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
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 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 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.