Pneumatic Injection Mould Machine Capability and

Techno-Economic Study

Matthew Paul Keyser

Department of Industrial Engineering

University of Stellenbosch

Study Leader: Theuns Dirkse van Schalkwyk

Final year project presented in partial fulfilment of the requirements for the degree of

Industrial Engineering at Stellenbosch University

B.Eng Industrial

i

Declaration

I, the undersigned, hereby declare that the work contained in this final year project is my own original

work and that I have not previously in its entirety or in part submitted it at any university for a degree.

…………………………… ………………………

Signature Date

ii

ECSA Exit Level Outcomes Reference

Exit level outcome Section(s) Page(s)

1. Problem solving All All

5. Engineering methods, skills & tools, incl. IT 4, 5 & 6 31 - 55

6. Professional & Technical communication All All

9. Independent learning ability 2 & 3 5 - 30

10. Engineering professionalism All All

iii

Abstract

The injection moulding process constitutes a vital part of the manufacturing sector and plastic

injection moulding (PIM) is one aspect of this process. The following report discusses the

characteristics and capabilities of a custom made injection moulding machine (CIMM) powered by

pneumatics, with the purpose of moulding plastic parts. These moulded parts will be measured and

analysed statistically in order to determine the optimal operational settings for the CIMM.

For the optimal settings to be determined, a set of experiments needs to be executed. Various

literature was studied to ensure an appropriate project methodology would be implemented to

successfully carry out the experimentation. Consequently, it was determined that a full factorial

design of experiments (DoE) would be executed with the assistance of Taguchi’s Optimisation

Method and various statistical analysis. Twenty-seven experiments were executed with each

experiment consisting of a unique combination of three factors, at three different levels. The three

factors are as follows:

1. The temperature at which the plastic is moulded (MT), in degrees Celsius (oC).

2. The length of time given for the plastic to fill the mould (FT), in seconds (sec).

3. The length of time kept in place before ejecting part (PT), in seconds (sec).

Once all twenty-seven experiments had been completed, each moulded part was inspected and

measured for data collection according to the following four criteria:

1. Visual inspection of part conformance

2. Rework time, measured in seconds (sec)

Rework time for runner

Rework time for finishing

3. Part weight, measured in grams (g)

4. Part thickness, measured in millimetres (mm)

iv

The above analysis provided the data that would be used to determine the optimal operational setting

for the CIMM by conducting the following forms of analysis:

Initial observation analysis

Techno-economic analysis

Statistical analysis

Descriptive statistical analysis

ANOVA analysis

Process control analysis

Waste Analysis

The statistical analysis was executed with the assistance of RSudio which is the interface for the open

source statistical software package R. All forms of analysis shared the same two objectives; total cost

per part (𝑇𝐶𝑝𝑎𝑟𝑡) must be minimised and part thickness must be maximised whilst minimising the

variation of part thickness in moulding process. The brief summary of the three possible operational

settings, based on their performance in the analysis process, are given in the table below.

A break-even analysis was then conducted to determine the single most optimal solution for the

CIMM so that a place for the machine in the business sector can be motivated. This resulted in

Experiment 14 being suggested as the most optimal solution due to its superior monthly profit which

will be the most beneficial factor moving forward, from a business perspective.

The findings of this project recommend that the CIMM would be best suited for small scale

production. This finding can lend itself to small business owners or enthusiasts as it is a compact and

mobile machine that can be operated and stored in a garage.

MT FT PT

14 190 10 5 1,26

23 200 10 5 1,26

26 200 15 5 1,67

Experiment TCpart (R )Operational settings

Table 1: Three possible optimal operational settings for the CIMM.

v

Opsomming

Die spuitgietings-proses speel 'n belangrike rol in die vervaardigingsektor en plastiese spuitvorms

(‘PIM’) is een aspek van hierdie proses. Hierdie dokument bespreek die kenmerke en vermoëns van 'n

doelvervaardigde spuitvormmasjien (‘CIMM’) wat pneumaties aangedryf word deur met die doel om

plastiese onderdele te giet. Hierdie gevormde onderdele sal gemeet en statisties geanaliseer word om

die optimale operasionele verstellings vir die CIMM te bepaal.

Om die optimale verstellings te bepaal, moet 'n stel eksperimente uitgevoer word. Verskeie bronne

uit die literatuur is bestudeer om ‘n toepaslike projek metodologie te identifiseer en te implimenteer

sodat die eksperiment suksesvol uitgevoer kan word. Gevolglik is vasgestel dat 'n volle faktoriale

ontwerp van eksperimente (DoE) uitgevoer sal word met behulp van Taguchi se Optimalisering-

metode asook verskeie statistiese analises. Sewe en twintig eksperimente is uitgevoer waar elke

eksperiment bestaan uit 'n unieke kombinasie van drie faktore, op drie verskillende vlakke. Die drie

faktore is soos volg:

1. Die temperatuur waarteen die plastiek gegiet is (‘MT’), in grade Celsius (oC).

2. Die tydsduur om die gietvorm met plastiek te vul (‘FT’), in sekondes (sek).

3. Die tydsduur wat die onderdeel in die gietvorm in plek gehou word voordat die onderdeel

uitgeset word (‘PT’), in sekondes (sek).

Nadat al sewe en twintig eksperimente afgehandel is, is elke gevormde deel geïnspekteer en gemeet

vir die versameling van data volgens die volgende vier kriteria:

1. Visuele inspeksie om te bepaal of die onderdeel aan die vooraf bepaalde part vereistes

voldoen

2. Herwerk tyd gemeet in sekondes (sek)

Herwerk-tyd vir die ‘loper’

Herwerk-tyd vir die afwerking

3. Die onderdeel se gewig, gemeet in gram (g)

4. Die onderdeel se dikte, gemeet in millimeters (mm)

vi

Die bogenoemde ontleding het die nodige data verskaf om die optimale operassionele verstellings vir

die CIMM vas te stel deur die volgende vorms van analise te onderneem:

Aanvanklike waarnemings analise

Tegno-ekonomiese analise

Statistiese analise

Beskrywende statistiese analise

ANOVA analise

Proses beheer ontleding

Verkwisting (afval) analise

Die statistiese analise is uitgevoer met die hulp van RSudio wat die koppelvlak is vir die “open

source” statistiese sagteware pakket R. Alle vorms van analise het dieselfde twee doelwitte gedeel;

totale koste per onderdeel (‘𝑇𝐶𝑝𝑎𝑟𝑡’) moet tot die minimum beperk word en die onderdeel dikte moet

gemaksimeer word terwyl die variasie van die onderdeel dikte in die gietproses beperk moet word.

Die kort opsomming van die drie moontlike operasionele verstellings, soos gebasseer op hul prestasie

in die analise proses, word in die onderstaande tabel aangebring.

A gelykbreek analise gevolglik uitgevoer om die optimale oplossing vir die CIMM te bepaal sodat 'n

plek vir die masjien in die sakesektor gemotiveer kan word. Daarvolgens word Eksperiment 14

aanbeveel as die optimale oplossing weens sy uitstekende maandelikse wins. Vanuit ‘n

besigheidsperspektief is hierdie wins die mees voordelige faktor om vooruit te gaan.

Die bevindinge van hierdie projek beveel aan dat die CIMM mees geskik sal wees vir kleinskaalse

produksie. Hierdie bevinding leen homself tot kleinsake eienaars of entoesiaste, aangesien dit 'n

kompakte en mobiele masjien is wat in ‘n motorhuis gebruik en gestoor kan word.

Table 2: Drie moontlike optimale operasionele vertsellings vir die CIMM.

MT FT PT

14 190 10 5 1,26

23 200 10 5 1,26

26 200 15 5 1,67

EksperimentOperasionele verstellings

TCpart (R )

vii

Acknowledgements

I would firstly like to thank my family for the love and support they have shown me over my years at

university. Special mention has to go to my parents Min and Randolf Keyser. If it were not for all the

sacrifices they have made for me, I would not have had the privilege of studying Industrial

Engineering at an amazing university.

I would like to thank my good friend Soren Bruce for his time and effort in helping me with the data

collection which was a time consuming and tedious process due to the nature of this project.

I would like to thank my study leader, Theuns Dirkse van Schalkwyk, for his for his guidance and

assistance in the execution of this project.

Lastly, I would like to acknowledge my friends, especially my classmates, who have made the

engineering course just a bit more fun and enjoyable.

Jeremiah 29:11

“I know the plans I have for you,” declares the Lord, “plans to prosper you and not to harm you,

plans to give you hope and a future.”

viii

Contents

Declaration ............................................................................................................................................... i

ECSA Exit Level Outcomes Reference .................................................................................................. ii

Abstract .................................................................................................................................................. iii

Opsomming ............................................................................................................................................. v

Acknowledgements ............................................................................................................................... vii

Contents ............................................................................................................................................... viii

List of Figures ........................................................................................................................................ xi

List of Tables ........................................................................................................................................ xii

Glossary ............................................................................................................................................... xiii

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

1.1 Project Background and Information ............................................................................................ 1

1.2 Problem Statement ........................................................................................................................ 2

1.3 Project Aim ................................................................................................................................... 2

1.4 Project Objectives ......................................................................................................................... 3

1.5 Limitations and Assumptions of the Study ................................................................................... 3

1.6 Proposed Study Approach and Methodology ............................................................................... 3

1.7 Structure of the Report .................................................................................................................. 4

1.8 Conclusion .................................................................................................................................... 4

2. Literature Review ................................................................................................................................ 5

2.1 Injection Moulding ........................................................................................................................ 5

2.1.1 Background ................................................................................................................................ 5

2.1.2 Pneumatic Injection Moulding ................................................................................................... 5

2.1.3 The Basic Process ...................................................................................................................... 6

2.1.4 Importance of Moulding Quality ............................................................................................... 7

2.1.4.1 Moulding Materials ............................................................................................................. 7

2.1.4.2 Mould Requirements ........................................................................................................... 8

2.1.4.3 Mould Performance............................................................................................................. 8

2.1.4.4 Accuracy and Finish............................................................................................................ 9

2.2 Design of Experiments ................................................................................................................ 10

2.3 Taguchi Optimization Method .................................................................................................... 11

2.4 RStudio ....................................................................................................................................... 14

2.4.1 R as a Software Development Platform ................................................................................... 14

2.4.2 Design of Experiments in R ..................................................................................................... 15

ix

2.5 Techno-economic Study ............................................................................................................. 16

2.5.1 Cost per part ............................................................................................................................. 16

2.5.2 Break-even Analysis ................................................................................................................ 18

3. Project Methodology ......................................................................................................................... 19

3.1 Experimental Procedure .............................................................................................................. 19

3.1.1 Quality Aspect to be Optimised ............................................................................................... 19

3.1.2 Identify the Noise Factors and Test Conditions ....................................................................... 19

3.1.3 Identify the Control Factors and their Alternative Levels........................................................ 19

3.1.4 Design Experimental Matrix .................................................................................................... 20

3.1.5 Conduct Matrix Experiment..................................................................................................... 21

3.2 Data Collection and Analysis Methodology ............................................................................... 25

3.2.1 Quality Inspection Criteria ....................................................................................................... 25

3.2.2 Quantitative Analysis of Part Quality ...................................................................................... 25

4. Analysis of Data ................................................................................................................................ 31

4.1 Initial Observation Analysis........................................................................................................ 31

4.2 Techno-economic Analysis ......................................................................................................... 33

4.3 Statistical Analysis ...................................................................................................................... 34

4.3.1 Descriptive Statistical Analysis ............................................................................................... 34

4.3.2 ANOVA Analysis .................................................................................................................... 39

4.3.3 Process Control Analysis ......................................................................................................... 45

4.4 Waste Analysis ............................................................................................................................ 48

5. Discussion of Results ........................................................................................................................ 51

5.1 Summary of Results .................................................................................................................... 51

5.1 Break-even Analysis ................................................................................................................... 52

6. Conclusion ........................................................................................................................................ 54

6.1 Suggested Operational Settings .................................................................................................. 54

6.2 Improvements and Recommendations for Future Studies .......................................................... 54

6.3 Skills Applied and Developed by Student .................................................................................. 55

6.4 Benefits to Society ...................................................................................................................... 55

7. References ......................................................................................................................................... 56

Appendix A ........................................................................................................................................... 58

CIMM Specifications ........................................................................................................................ 58

Appendix B ........................................................................................................................................... 59

Classification of Design of Experiments .......................................................................................... 59

Appendix C ........................................................................................................................................... 62

Factors for Control Charts ................................................................................................................ 62

x

Appendix D ........................................................................................................................................... 63

Coding used in RStudio .................................................................................................................... 63

Appendix E ........................................................................................................................................... 65

Digital Watt Meter ............................................................................................................................ 65

Appendix F............................................................................................................................................ 66

Planned Project Timeline .................................................................................................................. 66

xi

List of Figures

Figure 1: Image of the CIMM .................................................................................................................. 6

Figure 2: Cyclic Sequence in the PIM Process (Rosato, 2000) ................................................................ 7

Figure 3: Polypropylene granules. .......................................................................................................... 8

Figure 4: Mould for the part that will be manufactured for this project ............................................... 9

Figure 5: Taguchi method flow chart (Unal & Dean, 1991). ................................................................. 12

Figure 6: Orthogonal Array (OA) Based Simulation Algorithm (Unal & Dean, 1991). .......................... 13

Figure 7: Flow chart of the experimental procedure. ........................................................................... 23

Figure 8: The sequential numbering chart. ........................................................................................... 24

Figure 9: A visually conforming part, prior to rework. .......................................................................... 26

Figure 10: A visually non-conforming part. ........................................................................................... 26

Figure 11: Conforming part following rework for runner. .................................................................... 27

Figure 12: Conforming part following rework for finishing. ................................................................. 27

Figure 13: Weighing part prior to rework (PW). ................................................................................... 28

Figure 14: Weighing part after rework (PWA). ..................................................................................... 28

Figure 15: Illustrating where and how the part thickness was measured using the digital vernier

calliper. .................................................................................................................................................. 29

Figure 16: A bar graph showing the number of conforming parts produced per experiment. ............ 32

Figure 17: Removing moulded parts by hand. ...................................................................................... 32

Figure 18: Histogram showing the distribution of total cost per part. ................................................. 34

Figure 19: Statistical summary of measurable parameters and collected data. .................................. 35

Figure 20: Total cost per part as a function of mould temperature. .................................................... 35

Figure 21: Total cost per part as a function of filling time. ................................................................... 36

Figure 22: Total cost per part as a function of packing time. ............................................................... 36

Figure 23: Thickness as a function of mould temperature. .................................................................. 37

Figure 24: Thickness as a function of filling time. ................................................................................. 38

Figure 25: Thickness as a function of packing time. ............................................................................. 38

Figure 26: Total cost as a function of mould temperature with linear trend line. ............................... 39

Figure 27: Total cost as a function of filling time with linear trend line. .............................................. 40

Figure 28: Total cost as a function of packing time with linear trend line. .......................................... 40

Figure 29: Summary of ANOVA test between total cost per part and mould temperature................. 41

Figure 30: Part thickness as a function of mould temperature with linear trend line. ........................ 42

Figure 31: Part thickness as a function of filling time with linear trend line. ....................................... 42

Figure 32: Part thickness as a function of packing time with linear trend line. .................................... 43

Figure 33: 3D scatter plot showing total cost per part as a function of filling time and mould

temperature. ......................................................................................................................................... 44

Figure 34: 3D scatter plot showing part thickness as a function of filling time and mould temperature

.............................................................................................................................................................. 44

Figure 35: X-bar chart created in Excel. ................................................................................................ 47

Figure 36: Summary of waste analysis. ................................................................................................. 49

Figure 37: Appendix A - Specifications of the CIMM made by Lindmann Machines & Equipment

(Super Products Website, 2015). .......................................................................................................... 58

Figure 38: Digital watt meter that was used to measure energy consumption (kWh). ....................... 65

xii

List of Tables

Table 1: Three possible optimal operational settings for the CIMM. .................................................... iv

Table 2: Drie moontlike optimale operasionele vertsellings vir die CIMM. .......................................... vi

Table 3: Advantages and disadvantages of PIM (Rosato, 2000). ............................................................ 7

Table 4: Requisites and Tools for Sound Experimentation (Juran & Godfrey, 1998). .......................... 10

Table 5: Advantages and disadvantages of R (Williams, 2012). ........................................................... 15

Table 6: Control factors and their levels for experimentation. ............................................................ 20

Table 7: The L27 Orthogonal Array (OA) ............................................................................................... 21

Table 8: OA with Control Factors and their different levels for Experimentation. ............................... 22

Table 9: Table showing how the standard rework times were determined. ........................................ 27

Table 10: The data collected for experiment 8 is shown here as an example of the data collected for

each experiment. .................................................................................................................................. 30

Table 11: Unit costs for the measured cost parameters. ..................................................................... 33

Table 12: Summary of techno-economic analysis. ............................................................................... 33

Table 13: Optimal based on the variation of total cost per part. ......................................................... 37

Table 14: Optimal settings based on the variation of part thickness. .................................................. 39

Table 15: ANOVA results for total cost per part. .................................................................................. 41

Table 16: ANOVA results for part thickness. ......................................................................................... 43

Table 17: Optimal settings for total cost per part. ............................................................................... 45

Table 18: Optimal settings for part thickness. ...................................................................................... 45

Table 19: Summary of values used for the X-bar chart. ....................................................................... 46

Table 20: Summary of results for process control analysis. ................................................................. 48

Table 21: Experiments that are statistically in control. ........................................................................ 48

Table 22: Table showing wastage as a percentage of the material used for the completed part (i.e.

after rework). ........................................................................................................................................ 50

Table 23: Summary of possible operational settings based on the conducted analyses. .................... 51

Table 24: Remaining possible optimal operational settings. ................................................................ 51

Table 25: Summary of break-even analysis. ......................................................................................... 52

Table 26: Optimal operational settings for the CIMM. ......................................................................... 54

Table 27: Appendix B.1 - Classification of Designs (Juran & Godfrey, 1998). ....................................... 59

Table 28: Appendix B.2 - Classification of Designs (Continued). .......................................................... 60

Table 29: Appendix B.3 - Classification of Designs (Continued) ........................................................... 61

Table 30: Appendix C - Table showing factors for the different control charts (Evans & Lindsay, 2014).

.............................................................................................................................................................. 62

Table 31: Project timeline plan. ............................................................................................................ 66

xiii

Glossary

Acronyms

ANOVA Analysis of variance

BEP Break-even point

CIMM Custom injection moulding machine

CL Center Line (�̿�)

DoE Design of experiments

FT Filling time (sec)

LCL Lower control limit

MT Mould temperature (oC)

OA Orthogonal array

PIM Plastic injection moulding

PP Polypropylene

PT Packing time (sec)

PW Part weight (g)

PWA Part weight after rework (g)

SP Selling price per part (R)

UCL Upper control limit

VC Variable cost per part, in this project see 𝑇𝐶𝑝𝑎𝑟𝑡

Symbols

𝐶𝐸,𝑝𝑎𝑟𝑡 Energy cost per part (R)

𝐶𝐸,𝑇𝑜𝑡 Total energy cost (R)

𝐶𝑘𝑊ℎ Cost per kilowatt hour (R/kWh)

𝐶𝐿 Labour cost per hour (R/hr)

𝐶𝐿,𝑝𝑎𝑟𝑡 Labour cost per part (R)

𝐶𝐿,𝑇𝑜𝑡 Total labour cost (R)

𝐶𝑀,𝑝𝑎𝑟𝑡 Material cost per part (R)

𝐶𝑀,𝑇𝑜𝑡 Total material cost (R)

xiv

𝐶𝑅𝑀 Raw material cost (R/kg)

𝐸𝑇𝑜𝑡 Total energy consumption (kWh)

𝑛 Number of conforming parts per experimental run

𝑠 Standard deviation

�̅� Average standard deviation

𝑇𝐶 Cycle time per part (sec)

𝑇𝑅 Rework time per part (sec)

𝑇𝑅,𝑓𝑖𝑛𝑖𝑠ℎ Rework time for finishing (sec)

𝑇𝑅,𝑟𝑢𝑛𝑛𝑒𝑟 Rework time for runner (sec)

𝑇𝐶𝑝𝑎𝑟𝑡 Total cost per part (R)

�̅� Mean

�̿� Overall mean

1

1. Introduction

The first chapter will introduce the project that will be undertaken and the problem that must be

solved. This will be achieved by providing some background on the problem as well as the objectives

that need to be met, along with the methodology that will also be implemented.

1.1 Project Background and Information

The machine that will form the centre of this project is a custom injection moulding machine or more

simply known as a CIMM. It was designed and built by Lindmann Machines and Equipment, a South

African company based in Cape Town. This company produced the CIMM with the purpose of it to

be ideal for entry level entrepreneurs wanting to execute small scale production. Consequently it was

also designed to be simple to operate and maintain.

The CIMM is a pneumatic type injection moulder and this means it uses compressed air to drive the

moulding process. Essentially, the mould is opened and closed using pneumatics and the material that

fills the mould itself is also driven into the mould using pneumatics. This is different from

conventional injection moulders which typically use hydraulics or electricity to power the systems

that create the final mould. An added benefit of a pneumatic machine is that it automatically releases

the final moulded part which is indirectly achieved with pneumatics. For the purpose of this study,

plastic will be the material used in the moulding process and will thus form the structure of the parts

that are produced.

The injection moulding machine has a built in programmable logic controller (PLC) which is a digital

computer, and this is where the input parameters are entered into to. There are four parameters that

can be programmed into the CIMM on this PLC and they include:

1. Temperature at which the heating element turns off (T1).

2. Temperature at which the machine injects molten plastic into the mould (T2).

3. The total length of time that the machine injects molten plastic into the mould.

4. The length of time that the mould is held closed before ejecting the part.

Both of these temperature settings are interlinked as the PLC has a two-way controller that aims to

keep the temperature as constant as possible during the moulding process. This two-way controller is

the reason for the two temperature setting parameters, however, because they are interlinked they will

be presented as one parameter for the remainder of the report, namely mould temperature (MT).

It must also be mentioned that parameter 3 will be presented as the filling time for the mould (FT) and

that parameter 4 will be presented as the packing time for the mould (PT). The packing time

2

represents the time the mould is help in place to allow the mould to cool before it is ejected, following

the completion of the filling.

The variation of these three parameters will directly affect the quality of the finished parts as well the

time that it takes for each product to be produced. Since the aim of this project is to manufacture

products in the most efficient way while minimising the costs, the combination of these parameters

will prove imperative to the final results.

A machine is not able to perfectly replicate each part or product that it manufactures, due to natural

variations in the manufacturing process, and this is why tolerances exist. As long as the produced

parts conform to the specified tolerances, they will pass the quality inspections. The CIMM itself will

also produce parts that diverge from the specified tolerance levels as a result of these variations.

1.2 Problem Statement

It is unknown what combination of parameter settings on the CIMM will yield an optimal

performance. These parameters, outlined in the previous section, will have a direct effect on the

degree to which produced parts will conform to the tolerances. The parameters are formalised as:

1. The temperature of the molten plastic (MT).

2. The time allocated for the molten plastic to flow into the mould (FT).

3. The time allocated for the mould to be kept in place before the part is ejected (PT).

A combination of these three parameters must be determined in order to find the optimal operational

performance for the CIMM.

1.3 Project Aim

The purpose of this study is to identify the optimal economic value of the CIMM by determining the

optimal operational parameters of the CIMM to produce conforming parts and taking into account the

associated costs to manufacture these parts.

This will be achieved by statistically determining the optimal operational settings of the injection

moulder to produce these parts as efficiently as possible. Finally the production of parts has to be

accomplished as economically as possible in order to motivate a place for the machine in the business

sector.

3

1.4 Project Objectives

The objectives of this project will be satisfied by conducting the following research steps:

Experimentally determine the capabilities of the custom injection moulding machine.

Examining the characteristics of the moulded parts by measuring and inspecting the final

product.

Analysing the results using R statistics software.

Based on findings determine the optimal operating settings of the CIMM to produce the given

parts.

Complete a techno-economic study.

Make recommendations based on the analysis.

Motivate a business case for where the machine might be operated profitably.

1.5 Limitations and Assumptions of the Study

The study will acknowledge the following initial limitations:

A pneumatic-type injection moulding machine will be the only injection moulding machine

type to be used in this study.

Plastic will comprise the only material used for the moulding of the parts.

Only one design part will be tested.

The study will then also acknowledge the following assumptions:

Functions that are not available in R can be coded by the student.

All software required to conduct the study will be readily available.

A sufficient pneumatic-type injection moulder will be readily available to the student.

1.6 Proposed Study Approach and Methodology

A well thought and structured methodology needs to be put in place to insure that the objectives on

the problem are satisfied. Firstly, a literature study will be performed in order to gain a thorough

understanding of the problem at hand. This will include understanding the concept of injection

moulding and the fundamental properties of how it works. Additionally, it will look at the various

types of injection moulding and the different parts that are produced as a result of these variations.

The specific injection mould machine in question will also be analysed and assessed in order to

understand its functionality and how it should perform.

Parts will then be produced by this machine over a range of operational settings which will yield

significant data that will be documented. Firstly the quality of the finished parts will be assessed by

4

measuring and inspecting that they meet the design parameters and secondly the optimal operational

settings for the injection mould machine will also be determined. This will be determined by

recording all the results and analysing all the data using R software which is widely used for statistical

computing and graphics.

In addition to the optimal operational aspect of the injection mould machine, the economic aspect also

has to analysed. This will be achieved by developing a techno-economic assessment. From an

economic perspective it will be important to produce each part as inexpensively as possible and this

will be most effectively reached by using as little material as possible.

The goal will be to manufacture high quality parts in the quickest possible time while still trying to

manufacture each part in the most cost effective manner. This will provide a good case to motivate a

position for this injection moulding machine in the manufacturing industry.

1.7 Structure of the Report

Chapter 2 covers the literature review phase of the project which forms a large portion of the final

year project and aided with understanding the tools that will be required to satisfy the project

objectives. Chapter 3 addresses the methodologies that were used to conduct the experiments and

capture the necessary data. In Chapter 4, the collected data was compiled and statistically analysed,

and in addition to this a techno-economic analysis was also conducted. The results of the project are

investigated and discussed in Chapter 5 in order to determine the optimal operating regime for the

CIMM. The final chapter, Chapter 6, constitutes the conclusion for the final year project which

provides a solution to the Problem Statement (Section 1.2) by satisfying the Project Aim (Section

1.3).

1.8 Conclusion

This chapter introduced the final year project by identifying the Problem Statement along with the

purpose of the project, shown in the Project Aim section. In addition to this, the objectives were laid

out with the proposed methodology illustrating how these objectives will be reached. Chapter 2 will

focus on the literature study which forms a vital step in meeting the objectives of the final year

project.

5

2. Literature Review

This chapter will look at various areas of literature that deal with background information, research

and existing methods that relate to this research problem. The information will then be applied to the

project in order to successfully reach the outlined objectives.

2.1 Injection Moulding

Injection moulding is a significant a part of manufacturing. As the injection moulding industry has

evolved over the years so to have the machines that produce the final products.

2.1.1 Background

Nowadays there are a range of injection moulding machines, and this is because various models are

better equipped to manufacture specific parts over others, depending on the way that they operate. In

addition to different machine types there are also different materials that are involved in the injection

moulding process (Gauthier, 1995). The most common ones include plastic, metal and glass and for

this project only plastic will be analysed and this is termed Plastic Injection Moulding (PIM).

PIM is the most common manufacturing method for producing parts made out of plastic material. It is

an extremely versatile process that can produce parts with holes, springs, threads, hinges and

undercuts in a single operation (Gauthier, 1995). Moulded parts can be simple or complex and can be

solid, foamed, reinforced or filled. They can be small or large, thick or thin, flexible or rigid. Injection

moulded parts also lend themselves to endless decorative effects; they can be polished, textured, hot-

stamped, plated, coloured or clear (Gauthier, 1995). No other manufacturing process offers the range

of capabilities that injection moulding provides and this is what makes it such an appealing process.

Typical injection mouldings (moulded parts) can be found everywhere in daily life. Examples include

automotive parts, household articles, consumer electronic components and toys (Zhou, 2013).

In today’s manufacturing industry, there are four different types of injection moulding machines;

hydraulic, pneumatic, electric and hybrid (Thiriez & Gutowski, 2006). The classification is based on

the method of how each machine produces a part and this specifically looks at the driving system they

use. These different types of injection moulding machines range in size and complexity; from desk-

size units up to machines the size of a small house (Thiriez & Gutowski, 2006).

A custom pneumatic type injection moulding machine will be utilised for the purposes of this project.

2.1.2 Pneumatic Injection Moulding

Pneumatically operated injection moulding machines use compressed air to drive a plunger in the

injection moulding process. This makes them cheaper to run than the other types of injection

6

moulding machines (Shukla, 2013). By having less mechanical parts it also reduces the chance of

mechanical failure and additionally there are no problems with oil leakage and fire hazards.

Figure 1 below shows the pneumatic type CIMM that will be used to conduct this research project.

The CIMM was designed and built by Lindmann Machines and Equipment who have stated on their

website that the machine “suits industry for short runs and prototypes and educational institutions as

teaching instruments” (Super Products Website, 2015). The exact design specifications of the CIMM

can be found in Appendix A.

2.1.3 The Basic Process

PIM is basically a repetitive and cyclical process in which melted plastic at high pressure is injected

into a mould cavity, cooled and held under pressure until it can be ejected in a solid state, duplicating

the shape of the mould cavity. The mould may consist of a single cavity or a number of similar or

dissimilar cavities, each connected to flow channels, or runners, which direct the flow of the melted

plastic to the individual cavities (Rosato et al, 2000).

Figure 2, on the following page, shows the basic sequence of operations which occur in a moulding

cycle: (a) heating and injecting, (b) moulding, and (c) ejecting.

Figure 1: Image of the CIMM

7

To overview the benefits of the PIM, Table 2.1 presents the advantages and disadvantages of the PIM

enterprise (Rosato, 2000).

2.1.4 Importance of Moulding Quality

As with any manufacturing system, quality is of great importance when it comes to the product and

how it is manufactured due to the high level of competition in the industry. Therefore quality has

become a market differentiator for almost any manufactured product and manufactures are constantly

looking to enhance the quality of their product. When looking at quality in the PIM process, there are

a few important aspects to consider.

Table 3: Advantages and disadvantages of PIM (Rosato, 2000).

2.1.4.1 Moulding Materials

As mentioned in Section 2.2.1 plastic will constitute the only material to be used in this project as the

CIMM is only compatible with plastic material. According to Rosato et al (2000), the general

accepted definition for plastics is: “any one of a large and varied group of macromolecular materials

Advantages Disadvantages

High reproducibility

Low product cost for large volume

production

High tolerances

Wide range of plastic materials can

be used

Minimal scrap losses

No (very little) finishing required

Running costs may be high

Parts must be designed with

moulding consideration

Expensive equipment investment

Figure 2: Cyclic Sequence in the PIM Process (Rosato, 2000)

8

consisting wholly or in part of combinations of carbon with oxygen, hydrogen, nitrogen, and other

organic and inorganic elements. Although solid in the finished state, at some stage in its manufacture

it was made liquid, and thus is capable of being formed into various shapes. This is achieved through

the application, either singly or together, of heat and pressure”.

The great economic significance of plastics is ultimately tied to their properties such as low density,

easy to process, low thermal/electronic conductivity, high chemical resistance and reusability (Zhou,

2013).

A fundamental feature of plastics is their variety. There are over 17,000 plastic materials available

worldwide and within the most common plastic families there are five major thermoplastic types

(Zhou, 2013). These thermoplastics can be categorised as; low density polyethylene (LDPE),

polyvinyl chloride (PVC), low density polyethylene (HDPE), polypropylene (PP) and polystyrene

(PS) (Zhou, 2013).

The thermoplastic that will utilised in this project is polypropylene and it is supplied in the form

plastic granules which can be seen in Figure 3 below.

2.1.4.2 Mould Requirements

In practice, the requirements of an injection mould are heavily influenced by the customer expectation

towards the quality of the product as well as the performance of the mould (Rees, 1995).

2.1.4.3 Mould Performance

Given the expensive nature of a mould investment, the development of the mould is done with the

anticipation for it to have a useful lifetime (Avery, 1998). When considering the reliability of its

Figure 3: Polypropylene granules.

9

operation and life expectancy, as well as product quality and cost, mould performance is a measure of

its productivity. The productivity of a mould usually relates to the ability of the mould to produce a

certain number of products during a given timeframe (Rees, 1995).

2.1.4.4 Accuracy and Finish

Generally, the customer has two expectations when it comes to accuracy and finish: (1) parts

produced from a mould are dimensionally accurate by being within the requested tolerances, (2) the

moulded part complies with the specified finish or appearance (Rees, 1995). Therefore, it is

important to understand and consider the shrinkage of the plastic material used in order to

accommodate allowable cavity oversize for shrinkage (Rees, 1995).

From a mould design perspective, engineers will decide on the number of cavities needed for the

mould to successfully meet the customer requirements (Rees, 1995). In addition to this the engineers

will include a runner into the mould design, which serves as a channel for the molten plastic to flow

through on its way to the mould cavity. From a quality aspect, the runner ensures that molten material

can be packed into the cavity as it cools without any restriction (Rosato et al, 2000).

Figure 4 above shows the mould that will used for the manufacturing parts needed to conduct the

experiment in this project. From the figure it is evident that only one cavity will be used to mould the

part and this cavity will be supplied by a very large runner.

Runner

Mould

Cavity

Figure 4: Mould for the part that will be manufactured for this project

10

2.2 Design of Experiments

When conducting an experiment there are a few points to note before ‘just jumping in’ and

undertaking the experiment at hand. Juran & Godfrey (1998) describe these points as requisites and

tools that are necessary for sound experimentation and have summarised them in Table 4 below. This

checklist can be helpful in all phases of the experiment.

Table 4 discusses choosing ‘factors’ when defining the objectives of the experiment. A factor or

parameter is one of the controlled or uncontrolled variables whose influence upon a response is being

studied in the experiment (Juran & Godfrey, 1998). Each parameter may be quantitative (e.g.

temperature in degrees) or it may be qualitative (e.g. different machines, switch on or off). ‘Level’ is

another term that needs to be addressed, the levels of a parameter are the values of the parameter

being examined in the experiment (Juran & Godfrey). For example if the experiment is to be

conducted at three different speeds, then the parameter ‘speed’ has ‘three’ levels.

A very important aspect of conducting a sound experiment is to collect accurate data, and this is

effectively achieved by the principle of replication. Juan & Godfrey (1998) define replication as the

rerunning of an experiment or measurement in order to increase precision or to provide the means for

measuring precision. A single observation or experimental run comprises a single replicate.

Replication provides an opportunity for the effects of uncontrolled factors to balance out and thus acts

Table 4: Requisites and Tools for Sound Experimentation (Juran & Godfrey, 1998).

11

as a bias-decreasing tool. In order to collect accurate data in this project, each experiments level will

be rerun several times.

Juran & Godfrey (1998) define ‘design of experiments’ or DoE as an organised, statistical approach

that varies all parameters simultaneously to significantly reduce the number of experiments. With

DoE the entire experimental space can be explored efficiently by taking into account important

process parameters. The resulting data are used to generate a statistical model which is analysed to

support decision making. The areas where DoE is used in industrial research, development and

production include:

Screening: to determine which parameters are important in the process

Optimization: to find the optimal parameter settings for the process

Robustness testing: to investigate how adjusting different parameters affects the process

Juran & Godfrey also classified all the various experimental design techniques and their type of

application in a table which can be found in Appendix B. The table also mentions the structure of each

design type and the information that must be sort to adequately satisfy that particular design.

For the purpose of this project, a ‘Factorial’ design (second row of Table B.1) otherwise known as

“Full Factorial” design will be implemented. The three parameters that will be investigated (Namely:

molten plastic temperature (MT), molten plastic filling time (FT) and mould packing time (PT) as

mentioned above in the problem statement.) will be tested at k levels. Therefore the number of

experimental runs that will have to be conducted for this project, due to full factorial design, can be

determined by:

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑅𝑢𝑛𝑠 = 3𝑘 (2.1)

It will be important to note the interaction between these parameters as the objective of this industrial

research project is to find the optimal parameter settings for the injection moulding process.

2.3 Taguchi Optimization Method

Finding the optimal operational parameters of the CIMM forms the main focus of this final year

project and there are various optimization algorithms available to achieve this. DoE techniques,

especially the Taguchi Method, are widely used to generate meaningful data and determine optimal

process parameters for injection moulding (Zhou, 2013).

12

The Taguchi Method is a statistical method developed by Genichi Taguchi to improve the quality of

manufactured goods. Taguchi's approach provides a systematic and efficient method for determining

near optimum operating parameters for performance and cost (Unal & Dean, 1991). Figure 5 below

illustrates a flow chart of the Taguchi approach, as explained by Unal & Dean (1991).

The details of these steps will now be communicated as explained by Unal & Dean (1991).

1. The first step in the Taguchi Method is to determine the quality characteristic to be optimized.

The quality characteristic is a parameter whose variation has a critical effect on product

quality.

2. The next step is to identify the noise factors that can have a negative impact on system

performance and quality. Noise factors are those parameters which are either uncontrollable

or are too expensive to control.

3. The third step is to identify the control parameters thought to have significant effects on the

quality characteristic. Control parameters are those design factors that can be set and

maintained. The levels for each test parameter must be chosen at this point.

4. The next step is to design the matrix experiment and define the data analysis procedure. First,

an appropriate orthogonal array (OA) for the noise and control parameters to fit a specific

study are selected. Taguchi provides many standard OA’s and corresponding linear graphs for

this purpose. Taguchi proposes OA based simulation to evaluate the mean and the variance of

Figure 5: Taguchi method flow chart (Unal & Dean, 1991).

13

a product's response resulting from variations in noise factors. Figure 6 displays the OA based

simulation algorithm and structure.

5. The next step is to conduct the matrix experiment and record the results.

6. After the experiments have been conducted, the optimal test parameter configuration within

the experiment design must be determined. To analyse the results, the Taguchi Method uses a

statistical measure of performance called signal to-noise (S/N) ratio. The S/N ratio developed

by Taguchi is a performance measure to choose control levels that best cope with noise. In its

simplest form, the S/N ratio is the ratio of the mean (signal) to the standard deviation (noise).

There are three standard S/N ratios that can be used depending on the quality characteristic to

be optimized. The three different S/N ratios are:

Biggest-is-best quality characteristic

Smallest-is-best quality characteristic

Nominal-is-best quality characteristic

Whatever the type of quality characteristic is chosen, the transformations are such that the

S/N ratio is always interpreted in the same way: the larger the S/N ratio the better.

Figure 6: Orthogonal Array (OA) Based Simulation Algorithm (Unal & Dean, 1991).

14

7. For the final step, an experimental confirmation is run using the predicted optimum levels for

the control parameters being studied.

The Taguchi method may not necessarily provide the optimal solution as the experiment does not

contain all the possible combinations of parameters. However, it will provide a clear indication of

which parameters have the greatest effect on quality and cost. In this project, the Taguchi Method will

be implemented in conjunction with a full factorial experimental design to determine the optimal

operating regime of the CIMM.

2.4 RStudio

The software environment R is widely used for statistical computing and constructing graphics. It is

an easy to adopt coding language that allows for a user-created interface designed around a specific

(set of) problem(s) (Le Roux & Lubbe, 2013). RSudio provides the interface for the open source

statistical software package R.

2.4.1 R as a Software Development Platform

R is an open-source software system that is supported by a group of volunteers from many countries

with the central control being in the hands of a group called ‘R-Core’. Its base system provides a

general computer language for performing tasks like organising data, statistical analysis, model-

fitting, data visualisation, building of complex graphs etc. (Chambers, 2008). The R package hosts a

powerful and flexible set of statistical tools which are customisable on the platform to best suit the

required needs of the user. The platform itself facilitates both the handling and storage of data by

making use of a coherent collection of intermediate tools to analyse the data with (Chambers, 2008).

Williams (2012) has analysed R and compiled a list of advantages and disadvantages for the statistical

software system and some of those points are listed in the table on the following page.

15

Table 5: Advantages and disadvantages of R (Williams, 2012).

Advantages Disadvantages

R is the most comprehensive statistical analysis

package available. It incorporates all of the

standard statistical tests, models, and analyses, as

well as providing a comprehensive language for

managing and manipulating data. New technology

and ideas often appear first in R.

R has a steep learning curve (it does take a while

to get used to the power of R) but no steeper than

for other statistical languages. R is not so easy to

use for the novice. There are several simple-to

use graphical user interfaces (GUIs) for R that

encompass point and-click interactions, but they

generally do not have the polish of the commercial

offerings.

R is a programming language and environment

developed for statistical analysis by practising

statisticians and researchers. It reflects well on a

very competent community of computational

statisticians. R is now maintained by a core team

of some 19 developers, including some very senior

statisticians.

There is, in general, no one to complain to if

something doesn’t work. R is a software

application that many people freely devote their

own time to developing. Problems are usually

dealt with quickly on the open mailing lists, and

bugs disappear with lightning speed. Users who do

require it can purchase support from a number of

vendors internationally.

The graphical capabilities of R are outstanding,

providing a fully programmable graphics language

that surpasses most other statistical and graphical

packages. Because R is open source, unlike closed

source software, it has been reviewed by many

internationally renowned statisticians and

computational scientists.

Many R commands give little thought to memory

management, and so R can very quickly consume

all available memory. This can be a restriction

when doing data mining. There are various

solutions, including using 64 bit operating systems

that can access much more memory than 32 bit

ones.

R has over 4800 packages available from multiple

repositories specializing in topics like

econometrics, data mining, spatial analysis, and

bio-informatics.

Documentation is sometimes patchy and terse, and

impenetrable to the non-statistician. However,

some very high-standard books are increasingly

plugging the documentation gaps.

2.4.2 Design of Experiments in R

As a result of being an open-source system, R is exposed to continual scrutiny by the users. This

includes some algorithms for numerical computations and simulation that likewise reflect modern,

open-source computational standards in these fields (Chambers, 2008). This means that users not only

update current algorithms that solve long standing problems, but they also develop algorithm

packages that solve the problems of today. Essentially users can create packages to solve almost any

statistical problem that they can come up with. Looking at the problem related to this project, a

‘Design of Experiments’ is one such package that exists (Cano et al, 2012).

R is the software package that will be used for the statistical analysis of this project due to its

versatility and customisability.

16

2.5 Techno-economic Study

The assessment of the CIMM for its techno-economic feasibility is of utmost importance for the

motivation of the machine to be implemented into the business sector. This section will discuss the

techno-economic factors of the CIMM which consists of two stages; firstly the cost per part followed

by the break-even analysis.

2.5.1 Cost per part

One of the main objectives to this project is to successfully obtain the optimal operating settings for

the CIMM. This can be more accurately achieved by determining the cost per part, at each respective

operational setting combination, as this adds an extra dimension to finding an optimal solution. There

is no existing literature form this section of the report as the cost per part is unique to this project.

Each unique setting combination will be accounted for by producing a number of parts, for that

combination, in an experimental run. Factors that influence the cost per part are:

Energy consumption

Maintenance

Raw material

Compressed air

Labour

The CIMM’s compressed air usage and maintenance costs are not significantly affected by varying

the operational settings, so the incurred cost can be ignored. Therefore, for the purpose of this project,

only the energy, material and labour cost will be considered in determining the cost per part for the

CIMM.

The total labour cost (𝐶𝐿,𝑇𝑜𝑡) will be calculated by taking into account both the cycle and rework time

for each part, in an experimental run, and multiplying it by the labour cost per hour. The cycle time

per part will be obtained by timing the entire run from when the first part starts to mould until the

twenty-fifth part has been moulded. For each experimental run this cost can be represented in the

following equation:

𝐶𝐿,𝑇𝑜𝑡 = (𝑇𝐶 + 𝑇𝑅) × 𝐶𝐿 (2.2)

where 𝑇𝐶 is the cycle time per part, 𝑇𝑅 is the rework time per part and 𝐶𝐿 is the unit labour cost

measured as R/hr. From this result the labour cost per part (𝐶𝐿,𝑝𝑎𝑟𝑡) can be calculated by dividing the

labour cost by the number of conforming parts produced in that run (𝑛):

𝐶𝐿,𝑝𝑎𝑟𝑡 =𝐶𝐿,𝑇𝑜𝑡

𝑛 (2.3)

17

The manner in which the rework time per part shall be obtained is explained in Section 3.2.2.

Material costs forms the second aspect in the cost per part analysis. The total material cost (𝐶𝑀,𝑇𝑜𝑡)

will be calculated by making use of the total part weight (𝑃𝑊) and the raw material cost (𝐶𝑅𝑀) per

kilogram:

𝐶𝑀,𝑇𝑜𝑡 = 𝐶𝑅𝑀 × 𝑃𝑊 (2.4)

The material cost per part (𝐶𝑀,𝑝𝑎𝑟𝑡) can then be calculated by dividing 𝐶𝑀,𝑇𝑜𝑡 by the number of

conforming parts produced in that run (𝑛) as shown in the equation:

𝐶𝑀,𝑝𝑎𝑟𝑡 = 𝐶𝑀,𝑇𝑜𝑡

𝑛 (2.5)

The final cost that will be taken into consideration, for the techno-economic assessment, will be the

energy consumption cost. This consumption will be obtained by using an adaptor device (Appendix

E) which connects to the mains of the machine. This device will read and measure the total energy

consumption (𝐸𝑇𝑜𝑡 ) of the CIMM. The total energy usage cost (𝐶𝐸,𝑇𝑜𝑡) will then be calculated by

taking the energy consumption measurement for the run and multiplying it with the cost per kWh

(𝐶𝑘𝑊ℎ ) given by the local municipality. This can be depicted with the equation:

𝐶𝐸,𝑇𝑜𝑡 = 𝐶𝑘𝑊ℎ × 𝐸𝑇𝑜𝑡 (2.6)

The energy cost per part (𝐶𝐸,𝑝𝑎𝑟𝑡) can then be determined by dividing (𝐶𝐸,𝑇𝑜𝑡) by the number of

conforming parts produced in that run (𝑛):

𝐶𝐸,𝑝𝑎𝑟𝑡 = 𝐶𝐸,𝑇𝑜𝑡

𝑛 (2.7)

The above costs can then be combined to determine the total cost per part (𝑇𝐶𝑝𝑎𝑟𝑡) for each

experimental run with the equation:

𝑇𝐶𝑝𝑎𝑟𝑡 = 𝐶𝐿,𝑝𝑎𝑟𝑡 + 𝐶𝑀,𝑝𝑎𝑟𝑡 + 𝐶𝐸,𝑝𝑎𝑟𝑡 (2.8)

Substituting Equations 2.2 – 2.7 into Equation 2.8 will result in the finalised 𝑇𝐶𝑝𝑎𝑟𝑡 given below in

Equation 2.9:

𝑇𝐶𝑝𝑎𝑟𝑡 = (𝐶𝐿)(𝑇𝐶+ 𝑇𝑅)+(𝐶𝑘𝑊ℎ)(𝐸𝑇𝑜𝑡)+(𝐶𝑅𝑀)(𝑇𝑜𝑡𝑃𝑊)

𝑛 (2.9)

18

2.5.2 Break-even Analysis

Gutierrez and Dalsted (2012) provide a sufficient definition for break-even analysis: “Break- even

analysis is a useful tool to study the relationship between fixed costs, variable costs and returns. A

break-even point (BEP) defines when an investment will generate a positive return”. As its name

implies, this approach determines the sales needed to break even.

From a calculation perspective, the break-even point computes the volume of production at a given

price necessary to cover all costs (Gutierrez and Dalsted, 2012). The formula for this calculation is

given as:

𝐵𝐸𝑃 = 𝐹𝑖𝑥𝑒𝑑 𝐶𝑜𝑠𝑡𝑠

𝑆𝑃−𝑉𝐶 (2.8)

where SP is the selling price per part and VC is the variable cost per part.

In the case of this project, the fixed costs will only involve the cost of the CIMM and the VC will

include the three costs (𝑇𝐶𝑝𝑎𝑟𝑡) mentioned above in Section 2.6.1. From the result obtained in the

break-even analysis, a feasibility case can be provided for purchasing the CIMM.

19

3. Project Methodology

The project methodology will comprise of two sections; the experimental procedure followed by the

data collection and analysis methodology. These two sections will be outlined and discussed in this

chapter.

3.1 Experimental Procedure

In order to determine an optimal operating setting for the CIMM, experiments need to be executed.

These experiments will be performed by using the Taguchi Method in conjunction will a full factorial

experimental design.

The steps of the Taguchi method will now be implemented.

3.1.1 Quality Aspect to be Optimised The quality aspect that is to be optimised is the moulding finish of plastically moulded parts using the

CIMM. The side effects of this optimising process will include moulded parts with varying levels of

quality to the finished part. Each of these parts will be classified as either a conforming or non-

forming part.

3.1.2 Identify the Noise Factors and Test Conditions The experiments will be conducted in the Senrob Lab of the Mechanical and Industrial Engineering

Building. As explained in the Taguchi Optimization Method (Section 2.3), it is important to identify

the noise factors in this experiment as they can have a negative impact on the quality of the moulded

parts. The noise factors that could affect the mould operation on the CIMM are:

Variation in the raw material (plastic granules)

Machine condition

Ambient temperature of the Senrob Lab

Operator skill

3.1.3 Identify the Control Factors and their Alternative Levels As mentioned in step 3 of the Taguchi Method, the control factors (test parameters) are those that can

be set and maintained. Recapitulating from Section 1.2, the control factors are as follows:

The temperature of the molten plastic, or more simply the mould temperature (MT).

The time allocated for the molten plastic to flow into the mould, or more simply the filling

time (FT).

The time allocated for the mould to be kept in place before the part is ejected, or more simply

the packing time (PT).

20

The factors and their levels, for conducting the experiment, were decided upon by moulding a few

parts at random levels until several conforming parts were successfully produced. The control

parameters settings were noted and the levels for the experiment were consequently chosen based on

moderate variations on the noted parameter settings. The control factors and their respective levels for

experimentation are shown in Table 6.

3.1.4 Design Experimental Matrix An appropriate sized orthogonal array (OA) has to be used for conducting the experiments. Given that

a full factorial experimental design is to be executed, Equation 2.1 will be used to determine the size

of the (OA). This equation yields:

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑅𝑢𝑛𝑠 = 33 = 27

Therefore the most suitable orthogonal array for experimentation is an L27 array as shown in Table 7

on the next page. This means that a total of twenty seven experiments need to be carried out.

1 2 3

MT 180 190 200

FT 5 10 15

PT 3 5 7

FactorsLevels

Table 6: Control factors and their levels for experimentation.

21

Table 7: The L27 Orthogonal Array (OA)

3.1.5 Conduct Matrix Experiment In accordance with the above OA, experiments were conducted with the factors and their levels as

mentioned in Table 7. The experimental layout with the selected values of the factors is shown on the

following page in Table 8.

Experimental

No.

Control Factors

1 2 3

1 1 1 1

2 1 1 2

3 1 1 3

4 1 2 1

5 1 2 2

6 1 2 3

7 1 3 1

8 1 3 2

9 1 3 3

10 2 1 1

11 2 1 2

12 2 1 3

13 2 2 1

14 2 2 2

15 2 2 3

16 2 3 1

17 2 3 2

18 2 3 3

19 3 1 1

20 3 1 2

21 3 1 3

22 3 2 1

23 3 2 2

24 3 2 3

25 3 3 1

26 3 3 2

27 3 3 3

22

Table 8: OA with Control Factors and their different levels for Experimentation.

Experimental No.

Control Factors

MT FT PT

1 180 5 3

2 180 5 5

3 180 5 7

4 180 10 3

5 180 10 5

6 180 10 7

7 180 15 3

8 180 15 5

9 180 15 7

10 190 5 3

11 190 5 5

12 190 5 7

13 190 10 3

14 190 10 5

15 190 10 7

16 190 15 3

17 190 15 5

18 190 15 7

19 200 5 3

20 200 5 5

21 200 5 7

22 200 10 3

23 200 10 5

24 200 10 7

25 200 15 3

26 200 15 5

27 200 15 7

Each of the above 27 experiments will involve manufacturing 25 parts as to account for the variations

that may occur due to the noise factors. This means that a total of 675 moulds will be manufactured

by the CIMM during the experimentation process. The experiments shall be executed in the order they

are represented in Table 8. One of these experiments will represent the optimal operational settings

for the CIMM to produce the given part.

23

The manner in which each of these 27 experiments will be executed is visually outlined below in

Figure 7.

Figure 7: Flow chart of the experimental procedure.

24

As mentioned in 7, each moulded part will be placed on a sequential numbering chart as it is removed

from the CIMM as they are too hot mark with a permanent marker immediately after their removal.

The reason for numbering system is to track the quality of parts as the experiment is conducted to see

if any trends are identified. An image of the sequential chart is shown in Figure 8 below.

Once an experimental run has been completed and the total cycle time and power usage for that run

have been recorded, each part will be numbered with permanent marker. This number must correlate

with position on the chart.

Following the numbering procedure, the parts for each experimental run will then be placed into a bag

labelled with that experiments number. The data that has to be collected from these experiments and

the manner in which it is to be obtained will be discussed in the succeeding section.

Figure 8: The sequential numbering chart.

25

3.2 Data Collection and Analysis Methodology

This section will look at how the data was obtained from the moulded parts that were produced during

the experiments and the quality inspection procedure that was followed.

3.2.1 Quality Inspection Criteria The operational settings can only be considered optimal if conforming parts are produced and this can

be determined through a quality inspection. The quality inspection of each part was divided into four

inspection criteria:

1. Visual inspection of part conformance

2. Rework time, measured in seconds (sec)

Rework time for runner (𝑇𝑅,𝑟𝑢𝑛𝑛𝑒𝑟)

Rework time for finishing (𝑇𝑅,𝑓𝑖𝑛𝑖𝑠ℎ)

3. Part weight, measured in grams (g)

4. Part thickness, measured in millimetres (mm)

Each of the above criteria will now be discussed and insight will be given as to how each was

measured.

3.2.2 Quantitative Analysis of Part Quality As previously mentioned, visual inspection of part conformance formed the first criteria of the quality

inspection process. This aspect of the inspection process is important as it determines whether a part

was successfully been moulded or not. The result will have significant implications to the cost per part

value for that experimental run as shown in the equations from Section 2.5.1. An example of a

conforming part prior to rework, and non-conforming part can be seen in Figure 9 and Figure 10

respectively on the following page.

26

It is important to note that conformance is based on the circular section of the moulded part as it is the

circular section which constitutes the final part. The longitudinal section is the runner of the moulded

part which will be cut off during the rework cycle.

The second criteria, rework time, was measured for the purpose of determining the labour costs for

the production of the part. For the purposes of this project the rework time will involve two

dimensions; rework time for the runner (𝑇𝑅,𝑟𝑢𝑛𝑛𝑒𝑟), and rework time for the finishing (𝑇𝑅,𝑓𝑖𝑛𝑖𝑠ℎ) of the

part. Rework is a necessary process as it ensures the final part meets the design specifications. This is

a standard time and was determined by measuring the time to rework twenty-five parts which was

repeated three time to get an average. An average rework time per part was obtained from dividing the

average by twenty-five, as there were twenty-five parts produced per experiment.

Figure 9: A visually conforming part, prior to rework.

Figure 10: A visually non-conforming part.

27

The standard rework time of how long it will take to rework a single part was based on the average

rework time per part. The results of this are shown in Table 9 below.

Only conforming parts, as shown in Figure 9, will undergo rework as there is no point wasting time

on reworking non-conforming points as it will not add any value to the part. Figure 11 and 12 below

show a conforming part following rework for the runner and finishing respectively.

The third criteria is the part weight (PW) and it was measured using a very accurate scale which

measures to one-thousandth of a gram (10-3 g) or three decimal places. It is important to mention that

conforming parts were weighed before (PW) and after both rework procedures (PWA) had been

executed in order to get a measurement for the wasted material per part. This is illustrated in Figure

13 and 14 on the next page.

Trial 1 Trial 2 Trial 3

Runner 137 128 134 133 5.32 6

Finishing 368 356 377 367 14.68 15

Rework DimensionTimes (sec) Average Time

per Trial

Average Rework

Time per Part

Standard Rework

Time per Part

Table 9: Table showing how the standard rework times were determined.

Figure 12: Conforming part following rework for finishing.

Figure 11: Conforming part following rework for runner.

28

The fourth and final criteria is the part thickness which was measured using a very accurate digital

vernier calliper which can also measure to one-thousandth of a millimetre (10-3 mm) or three decimal

places. The thickness measurement was chosen at an arbitrary point as the part had no existing

dimensional tolerances.

Non-conforming parts were not given a thickness measurement as they have already been declared as

not being useful. Figure 15 on the following page shows where and how the part thickness was

measured.

Figure 13: Weighing part prior to rework (PW). Figure 14: Weighing part after rework (PWA).

29

All the data that was measured from implementing the four inspection criteria was captured and

entered into excel sheets for each experiment. An example of the data that was captured can be seen

below in Table 10 which displays an excel sheet with the data that was captured for Experiment 8.

This table is displayed on the page that follows.

Figure 15: Illustrating where and how the part thickness was measured using the digital vernier calliper.

30

Table 10: The data collected for experiment 8 is shown here as an example of the data collected for each experiment.

Visually conforming and non-conforming parts are represented with a ‘1’ and ‘0’ respectively in the

‘Conformance’ column. The ‘Waste Material’ column values were achieved by subtracting the PWA

from the PW.

Once the data for all the experiments have been captured into excel sheets, the statistical analysis of

this data can commence. Chapter 4 outlines the steps followed and results obtained from the techno-

economic analysis and statistical analysis.

Experiment No. 8

Temp (oC) 180 Cycle Time (min) 13:54

FT (sec) 15 Power Usage (kWh) 0.0656

PT (sec) 5

Time Started 13:09

1 0 0.607 0.607

2 0 1.589 1.589

3 0 1.751 1.751

4 1 6 15 1.810 0.873 0.937 2.254

5 1 6 15 1.900 0.896 1.004 2.287

6 1 6 15 1.876 0.907 0.969 2.305

7 1 6 15 1.917 0.922 0.995 2.332

8 1 6 15 1.880 0.914 0.966 2.337

9 1 6 15 1.914 0.915 0.999 2.313

10 1 6 15 1.817 0.914 0.903 2.354

11 1 6 15 1.854 0.912 0.942 2.355

12 1 6 15 1.852 0.914 0.938 2.324

13 1 6 15 1.950 0.933 1.017 2.379

14 1 6 15 1.861 0.920 0.941 2.348

15 1 6 15 1.877 0.917 0.960 2.322

16 1 6 15 1.906 0.906 1.000 2.298

17 1 6 15 1.879 0.912 0.967 2.319

18 1 6 15 1.852 0.916 0.936 2.309

19 1 6 15 1.861 0.927 0.934 2.325

20 1 6 15 1.873 0.928 0.945 2.328

21 1 6 15 1.899 0.918 0.981 2.321

22 1 6 15 1.842 0.922 0.920 2.317

23 1 6 15 1.855 0.920 0.935 2.350

24 1 6 15 1.866 0.915 0.951 2.320

25 1 6 15 1.930 0.918 1.012 2.326

Thicknes

s (mm)Part No.

Conformance

(0/1)

Rework Time

(Runner)

Rework Time

(Finish)PW (g) PWA (g)

Waste

Material (g)

31

4. Analysis of Data

To successfully determine the optimal operational settings for the CIMM the data collected in Section

3.2 has to statistically analysed. The following section will review the procedures that were used to

analyse the data that was captured into Excel. Graphs that could not be created in RStudio will be

created in Excel. A copy of the R coding that was used in this project can be found in Appendix D.

Three different analyses will be used:

Initial observation analysis

Techno-economic analysis

Statistical analysis

Descriptive statistical analysis

ANOVA analysis

Process control analysis

Waste Analysis

The two main objectives of the analysis in this section are:

1. Total cost per part (𝑇𝐶𝑝𝑎𝑟𝑡) must be minimised

2. Part thickness must be maximised

These two objectives have to be achieved whilst minimising the variation of part thickness in

moulding process.

4.1 Initial Observation Analysis

Once all the data had been captured into the excel sheets for all the conducted experiments, two initial

observations were noted before any statistical analysis needed to be performed. The first observation

pertained to the number of conforming parts that were produced for each of the conducted

experiments. A summary of these results can be seen in Figure 16 on the following page.

The second observation was that the moulded parts did not automatically eject as expected. This not

only meant that the experimental runs acquired a greater cycle time (𝑇𝐶), it also posed a safety hazard

as the parts now had to be removed by hand as shown in Figure 17 on the following page.

32

From Figure 16 it is evident that only “Experiments; 7, 8, 9, 13, 14, 15, 16, 17, 18, 22, 23, 24, 25, 26,

27” produced conforming parts. Therefore, only these experiments will be considered for the techno-

economic and statistical analyses that follow.

Figure 16: A bar graph showing the number of conforming parts produced per experiment.

Figure 17: Removing moulded parts by hand.

33

4.2 Techno-economic Analysis

The first part of the techno-economic analysis involves determining the cost per part for each

experiment as outlined in Section 2.5. These values are needed for the second stage of the techno-

economic analysis, namely the break-even analysis. Section 2.5.1 stated that only the energy

consumption, labour and raw material costs would be considered for the techno-economic analysis

and their unit costs are summarised in the following table:

Substituting the above unit costs along with the captured data into Equations 2.2, 2.4 and 2.6 will

result in the total costs for labour (𝐶𝐿,𝑇𝑜𝑡), material (𝐶𝑀,𝑇𝑜𝑡) and energy (𝐶𝐸,𝑇𝑜𝑡) respectively. These

values can been seen in Table 12 below:

Cost Parameters Unit Source

Energy (CkWh) R0.88/kWh Stellenbosch Municipality, 2015

Labour (CL) R85/hr Trading Economics Website, 2015

Material (CRM) R20.75/Kg Plastomark PTY LTD Quote, 2015

Table 11: Unit costs for the measured cost parameters.

Usage (kWh) Cost (R ) Usage (hr) Cost (R ) Usage (Kg) Cost (R )

7 0.0677 0.06 0.35 29.54 0.045351 0.94 30.54 19 1.61

8 0.0656 0.06 0.36 30.60 0.045218 0.94 31.60 22 1.44

9 0.0651 0.06 0.37 31.04 0.045824 0.95 32.05 21 1.53

13 0.0537 0.05 0.32 27.08 0.045402 0.94 28.07 22 1.28

14 0.0598 0.05 0.34 29.27 0.048003 1.00 30.32 24 1.26

15 0.0592 0.05 0.34 28.74 0.046316 0.96 29.75 22 1.35

16 0.0725 0.06 0.39 33.43 0.046267 0.96 34.45 24 1.44

17 0.0733 0.06 0.39 33.52 0.047838 0.99 34.58 24 1.44

18 0.0746 0.07 0.40 34.33 0.047669 0.99 35.39 25 1.42

22 0.0618 0.05 0.35 29.54 0.047371 0.98 30.57 24 1.27

23 0.0639 0.06 0.36 30.34 0.048262 1.00 31.39 25 1.26

24 0.0648 0.06 0.36 30.60 0.046800 0.97 31.63 25 1.27

25 0.0875 0.08 0.42 35.96 0.046611 0.97 37.01 25 1.48

26 0.0991 0.09 0.48 40.68 0.045047 0.93 41.70 25 1.67

27 0.0996 0.09 0.48 40.80 0.045971 0.95 41.84 25 1.67

Total Cost

per part (R )

Costs

Experiment Energy (CE,Tot) Labour (CL,Tot) Material (CM,Tot) Total

Cost (R )n

Table 12: Summary of techno-economic analysis.

34

The total cost per part (𝑇𝐶𝑝𝑎𝑟𝑡) for each experimental run can then be calculated by using Equation

2.9 and converting input values in the following way:

𝑇𝐶𝑝𝑎𝑟𝑡 = (𝐶𝐿)(

𝑇𝐶60

+ 𝑇𝑅

3600)+(𝐶𝑘𝑊ℎ)(𝐸𝑇𝑜𝑡)+(𝐶𝑅𝑀)(

𝑇𝑜𝑡𝑃𝑊1000

)

𝑛

These conversions had to be made in order to get the cycle time and rework times into hours along

with the total part weight into kilograms as these are the units in which the cost parameters are

supplied. The 𝑇𝐶𝑝𝑎𝑟𝑡 values constitute the main focus of the techno-economic analysis and they can

be found in the final column of Table 12. A summary of these results can be illustrated in the

histogram that follows below.

From Table 12 and Figure 18 it can be observed that five of the fifteen experiments have a total cost

per part value between R1.20 and R1.30, the lowest interval. It can also be noted that the lowest total

cost per part is R1.26 achieved by Experiment 14 and Experiment 23.

4.3 Statistical Analysis

A statistical analysis of the collected data will be discussed in the following section. The statistical

analysis was executed with the assistance of RStudio (an open source software package for the R

environment).

4.3.1 Descriptive Statistical Analysis A basic summary of the measurable parameters and collected data is given in Figure 19 as this is a

good way to ensure data integrity. The data only relates to the conforming parts that were produced in

the experimental runs. This is due to the purpose of the project, namely finding the optimal

operational settings of the CIMM which involves maximising the number of conforming parts

produced.

Figure 18: Histogram showing the distribution of total cost per part.

35

Looking at the Figure 19 above and recapitulating from Section 3.1.3; the MT, FT and PT are the

moulding temperature (oC), filling time (sec) and packing time (sec) respectively. Together they form

the control factors (or parameters) of the CIMM. PW, PWA, Waste and Thickness are the measurable

data that was captured during the experimental runs in Section 3.2. In Figure 19; PW, PWA and

Waste are represented in grams while Thickness is represented in millimetres. The final column TC is

the total cost per part (𝑇𝐶𝑝𝑎𝑟𝑡) that was calculated in Section 4.2 and it is represented in Rands.

In order to ensure a thorough descriptive analysis of the data is conducted, various plots were

generated in RStudio that are illustrated throughout this chapter. These plots compare the

relationships of the control parameters with the two main objectives that need to be met, namely

minimising 𝑇𝐶𝑝𝑎𝑟𝑡 and maximising part thickness. Box plots were the first chosen graph-type as they

show variation which is another criteria that has to be minimised as this indicates how controlled the

process is.

The first set of graphs look to compare the control parameters against the first objective, namely

minimising 𝑇𝐶𝑝𝑎𝑟𝑡.

Figure 19: Statistical summary of measurable parameters and collected data.

Figure 20: Total cost per part as a function of mould temperature.

36

Box plots of the 𝑇𝐶𝑝𝑎𝑟𝑡 versus mould temperature, filling and packing time are displayed in Figure

20, 21 and 22 respectively. Looking at these box plots from a variation perspective it is clear that:

Increasing the mould temperature from 180oC to 190oC results in very little change in

variation but a further increase to 200oC produces a greater variation

Increasing the filling time from 10 seconds to 15 seconds results in a greater variation

Increasing the packing time from 3 seconds to 5 seconds produces a slight increase in

variation but there is no significant variation change between 5 and 10 seconds

Figure 22: Total cost per part as a function of packing time.

Figure 21: Total cost per part as a function of filling time.

37

Figure 23: Thickness as a function of mould temperature.

Based on variation of the 𝑇𝐶𝑝𝑎𝑟𝑡 alone, the optimal operating parameters are shown in the table

below. It is important to note that these operational settings represent Experiment 13.

The second set of box plots look to compare the control parameters against the second

objective, namely maximising part thickness.

Control Parameter Setting

MT 190

FT 10

PT 3

Table 13: Optimal based on the variation of total cost per part.

38

Box plots of part thickness versus mould temperature, filling and packing time are displayed in Figure

23, 24 and 25 respectively. Looking at these box plots from a variation perspective it is clear that:

Increasing the mould temperature shows no significant relationship with regards to a constant

increase/decrease in variation of part thickness

Increasing the filling time from 10 seconds to 15 seconds results in a slightly greater variation

of part thickness

Increasing the packing time shows no significant relationship with regards to a constant

increase/decrease in variation of part thickness

Figure 25: Thickness as a function of packing time.

Figure 24: Thickness as a function of filling time.

39

Figure 26: Total cost as a function of mould temperature with linear trend line.

Based on variation of the part thickness alone, the optimal operating parameters are shown in the table

below. It is important to note that these operational settings represent Experiment 23.

4.3.2 ANOVA Analysis The six plots generated in Section 4.3.1 were plotted again; however, in this section they were plotted

as scatterplots and given a trend line. This trend line is necessary to successfully conduct ANOVA

(Analysis of Variance) which will test to see if there is a linear relationship between the three control

parameters and the two main objectives mentioned in the previous section.

Once again, the first set of graphs look to compare the control parameters against the first objective,

namely minimising 𝑇𝐶𝑝𝑎𝑟𝑡.

Control Parameter Setting

MT 200

FT 10

PT 5

Table 14: Optimal settings based on the variation of part thickness.

40

Figure 27: Total cost as a function of filling time with linear trend line.

Scatterplots of the 𝑇𝐶𝑝𝑎𝑟𝑡 versus mould temperature, filling and packing time are displayed in Figure

26, 27 and 28 respectively. Looking at the trend lines on these plots it is clear that:

Increasing the mould temperature results in a moderate decrease in the 𝑇𝐶𝑝𝑎𝑟𝑡

Increasing the filling time results in a relatively large increase in the 𝑇𝐶𝑝𝑎𝑟𝑡

Increasing the packing time results in a slight increase in the 𝑇𝐶𝑝𝑎𝑟𝑡

Figure 28: Total cost as a function of packing time with linear trend line.

41

ANOVA was then conducted for each graph (Figure 26, 27 and 28) by using the ‘anova()’ function in

R. The aim of the ANOVA test is to statistically confirm if a linear relationship does indeed exist

between the control parameters and the 𝑇𝐶𝑝𝑎𝑟𝑡 as mentioned in the bullet points above. A relationship

can be confirmed if the ‘Pr(> 𝐹)’ value is less than 0.05 (5%). The Pr(> 𝐹) represents the

probability of finding data along the existing trend line for the x-value on the graph, in this case the

control parameters. In other words it is the strength of the correlation between the two parameters, the

smaller the Pr(> 𝐹) value the greater the correlation. An example of the ANOVA test for the linear

test between 𝑇𝐶𝑝𝑎𝑟𝑡 and mould temperature (MT) is shown in Figure 29 below:

Looking at the figure it can be seen that the Pr(> 𝐹) value is 0.03647 which is less than 0.05 and this

statistically confirms that a linear relationship exists. The results of the ANOVA tests for 𝑇𝐶𝑝𝑎𝑟𝑡

against all three control parameters is shown in the table that follows. It is clear that the two remaining

Pr(> 𝐹) values are also less than 0.05 which statistically confirms all three control parameters have a

linear relationship with 𝑇𝐶𝑝𝑎𝑟𝑡 as mentioned in the three bullet points on the previous page.

The second set of graphs look to compare the control parameters against the second objective, namely

maximising part thickness. These graphs start on the following page.

Figure 29: Summary of ANOVA test between total cost per part and mould temperature.

MT 0.03647

FT 2.2 x10-16

PT 0.0444

Control Parameter

tested against TCpartPr (>F)

Table 15: ANOVA results for total cost per part.

42

Figure 30: Part thickness as a function of mould temperature with linear trend line.

Figure 31: Part thickness as a function of filling time with linear trend line.

43

Scatterplots of part thickness versus mould temperature, filling and packing time are displayed in

Figure 30, 31 and 32 respectively. Looking at the trend lines on these plots it is clear that:

Increasing the mould temperature results in a slight decrease in part thickness

Increasing the filling time results in a slight increase in part thickness

Increasing the packing time results in a slight decrease in part thickness

Once again, ANOVA was conducted for each graph (Figure 30, 31 and 32) to statistically confirm if a

linear relationship does indeed exist between the control parameters and the part thickness as

mentioned in the bullet points above. The results of the ANOVA tests for part thickness against all

three control parameters is shown in the table that follows. It is clear that all three Pr(> 𝐹) values are

also less than 0.05 which statistically confirms all the control parameters have a linear relationship

with part thickness as mentioned in the three bullet points above.

Figure 32: Part thickness as a function of packing time with linear trend line.

MT 8.22 ×10-4

FT 9.04 ×10-8

PT 4.29 ×10-3

Control Parameter tested

against part thicknessPr (˃F)

Table 16: ANOVA results for part thickness.

44

Two separate three-dimensional scatterplots were generated and are displayed in Figure 33 and 34

below as they give a good visual representation of the data. The factors plotted were selected

according to the level of correlation (lowest Pr(> 𝐹) values) with 𝑇𝐶𝑝𝑎𝑟𝑡 and part thickness

respectively. From Table 15 and 16 it is evident that the PT Pr(> 𝐹) values were the largest for both

𝑇𝐶𝑝𝑎𝑟𝑡 and part thickness, hence PT had very little influence on the two objectives and was

subsequently neglected from the following two graphs.

Figure 33: 3D scatter plot showing total cost per part as a function of filling time and mould temperature.

Figure 34: 3D scatter plot showing part thickness as a function of filling time and mould temperature

45

Looking at Figure 33 it is clear that an FT value of 10 seconds provides a lower 𝑇𝐶𝑝𝑎𝑟𝑡 which will be

decreased slightly further with a MT of 180oC. However, it must be noted that a combination of these

two control parameters did not produce any conforming parts. Therefore, in terms of 𝑇𝐶𝑝𝑎𝑟𝑡, the

optimal operational settings are shown in Table 17. It is important to note that these operational

settings represent Experiment 13, 14 and 15.

Figure 34 shows that part thickness is not significantly affected by changing the control parameters. It

can be deduced that an FT value of 15 seconds and an MT value of 180oC produce a slightly greater

part thickness but this comes at the expense of a very large variation. Due to the large variation shown

at the 180oC MT value, 190oC was chosen as the preferred MT value. The 190oC setting along with

the additional two operational settings in the table below, show the optimal operational settings in

terms of part thickness. It is important to note that these operational settings represent Experiment 16,

17 and 18.

From Table 17 and 18 above, it is clear that the only different operational parameter is FT. A re-

examination of the three-dimensional plots shows that part thickness is not as significantly affected by

changing the FT from 10 seconds to 15 seconds as comparison to the 𝑇𝐶𝑝𝑎𝑟𝑡. Therefore, the

operational settings in terms of 𝑇𝐶𝑝𝑎𝑟𝑡 were selected as the optimal operational settings for the

ANOVA analysis.

4.3.3 Process Control Analysis

This section will outline the process control study that was conducted from the collected data. Process

control is the ability of a process to produce outputs that conform to the required specifications. The

process control study is visually aided with control charts of which the most common are the ‘R-

chart’ and ‘�̅�-chart’. The �̅�-chart is used to monitor the centring of a process while the R-chart

monitors the variation of a process. A variation analysis has already been covered in Section 4.3.1 and

Section 4.3.2, therefore an �̅�-chart will be used for this analysis.

Control Parameter Setting

MT 190

FT 10

PT 3,5,7

Table 17: Optimal settings for total cost per part.

Control Parameter Setting

MT 190

FT 15

PT 3,5,7

Table 18: Optimal settings for part thickness.

46

An �̅�-chart will be generated in Excel to analyse the CIMM’s ability to centre the part thickness, of

conforming parts, in the injection moulding process. The first step is to generate the mean thickness

value (denoted by �̅�) and standard deviation value (denoted by 𝑠) for each experiment i, that

produced conforming parts. Recapitulating from section 4.1 these experiments were; “Experiments; 7,

8, 9, 13, 14, 15, 16, 17, 18, 22, 23, 24, 25, 26, 27”. Next, the overall mean (�̿�) and average standard

deviation (�̅�) calculations are made as shown in the equations below.

�̿� = ∑ �̅�𝑖

𝑘𝑖=1

𝑘 (4.1)

𝑆̅ = ∑ 𝑠𝑖

𝑘𝑖=1

𝑘 (4.2)

The variable 𝑘 represents the number of experimental samples, which in this case is fifteen as only

fifteen experiments produced conforming parts. The overall mean and average range are then used to

compute the center line (CL) along with the lower and upper control limits (LCL and UCL) for the �̅�-

chart using the following equations:

𝐶𝐿 = �̿� (4.3)

𝐿𝐶𝐿 = �̿� − 𝐴3�̅� (4.4)

𝑈𝐶𝐿 = �̿� + 𝐴3�̅� (4.5)

The variable A3 is a constant dependant on the sample size (in this case number of conforming parts,

n, in each experiment) found in Appendix C, provided by Evans & Lindsay (2014). Due to the

different number of conforming parts per experiment (Figure 16), an average value was used. This

average value, along with the additional values calculated in Equations 4.1 – 4.5 are shown in the

table that follows.

Table 19: Summary of values used for the X-bar chart.

2.336 0.047809 2.336 2.306 2.365 23.47 24

47

The resulting �̅�-chart, generated in Excel, is shown in Figure 35 below.

The control limits (LCL and UCL) represent the range between which all points need to occur for the

process to be in statistical control. If an �̅� value falls outside this range it shows the CIMM was not

able to successfully centre the part thickness for the control parameters of that experiment. A

summary of this analysis is given in the table on the succeeding page.

Figure 35: X-bar chart created in Excel.

48

Therefore, based on the process control analysis, the optimal operational settings are shown in the

table below.

4.4 Waste Analysis

Due to the limited amount of data points for the 𝑇𝐶𝑝𝑎𝑟𝑡 in the statistical analysis as well as the large

wastage that was observed during that data capturing process, a wastage analysis was also conducted.

The limited amount of data points in the descriptive analysis is as a result of the energy consumption

cost and labour cost being divided by the number of conforming parts for that experiment (Equation

2.3 and 2.7). This was done as due to the difficulty of individually measuring the energy consumption

and labour time for each part.

7 Yes

8 Yes

9 No

13 No

14 Yes

15 No

16 No

17 No

18 No

22 Yes

23 Yes

24 Yes

25 No

26 Yes

27 Yes

Statistically

in ControlExperiment

Table 20: Summary of results for process control analysis.

MT FT PT

7 180 15 3

8 180 15 5

14 190 10 5

22 200 10 3

23 200 10 5

24 200 10 7

26 200 15 5

27 200 15 7

ExperimentControl Parameters

Table 21: Experiments that are statistically in control.

49

However, it was possible to individually measure the material cost per part as each part was weighed

before (PW) and after rework (PWA) as mentioned in Section 3.2.2. Therefore, a double bar graph

was plotted showing the waste material compared to the actual material that was used for the finished

part (PWA). Once again only conforming parts were considered for the analysis and the generated

graph is displayed below in Figure 36.

From the graph it is evident clear that more material went to waste than that actual mould itself for

every experiment conducted, except for Experiment 26 which had the same value for both weights. A

summary of the results, given in the table on the following, shows the waste material as a percentage

of the material used in the mould.

Figure 36: Summary of waste analysis.

50

Therefore, in terms of wastage, the control parameters for Experiment 26 serve as the optimal

operational settings as this experiment had the lowest wastage per conforming part.

Experiment PWA (g) Waste (g) Wastage as Percentage of PWA

7 17.434 18.024 103%

8 20.119 21.152 105%

9 18.936 20.262 107%

13 19.171 21.364 111%

14 21.207 25.103 118%

15 19.225 22.234 116%

16 22.521 23.109 103%

17 22.619 23.497 104%

18 23.463 24.206 103%

22 21.742 24.062 111%

23 22.812 25.45 112%

24 22.553 24.247 108%

25 22.856 23.755 104%

26 22.558 22.489 100%

27 22.438 23.533 105%

Table 22: Table showing wastage as a percentage of the material used for the completed part (i.e. after rework).

51

5. Discussion of Results

The following chapter will look to discuss the results captured in Chapter 4 in order to determine the

optimal operating settings for the CIMM. This will be achieved by looking at the results obtained for

each analysis conducted in Chapter 4.

5.1 Summary of Results

Table 23 shows a summary of the results of all the analyses conducted in Chapter 4. These

experiments represent the possible operating settings that will constitute the optimal operational

settings for the CIMM.

Looking at the best performing experiments for each analysis and taking into account process control,

Experiment 13 and 15 will be discarded as they do not conform to the process control measures which

can be reviewed in Figure 35. Although Experiments 7, 8, 22, 24, 25 and 27 conform to the process

control measures, they did not perform optimally in any other analysis and will subsequently also be

discarded. Therefore, the remaining possible optimal settings are shown in the following Table:

MT FT PT

14 190 10 5

23 200 10 5

26 200 15 5

Operational SettingsExperiment

Table 24: Remaining possible optimal operational settings.

Table 23: Summary of possible operational settings based on the conducted analyses.

Experiment 14 / 23 13 / 23 13 / 14 / 15 26

MT 190 / 200 190 / 200 190 200

FT 10 10 10 15

PT 5 3 / 5 3 / 5 / 7 5

See

Table 21

Techno-

economic

Analysis

Descriptive

Statistical

Analysis

ANOVA

Analysis

Process

Control

Analysis

Waste

Analysis

52

5.1 Break-even Analysis

In order to motivate a place for the CIMM in industry, a business case needs to be presented on how

the machine will perform in the work environment. The most simple and effective way of illustrating

this is through a break-even analysis. The remaining three possible optimal operating settings will be

put through a break-even analysis in order to determine which combination of settings is the single

most optimal solution.

The following assumptions will be taken into account for the break-even analysis:

The purchase of the CIMM (R80 000) is made with a once off payment

A provision was made for a 30% gross profit margin

The machine would operate 8 hours a day, 5 days a week at an availability of 80%

Although the break-even analysis doesn’t cover all the costs associated with operation of the CIMM it

still provides useful insight into which operational settings would be optimal. Table 25 shows that all

three experiments are financially feasible. The results for Experiment 14 and 23 are very similar as

they have the same total cost per part (𝑇𝐶𝑝𝑎𝑟𝑡). As a result they share the same break-even point of

158,731 which is greater than that of Experiment 26 at 119,761. This means that a greater quantity of

parts has to be produced for Experiment 14 and 23, compared to Experiment 26, in order to start

making a profit. However, due to the longer cycle time of Experiment 26, less parts will be produced

per month and subsequently less profit will be made.

14 23 26

1.26 1.26 1.67

40% 40% 40%

1.764 1.764 2.338

0.50 0.50 0.67

158,731 158,731 119,761

0.85 0.86 1.15

480 480 480

80% 80% 80%

384 384 384

452 447 334

9035 8930 6678

15,938.26 15,752.93 15,613.77

4,553.79 4,500.84 4,461.08

Experiment

R80 000

Max Production (per month)

Profit (per part)

Daily Usage (min)

Availability

Time in Use (min)

Max Production (per day)

Expected Monthly Income (R )

Expected Monthly Profit (R )

CIMM Cost

Total Cost per part (R )

Gross Profit Margin

Selling Price (R )

Break-even Units

Total Cycle Time per part (min)

Table 25: Summary of break-even analysis.

53

While all experiments are financially feasible a decision has to be made for an optimal solution for the

CIMM. Experiment 14 will be the suggested optimal solution for the operational settings of the

CIMM. This is due to its superior expected monthly profit which will be the most beneficial factor,

from a business point of view, moving forward.

54

6. Conclusion

The following chapter will conclude the project by summarising the findings of Chapter 5 with the

main focus of providing the optimal operational settings for the CIMM.

6.1 Suggested Operational Settings

As stated in Section 1.3 the aim of this study was to identify was to solve the problem statement in

Section 1.2. This would be achieved by finding the optimal economic value of the CIMM and

determining the optimal combination of operational parameters for the CIMM to produce conforming

parts, taking into account the associated costs to manufacture these parts.

Based on the results of the experimentation that was executed, Experiment 14 provides the optimal

operational settings for the CIMM. The results from this experiment show that good quality parts will

be produced at a low production cost. These optimal settings are shown in the following table:

6.2 Improvements and Recommendations for Future Studies

In terms of the study, the results obtained from this project are very subjective as they are only based

one design part at a limited range of moulding parameters. For future studies and experimentation, the

following can be considered:

Replicate the experimental procedure with different moulds by varying the part geometry,

size and complexity.

Replicate the experimental procedure over a larger range of moulding parameters (i.e. a

greater number of experiments).

Look to include the cost of the compressed air and maintenance for the CIMM to ensure

greater accuracy for the techno-economic assessment.

MT 190oC

FT 10 seconds

PT 5 seconds

Table 26: Optimal operational settings for the CIMM.

55

In terms of the CIMM itself, the following improvements can be considered:

The CIMM automatically ejecting parts as this would reduce the 𝑇𝐶 per part and further

optimise the machine.

Improve the design of the mould to ensure that less raw material is wasted as this results in

unnecessary loss in capital.

6.3 Skills Applied and Developed by Student

Prior to the final year project, the student had very limited experience or knowledge of optimising a

manufacturing machine. Undertaking this project expanded the student’s knowledge tremendously.

The student gained insight into various statistical analysis and optimisation techniques. Most of the

statistical analysis was conducted using the RStudio software package and this improved the student’s

ability in the field of programming.

The student applied many techniques and practices learnt through the academic years of Industrial

Engineering course. The most prominent of these came from the Engineering Statistics and Quality

Assurance courses.

On a personal level the student learnt a lot in the areas of time management, perseverance and

problem solving. The most notable of these was time management as the student struggled at times

with time management, due to having many projects with other modules and extra-curricular activities

during the same time period.

6.4 Benefits to Society

The implementation of the projects recommendations will provide useful insight for potential small

business owners in the field of PIM looking to make use of the CIMM. The optimal operational

settings that were determined in this project will provide the owners of the machine with the

opportunity to produce similar parts in the most optimal way. Optimisation is highly beneficial in the

manufacturing sector as businesses in this industry look to manufacture high quality parts in the most

optimal way as this allows the business to generate a greater income.

56

7. References

AVERY, J. (1998). Injection Molding Alternatives: A Guide for Designers and Product Engineer.

Hanser Publishers.

CANO, E.L., MOGUERZA, J.M & REDCHUK, A. (2012). Six Sigma with R. Springer.

CHAMBERS, J. (2008). Software for Data Analysis: Programming with R.

EVANS, J.R. & LINDSAY, W.M. (2014). Managing for Quality and Performance Excellence.

SOUTH-WESTERN CENGAGE Learning, 9th edition.

GAUTHIER, M. (1995). Injection Molding. Engineered Materials Handbook Desk Edition.

GUTIERREZ, P.H. & DALSTED, N.L. (2012). Break-Even Method of Investment Analysis.

Colorado State University. [ONLINE] Available at:

http://www.ext.colostate.edu/pubs/farmmgt/03759.html. [Accessed 21 August 2015].

JURAN, J. & GOFREY, A. (1998). Juran’s Quality Handbook. McGraw-Hill, 5th edition.

REES, H. (1995). Mold Engineering. Hanser Publishers.

ROSATO, D.V., ROSATO, D.V & ROSATO, M.G. (2000). Injection Moulding Handbook. Kluwer

Academic Publishers, 3rd edition.

SHUKLA, G. & SHUKLA, P. (2013). Design & Fabrication of Pneumatically Operated Plastic

Injection Molding Machine. International Journal of Engineering and Innovative Technology (IJEIT)

Volume 2, Issue 7.

STELLENBOSCH MUNICIPALITY (2015). Appendix 1B: Electricity Tariffs Applicable To

Services Rendered From 1 July 2015.

SUPER PRODUCTS WEBSITE (2015). ASH Service 1: Lindmann Products & Equipment.

[ONLINE] Available at: http://www.superproducts.co.za/Lindmann%20Machines.htm. [Accessed 05

June 2015].

THIRIEZ, A. & GUTOWSKI, T. (2006) An Environmental Analysis of Injection Molding.

Department of Mechanical Engineering Massachusetts Institute of Technology.

TRADING ECONOMICS WEBSITE (2015). South Africa Average Monthly Wages in

Manufacturing. [ONLINE] Available at: http://www.tradingeconomics.com/south-africa/wages-in-

manufacturing. [Accessed 21 August 2015].

UNAL, R. & DEAN, E. (1991). Taguchi Approach to Design Optimization for Quality and Cost: An

Overview. Annual Conference of the International Society of Parametric Analysts.

WILLIAMS, G. (2012). Data Mining with Rattle and R. [ONLINE] Available at:

http://www.analyticstrainings.com/?p=101. [Accessed 15 August 2015].

57

ZHOU, H. (2013). Computer Modeling for Injection Moulding. Huazhong University of Science and

Technology. John Wiley & Sons.

58

Appendix A

CIMM Specifications

Figure 37: Appendix A - Specifications of the CIMM made by Lindmann Machines & Equipment (Super Products Website, 2015).

59

Appendix B

Classification of Design of Experiments

Table 27: Appendix B.1 - Classification of Designs (Juran & Godfrey, 1998).

60

Table 28: Appendix B.2 - Classification of Designs (Continued).

61

Table 29: Appendix B.3 - Classification of Designs (Continued)

62

Appendix C

Factors for Control Charts

Table 30: Appendix C - Table showing factors for the different control charts (Evans & Lindsay, 2014).

63

Appendix D

Coding used in RStudio

#Set Work Directory

setwd("C:/Users/Matthew/Desktop/RStudio")

#Barplot For Conforming Parts

NumOfConfParts <- c(0,0,0,0,0,0,19,22,21,0,0,0,22,24,22,24,24,25,0,0,0,24,25,25,25,25,25)

barplot(NumOfConfParts, names.arg = c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27"),xlab = "Experiment",ylab = "Frequency",main ="Number Of Conforming Parts per Experiment" )

#Histogram for Summary Of Total Cost (TC) per Part

TC <- c(1.61,1.44,1.53,1.28,1.26,1.35,1.44,1.44,1.42,1.27,1.26,1.27,1.48,1.67,1.67)

hist(TC, main = "Histogram of Total Cost per Part", xlab = "TC (R)")

#Summary Of Conforming Parts

Con <- read.csv2("C:/Users/Matthew/Desktop/RStudio/Skripsie/ConformingParts.csv")

summary(Con)

#BoxPlots for Conforming Parts (Descriptive Statistics)

boxplot(Con$Thickness ~ Con$MT, main="Box plot of Thickness vs Mould Temperature", xlab="MT", ylab="Thickness" )

boxplot(Con$TC ~ Con$MT, main="Box plot of Total Cost vs Mould Temperature", xlab="MT", ylab="TC" )

boxplot(Con$Thickness ~ Con$FT, main="Box plot of Thickness vs Filling Time", xlab="FT", ylab="Thickness" )

boxplot(Con$TC ~ Con$FT, main="Box plot of Total Cost vs Filling Time", xlab="FT", ylab="TC" )

boxplot(Con$Thickness ~ Con$PT, main="Box plot of Thickness vs Packing Time", xlab="PT", ylab="Thickness" )

boxplot(Con$TC ~ Con$PT, main="Box plot of Total Cost vs Packing Time", xlab="PT", ylab="TC" )

#ScatterPlots Of Conforming Parts with Trendlines for ANOVA Analysis

plot(Con$Thickness ~ Con$MT, main="Scatterplot of Thickness vs Mould Temperature", xlab="MT", ylab="Thickness" )

THvMT<-lm(Con$Thickness~Con$MT)

abline(THvMT)

plot(Con$TC ~ Con$MT, main="Scatterplot of Total Cost vs Mould Temperature", xlab="MT", ylab="TC" )

TCvMT<-lm(Con$TC~Con$MT)

abline(TCvMT)

plot(Con$Thickness ~ Con$FT, main="Scatterplot of Thickness vs Filling Time", xlab="FT", ylab="Thickness" )

THvFT<-lm(Con$Thickness~Con$FT)

abline(THvFT)

64

plot(Con$TC ~ Con$FT, main="Scatterplot of Total Cost vs Filling Time", xlab="FT", ylab="TC" )

TCvFT<-lm(Con$TC~Con$FT)

abline(TCvFT)

plot(Con$Thickness ~ Con$PT, main="Scatterplot of Thickness vs Packing Time", xlab="PT", ylab="Thickness" )

THvPT<-lm(Con$Thickness~Con$PT)

abline(THvPT)

plot(Con$TC ~ Con$PT, main="Scatterplot of Total Cost vs Packing Time", xlab="PT", ylab="TC" )

TCvPT<-lm(Con$TC~Con$PT)

abline(TCvPT)

#ANOVA Analysis

anova(TCvMT)

anova(TCvFT)

anova(TCvPT)

anova(THvMT)

anova(THvFT)

anova(THvPT)

#3D Scatter Plots

Scat3D4 <- scatterplot3d(Con$FT, Con$MT, Con$TC,highlight.3d=TRUE, type="h", xlab="FT", ylab="MT",zlab="Total Cost")

plane4 <- lm(Con$TC ~ Con$FT + Con$MT)

Scat3D4$plane3d(plane4)

Scat3D2 <- scatterplot3d(Con$FT, Con$MT, Con$Thickness,highlight.3d=TRUE, type="h", xlab="FT", ylab="MT",zlab="Thickness")

plane2 <- lm(Con$Thickness ~ Con$FT + Con$MT)

Scat3D2$plane3d(plane2)

65

Appendix E

Digital Watt Meter

Figure 38: Digital watt meter that was used to measure energy consumption (kWh).

66

Appendix F

Planned Project Timeline

Start Date End Date Number Of Days

16 March 2015 13 April 2015 28

19 March 2015 20 May 2015 62

21 May 2015 27 May 2015 6

20 July 2015 02 August 2015 13

03 August 2015 09 August 2015 6

20 August 2015 28 August 2015 8

07 September 2015 14 September 2015 7

21 September 2015 05 October 2015 14

12 October 2015 26 October 2015 14

28 October 2015 01 November 2015 4

17 November 2015 18 November 2015 1

Final Report

Oral Preparation

Oral Assessment

Research Literature

Conduct Experimentation

Obtain Results

Task Definition

Project Proposal

First Examination Copy

Project Summary

70% Draft

Preliminary Examination Copy

Table 31: Project timeline plan.