Micro-O Micro Milling Process OptimizationProject topic: Micro Milling Process Optimization...

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Micro-O Micro Milling Process Optimization Technische Universität Berlin (TUB) Prof. Dr. h. c. Dr.-Ing. Eckart Uhlmann Instituto de Pesquisas Tecnológicas (IPT) Dr. Luciana W. S. L. Ramos Universidade Metodista de Piracicaba (UNIMEP) Prof. Dr.-Ing. Klaus Schützer Universidade Federal de ABC (UFABC) Prof. Dr. Erik del Conte Prof. Dr. Crhistian Baldo

Transcript of Micro-O Micro Milling Process OptimizationProject topic: Micro Milling Process Optimization...

Micro-O

Micro Milling Process Optimization

Technische Universität Berlin (TUB)

Prof. Dr. h. c. Dr.-Ing. Eckart Uhlmann

Instituto de Pesquisas Tecnológicas (IPT)

Dr. Luciana W. S. L. Ramos

Universidade Metodista de Piracicaba (UNIMEP)

Prof. Dr.-Ing. Klaus Schützer

Universidade Federal de ABC (UFABC)

Prof. Dr. Erik del Conte

Prof. Dr. Crhistian Baldo

Slide 2

Micro-O Micro milling process optimization

Project information

– General data

– Motivation

– Approach

– Team members

– Researcher exchange

and publications

– Project development status

Content

Project results

– Analysis and improvement

of cutting process planning (WP A)

– Improvement of process planning

and machining parameter (WP B)

– Process monitoring and improvement

of part control (WP C)

– Utilization of simulations

for improvement of micro milling (WP D)

– Micro-milling of molds

for micro-injection molding (WPE)

Outlook, remarks

and upcoming research period

Slide 3

Micro-O Project data

Project start with 3rd BRAGECRIM phase

on 1st August 2014

Project duration: 2 years (+ 2 years planned)

Project topic: Micro Milling Process Optimization

Participating universities and institutes:

– Institute for Machine Tools and Factory Management (IWF)

- Technische Universität Berlin (TUB)

– Institute of Technological Research (IPT)

– Lab. for Computer Application in Design and Manufacturing

(SCPM) - Methodist university of Piracicaba (UNIMEP)

– Engineering, Modeling and Applied Social Sciences Center

(CECS) - Federal University of Santo André and São

Bernardo do Campo (UFABC)

UNIMEP (Piracicaba)

UFABC (Santo André)

IPT (São Paulo)

TU (Berlin)

Slide 4

Increased pressure on highly developed industry

due to strong competition and low labour cost

Instead of competing with low prices, focus should be

on high quality and improvement of innovative,

reliable and advanced manufacturing technologies

Manufacturing technologies and machine tools

for micro-machining have achieved

a high proficiency level in industry and research

Increasing demand for micro-structured parts and products

requires enhancing the productivity, to develop new

and to improve existing micro-manufacturing technologies

Introduction and motivation

Micro-O Motivation

Micromould for microfluidic device with basic microfeatures

Section Parameter Value Tolerance

Mould mould overall size 15 mm x 15 mm ± 2mm

Microchannel

walls

length 500 µm - 10 mm ± 10 µm

width 100 µm – 500 µm ± 5 µm

height 100 µm – 500 µm ± 5 µm

inclination angle 3°- 50° ± 5°

Inlet/Outlet

Circular inlet-outlets ratio of 50 µm ± 5 µm

Angles for T junction inlets 26°-40° ± 10°

Surface

microchannelsRoughness Ra Ra < 0.5 µm ± 0.01 µm

CAD/CAM

processing

Final part for

quality inspection

Requirements

and tolerances

Slide 5

Micro-O Project approach 1st phase

Process

WP A Analysis and

improvement of

SCPM cutting process

IWF planning

WP C Process

monitoring and

IPT improvement of

part control

WP D Utilization of simulations for improvement of micro milling IWF

CAM

S P

System functions

„Referencing“

„Measure tool“

Parameter set

Tolerance band

Cutting speed

Cutting depth

S P

Inspection

S P

WP B Improvement of

process setup

IPT and machining

SCPM parameter

IWF

Microfluidics

application and

product design

Mold with micro-

features

Slide 6

Micro-O Project approach 2nd phase

WP E Micro milling of molds for micro-injection molding IPT IWF SCPM CECS

Application

CAD Design

time for … Initial … Improved

CAM processing 1.0 h 0.5 h

Process setup 1.5 h 1.2 h

Machining 1.5 h 1.0 h

Part control 4.0 h 2.0 h

Micro-

injection

molding

WP D Utilization of simulations for improvement of micro milling IWF

CAM Design

Mold

manu-

facturing

WPA

SCPM

IWF

Analysis and

improvement of

cutting process

planning

WP B

IPT

SCPM

IWF

CECS

Improvement of

process setup and

machining

parameter

WP C

IPT

CECS

IWF

Process monitoring

and improvement of

part control

Feedback

Part

controlMicrofluidic devicePart

control

Project main objective

Application oriented

optimization of

productivity and accuracy

1st project period main results:

- Determined productivity potential of CAM processing

- Optimized set-up procedure and finishing cutting parameter

- New evaluation strategies for critical micro-feature inspection

- Enhanced simulation models for micro-milling

Micro-structured part

2n

dp

roje

ct

peri

od

1stp

roje

ct

peri

od

Slide 7

Micro-O Project Team 2014-2017INSTITUTION NAME EMAIL ACTIVITY

IWF Prof. Dr. h. c. Dr.-Ing. Eckart Uhlmann [email protected] Principal researcher / Coordinator

IWF Jan Mewis [email protected] Researcher

IWF Simon Thom [email protected] Researcher

IWF Dr.-Ing. Lukas Prasol [email protected] Researcher

IWF Raphael Rathje [email protected] Student

IWF Hoang Minh Nguyen Student

IWF Enrico Seiler [email protected] Student

UNIMEP Prof. Dr.-Ing. Klaus Schützer [email protected] Principal researcher

UNIMEP Tiago Picarelli [email protected] Student

UNIMEP Felipe Perroni [email protected] Student

UNIMEP Luiz Guilherme [email protected] Student

UNIMEP Marcelo Octavio Tamborlin [email protected] Student

UFABC Prof. Dr. Erik Gustavo Del Conte [email protected] Principal researcher

UFABC Prof. Dr. Crhistian Baldo [email protected] Principal researcher

UFABC Dr. Manuel Alberteris-Campos [email protected] Researcher

UFABC Bruna Castilho dos Santos [email protected] Student

UFABC Gabriel de Andrade [email protected] Student

UFABC Elvis Fernando Cipriano de Lima Student

UFABC João Gabriel Franchi Briotto Student

UFABC Cinthia Soares Manso [email protected] Student

IPT Dr. Luciana Wasnievski da Silva de Luca Ramos [email protected] Principal researcher / Coordinator

IPT Dr. Liz Katherine Rincon [email protected] Researcher

IPT Diogo Borges [email protected] Researcher

IPT Antonio Militão [email protected] Technican

IPT Renato Spacini de Castro [email protected] Technican

Slide 8

Micro-O Exchanges and publications

Work and study missions

throughout the whole project period

As the project has evolved,

work missions were conducted especially in 2016

and 2017 to catch up with the work plan

Results of experimental research conducted

in the time from April 2016 to September 2016

have been published in 2017

As can be seen from the table to the left,

the research output is still rising

2014 2015 2016 2017

Work missions 2 2 5 4

Study

missions2 - 2 2

Journal

publications- 1 2 2

Conferences 1 4 2 4

B.Sc., M.Sc.

and Ph.D.

theses

1 2 2 1

Other

publications- - - -

Number of exchanges and publications

Work and study missions

Publications

Slide 9

Micro-O Project development status

Activities completed Activities ongoing

Slide 10

Micro-O Project development status

Activities completed Activities ongoing

Slide 11

Micro-O Micro milling process optimization

Project information

– General data

– Motivation

– Approach

– Team members

– Researcher exchange

and publications

– Project development status

Content

Project results

– Analysis and improvement

of cutting process planning (WP A)

– Improvement of process planning

and machining parameter (WP B)

– Process monitoring

and improvement of part control (WP C)

– Utilization of simulations

for improvement of micro milling (WP D)

– Micro-milling of molds

for micro-injection molding (WP E)

Outlook, remarks

and upcoming research period

Slide 12

Micro-O Project approach

WP A Analysis and

improvement of

SCPM cutting process

IWF planning

WP C Process

monitoring and

IPT improvement of

part control

WP D Utilization of simulations for improvement of micro milling IWF

WP B Improvement of

process setup

IPT and machining

SCPM parameter

IWF

ProcessCAM

S P

System functions

„Referencing“

„Measure tool“

Parameter set

Tolerance band

Cutting speed

Cutting depth

S P

Inspection

S P

Microfluidics

application and

product design

Mold with micro-

features

Slide 13

Micro-O Cutting process planning (WP A)

Analysis of impact factors

in micro milling tool path generation

Tool paths of cutting strategies: follow part, zig and Profile

Comparation of tool path generation and simulation times

finish op. 0.4mm tool

Cutting Strategy Tolerance (mm) 1 gen 2 gen 3 gen average CAM Simulation Time (s)

Follow Part 0.0001 43,31 43,2 43,9 43,47 1145

Follow Part 0.0002 34,3 34,26 35,02 34,53 1145

Follow Part 0.0010 19,08 19,5 19,51 19,36 1145

Zig 0.0001 62,09 62,17 62,16 62,14 3331

Zig 0.0002 51,64 52,84 52,5 52,33 3331

Zig 0.0010 35,23 33,51 34,08 34,27 3331

Profile 0.0001 42,34 42,55 42,43 42,44 105

Profile 0.0002 33,61 33,02 33,85 33,49 105

Profile 0.0010 18,03 17,81 18,82 18,22 105

Generating Time (s) only finish op. 0.4mm toolfinish op. 0.4mm tool

Cutting Strategy Tolerance (mm) 1 gen 2 gen 3 gen average CAM Simulation Time (s)

Follow Part 0.0001 43,31 43,2 43,9 43,47 1145

Follow Part 0.0002 34,3 34,26 35,02 34,53 1145

Follow Part 0.0010 19,08 19,5 19,51 19,36 1145

Zig 0.0001 62,09 62,17 62,16 62,14 3331

Zig 0.0002 51,64 52,84 52,5 52,33 3331

Zig 0.0010 35,23 33,51 34,08 34,27 3331

Profile 0.0001 42,34 42,55 42,43 42,44 105

Profile 0.0002 33,61 33,02 33,85 33,49 105

Profile 0.0010 18,03 17,81 18,82 18,22 105

Generating Time (s) only finish op. 0.4mm tool

Interpolation method has a significant impact

on NC program size and the post-processing time

As expected, the decrease of tolerance increases

CAM processing times (tool path generation times)

and the NC program size, but has no significant impact

on the machining time

The cutting strategy has significant impact on all target

values, mainly as result of non-cutting movements

and therefore reveals highest potential for optimization

But cutting strategies generally cannot be easily

replaced and, therefore compared (knowledge of the

most appropriate cutting strategies for each case is

essential)

Slide 14

Micro-O Cutting process planning (WP A)

Methodical reduction of process times

In roughing and semi-finishing operations always leave

the minimum stock for remaining operations

Reduce non cutting moves by lowering clearance planes

and individual evaluation of small closed areas

In some cases more transitions

between areas can reduce machining time

8th generation CAM program - machining time: 2:00:06

9th generation CAM program - machining time: 1:40:23

Reduction of time

only in this step:

16.4 %

Comparing machining times: Version 8 on the left and version 9 right

Slide 15

Micro-O Cutting process planning (WP A)

Walls test with 10 mm cuting segments

Real time is from 1,5 to 6x the simulated time

Ratio o

fm

easure

dand

sim

ula

ted

ma

ch

inin

gtim

e

Cutting depth ap [µm]

4 20

40

4.0

2.0

0.0

6.0

4 40 80

Prediction of machining times and accuracy

On the short distance movements the feed rate

programmed is different from real feed rate

The difference is substantial in micro-machining

and common CAM software don’t show this effect

In 10 mm segments with feed per tooth fz = 4…40 µm

the ratio between real machining time and simulated

time is between 1.5 and 6

Slide 16

Micro-O Cutting process planning (WP A)

Tuning of CAM processing

regarding micro-machining operations Kinematic and geometric model of KERN Evo

micro-milling center has been implemented in NX 11.0

The axis speed, acceleration, jerk and control specific

parameter of the virtual machine were adjusted

according to the values given in the real machine tool

The model has been applied to detect collisions,

reduce non cutting moves and study material

remaining for optimization purposes

Routines, parameters, cutting methods,

engage and retract strategies has been improved

and added as default on the CAM system

On the improved virtual machine tool

the simulated error lowered to 3...5 %

Virtual model of KERN Evo micro-milling center

Axis control on NX 11.0

(a) (b)

Slide 17

Micro-O Project approach

WP A Analysis and

improvement of

SCPM cutting process

IWF planning

WP C Process

monitoring and

IPT improvement of

part control

WP D Utilization of simulations for improvement of micro milling IWF

WP B Improvement of

process setup

IPT and machining

SCPM parameter

IWF

ProcessCAM

S P

System functions

„Referencing“

„Measure tool“

Parameter set

Tolerance band

Cutting speed

Cutting depth

S P

Inspection

S P

Microfluidics

application and

product design

Mold with micro-

features

Slide 18

Micro-O Planning and parameter (WP B)

Machine tool set-up improvement

In-situ magnetic inspection of the part fixture

The effect of the elastic deflection produced

by the clamping forces was more influential

on the MBNenergy than the effect of the plastic

deformation produced by the micro-milling tool

Magnetic Barkhausen Noise (MBN) signals in feed

direction at the second and the fourth stage are shown

Comparing the two signals, it is possible to see the

differences in amplitude caused by the clamping forces

The fixture of workpiece in case of clamping systems

has an impact on the machining accuracy,

because of elastic deformation of the workpiece

Experimental setup

MBN signals [CAM17]

Slide 20

Micro-O Planning and parameter (WP B)

Improvement of cutting parameter

Results of analysis on steel:

– Cutting depth ap has high, cutting width ae has none and feed

per tooth fz has low correlation with line roughness

– Mean chip thickness is lower than the minimum chip

thickness - this significantly effects cutting process dynamics -

the cutting parameter range needs to be adjusted

Results of analysis on brass:

– Feed per tooth has highest correlation with line roughness,

cutting depth and width have similar and low correlation

– Optimizations: bigger cutting depth and width with low feed,

saves time without reducing surface quality

Some parameter set result in roughness as good as other

fine finishing processes like grinding and polishing

(a) Picture steel part (b) Measured surface with detail

Experimental setup

Material: X40CrMoV5-1

Tool: end mill d = 0.4 mm

ap: 4 µm, 40 µm, 80 µm

ae: 100 µm, 200 µm

fz: 4 µm, 20 µm, 40 µm

(a) (b)

[TAM17](a) Picture brass part (b) Measured surface with detail

500 µm

Experimental setup

Material: CuZn39Pb

Tool: end mill d = 0.4 mm

ap: 4 µm, 40 µm, 80 µm

ae: 100 µm, 200 µm

fz: 4 µm, 20 µm, 40 µm

(a) (b)

Slide 21

Micro-O Planning and parameter (WP B)

Analysis of process stability

A stability analysis was performed on Kern Evo in Brazil

and Primacon in Germany with the workpiece material

X40CrMoV5-1

On both the cutting forces were recorded and

comparedby applyíng a force measurement platform

Kern Evo: During the experiments neither chatter marks

nor vibrations at chatter frequencies could be observed

Primacon: A clear stability limit could be observed

Kern Evo has improved frame shape and materials,

leading to a more stable process and higher accuracy

Stability lobe diagram for PrimaCON

A X X X X X X X X X X X X X X

A A X X X X X X X X X X A X X

O A X X X X X X X X X X A X X

O O X A X X X X X X X O O O X

O O O O X X X A O O O O O O O20

40

µm

25

30

29 37 krpm 4533

Tool

Micro end mill, d = 0.4 mm, z = 2

Material

X40CrMoV5-1

Process parameter

fz = 4 µm

ae = 400 µm

n = 45 krpm

Lubrication

Dry

O stable processA uncertain stateX unstable process

Cuttin

gdepth

ap

Spindle speed n

Machine tool

PrimaCON

Tool

Micro end mill, d = 0.4 mm, z = 2

Material

X40CrMoV5-1

Process parameter

fz = 4 µm

ae = 400 µm

n = 45 krpm

Lubrication

Dry

Stability lobe diagram of PrimaCON

A X X X X X X X X X X X X X X

A A X X X X X X X X X X A X X

O A X X X X X X X X X X A X X

O O X A X X X X X X X O O O X

O O O O X X X A O O O O O O O20

40

µm

25

30

29 37 krpm 4533

Tool

Micro end mill, d = 0.4 mm, z = 2

Material

X40CrMoV5-1

Process parameter

fz = 4 µm

ae = 400 µm

n = 45 krpm

Lubrication

Dry

O stable processA uncertain stateX unstable process

Cuttin

gdepth

ap

Spindle speed n

Machine tool

PrimaCON

Slide 23

Micro-O Project approach

WP A Analysis and

improvement of

SCPM cutting process

IWF planning

WP C Process

monitoring and

IPT improvement of

part control

WP D Utilization of simulations for improvement of micro milling IWF

WP B Improvement of

process setup

IPT and machining

SCPM parameter

IWF

ProcessCAM

S P

System functions

„Referencing“

„Measure tool“

Parameter set

Tolerance band

Cutting speed

Cutting depth

S P

Inspection

S P

Microfluidics

application and

product design

Mold with micro-

features

Slide 24

Micro-O Monitoring and control (WP C)

Investigation of measurement processes

for roughness analysis

Line roughness parameters selected and employed

in accordance with ISO 4288:1996 (Ra, Rz, Rt)

– Long wavelength components separated from short

wavelength components using Gaussian regression filter

ANOVA approach employed to identify and quantify

individual random effects in a measurement

– Measurements carried out in the feed direction

define the within-track variation (blue)

– Measurements carried out in different positions

define the between-track variation (red)

Expanded uncertainty of each roughness parameter

estimated as the combination of the within-track and

between-track standard uncertainties in a RSS manner[BAL17]

Slide 25

Micro-O Monitoring and control (WP C)

Mold dimensional results

Features of size of the microfluidic mold checked on a

multisensor CMM using an inbuilt image processing unit

– Major uncertainty factor: surface variation, directly

associated with the machining process, beyond the other

uncertainty components

Large form error (burrs): an relevant finding of the

measurement task, and reported to the machine shop

staff for adjusting the cutting parameters

For checking the micro-channel height, transverse tracks

sampled with the stylus contact profiler

– Large amount of points sampled on the surface, for each

track; the distance between the two ideal features of type

straight line was taken as the channel height, using the

least-squares method[BAL17]

Slide 26

Micro-O Monitoring and control (WP C)

Measurement result analysis

Concluding remarks:

– Two roughness measurement technologies tested and their

results compared using reference dimensional standards

– Stylus contact profiler preferred to the interferometer-based

profiler because of improved overall measurement

performance

– Outputs for both samples not significantly influenced by the

pure repeatability, but mainly by the within-sample texture

variation

– The workpiece itself is the major contributor of the

uncertaintity of the results

– Reliable measurement data could be supplied for micro-

milling process diagnostics and improvements

Mold Drawing [BAL17]

Slide 27

Micro-O Project approach

WP A Analysis and

improvement of

SCPM cutting process

IWF planning

WP C Process

monitoring and

IPT improvement of

part control

WP D Utilization of simulations for improvement of micro milling IWF

WP B Improvement of

process setup

IPT and machining

SCPM parameter

IWF

ProcessCAM

S P

System functions

„Referencing“

„Measure tool“

Parameter set

Tolerance band

Cutting speed

Cutting depth

S P

Inspection

S P

Microfluidics

application and

product design

Mold with micro-

features

Slide 28

Micro-O Micro-milling simulations (WP D)

Simulation based improvement of part fixture

Simulation-based analysis of the deformation of the

workpiece in the micro-milling process and derivation

of appropriate compensation measures were elaborated

Deflections of clamping jaws due to clamping process

have been determined -> significant lift

in positive z-direction and rotation around y-axis

Milling test and simulation results are in good agreement

(difference between simulation and measurement less

than 1 µm)

The FE results can be applied to reduce the impact of a

clamping system by a significant amount and therefore,

significantly increase form accuracy of the workpiece

Exemplary Simulated and machined relative surface height

deflection resulting from clamping, Fc = 15 kN

Measurement points for determination of deflection of

clamping jaws in z-direction

1

2

3

4

xz

y

8

µm

6

4

3

2

0

Refe

ren

ce

dsu

rfa

ce

he

igh

tz

r

5

1

(a) (b) SimulatedMeasured

[CAM17]

Slide 29

Micro-O Micro-milling simulations (WP D)

Simulation based prediction

and avoidance of chatter Difficulties to determine frequency response function

(FRF) at tool center point (TCP) (currently not possible to

excite with reproducible and known force)

Dynamic sub-structuring

combining measured and simulated FRF

Impact of damping, cantilever length, end mill form

and clamping device was considered in FE Simulation

using ANSYS

For micro-milling tools, tool dynamics (1st and 2nd

Eigenfrequency) dominate the compliance at TCP

Damping (structural and process related) has to be

adjusted according to results of preliminary cutting tests

FRF at TCP

Measured FRF at chuck Simulated FRF at TCP

Procedure for determination of TCP compliance

40

10

Axia

l d

ep

th o

f cu

t a

p

20

µm

10 20 30 103rpm 50Spindle speed n

06.2

5.8

kHz

5.6

Cha

tte

r fr

eq

ue

ncy f

c

Stability limit

Predicted stability limit for tool with diameter d = 0.4 mm

Slide 30

Micro-O Micro-milling simulations (WP D)

Integrated simulation

of micro-milling processes GUI for simulation of milling processes

has been developed in Matlab and tested

– Parser and Interpolator has been implemented

– Axis control behavior

– Geometric behavior (volumetric)

– Dynamic behavior (p-LSCF synthesized MIMO system)

– Cutting force models (macro and micro milling)

– Time domain cutting model (end and radius milling tool)

Possible applications

– Prediction of resulting surface quality and cutting forces

– Differentiation of impacts (geometric, dynamic, control, tool

geometry) on local machining errorFunctionalities of GUI for virtual machining

CNC &

axis control

Tool

definition

CAD &

CAM

Geometric

behavior

Dynamic

behavior

Cutting

process

Slide 31

Micro-O Micro-milling simulations (WP D)

Integrated simulation

of micro-milling processes Comparison of optically observed topography and virtually

determined topography shows good agreement

Comparing the simulated and measured cutting forces

yield reasonable results regarding the amplitude

for micro-milling processes

Although the dynamic model has been applied

and the sample rate of the simulation fs = 40 kHz

is comparably high, differences in high-frequency dynamic

effects can be observed due tool simplifications

The virtual machine tool allows a good preview of cutting

forces, avoidance of chatter and surface quality,

leading to process optimizations.

-15.8 -15.4µm

-36.66 -35.0µm

ap = 15µm

ap = 35µm

(left) surface topography, (right) force spectra

surface topography

Slide 32

Micro-O Micro-milling simulations (WP D)

Mold floorChannel top

-0.5

-1.0

µmH

eig

ht

of

sim

ula

ted p

rofile

[UHL17]

Slide 33

Micro-O Micro-milling simulations (WP D)

Integrated simulation

of micro-milling processes Next steps:

– Evaluate predicted stability lobes

on target machine tool KERN Evo in Brasil

– Implement tool wear model based on real results

– Improve geometric cutting model for end milling

– Conduct further simulations

and validate improved simulation models

– Generate data base for cutting force model coefficients

– Simulation performance improvement

Simulation of cutting process

Visualization of virtual machining process

Microstructure

surface

Milled surface

Visualized

cutting edge

300 µm

Slide 34

WP E Micro milling of molds for micro-injection molding IPT IWF SCPM CECS

Application

CAD Design

time for … Initial … Improved

CAM processing 1.0 h 0.5 h

Process setup 1.5 h 1.2 h

Machining 1.5 h 1.0 h

Part control 4.0 h 2.0 h

Micro-

injection

molding

WP D Utilization of simulations for improvement of micro milling IWF

CAM Design

Mold

manu-

facturing

WPA

SCPM

IWF

Analysis and

improvement of

cutting process

planning

WP B

IPT

SCPM

IWF

CECS

Improvement of

process setup and

machining

parameter

WP C

IPT

CECS

IWF

Process monitoring

and improvement of

part control

Feedback

Part

controlMicrofluidic devicePart

control

Project main objective

Application oriented

optimization of

productivity and accuracy

1st project period main results:

- Determined productivity potential of CAM processing

- Optimized set-up procedure and finishing cutting parameter

- New evaluation strategies for critical micro-feature inspection

- Enhanced simulation models for micro-milling

Micro-structured part

2n

dp

roje

ct

peri

od

1stp

roje

ct

peri

od

Micro-O Project approach 2nd phase

Slide 35

Micro-O Molds for micro-injection (WP E)

Next steps

CAM Integration for molds with micro-features (E1)

– Integration of knowledge based support in the CAM system

regarding manufacturing of micro-molds

Process set-up and parameter improvement

for micro mold machining (E2)

– Cutting parameter analysis e.g. in relation to cutting width ae

Process monitoring during machining of micro mold (E3)

– Tool wear monitoring

Analysis and improvement of process stability (E4)

– Impact of material characteristics

regarding process stabilityMicro-fluidic devices

Slide 36

Micro-O Molds for micro-injection (WP E)

Next steps

Simulation of micro-injection molding processes (E5)

– Impact of demolding

after micro-injection molding on final part quality

– Simulation of demolding process with ANSYS

Quality inspection of injection molded parts (E6)

– Development of methods to determine final polymer part

quality in terms of dimensional and geometrical accuracy

Final holistic optimization of micro mold and evaluation

– Set-up of a practical relevant use case

– Evaluation of complete manufacturing chain

in terms of accuracy and productivityMicro-fluidic devices

Slide 38

Thank you for your attention!

Slide 39

References

BAL17 Baldo, C. R.; Ramos, L. W. S. L.; Mewis, J.; del Conte, E. G.; Uhlmann, E.; Schützer, K.: Measurement design for

dimensional control of functional micro-scale features on microfluid-ic moulds. In: Proceedings of 9th Brazilian

Congress on Manufacturing Engineering, Joinville, Brazil, 2017.

CAM17 Camposa, M. A.; Mewis, J.; del Conte, E. G.: In-situ magnetic inspection of the part fixture and the residual stress in

micromilled hot-work tool steel. NDT & E International - international journal of nondestructive testing and

evaluation,

Vol. 90 (2017), pp. 33 - 38.

TAM17 Tamborlin, M. O.; Mewis, J.; Ramos, L. W. S. L.; Schützer, K.: Influence of cutting parameters in micro-milling of

moulds for micro-components. 16th international scientific conference on production engineering, Zadar, Croatia,

08.06. - 10.06.2017.

UHL17 Uhlmann, E.; Mewis, J.; Baldo, R. C.; Ramos, L. W. S. L.; Peukert, B.; Schützer, K.; del Conte, E.; Tamborlin, M.:

Virtual machining of micro-milling processes for prediction of cutting forces and surface quality. 6th International

Conference on Virtual Machining Process Technology (VMPT), Montréal, Canada, 29.05.2017 - 02.06.2017.