fraction of each image and powder is seen in appendix 8.1.

A summarized histogram of the resulting mean porosity and particle density is given in Figure

40. The image analysis results give that Powder B has the largest mean pixel area fraction of

0,207% with Powder E not far behind that value of 0,174%. Powder A and C have the lowest

mean values of only 0,060% and 0,063% respectively. Powder D shows a value close to

Powder A and C of 0,078%.

Particle Density

Powder Density (%)

A 99,940

C 99,937

D 99,922

E 99,826

B 99,793

Figure 40. Histogram of the averaged pixel area fraction for each powder (left) and the resulting

particle density ranked (right).

The pore sizes were also measured from ten images for each powder to obtain data to plot as a

size frequency distribution. The number of images is equal in order to have the same area

analyzed for all powders. The pores are measured and plotted into four bin ranges: 5-10, 11-

A B C D E

Mean 0,060 0,207 0,064 0,078 0,174

0,000

0,100

0,200

Pix

el A

rea

Fra

ctio

n (

%)

Porosity

43

25, 26-50 and above 50 µm. A frequency distribution is plotted in Figure 41 and data are seen

in Table 6. Results show that Powder B has a substantially higher number of pores than the

other powders. On the other hand, the pores in B are in the lower size ranges if compared to

the other powders. Powder A and C distinguish from the others with the lowest number of

pores, only 22 and 26 respectively, where C shows the largest mean size of 22 µm. The

measured pores for each image and powder are seen in appendix 8.2.

Table 6. Data from pore diameter measurements.

Particle Pore Diameter

Powder Mean (µm) Max (µm) No.

A 17 42 22

B 13 46 122

C 22 48 26

D 15 40 71

E 18 54 81

Figure 41. Frequency distribution of pore sizes for all five powders.

0

20

40

60

5 10 25 50 More

Fre

quen

cy

Bin range (µm)

A - Pore diameter

0

20

40

60

5 10 25 50 More

Fre

quen

cy

Bin range (µm)

B - Pore diameter

0

20

40

60

5 10 25 50 More

Fre

quen

cy

Bin range (µm)

C - Pore diameter

0

20

40

60

5 10 25 50 More

Fre

quen

cy

Bin range (µm)

D - Pore diameter

0

20

40

60

5 10 25 50 More

Fre

quen

cy

Bin range (µm)

E - Pore diameter

44

There is a large difference in pore content even among the powders that are produced by the

same method. This shows a clear supplier dependency. On the other hand Powder A and C

show similar results despite the different production methods. Powder A was expected to

have the highest quality since it is produced by plasma atomization compared to Powder C

and its gas atomization technique, as presented by Zhong et al. (2016). This shows that it is

possible to produce a powder by GA with an equivalent quality as PA or PREP by what is

believed to be process optimization.

4.1.3 Particle Size Distribution

4.1.3.1 Image Analysis

The image analysis results are summarized and averaged for all three slides that were

prepared for each powder in Figure 43. The results are given as a volume distribution of the

particle sizes. Individual distribution result for each powder is also viewed where statistical

values of Dv90, Dv50 and Dv10 are given in Figure 42. The distribution span is also calculated

for all powders and is given by:

It is seen from resulting statistical values that B, C, D and E have a similar distribution width.

The median value of A,B and E is similar at ranging from 74,4-76,1 µm. Powder C and D are

close at a value of approximately 69 µm. Powder A differentiate from the other powders with

a wider span which is clearly seen in Figure 43. All powders have a PSD within material

specifications.

A – Statistics

D10 D50 D90 Span

58,7 76,1 99,3 0,533

0

10

20

30

40

0 50 100 150

rela

tive

vo

l%

Bin range (µm)

A - IA

A - averaged

45

B – Statistics

D10 D50 D90 Span

63,1 74,4 87,5 0,329

C – Statistics

D10 D50 D90 Span

60,9 69,8 79,5 0,266

D – Statistics

D10 D50 D90 Span

59,1 69,4 80,6 0,310

E – Statistics

D10 D50 D90 Span

66,1 75,4 84,7 0,247

Figure 42. Particle size distribution for all five powders from image analysis measurements.

0

10

20

30

40

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

B - IA

B - averaged

0

10

20

30

40

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

C - IA

C - averaged

0

10

20

30

40

0 50 100 150

Rel

atie

vo

l%

Bin range (µm)

D - IA

D - averaged

0

10

20

30

40

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

E - IA

E - averaged

46

Figure 43. Particle size distribution from image analysis measurements.

4.1.3.2 Laser Diffraction

Results from laser diffraction measurements are given below where the average size

distribution is plotted in Figure 44. In Table 7 the values of Dv90, Dv50 and Dv10 in µm are

seen for all three tests performed per powder. The distribution span is also calculated for all

powders.

It is seen that Powder C and D have a similar distribution in terms of span width and a lowest

PSD. The same similarities follow for Powder B and E and a resulting higher PSD. A slightly

wider distribution width is seen with Powder A which has a similar D10 as B and E but a

higher D90 value, also seen in Figure 44. All powders are within GKN material

specifications.

Figure 44. Particle size distribution results from laser diffraction measurements.

0

10

20

30

40

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

PSD - Image analysis

A - averaged

B - averaged

C - averaged

D - averaged

E - averaged

0

5

10

15

20

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

PSD - Laser diffraction

A - averaged

B - averaged

C - averaged

D - averaged

E - averaged

47

Table 7. Summary of statistical result from laser diffraction measurements.

Particle Size Distribution

Powder D10 (µm) D50 (µm) D90 (µm) Span

A1 55,0 75,8 104,2

A2 54,9 75,6 104,1

A3 55,1 75,8 104,4

A - averaged 55,0 75,7 104,2 0,650

B1 53,7 72,9 99,7

B2 53,7 72,9 99,6

B3 53,7 73,0 99,7

B - averaged 53,7 72,9 99,7 0,630

C1 48,6 66,2 90,1

C2 48,8 66,4 90,2

C3 48,8 66,4 90,3

C - averaged 48,7 66,3 90,2 0,626

D1 48,1 65,4 89,4

D2 48,1 65,4 89,5

D3 48,1 65,5 89,6

D - averaged 48,1 65,4 89,5 0,633

E1 53,7 72,6 98,7

E2 53,9 72,8 98,9

E3 53,8 72,7 98,8

E - averaged 53,8 72,7 98,8 0,619

4.1.3.3 Comparison of Image Analysis & Laser Diffraction

If the results from image analysis and laser diffraction are compared to each other, it is clear

that the two methods do not produce equivalent results. The distribution span calculated for

laser diffraction is much wider than for image analysis, with the exception of Powder A

which is slightly closer, see Figure 45. The median value also shows a slightly higher value

by image analysis measurement. This shows a limitation of the image analysis equipment

used, which was somewhat expected. The image analysis method present at GKN is a cheap

and simple method to investigate the PSD, but used with knowledge of possible limited

accuracy. The method of laser diffraction was then performed, by an external part, to

investigate the differences. By the results one can say that image analysis equipment at GKN

should not be used to obtain an accurate PSD but can give an approximate value of the

median size. To be mentioned is also the number of particle analyzed by each method. The

number of particles analyzed by image analysis varied from 5200 particles for Powder B to

18000 particles for Powder A. This can be compared to laser diffraction where a countless

number of particles were analyzed due to a much larger sample size.

48

A

D10 D50 D90 Span

IA 58,7 76,1 99,3 0,533

LD 55,0 75,7 104,2 0,650

B

D10 D50 D90 Span

IA 63,1 74,4 87,5 0,329

LD 53,7 72,9 99,7 0,630

C

D10 D50 D90 Span

IA 60,9 69,8 79,5 0,266

LD 48,7 66,3 90,2 0,626

D

D10 D50 D90 Span

IA 59,1 69,4 80,6 0,310

LD 48,1 65,4 89,5 0,633

0

10

20

30

40

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

A

A - IA

A - LD

0

10

20

30

40

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

B

B - IA

B - LD

0

10

20

30

40

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

C

C - IA

C - LD

0

10

20

30

40

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

D

D - IA

D- LD

49

E

D10 D50 D90 Span

IA 66,1 75,4 84,7 0,247

LD 53,8 72,7 98,8 0,619

Figure 45. PSD comparison between image analysis and laser diffraction for all five powders.

4.1.4 Rheology

4.1.4.1 Hall Flow Rate

The Hall Flow rate measurements for all powders are seen in Table 8 below. The averaged

flow rate measured shows that Powder A and C have the lowest value, 13 s/50g, and thus the

best flow rate. Powder B and E show the highest value of 16 s/50g with Powder D next in

ranking with 15 s/50g. But overall one can view the results as two groupings. All powders are

within GKN material specification.

Table 8. Hall Flow rate from Hall Flowmeter measurements.

Hall Flow Rate

Powder Test 1 Test 2 Test 3 FRH (s/50g)

A 12,74 12,70 12,67 13

B 16,35 16,59 16,54 16

C 13,21 13,33 13,12 13

D 15,13 15,02 15,04 15

E 15,73 15,55 15,49 16

4.1.4.2 Stability, Variable Flow & Consolidation

The stability and variable flow energy test are results obtained from the confined test of the

FT4 Freeman rheometer. The basic flow energy results from all five powders are plotted and

seen in Figure 46. The exact values obtained are found in appendix 8.4. A summary of the

various test results and calculated indexes are seen in Table 9. It is the seventh and last basic

flow energy value from the stability test that is given in Table 9.

The stability test, i.e. test cycle 1-7, show a small deviation between the first and last test

cycle for all five powders and thus good results. This is also shown from the calculated

0

10

20

30

40

0 50 100 150

Rel

ativ

e vo

l%

Bin range (µm)

E

E - IA

E - LD

50

stability index (SI) as it is close to one. The lowest flow energies and thus the best result are

found for Powder A and C. Powder E show the highest flow energies and thus the worst

result. The results for Powder B and D have similar results fall into an intermediate group of

ranking.

The results of the unconfined test, i.e. the resulting specific energy (SE), are also seen in

Table 9. The specific energy results for Powder A and C are the lowest which gives the

lowest resistance to unconfined flow. The result for Powder E is the highest and thus the

highest resistance to flow. In the intermediate level are B and D with similar SE values. The

specific energy is calculated as:

The variable flow energy test results are also seen in Figure 46 as test cycle 8-11. From this

figure and Table 9 it is seen that the BFE value of Powder A continuously increases with a

decreasing blade tip speed. A small difference is seen for Powder D, C and E where the BFE

value increases with speed of 70 mm/s but then decreases again as the speed is decreased.

Powder C on the other hand shows a small increase at 70 mm/s but then remains at a stable

value. However, the increases and decreases are overall small which is shown by the

calculated flow rate index (FRI) in Table 9. The FRI is the ratio between test cycle 8 and 11

where all powders have a ratio close to 1. The consolidation test and the resulting

consolidation indexes (CI) are also seen in Table 9. From the CI values it is seen that Powder

A have the lowest flow energy increase by 31% when subjected to simulated vibrations.

Powder B and D reviews the highest flow energy increase close to 70%. Powder C and E

have results in between these.

Table 9. Summary of rheometer measurement results.

Summarized Flow Data

Powder BFE (mJ) SI FRI SE (mJ/g) CEtap50 (mJ) CI

A 575 1,03 1,07 1,62 754 1,31

B 609 0,93 1,01 2,05 1032 1,69

C 435 0,96 1,01 1,61 608 1,40

D 644 1,04 1,01 1,96 1070 1,66

E 891 1,10 0,96 2,63 1299 1,46

51

Figure 46. Stability and variable flow energy results.

These flowability results show a limitation of Hall Flow measurements. The confined test and

resulting basic flow energies identify two powders that distinguishes as two extremes from

the others, which is not identified by Hall Flowmeter. This shows the lack of sensitivity by

the Hall Flowmeter which corresponds to previous observations by Clayton, Millington-

Smith & Armstrong (2015).

4.1.4.3 Aeration Test

The basic flow energy was measured by the influence of air flow supplied to the powder

sample. The measured flow energies for each powder are seen in Table 10 and plotted in

Figure 47. From the graph it is seen that Powder E shows no decrease in flow energy until an

air velocity of 4 mm/s is supplied and any larger decrease is not seen until an air velocity of 8

mm/s. Powder A and D show a decrease after 2 mm/s and thus have less cohesive forces

between particles than E. The best result is observed for C which has the largest decrease in

flow energy as the air velocity increases.

Figure 47. Flow energies plotted with increasing air velocity supplied.

0

200

400

600

800

1000

0 1 2 3 4 5 6 7 8 9 10 11

Bas

ic fl

ow

ener

gy (

mJ)

Test cycle

Stability & variable flow energy test

A

B

C

D

E

0

100

200

300

400

500

600

700

0 1 2 3 4 5 6 7 8 9 10

Bas

ic f

low

ener

gy (

mJ)

Air velocity (mm/s)

Aeration test

A

B

C

D

E

52

The aeration ratio (AR) is another measure of this which makes is easy to compare the

powders against each other and given as:

The ratio is calculated for values obtained at 5 mm/s and 10 mm/s noted as AR5 and AR10

respectively in Table 10. The general guideline of a cohesive or non-cohesive powder is by

the value of the aeration ratio given as:

AR ≈ 1 2 < AR < 20 AR >> 20

The powder is not

sensitive to aeration.

Usually a very cohesive

powder.

Average sensitivity to

aeration. Most common

range value for

powders.

Highly sensitive to

aeration. Very low

cohesive strength and

likely in a fluidized

state.

Table 10. Summary of results from aeration test.

Aeration Test

Powder AR0 (mJ) AE5 (mJ) AR5 AE10 (mJ) AR10

A 553 391 1,41 137 4,04

B 615 390 1,58 185 3,32

C 443 244 1,82 53 8,29

D 505 365 1,38 95 5,29

E 597 548 1,09 280 2,13

By the aeration energy at a velocity of 5 mm/s it is seen that none of the powders are

considered as sensitive to aeration. Powder E have a value close to 1 and can be considered as

a very cohesive powder at this velocity. On the other hand Powder C has the value closest to

2 and thus the most sensitive powder at 5 mm/s. If the air velocity is increased to 10 mm/s all

powders are above the value of 2 and are considered to have an average sensitivity to

aeration. However, Powder E still has the lowest value and is in the very bottom scale of the

range and thus considered to show the worst result. Powder C still shows the best result with a

value over 8 and can be concluded to have the lowest cohesive forces between particles. The

ranking of Powder A, B and D is harder to establish by certainty. All measured flow energies

at various air velocities are seen in appendix 8.5.

53

4.1.5 Bulk Properties

4.1.5.1 Apparent Density

The measured apparent density for all five powders is found in Table 11. The results show

that Powder A has the largest apparent density, i.e. the best ability to freely pack a defined

volume. The powder with the lowest ability to freely pack, i.e. lowest apparent density, is B.

The other three powders show more similar intermediate results.

Table 11. Apparent density results from Hall Flowmeter measurements.

Apparent Density

Powder Powder mass (g) Cup volume (cm3) )

A 116,8 25,01 4,67

B 103,3 25,01 4,13

C 111,5 25,01 4,46

D 109,4 25,01 4,37

E 106,9 25,01 4,27

4.1.5.2 Conditioned Bulk Density & Tap Density

The measured conditioned bulk density (CBD) from the Freeman rheometer equipment, see

Table 12, all show a higher value if compared to the apparent density which was expected.

The conditioning sequence removes possible air pockets and homogenizing the powder which

on a positive response should increase the density of the powder. The density increase is

similar for all powder aside from Powder A with a slightly smaller increase.

The conditioned bulk density is also compared to the tap density where a density difference is

given in Table 12. A lower density difference gives a powder less sensitive to vibrations. The

best results are shown by Powder A followed by Powder C. The remaining powders show

very similar results with a slightly larger difference in density. The level of density increase is

somewhat reflected on the resulting consolidation energy and consolidation index as viewed

in Table 9. The smallest density increase by A and C can be reflected to the lowest

consolidation indexes from rheological measurements.

54

Table 12. Density results from FT4 Freeman rheometer measurements.

Conditioned Bulk Density & Tap Density

Powder CBD (g/cm3) (g/cm

3) Diff.

A 4,88

5,12 0,24

4,84 0,28

B 4,38

4,80 0,42

4,39 0,41

C 4,75

5,08 0,33

4,74 0,34

D 4,66

5,06 0,40

4,63 0,43

E 4,56

4,98 0,42

4,55 0,43

4.2 Deposit Evaluation

4.2.1 Powder Efficiency

The powder efficiency (µ) during deposition is calculated from the ratio of the mass actually

deposited ( ) and the powder delivered during deposition ( ). The actual mass of

deposited powder is given from the difference in sheet weight before and after deposition.

The powder mass delivered during time of deposition is calculated by the total length of

deposited powder multiplied by the powder mass flow rate ( ) all divided by the scanning

speed ( ):

In total there are 71 beads deposited with a single length of 30 mm on each plate. This gives a

delivered powder mass of 5,11 g. The powder efficiency for each powder can now be

calculated and is seen in Table 13. The lowest efficiency is given for Powder C at 73,6% and

the highest for Powder E at 88,6%. The remaining powders show comparable results in

between C and E. However, the standard deviation of the deposited powder mass is also

calculated in Table 13 where a larger scatter in results is found for Powder E. This shows a

less repeatable process for E in terms of deposited powder mass. However, all powders show

an efficiency above 70% which is considered a well approved result.

55

Table 13. Powder efficiency during deposition.

Powder Efficiency

Powder Plate mD(g) Mean mD (g) StDev (g) (g) µ (%) Mean µ (%)

A

1 4,15

4,17 0,02 5,11

81,2

81,6

2 4,16 81,4

3 4,19 82,0

4 4,19 82,0

5 4,17 81,6

B

1 4,18

4,22 0,04 5,11

81,8

82,6

2 4,20 82,2

3 4,20 82,2

4 4,26 83,3

5 4,26 83,4

C

1 3,80

3,76 0,04 5,11

74,4

73,6

2 3,80 74,4

3 3,72 72,8

4 3,74 73,2

5 3,76 73,6

D

1 4,33

4,32 0,04 5,11

84,7

84,5

2 4,36 85,3

3 4,25 83,2

4 4,35 85,1

5 4,31 84,3

E

1 4,41

4,53 0,11 5,11

86,3

88,6

2 4,55 89,0

3 4,58 89,6

4 4,68 91,6

5 4,42 86,5

The difference between the smallest and largest mass of deposited powder is believed to be

linked to a powder’s ability to flow or be aerated. Powder E shows the highest deposited mass

with a mean value of 4,53 g, close to a 0,8 g difference from the lowest value of 3,76 g by

Powder C. This is a big variation considering the small deposited volume. The BFE values of

Powder C and E show the highest and lowest flowability respectively. This is also true for the

aeration ratio measured for 5 and 10 mm/s. One theory is that Powder E has a larger

difference in dispersion of the powder in carrier gas. In Figure 48 there are illustrations of a

well aerated powder and less aerated, perhaps the slight difference between Powder C and E.

A well dispersed and flowable powder, as Powder C, might be lost to a greater extent when it

exits the nozzle by the carrier gas. This correlation is hard to prove since the process today

has no ways available to measure or detect these differences of a powder feeding through the

hose and the nozzle outlet. There is also the possibility of a powder to attach to the inner

walls of the hose and nozzle, which is difficult to perceive.

56

Figure 48. Example of a well aerated (left) and less aerated powder (right) (Freeman

Technology, n.d.).

The work done by Kong, Carroll, Brown & Scudamore (2007), as presented in section 2.5.3,

showed a relationship between PSD and powder efficiency where a powder of similar PSD as

this project produced the highest efficiency. A powder with smaller PSD appeared to pulse,

most likely clogging inside the nozzle whereas a larger PSD created a too large powder beam

spot size. The PSD differences between the powders in this project are not as big as for Kong

et al., but the reason for differences is believed to be somewhat similar in terms of clogging.

4.2.2 Geometry

The average geometries for the single- and multi-beads are seen in Figure 49 to Figure 54

below. The five obtained values for each section and powder are averaged and seen in

appendix 8.6.

The results show that Powder C differentiates from the others with the lowest multi-bead

height, seen in Figure 49. It can also be seen that Powder D and E have similar height. The

same similarity is found for A and B. The standard deviation for each powder is small which

shows a repeatable process. A small decrease in height is seen for section B, which is

considered as the stable deposition area. The measured multi-bead width shows comparable

results for all five powders.

The penetration depths are important where Powder A shows the best results for both the

minimum and maximum values. The corresponding lowest penetration is found for Powder E.

The minimum required penetration is however 0,1 mm which all powders have exceeded.

The differences in single-bead height and width between powders are small. One observation

is however that Powder C floats out slightly more, i.e. a lower and wider single- and multi-

bead in comparison to the others.

57

Figure 49. Average multi-bead height for all powders.

Figure 50. Average multi-bead width for all powders.

Figure 51. Average multi-bead minimum and maximum penetration depth of all powders.

1,50

1,60

1,70

1,80

1,90

A B C

Hei

ght

(mm

)

Area section

Multi-bead: Height

A

B

C

D

E

11,35

11,40

11,45

11,50

11,55

11,60

A B C

Wid

th (

mm

)

Section area

Multi-bead: Width

A

B

C

D

E

0,10

0,15

0,20

0,25

0,30

0,35

A B C

Pen

etra

tio

n (m

m)

Area section

Multi-bead: Min. and Max. Penetration

Amax

Amin

Bmax

Bmin

Cmax

Cmin

Dmax

Dmin

Emax

Emin

58

Figure 52. Average single-bead height for all powders.

Figure 53. Average single-bead width for all powders.

Figure 54. Average single-bead penetration depth of all powders.

0,15

0,20

0,25

0,30

A B C

Hei

ght

(mm

)

Area section

Single-bead: Height

A

B

C

D

E

1,45

1,50

1,55

1,60

A B C

Wid

th (

mm

)

Area section

Single-bead: Width

A

B

C

D

E

0,20

0,22

0,24

0,26

0,28

0,30

A B C

Pen

etra

tio

n (

mm

)

Area section

Single-bead: Penetration

A

B

C

D

E

59

4.2.3 Defects

All detected and measured pores for all five plates and three section areas, in total 15 surfaces

analyzed per powder, are found in Table 14. No lack of fusion or micro-cracks was detected

in the samples.

The powders with lowest part quality, in terms of apparent pores, are found to be B and E

with the highest number of pores counted, over 200 in total. Powder A and C have the lowest

number of pores counted, 78 and 31 pores respectively. The measured pores were also sorted

into bin ranges 10, 25, 50 and above 50 µm and plotted as a frequency distribution, see Figure

55 to Figure 59. The frequency distributions show that B has the largest number of pores but

the most in a smaller size range. On the opposite side of the scale is C with lowest number of

pores and which many are in a larger size range in comparison. Powder A shows the second

lowest number of pores with more in a larger size range as C. It is also interesting to compare

detected pores in the single-beads. The result for Powder B and E shows the largest number

of pores, 16 and 11 respectively, which is a great amount for the small area of a single-bead.

This show the substantially lower quality of E and B compared to A and C which had no

pores.

The pore frequency in the three area sections, A, B and C, was also investigated in order to

see if there could be a difference, see Figure 55 to Figure 59. The results for each powder are

seen in Table 15 to Table 19. Since section A and C are cuts before and/or after start-and-

stop, i.e. the turn of path for each bead and layer, it was believed that more pores could be

detected here. This is true for section A which show a higher number of pores compared to

section B for all five powders. However, this is not a general trend for section C and the

differences between the more process-stable part, i.e. section B, are not always big.

These results raise the question whether few but larger pores are better or worse than many

but smaller in this project. However, when concluding on part quality the amount of pores is

considered the most importance factor in comparison to powder efficiency or geometry. The

present implementation of laser metal deposition in production at GKN, have fairly low

deposition volumes which make the efficiency a secondary quality factor. If the deposition

volumes would increase the powder efficiency would of course be more important. Powder C

is then concluded to have the highest part quality and Powder A as secondary best.

60

Table 14. Summary of data of apparent pores in all powders.

Summarized Part Pore Diameter

Multi-bead (µm) Single-bead (µm)

Powder Max size Median size No. Max size Median size No.

A 128 23 78 - - -

B 63 16 222 40 15 16

C 53 22 31 - - -

D 57 19 130 20 - 2

E 69 21 247 39 17 11

Table 15. Summary of section data of apparent pores in Powder A.

Powder A

Multi-bead (µm) Single-bead (µm)

Section Max size Median size No. Max size Median size No.

A 75 23 38 - - -

B 128 28 17 - - -

C 90 22 23 - - -

All 128 24 78 - - -

Figure 55. Frequency distribution of pores for Powder A in all 5 plates (left) and sorted by area section

(right).

Table 16. Summary of section data of apparent pores in Powder B.

Powder B

Multi-bead (µm) Single-bead (µm)

Section Max size Median size No. Max size Median size No.

A 56 16 94 26 16 5

B 48 15 76 40 17 7

C 63 17 52 35 12 4

All 63 16 222 40 15 16

0

50

100

150

10 25 50 More

Fre

quen

cy

Bin range (µm)

A - Multi-bead

0

20

40

60

80

100

A B C

Fre

quen

cy

A - Multi-bead

61

Figure 56. Frequency distribution of pores for Powder B in all 5 plates (left) and sorted by area section

(right).

Table 17. Summary of section data of apparent pores in Powder C.

Powder C

Multi-bead (µm) Single-bead (µm)

Section Max size Median size No. Max size Median size No.

A 56 22 18 - - -

B 43 21 6 - - -

C 36 25 10 - - -

All 56 22 34 - - -

Figure 57. Frequency distribution of pores for Powder C in all 5 plates (left) and sorted by area section

(right).

Table 18. Summary of section data of apparent pores in Powder D.

Powder D

Multi-bead (µm) Single-bead (µm)

Section Max size Median size No. Max size Median size No.

A 57 20 45 - - -

B 47 19 42 14 - 1

C 48 20 4 20 - 1

All 57 19 129 20 - 2

0

50

100

150

10 25 50 More

Fre

quen

cy

Bin range (µm)

B - Multi-bead

0

20

40

60

80

100

A B C

Fre

quen

cy

B - Multi-bead

0

50

100

150

10 25 50 More

Fre

quen

cy

Bin range (µm)

C - Multi-bead

0

20

40

60

80

100

A B C

Fre

quen

cy

C - Multi bead

62

Figure 58. Frequency distribution of pores for Powder D in all 5 plates (left) and sorted by area section

(right).

Table 19. Summary of section data of apparent pores in Powder E.

Powder E

Multi-bead (µm) Single-bead (µm)

Section Max size Median size No. Max size Median size No.

A 68 22 89 35 22 4

B 69 20 75 14 14 3

C 54 20 79 39 17 5

All 69 21 243 Sum 17 12

Figure 59. Frequency distribution of pores for Powder E in all 5 plates (left) and sorted by area section

(right).

4.2.4 Microstructure

The microstructure for each powder is viewed is with an optical microscopy image taken for

the multi- and single-bead, see Figure 60 and Figure 61 respectively. It is seen in the multi-

bead build up that a finer columnar structure is achieved in the first layer to the substrate. This

correlates to the heat sinking effect explained in section 2.2.5. The first layer has a higher

cooling rate and thus results in a finer microstructure. The subsequent layers show larger

grains that grow from previous layer often over multiple layers which are especially evident

for Powder C.

0

50

100

150

10 25 50 More

Fre

quen

cy

Bin range (µm)

D - Multi-bead

0

20

40

60

80

100

A B C

Fre

quen

cy

D - Multi-bead

0

50

100

150

10 25 50 More

Fre

quen

cy

Bin range (µm)

E - Multi-bead

0

20

40

60

80

100

A B C

Fre

quen

cy

E - Multi-bead

63

Powder A

Powder B

64

Powder C

Powder D

65

Figure 60. OM images showing the microstructure of a multi-bead for all five powders.

Powder A

Powder E

66

Powder B

Powder C

67

Figure 61. OM images showing the microstructure of a single-bead for all five powders.

Powder D

Powder E

68

4.3 Statistical Evaluation

A statistical evaluation is made for various powder evaluations as well as properties of the

deposited material. This evaluation is done by the statistical method Analysis of Variance

(ANOVA) with the software Minitab. As described in section 2.6.1, ANOVA is a method to

determine whether the mean differs between two or more groups. If the P-value is less or

equal to the significance level one can, with a statistical significance, reject the null

hypothesis:

H0: All population means are equal

A high F-value in terms leads to a low P-value. The multiple groups that are the factors of the

null hypothesis is the various powder suppliers, i.e. A, B, C, D and E. If the P-value for each

test is below 0,05 there is statistical significant evidence that the two or more groups mean are

not from the same population. If the P-value is above 0,05 one cannot with statistical

significant certainty discard the null hypothesis that the groups come from the same large

population. This also means that these factors cannot be further used for any statistical

correlation, due to lack of statistical evidence that the powders are different. For each test the

groups are tested against various responses, i.e. characterization or experimental results. For

the measured multi-bead and single-bead geometry, noted MB and SB respectively, the three

area sections are analyzed individually.

All tests performed are summarized in Table 20. It is seen that the ANOVA test for multi-

bead width for all area sections have a P-value larger than the significance level. The same

result is given for the width of single-bead area section A and B. This gives that the groups

are not statistically significantly different. All the remaining responses show a P-value lower

than 0,05 whereby the null hypothesis can, by a statistical significance, be rejected. A boxplot

is also reported for responses that show a p-value below 0,05. These boxplots gives a

possibility to see the distribution of data and a trend between different responses. The

responses that show a p-value below 0,05 and are of interest for the subsequent statistical

correlation are shown below. Additional boxplots are seen in appendix 8.7.

69

Table 20. Summary of results from ANOVA tests.

ANOVA

Response F-value P-value Response F-value P-value

Hall Flow rate 807,87 0,000 Basic flow energy 487,70 0,000

Particle porosity 26,34 0,000 Deposit pore freq. 20,60 0,000

Particle pore freq. 9,89 0,000 Deposit pore dia. 11,64 0,000

Particle pore dia. 6,65 0,000 Powder efficiency 109,56 0,000

ShapeFactor 110,79 0,000

IA – D10 4764 0,000 LD – D10 8343,88 0,000

IA – D50 9,80 0,002 LD – D50 10547,83 0,000

IA – D90 99,50 0,000 LD – D90 13460,25 0,000

MB height – A 60,04 0,000 SB height – A 15,57 0,000

MB height – B 62,87 0,000 SB height – B 4,87 0,007

MB height – C 81,44 0,000 SB height – C 9,32 0,000

MB width – A 1,12 0,374 SB width – A 1,11 0,380

MB width – B 1,21 0,339 SB width – B 2,17 0,110

MB width – C 0,55 0,698 SB width – C 6,82 0,001

MB Pmax – A 8,26 0,000 SB Pmax – A 4,39 0,010

MB Pmax – B 7,06 0,001 SB Pmax – B 4,82 0,007

MB Pmax – C 2,91 0,048 SB Pmax – C 3,92 0,016

MB Pmin – A 6,99 0,001

MB Pmin – B 4,38 0,011

MB Pmin – C 5,28 0,005

The particle size distribution obtained from laser diffraction measurements, noted as LD in

boxplot, is seen in Figure 62. All three boxplots of D10, D50 and D90 show the same

response trend between suppliers. The boxes itself are also very narrow which shows a low

standard deviation between individual measurements.

70

EDCBA

56

55

54

53

52

51

50

49

48

Dia

mete

r (u

m)

LD - D10

EDCBA

75,0

72,5

70,0

67,5

65,0

Dia

mete

r (u

m)

LD - D50

EDCBA

106

104

102

100

98

96

94

92

90

Dia

mete

r (u

m)

LD - D90

Figure 62. Boxplot of D10, D50 and D90 values obtained by laser diffraction measurements for all

five powders.

71

When looking at the boxplot of basic flowability energy and powder efficiency, see Figure 63

and Figure 64, a response trend can be spotted between them two. The two responses seem to

follow the same pattern for the suppliers. This indicates that the basic flowability influence

the resulting powder efficiency during deposition. The multi-bead height also follows the

same response trend as BFE and efficiency between the suppliers, seen in Figure 69.

EDCBA

900

800

700

600

500

400

Basic

flow

energ

y (

mJ)

Basic flowability energy

Figure 63. Boxplot of the basic flowability energy obtained by rheometer measurements for all five

powders.

EDCBA

90

85

80

75

70

Eff

eciency

(%

)

Powder efficiency

Figure 64. Boxplot of measured powder efficiency for all five powders.

72

The particle porosity in terms of pixel area fraction is seen in Figure 65 and the measured

ShapeFactor by image analysis is seen in Figure 66.

EDCBA

0,5

0,4

0,3

0,2

0,1

0,0

Pix

el a

rea f

ract

ion (

%)

Particle porosity

Figure 65. Boxplot of the measured pixel area fraction for all five powders.

EDCBA

1,075

1,070

1,065

1,060

1,055

1,050

ShapeFact

or

ShapeFactor

Figure 66. Boxplot of measured ShapeFactor for all five powders.

73

A clear response trend is seen in the boxplot between the particle pore frequency and deposit

pore frequency, see Figure 67. A response trend is also seen between the particle pore

diameter and deposit pore diameter even if it is more subtle, see Figure 68.

EDCBA

35

30

25

20

15

10

5

0

Fre

quency

Particle pore frequency

EDCBA

60

50

40

30

20

10

0

Fre

quency

Deposit pore frequency

Figure 67. Boxplot of measured particle and deposit pore frequency for all five powders.

74

EDCBA

60

50

40

30

20

10

0

Dia

mete

r (u

m)

Particle pore diameter

EDCBA

140

120

100

80

60

40

20

0

Dia

mete

r (u

m)

Deposit pore diameter

Figure 68. Boxplot of the measured particle and deposit pore diameter for all five powders.

The multi-bead height for all three area sections are seen in Figure 69. All three area sections

follow the same response trend. A response trend is also found between the multi-bead

heights and the boxplots of basic flow energy and powder efficiency in Figure 63 and Figure

64.

75

E-AD-AC-AB-AA-A

1,9

1,8

1,7

1,6

1,5

Dista

nce

(m

m)

MB height - Section A

E-BD-BC-BB-BA-B

1,9

1,8

1,7

1,6

1,5

Dista

nce

(m

m)

MB height - Section B

E-CD-CC-CB-CA-C

1,9

1,8

1,7

1,6

1,5

Dista

nce

(m

m)

MB height - Section C

Figure 69. Boxplot of the measured multi-bead width for all area sections and powders.

76

4.4 Statistical Correlation

A regression analysis was performed with the software Minitab in order to statistically

correlate the input data, known as predictors, to the output data, known as responses. The

predictor and response also need to have an equal sample size. The analysis will show how

well one can describe and predict a certain output from the input data by a linear regression

model. This fit of data to the model can also be graphically viewed by a Fitted Line Plot. The

result gives a value of R-sq which is a statistical measure of how well the regression model

describes the data. This value is given as a percentage of the response variation that is

described by the linear model. The higher the R-sq value the more data fits the regression

model. A general rule of thumb for a high enough R-sq value is 60%.

Due to the time restriction of this project only three or five predictors and responses are

analyzed, which is in the very bottom of accepted sample sizes. The suggested sample size to

ensure a precise estimate of model strength is 40 samples or more. With this is mind, a

response fit to the model should perhaps be considered more as a statistical trend than a

precise prediction for high R-sq values. The same goes for lower values around 50% which

may view a good trend considering the small sample size. This also gives that a residual

pattern is hard to identify, whereby no residual plots are reported.

4.4.1 Median Particle Size – Basic Flow Energy

A regression analysis is made between the median particle size obtained from laser diffraction

measurements and the basic flowability energy. Since the laser diffraction measurements

were performed three times on each powder and basic flowability seven times, a selection of

flowability measurements had to be made to ensure equal sample sizes. For each powder the

flowability measurement 1, 4 and 7 was chosen.

A fitted line plot for the analysis is seen in Figure 70. The resulting analysis showed an R-sq

value above 60% for all powders with the exception of Powder C. Powder B, D and E show

an R-sq value above 90% and thus a model that describes the responses very well. Powder C

has a value at 42,9% which indicate that the model cannot statistically predict the basic

flowability by the measured median particle size. However, with only three measured values

the fit of 42,9% is not necessarily bad and could indicate a statistical correlation. These

results statistically prove what has been presented from literature and in house-knowledge

that the particle size affects the flowability of the powder.

If one look at the regression coefficients at each boxplot, it is clear that a small increase in

median particle size shows a large increase in BFE. One remark is given to the median values.

The repeatability of laser diffraction gives that there are very small differences between the

77

three measurements. The difference between the three measured flow energies is larger which

reflects on the regression coefficient. Powder E shows this clearly with a very small

difference between LD measurements and a much larger difference between flow energies,

giving the largest coefficient.

75,8475,7875,7275,6675,60

575

570

565

560

555

D50 (um)

Basi

c flow

energ

y (m

J)

S 7,44670

R-Sq 64,7%

R-Sq(adj) 29,3%

A - D50 & BFEBFE - A = - 4145 + 62,20 A - D50

72,94872,93672,92472,91272,900

650

640

630

620

610

D50 (um)

Basi

c flow

energ

y (m

J)

S 9,34084

R-Sq 90,8%

R-Sq(adj) 81,6%

B - D50 & BFEBFE - B = - 52037 + 722,2 B - D50

78

66,3666,3266,2866,2466,20

452,5

450,0

447,5

445,0

442,5

440,0

437,5

435,0

D50 (um)

Basi

c flow

energ

y (m

J)

S 9,35311

R-Sq 42,9%

R-Sq(adj) 0,0%

C - D50 & BFEBFE - C = - 4007 + 67,10 C - D50

65,4665,4465,4265,4065,38

645

640

635

630

625

620

D50 (um)

Basi

c flow

energ

y (m

J)

S 3,43253

R-Sq 97,0%

R-Sq(adj) 94,0%

D - D50 & BFEBFE - D = - 19757 + 311,6 D - D50

72,8072,7672,7272,6872,64

890

880

870

860

850

840

830

820

810

800

D50 (um)

Basi

c flow

energ

y (m

J)

S 4,90294

R-Sq 99,3%

R-Sq(adj) 98,5%

E - D50 & BFEBFE - E = - 37816 + 531,8 E - D50

Figure 70. Fitted line plot of the median particle size to the basic flow energy for all five powders.

79

4.4.2 Median Particle Size – Powder Efficiency

A regression analysis is made between the median particle size and deposition efficiency.

Since we have three values for each particle size value, the calculated efficiency of plate 1, 3

and 5 was used in this analysis.

A fitted line plot for the analysis is seen in Figure 71. All powders show a value above 60 %.

This gives that there is a statistical correlation between median particle size and powder

efficiency. But if one reflects over the actual values of efficiency, despite some large

coefficients, the very small increase in PSD values does not greatly affect efficiency. The

exception is found for Powder E since the standard deviation for efficiency is much larger.

This in combination with the same small differences in median size also gives a larger slope

coefficient for Powder E than the others.

75,8475,8075,7675,7275,6875,6475,60

82,0

81,9

81,8

81,7

81,6

81,5

81,4

81,3

81,2

81,1

D50 (um)

Effic

iency

(%

)

S 0,0739440

R-Sq 98,2%

R-Sq(adj) 96,4%

A - D50 & EfficiencyEffic. - A = - 174,9 + 3,387 A - D50

72,9572,9472,9372,9272,9172,9072,89

83,5

83,0

82,5

82,0

81,5

D50 (um)

Effic

iency

(%

)

S 0,678545

R-Sq 65,3%

R-Sq(adj) 30,6%

B - D50 & Efficiency Effic. - B = - 1589 + 22,92 B - D50

80

66,35066,32566,30066,27566,25066,22566,200

74,5

74,0

73,5

73,0

D50 (um)

Effic

iency

(%

)

S 0,553508

R-Sq 75,0%

R-Sq(adj) 50,0%

C - D50 & EfficiencyEffic. - C = - 452,4 + 7,934 C - D50

65,47565,46065,44565,43065,41565,40065,385

85,5

85,0

84,5

84,0

83,5

83,0

D50 (um)

Effic

iency

(%

)

S 0,826947

R-Sq 72,4%

R-Sq(adj) 44,8%

D - D50 & Efficiency Effic. - D = - 1309 + 21,30 D - D50

72,77072,74572,72072,69572,67072,64572,620

92

91

90

89

88

87

86

D50 (um)

Effic

iency

(%

)

S 0,136642

R-Sq 99,9%

R-Sq(adj) 99,7%

E - D50 & Efficiency Effic. - E = - 2437 + 34,74 E - D50

Figure 71. Fitted line plot of the median particle size to the powder efficiency for all five powders

81

4.4.3 ShapeFactor – Basic Flow Energy

A regression analysis is made between a particle shape factor, denoted ShapeFactor, and the

basic flowability energy. The average ShapeFactor was calculated five times per powder

giving that five basic flowability values are needed. Measurement 1,3,5,6 and 7 was chosen

for this analysis.

A fitted line plot for the analysis is seen in Figure 72. A correlation between ShapeFactor and

BFE can be made for Powder B, D and E due to a resulting R-sq value above 60%. Powder A

gives a value of 47% which could indicate a statistical correlation due to the low sample

sizes. The lowest R-sq value of 35,5% is found for Powder C which gives that the

ShapeFactor and flow energy cannot be correlated.

1,05551,05501,05451,05401,05351,05301,0525

575

570

565

560

555

ShapeFactor

Basi

c flow

energ

y (m

J)

S 5,76124

R-Sq 47,0%

R-Sq(adj) 29,3%

A - ShapeFactor & BFEBFE - A = - 3745 + 4086 ShapeFactor-A

1,0741,0731,0721,0711,0701,0691,068

650

640

630

620

610

600

ShapeFactor

Basi

c flow

energ

y (m

J)

S 11,2749

R-Sq 68,7%

R-Sq(adj) 58,3%

B - ShapeFactor & BFEBFE - B = - 5784 + 5985 ShapeFactor-B

82

1,06081,06021,05961,05901,05841,05781,0572

450

445

440

435

ShapeFactor

Basi

c flow

energ

y (m

J)

S 6,31292

R-Sq 35,5%

R-Sq(adj) 14,0%

C - ShapeFactor & BFEBFE - C = - 2509 + 2783 ShapeFactor-C

1,06561,06521,06481,06441,06401,06361,0632

645

640

635

630

625

620

615

610

ShapeFactor

Basi

c flow

energ

y (m

J)

S 4,36098

R-Sq 88,9%

R-Sq(adj) 85,3%

D - ShapeFactor & BFEBFE - D = - 12473 + 12309 ShapeFactor-D

1,07101,07051,07001,06951,06901,06851,0680

900

880

860

840

820

800

ShapeFactor

Basi

c flow

energ

y (m

J)

S 15,7474

R-Sq 84,9%

R-Sq(adj) 79,8%

E - ShapeFactor & BFEBFE - E = - 29825 + 28679 ShapeFactor-E

Figure 72. Fitted line plot of the particle ShapeFactor to the basic flow energy for all five powders

83

4.4.4 ShapeFactor – Powder Efficiency

A regression analysis is made between a particle shape factor, denoted ShapeFactor, and the

powder efficiency. The powder efficiency was calculated for all five plates giving that five

basic flowability values are needed. Measurement 1,3,5,6 and 7 was chosen for this analysis.

A fitted line plot for the analysis is seen in Figure 73. A correlation between ShapeFactor and

powder efficiency can be made for Powder A, D and E due to a resulting R-sq value above

60%. Powder B resulted in a value of 57,4% which is close to the threshold and could

indicate a statistical correlation due to the low sample sizes. The lowest R-sq value of 15% is

found for Powder C which gives that the ShapeFactor and powder efficiency cannot be

correlated.

1,05551,05501,05451,05401,05351,05301,0525

82,0

81,8

81,6

81,4

81,2

81,0

ShapeFactor

Effic

iency

(%

)

S 0,201057

R-Sq 75,3%

R-Sq(adj) 67,0%

A - ShapeFactor & EfficiencyEffic. - A = - 196,8 + 264,0 ShapeFactor-A

1,0741,0731,0721,0711,0701,0691,068

83,5

83,0

82,5

82,0

81,5

ShapeFactor

Effic

iency

(%

)

S 0,551907

R-Sq 57,4%

R-Sq(adj) 43,2%

B - ShapeFactor & EfficiencyEffic. - B = - 163,4 + 229,5 ShapeFactor-B

84

1,06081,06021,05961,05901,05841,05781,0572

74,5

74,0

73,5

73,0

ShapeFactor

Effic

iency

(%

)

S 0,745286

R-Sq 15,0%

R-Sq(adj) 0,0%

C - ShapeFactor & EfficiencyEffic. - C = - 123,6 + 186,2 ShapeFactor-C

1,06561,06521,06481,06441,06401,06361,0632

85,5

85,0

84,5

84,0

83,5

83,0

ShapeFactor

Effic

iency

(%

)

S 0,363360

R-Sq 86,4%

R-Sq(adj) 81,9%

D - ShapeFactor & EfficiencyEffic. - D = - 885,2 + 911,3 ShapeFactor-D

1,07101,07051,07001,06951,06901,06851,0680

92

91

90

89

88

87

86

85

ShapeFactor

Effic

iency

(%

)

S 0,888972

R-Sq 88,1%

R-Sq(adj) 84,1%

E - ShapeFactor & EfficiencyEffic. - E = - 1899 + 1858 ShapeFactor-E

Figure 73. Fitted line plot of the particle ShapeFactor to the powder efficiency for all five powders

85

4.4.5 Basic Flow Energy – Powder Efficiency

A regression analysis is made between the basic flowability and powder efficiency. The

powder efficiency was calculated for all five plates giving that five basic flowability values

are needed. Measurement 1,3,5,6 and 7 was chosen for this analysis.

A fitted line plot for the analysis is seen in Figure 74. All five results show an R-sq value

above 60%. Powder E has the best fit with an value of 94,2%. This concludes that there is a

correlation between flow energy and powder efficiency. However, when looking at the slope

coefficients it is seen that a large increase in BFE does not largely affect the efficiency.

575570565560555

82,1

82,0

81,9

81,8

81,7

81,6

81,5

81,4

81,3

81,2

Basic flow energy (mJ)

Effic

iency

(%

)

S 0,230778

R-Sq 67,4%

R-Sq(adj) 56,5%

A - BFE & Efficiency Effic. - A = 58,00 + 0,04193 BFE - A

650640630620610

83,6

83,2

82,8

82,4

82,0

Basic flow energy (mJ)

Effic

iency

(%

)

S 0,473346

R-Sq 68,7%

R-Sq(adj) 58,2%

B - BFE & EfficiencyEffic. - B = 60,74 + 0,03477 BFE - B

86

452448444440436

74,5

74,0

73,5

73,0

Basic flow energy (mJ)

Effic

iency

(%

)

S 0,507029

R-Sq 60,7%

R-Sq(adj) 47,6%

C - BFE & Efficiency Effic. - C = 38,44 + 0,08011 BFE - C

648640632624616

86,0

85,5

85,0

84,5

84,0

83,5

83,0

Basic flow energy (mJ)

Effic

iency

(%

)

S 0,535104

R-Sq 70,5%

R-Sq(adj) 60,6%

D - BFE & EfficinecyEffic. - D = 45,04 + 0,06306 BFE - D

880860840820800

92

91

90

89

88

87

86

Basic flow energy (mJ)

Effic

iency

(%

)

S 0,617168

R-Sq 94,2%

R-Sq(adj) 92,3%

E - BFE & Efficiency Effic. - E = 35,97 + 0,06174 BFE - E

Figure 74. Fitted line plot of the basic flow energy to the powder efficiency for all five powders

87

4.4.6 Basic Flow Energy – Multi-bead Height

A regression analysis is made between the BFE and multi-bead height. Five multi-bead

heights are available which gives that five basic flowability values are needed. Measurement

1,3,5,6 and 7 was chosen for this analysis.

A fitted line plot for the analysis is seen in Figure 75. All resulting R-sq values are above

60% with the exception of Powder C. A value of fit at 57,5% gives that the model cannot

statistically predict the multi-bead height by the basic flow energy. However, with only five

values a fit of 57,5% could indicate a statistical correlation for Powder C.

575570565560555

1,79

1,78

1,77

1,76

1,75

1,74

Basic flow energy (mJ)

Heig

ht (m

m)

S 0,0117927

R-Sq 67,4%

R-Sq(adj) 56,5%

A - BFE & Heighth - A = 0,5539 + 0,002143 BFE - A

650640630620610

1,79

1,78

1,77

1,76

1,75

1,74

1,73

1,72

Basic flow energy (mJ)

Heig

ht (m

m)

S 0,0088168

R-Sq 88,8%

R-Sq(adj) 85,1%

B - BFE & Heighth - B = 0,9786 + 0,001231 BFE - B

88

452448444440436

1,61

1,60

1,59

1,58

1,57

1,56

1,55

1,54

1,53

1,52

Basic flow energy (mJ)

Heig

ht (m

m)

S 0,0234375

R-Sq 57,5%

R-Sq(adj) 43,4%

C - BFE & Heighth - C = 0,0424 + 0,003470 BFE - C

648640632624616

1,84

1,83

1,82

1,81

1,80

1,79

1,78

Basic flow energy (mJ)

Heig

ht (m

m)

S 0,0132929

R-Sq 80,2%

R-Sq(adj) 73,6%

D - BFE & Heighth - D = 0,5232 + 0,002041 BFE - D

880860840820800

1,875

1,850

1,825

1,800

1,775

1,750

Basic flow energy (mJ)

Heig

ht (m

m)

S 0,0111899

R-Sq 93,7%

R-Sq(adj) 91,5%

E - BFE & Height h - E = 0,9101 + 0,001062 BFE - E

Figure 75. Fitted line plot of the basic flow energy to the multi-bead height for all five powders.

89

4.4.7 Particle Pore Frequency – Deposit Pore Frequency

A regression analysis is made for the particle pore frequency and deposit pore frequency. The

pores counted in each area section and plate is summarized giving one total pore frequency

value for each one of the five plates. To obtain an equal sample size for the powder

frequency, the counted pores in the powder needed some adjustment. The pores were initially

measured and counted on ten images making it easy to combine the results for every other

two images into one, resulting in five frequency values. Keep in mind that the number of

pores counted in the powder does not correspond to the same area of analyzed material in the

part, i.e. the area of powder analyzed is not normalized or extrapolated to the part area.

A fitted line plot for the analysis is seen in Figure 76. The analysis shows a good correlation

between initial and resulting pore frequency for all five powders. All powders have an R-sq

value above 60%. Powder A, D and E show a strong correlation with a value above 90%.

When viewing the regression coefficients for each powder, the level of increase is the lowest

for powder C and the highest for powder E. Powder E has a high regression constant of 36

which influences the larger pore frequency increase. When looking at these results it should

be remembered that the process parameters are optimized for the reference powder, Powder

C. The various values in regressions coefficients between powders indicate that process

parameters need individual optimization for each powder.

6,05,55,04,54,03,53,0

25,0

22,5

20,0

17,5

15,0

12,5

10,0

Powder frequency

Part

fre

quency

S 1,75046

R-Sq 93,3%

R-Sq(adj) 91,1%

A - Powder & PartA-part = - 6,231 + 4,962 A-powder

90

36333027242118

70

60

50

40

30

Powder frequency

Part

fre

quency

S 7,87883

R-Sq 78,1%

R-Sq(adj) 70,8%

B - Powder & PartB-part = - 2,60 + 1,926 B-powder

7,26,45,64,84,03,22,4

10

9

8

7

6

5

4

Powder frequency

Part

fre

quency

S 1,29921

R-Sq 75,7%

R-Sq(adj) 67,5%

C - Powder & PartC-part = 2,043 + 0,9149 C-powder

282420161284

45

40

35

30

25

20

15

Powder frequency

Part

fre

quency

S 2,45463

R-Sq 96,0%

R-Sq(adj) 94,6%

D - Powder & PartD-part = 8,316 + 1,231 D-powder

91

282420161284

58

56

54

52

50

48

46

44

42

40

Powder frequency

Part

fre

quency

S 0,875022

R-Sq 98,1%

R-Sq(adj) 97,5%

E - Powder & PartE-part = 36,39 + 0,7538 E-powder

Figure 76. Fitted line plot of the powder pore frequency to the part pore frequency for all five powders

4.4.8 Particle Pore Size – Deposit Pore Size

A regression analysis is also interesting for the particle pores sizes and deposit pore sizes. For

this analysis the total number of pores in the five multi-beads was sorted by bin ranges 10, 25

and 50 µm. To obtain an equal sample size per bin range, the measured and counted pores in

the powder needed some adjustment as in section 4.4.7. The measured pores were combined

for every other two images into one and sorted into bin ranges. This gives five frequency

values for each bin range. The particle frequency for powder is noted as P and deposited part

as D with corresponding bin range in combination. Keep in mind that the number of pores

counted in the powder do not correspond to the same area of analyzed material in the part, i.e.

the area of powder analyzed is not normalized or extrapolated to the part area. The

summarized data is found in appendix 8.3.

Fitted line plots for the analyses are seen in Figure 77 to Figure 81. The results show a clear

correlation between the initial pores in particle and resultant pores in part. All analysis shows

an R-sq value above 60%. An interesting connection between the various bin ranges for each

individual powder is observed. If the plots of Powder A, B, D and E are analyzed, it is seen

that larger pore sizes in the powder gives a significantly higher number of large pores in the

deposited part. A small frequency increase in the range of 25 and 50 µm in the powder results

in a larger increase in part. This is reflected by a generally higher regression constant and

slope coefficient if compared to bin range 10 µm. This leads to the conclusion that a powder

with large pores statistically results in a part with a significantly higher number of large pores

than a powder with smaller pores. Since Powder C did not measure any pores within bin

range 10 µm, can this connection not be fully made.

92

Powder A

2,01,81,61,41,21,0

2,0

1,5

1,0

0,5

0,0

P-10

D-1

0

S 0,471405

R-Sq 76,2%

R-Sq(adj) 68,3%

A - Particle & Deposit Pores: Bin Range 10 umD - 10 = - 0,6667 + 1,333 P - 10

3,02,52,01,51,0

11

10

9

8

7

6

5

4

P-25

D-2

5

S 1,28452

R-Sq 85,9%

R-Sq(adj) 81,3%

A - Particle & Deposit Pores: Bin Range 25 umD - 25 = 1,900 + 2,750 P - 25

2,01,51,00,50,0

12

10

8

6

4

2

0

P-50

D-5

0

S 0,632456

R-Sq 97,7%

R-Sq(adj) 96,9%

A - Particle & Deposit Pores: Bin Range 50 umD - 50 = 0,4000 + 5,000 P - 50

Figure 77. Fitted line plot of Powder A and the pore size frequency for each bin range.

93

Powder B

2015105

8

7

6

5

4

3

2

P-10

D-1

0

S 0,628113

R-Sq 93,7%

R-Sq(adj) 91,6%

B - Particle & Deposit Pores: Bin Range 10 umD - 10 = 1,436 + 0,2851 P - 10

14131211109

45

40

35

30

25

20

15

P-25

D-2

5

S 5,22529

R-Sq 83,4%

R-Sq(adj) 77,8%

B - Particle & Deposit Pores: Bin Range 25 umD - 25 = - 23,36 + 4,778 P - 25

543210

17,5

15,0

12,5

10,0

7,5

5,0

P-50

D-5

0

S 0,609994

R-Sq 98,5%

R-Sq(adj) 98,0%

B - Particle & Deposit Pores: Bin Range 50 umD - 50 = 6,488 + 2,070 P - 50

Figure 78. Fitted line plot of Powder B and the pore size frequency for each bin range.

94

Powder C

54321

7

6

5

4

3

2

1

P-25

D-2

5

S 0,948683

R-Sq 81,8%

R-Sq(adj) 75,7%

C - Particle & Deposit Pores: Bin Range 25 umD - 25 = 0,5000 + 1,100 P - 25

3,02,52,01,51,00,50,0

4,0

3,5

3,0

2,5

2,0

P-50

D-5

0

S 0,320256

R-Sq 89,0%

R-Sq(adj) 85,3%

C - Particle & Deposit Pores: Bin Range 50 umD - 50 = 1,692 + 0,6923 P - 50

Figure 79. Fitted line plot of Powder C and the pore size frequency for each bin range.

95

Powder D

14121086420

3,0

2,5

2,0

1,5

1,0

0,5

0,0

P-10

D-1

0

S 0,346028

R-Sq 94,0%

R-Sq(adj) 92,0%

D - Particle & Deposit Pores: Bin Range 10 umD - 10 = - 0,1733 + 0,2256 P - 10

111098765432

30

25

20

15

10

P-25

D-2

5

S 2,05818

R-Sq 94,5%

R-Sq(adj) 92,7%

D - Particle & Deposit Pores: Bin Range 25 umD - 25 = 4,917 + 2,042 P - 25

43210

9

8

7

6

5

4

P-50

D-5

0

S 1,29099

R-Sq 66,2%

R-Sq(adj) 55,0%

D - Particle & Deposit Pores: Bin Range 50 umD - 50 = 3,875 + 0,8750 P - 50

Figure 80. Fitted line plot of Powder D and the pore size frequency for each bin range.

96

Powder E

654321

6

5

4

3

2

1

0

P-10

D-1

0

S 1,83738

R-Sq 52,2%

R-Sq(adj) 36,3%

E - Particle & Deposit Pores: Bin Range 10 umD - 10 = - 1,291 + 0,8023 P - 10

141312111098765

40,0

37,5

35,0

32,5

30,0

27,5

25,0

P-25

D-2

5

S 2,29362

R-Sq 91,7%

R-Sq(adj) 89,0%

E - Particle & Deposit Pores: Bin Range 25 umD - 25 = 15,06 + 1,768 P - 25

7654321

22

20

18

16

14

12

10

8

P-50

D-5

0

S 3,37553

R-Sq 65,8%

R-Sq(adj) 54,4%

E - Particle & Deposit Pores: Bin Range 50 umD - 50 = 9,308 + 1,779 P - 50

97

1,00,80,60,40,20,0

3,0

2,5

2,0

1,5

1,0

0,5

0,0

P-more

D-m

ore

S 0,471405

R-Sq 90,7%

R-Sq(adj) 87,7%

E - Particle & Deposit Pores: Bin Range 'more'D - more = 0,6667 + 2,333 P - more

Figure 81. Fitted line plot of Powder E and the pore size frequency for each bin range.

98

5 Conclusions

The conclusions of this project are presented below:

The various methods of powder characterization show:

□ Powder A shows the highest rank of qualitative powder morphology.

□ Powder A shows the highest rank of quantitative powder morphology.

□ Powder A shows the highest rank of powder density.

□ All five powders meet the specifications of particle size distribution.

□ Image analysis is a limited method of measuring particle size distribution at

GKN. The resulting median particle size can be viewed as an approximate.

□ Hall Flowmeter is a method not sensitive enough to detect large differences

in flowability. Various rheological measurements are needed to explain the

flowability of a powder.

□ Powder C shows the lowest resistance to confined and unconfined flow.

Powder A shows the second best result.

□ Powder C shows the best ability to be aerated. Powder D shows the second

best result.

□ Powder A is least sensitive to vibrations and the ability to pack.

□ Powder E shows the highest powder efficiency but also the lowest process

repeatability. Powder C shows the lowest efficiency.

□ Powder C shows the lowest counts of pores and thus highest part quality.

Powder A shows the second highest quality.

A statistical correlation is found between:

□ Median particle size & basic flow energy for Powder A, B, D and E.

□ Median particle size & powder efficiency for all five powders.

□ ShapeFactor & basic flow energy for Powder B, D and E.

□ ShapeFactor & powder efficiency for Powder A, D and E.

□ Basic flow energy & powder efficiency for all five powders.

□ Basic flow energy & multi-bead height for Powder A, B, D and E

□ Particle pore frequency & deposit pore frequency for all five powders.

□ Particle pore sizes & deposit pore sizes for all five powders. The frequency

of large pores, between 25-50 µm, in powder most drastically increases the

amount of large pores in a deposited part.

99

Final conclusions for the resulted powder characteristics and final part quality are:

The current powder characteristics defined in GKN’s material specification, i.e.

composition, manufacturing method, Hall Flow rate and particle size distribution,

are insufficient to alone specify and predict a powders behavior and impact on

part quality. The powder variances detected with rheological measurements and

the correlation between basic flow energy to the powder efficiency, indicates that

not only Hall Flow rate should be specified. The calculated ShapeFactor for each

powder was also reflected by the quantitative ranking of the powders. The

correlation between ShapeFactor, flow energy and efficiency for some powders

indicates that a specification towards morphology could be needed. We can also

see a clear correlation between powder pore frequency, pore size and final part

quality. This means that the powder density should be considered to be somewhat

specified. How these characteristics should be specified and by what limits cannot

be concluded in this project.

With Powder C as a reference in this project, it is Powder A that shows the highest

powder quality and highest quality of deposited material. If a secondary source

should be considered from the four powders in this project, it ought to be Powder

A.

100

6 Future Work

There are a few points that can be considered for future research to this master thesis project:

Individual process optimization (design of experiments) of second source – If

GKN sees the potential to use Powder A.

□ The question to answer is: Can we, by optimizing the process for Powder A,

lower the amount of particles in built material to match the quality of

Powder C that is in production today?

Mechanical testing of laser metal deposited parts – Assess the impact of large

and/or small pores.

□ The question to answer is: Can we find a more distinct criterion for the

material specification?

Investigation of powder efficiency – High-speed camera at the outlet of nozzle.

□ The question to answer is: Can we detect the reason for loss of powder by

implementing a high-speed camera at the outlet of nozzle? Are there

powder losses by the outlet and if not, is clogging the reason?

101

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106

8 Appendix

In this section various graphs and data are presented to add additional information and

understanding to previously presented methods and results.

8.1 Particle Pixel Area Fraction

These graphs are presented to view that a stable average value is reached for each powder.

Mean value: 0,060%

Mean value: 0,207%

Mean value: 0,064%

Mean value: 0,078%

Mean value: 0,174%

0,00

0,10

0,20

0,30

0 10 20 30 Pix

el a

rea

frac

tio

n (

%)

Image no.

A - Porosity

Porosity Mean porosity

0,00 0,10 0,20 0,30 0,40 0,50

0 10 20 30 40 Pix

el a

rea

frac

tio

n

(%)

Image no.

B - Porosity

Porosity Mean porosity

0,00

0,10

0,20

0 10 20 30 Pix

el a

rea

frac

tio

n (

%)

Image no.

C - Porosity

Porosity Mean porosity

0,00

0,10

0,20

0,30

0 10 20 30

Pix

el a

rea

frac

tio

n (

%)

Image no.

D - Porosity

Porosity Mean porosity

0,00

0,20

0,40

0,60

0 10 20 30

Pix

el a

rea

frac

tio

n (

%)

Image no.

E - Porosity

Porosity Mean porosity

107

8.2 Particle Pore Diameter

These graphs are presented to view that a stable average diameter is reached for each powder.

All graphs are the results from 10 analyzed images taken by optical microscope with a 50x

magnification.

0

20

40

0 10 20

Dia

met

er (

µm

)

Pore no.

A - Pore diameter

Pore dia. Average dia.

0

20

40

60

0 50 100

Dia

met

er (

µm

)

Pore no.

B - Pore diameter

Pore dia. Average dia.

0

20

40

0 10 20 30

Dia

met

er (

µm

)

Pore no.

C - Pore diameter

Pore dia. Average dia.

0

20

40

0 20 40 60 80

Dia

met

er (

µm

)

Pore no.

D - Pore diameter

Pore dia. Average dia.

0

20

40

60

0 20 40 60 80

Dia

met

er (

µm

)

Pore no.

E - Pore diameter

Pore dia. Average dia.

108

8.3 Summary of Pore Data in Powder & Deposited Part

These tables are presented to view which bin ranges that measured 0 pores. These are the

reason for a missing fitted line plot during statistical correlation. A reminder to the measured

and counted pores in the powder; investigation is made on ten images of 50x magnification

which are not of equal area as for part analysis.

Powder A – Pores

Powder (µm) Part (µm)

10 25 50 More 10 25 50 More

1 1 0 0 0 4 1 1

1 1 1 0 1 6 5 1

1 2 1 0 1 6 5 2

2 3 1 0 2 10 5 2

2 3 2 0 2 11 11 2

Powder B – Pores

Powder (µm) Part (µm)

10 25 50 More 10 25 50 More

3 9 0 0 3 15 6 0

8 9 0 0 3 20 7 0

10 11 1 0 4 33 9 1

16 12 2 0 6 39 10 1

22 14 5 0 8 39 17 1

Powder C – Pores

Powder (µm) Part (µm)

10 25 50 More 10 25 50 More

0 1 0 0 0 2 2 0

0 2 1 0 0 3 2 0

0 3 2 0 0 3 3 0

1 4 2 0 0 4 3 0

2 5 3 0 0 7 4 1

Powder D – Pores

Powder (µm) Part (µm)

10 25 50 More 10 25 50 More

1 3 0 0 0 10 4 0

2 3 1 0 0 13 5 0

3 8 2 0 1 19 5 0

6 9 4 0 1 23 6 0

14 11 4 0 3 29 9 1

Powder E – Pores

Powder (µm) Part (µm)

10 25 50 More 10 25 50 More

1 5 1 0 0 24 8 0

5 5 2 0 1 24 12 1

5 7 3 0 2 25 16 1

6 9 3 1 3 34 19 3

6 14 7 1 6 39 20 3

109

8.4 Morphology

All five ShapeFactor values obtained from image analysis for all powders are presented in the

table below.

Particle ShapeFactor

A B C D E

Mean StDev Mean StDev Mean StDev Mean StDev Mean StDev

1,0547 0,0151 1,0713 0,0225 1,0600 0,0211 1,0631 0,0204 1,0682 0,0218

1,0547 0,0202 1,0728 0,0226 1,0593 0,0207 1,0637 0,0213 1,0698 0,0224

1,0526 0,0148 1,0726 0,0228 1,0569 0,0203 1,0645 0,0215 1,0693 0,0225

1,0554 0,0154 1,0734 0,0242 1,0608 0,0224 1,0644 0,0211 1,0700 0,0228

1,0554 0,0153 1,0674 0,0203 1,0593 0,0219 1,0654 0,0232 1,0713 0,0224

8.5 Rheometer

All the values obtained from the stability and variability test are viewed below. From the BFE

results it is seen that the second test for all powders show a higher value which is believed to

be influenced by external surroundings. For that reason it is the first test results that are

plotted in Figure 46 and further analyzed.

All values from the aeration test are also presented below. The plotted values for the aeration

test are seen previously in Figure 47.

Stability & Variable Flow Energy

Basic Flowability Energy (mJ)

Test no. A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 Blade tip speed (mm/s)

1 560 562 652 610 451 445 620 636 810 832 100

2 557 565 645 618 445 451 609 629 807 849 100

3 558 567 632 618 440 449 615 641 824 854 100

4 560 582 632 638 438 455 619 653 843 858 100

5 562 585 614 637 436 454 622 657 857 863 100

6 564 578 634 655 437 452 631 665 881 872 100

7 575 588 609 665 435 465 644 677 891 896 100

8 579 582 622 653 439 459 653 691 902 899 100

9 589 602 634 685 445 470 671 729 921 943 70

10 600 615 634 679 444 471 663 725 915 918 40

11 622 637 630 677 445 468 659 717 869 887 10

110

Aeration Test Data

Basic flow energy (mJ)

Air velocity (mm/s) A B C D E

0 553 615 443 505 597

1 545 561 394 499 609

2 513 509 355 452 594

3 474 465 316 427 591

4 434 425 279 397 565

5 391 390 244 365 548

6 339 351 205 324 517

8 221 249 104 186 384

10 137 185 53 95 280

8.6 Part Geometry

The measured height, width and penetration for each area section of multi and single-beads

are given in the tables below. The plotted values for all five powders are seen previously in

Figure 49 to Figure 54.

A – Average Geometry for Each Area Section

Multi-bead Single-bead

(mm) A StDev B StDev C StDev A StDev B StDev C StDev

h 1,76 0,02 1,76 0,02 1,80 0,02 0,24 0,01 0,24 0,01 0,24 0,01

w 11,52 0,05 11,54 0,05 11,53 0,04 1,54 0,01 1,53 0,02 1,52 0,01

Pmin 0,20 0,02 0,20 0,02 0,19 0,01 - - - - - -

Pmax 0,30 0,01 0,33 0,02 0,30 0,01 0,25 0,01 0,25 0,01 0,24 0,01

B – Average Geometry for Each Area Section

Multi-bead Single-bead

(mm) A StDev B StDev C StDev A StDev B StDev C StDev

h 1,77 0,03 1,75 0,02 1,81 0,02 0,25 0,01 0,24 0,01 0,26 0,01

w 11,55 0,04 11,50 0,04 11,51 0,06 1,55 0,01 1,53 0,03 1,54 0,01

Pmin 0,19 0,02 0,19 0,02 0,19 0,02 - - - - - -

Pmax 0,31 0,02 0,31 0,02 0,31 0,03 0,25 0,01 0,25 0,01 0,25 0,01

C – Average Geometry for Each Area Section Multi-bead Single-bead

(mm) A StDev B StDev C StDev A StDev B StDev C StDev

h 1,58 0,03 1,57 0,03 1,62 0,02 0,21 0,01 0,22 0,01 0,21 0,01

w 11,48 0,07 11,48 0,05 11,48 0,06 1,55 0,01 1,55 0,00 1,56 0,01

Pmin 0,17 0,01 0,18 0,01 0,18 0,01 - - - - - -

Pmax 0,27 0,02 0,28 0,02 0,28 0,01 0,23 0,01 0,23 0,01 0,23 0,01

111

D – Average Geometry for Each Area Section Multi-bead Single-bead

(mm) A StDev B StDev C StDev A StDev B StDev C StDev

h 1,82 0,03 1,80 0,03 1,83 0,03 0,25 0,01 0,24 0,01 0,24 0,02

w 11,51 0,04 11,53 0,07 11,51 0,07 1,56 0,02 1,56 0,02 1,55 0,01

Pmin 0,17 0,01 0,17 0,02 0,18 0,01 - - - - - -

Pmax 0,28 0,01 0,29 0,02 0,29 0,01 0,24 0,01 0,24 0,01 0,24 0,02

E – Average Geometry for Each Area Section Multi-bead Single-bead

(mm) A StDev B StDev C StDev A StDev B StDev C StDev

h 1,83 0,03 1,82 0,04 1,84 0,03 0,25 0,01 0,25 0,01 0,25 0,02

w 11,49 0,05 11,49 0,02 11,48 0,06 1,55 0,02 1,54 0,01 1,55 0,02

Pmin 0,15 0,02 0,16 0,02 0,16 0,02 -

- - Pmax 0,26 0,02 0,28 0,02 0,28 0,03 0,24 0,01 0,23 0,01 0,23 0,01

8.7 Statistical Evaluation

The following boxplots visually show the response data of particle size distribution from

image analysis results and various geometrical measurements. These are presented in

appendix since they were not in subject for a subsequent statistical correlation in this project

but still of interest for additional statistical observations.

EDCBA

16

15

14

13

12

Hall Flo

w r

ate

(s/

50g)

Hall Flow rate

112

EDCBA

67

66

65

64

63

62

61

60

59

58

Dista

nce

(m

m)

IA - D10

EDCBA

78

76

74

72

70

Dista

nce

(m

m)

IA - D50

EDCBA

105

100

95

90

85

80

Dista

nce

(m

m)

IA - D90

113

E-AD-AC-AB-AA-A

11,65

11,60

11,55

11,50

11,45

11,40

11,35

Dista

nce

(m

m)

MB width - Section A

E-BD-BC-BB-BA-B

11,65

11,60

11,55

11,50

11,45

11,40

11,35

Dis

tance

(m

m)

MB width - Section B

E-CD-CC-CB-CA-C

11,65

11,60

11,55

11,50

11,45

11,40

11,35

Dista

nce

(m

m)

MB width - Section C

114

E-AD-AC-AB-AA-A

0,375

0,350

0,325

0,300

0,275

0,250

Dista

nce

(m

m)

MB Pmax - Section A

E-BD-BC-BB-BA-B

0,375

0,350

0,325

0,300

0,275

0,250

Dista

nce

(m

m)

MB Pmax - Section B

E-CD-CC-CB-CA-C

0,375

0,350

0,325

0,300

0,275

0,250

Dista

nce

(m

m)

MB Pmax - Section C

115

E-AD-AC-AB-AA-A

0,24

0,22

0,20

0,18

0,16

0,14

0,12

Dista

nce

(m

m)

MB Pmin - Section A

E-BD-BC-BB-BA-B

0,24

0,22

0,20

0,18

0,16

0,14

0,12

Dista

nce

(m

m)

MB Pmin - Section B

E-CD-CC-CB-CA-C

0,24

0,22

0,20

0,18

0,16

0,14

0,12

Dista

nce

(m

m)

MB Pmin - Section C

116

E-AD-AC-AB-AA-A

0,30

0,28

0,26

0,24

0,22

0,20

Dista

nce

(m

m)

SB height - Section A

E-BD-BC-BB-BA-B

0,30

0,28

0,26

0,24

0,22

0,20

Dista

nce

(m

m)

SB height - Section B

E-CD-CC-CB-CA-C

1,60

1,58

1,56

1,54

1,52

1,50

1,48

Dista

nce

(m

m)

SB width - Section C

117

E-AD-AC-AB-AA-A

0,28

0,27

0,26

0,25

0,24

0,23

0,22

0,21

0,20

Dista

nce

(m

m)

SB Pmax - Section A

E-BD-BC-BB-BA-B

0,28

0,27

0,26

0,25

0,24

0,23

0,22

0,21

0,20

Dista

nce

(m

m)

SB Pmax - Section B

E-CD-CC-CB-CA-C

0,28

0,27

0,26

0,25

0,24

0,23

0,22

0,21

0,20

Dista

nce

(m

m)

SB Pmax - Section C