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Fundamental Study of Fundamental Study of Corrective Abrasive Corrective Abrasive Machining TechnologyMachining Technology
Dr Xun ChenDr Xun Chen
Advanced Machining Technology GroupAdvanced Machining Technology Group
Centre for Precision TechnologiesCentre for Precision Technologies
School of Computing and EngineeringSchool of Computing and Engineering
CIRP UK Meeting 8th May 2009
Centre for Precision Technologies
Precision machining technology
Current Research ThemesCurrent Research Themes� Precision machining
� Abrasive machining
� Diamond turning
� Finite element analysis and molecular dynamics
� Intelligent process monitoring and control � Contact detection using acoustic emission
� Abrasive machining monitoring using AI techniques
� Knowledge support systems Database / Knowledge warehouse
Unique pioneer challenge in developing
next generation machining technology
Educating tomorrow’s professionals
Electrical & Electronic
Engineering
Mechanical & Manufacturing
Engineering
Computing & information
Technology
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Basic principle of precision corrective machining
Sa = 344.8nm Sa = 9.7nm
Pelvis
Acetabulum cup
Cup holder
Stem
Tribological analysis
AE pream-plifier 1& 2
Acc. coupler
Dynamo-amplifier
Thermo-amplifier
Data Acquisition card
SCB100
Spindle power
PAC signal card
On-line monitoring
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Framework of a grinding process control
Control methods
• adaptive control,
• constraint control.
Monitoring & control; Surface integrity; Coolant delivery; Wheel dressing
Environmental factors:
•Machine
•Workpiece
•Coolant
Wheel preparation:
•Wheel type
•Dressing conditions
•dressing depth
•dressing lead
Grinding process:
•Force
•Power
•Vibration
•Temperature
•Acoustic emission
•Material removal
•Displacement
•Wheel wear
•Grinding burn
Control parameters:
•Grinding conditions
•grinding speed
•workspeed
•feedrate
•Coolant delivery
•Grinding cycle
Wheel conditioning:
•Wheel balance
•Wheel topography
Control strategies
Quality inspection:
•Surface roughness
•Surface wave
•Surface integrity
•crack
•hardness change
•residual stress
•phase changes
•Form error
•Size accuracy
Process parameters Quality parameters
Outputs
Three major measures of
grinding qualities
• accuracy of size and form,
• surface roughness,
• surface integrity.
Sensors:• Power and torque• Forces• Size gauging• Grinding force• Vibration monitoring• Temperature sensing• Eddy current sensing • Acoustic emission• Image sensing
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From the macroworld to the nanoworld (The Hitchhiker’s Guide to Nanotechnology, 2006)
MicroMicro--machiningmachining is considered to cover the production of minute components and features from a wide range of materials, generally in the size range of 200 microns to a few nanometres and may also be known as micro drilling, micro cutting, micro milling, micro grinding and micro etching.
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Challenge of nanometre scale machiningChallenge of nanometre scale machining
Grain boundary indicates the limitation of the conventional turning operation limitation.What can be done?
Aluminum (Al) Sa = 0.742 nm Nickel (Ni) Sa = 0.924 nm
• Spindle RPM: 2000
• Finish Feedrate: 7.5 mm/min
• Finish Depth of Cut: 2 µm
• Coolant: Odorless Mineral Spirits
• Spindle RPM: 3000
• Finish Feedrate: 5 mm/min
• Finish Depth of Cut: 4 µm
• Coolant: Odorless Mineral Spirits
Sa < 1 nmSa < 1 nmForm error < 0.1 Form error < 0.1 µµmm
It was claimed that grinding has no minimum depth of cut.It was claimed that grinding has no minimum depth of cut.6
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grinding forces wheel wear
grinding wheel topography
surfaceintegrity
surfaceroughness
grindingtemperature
grindingvibration
size error
Outputs of grinding process
grinding power
grindingwheel
workpiecedressing
tooldressing
kinematicsgrinding
kinematics
Inputs of grinding process
chip geometry
Grinding process
single grain load
environment
grinding
deformation
form error
Basic Relationships of a Grinding Process and Simulation
Input : specifications of the wheel and the workpiece, dressing and grinding conditions.
Generating the cutting surface of the grain
Simulation based on a single grain in order k, j, i.
Distributing the grain centres
X (i = 1, 2, •••)
Y (j = 1, 2, •••)
Z (
k =
1, 2,
3)
Simulating the grinding action of the grain
All grains simulated?
no
Output results
yes
Accumulating the actions of the grains
workpiece surface before grinding
workpiece
Z
X
Simulating the wear of the grain
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Fundamental Mechanism of Grinding
wheel
vw
vs
workpiece
a
a
chip formation ploughing sliding
grithm
l k
vs
Three stages of chip generation
AE signals would have certain features that present three stages of chip generation.
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Rigs for single grit grinding tests
Workpiece (2 workpieces 180° apart from each other)
Single grit holder
Force sensor
AE Sensor and protective housing
Material
v
Str
oke
v
Grit tip
Scratch
Stroke
v
Grit tip
Scratch
Stroke
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AE signals of single grit scratch
0 0.1 0.2 0.3 0.4-0.2
-0.1
0
0.1
0.2Test 212 SG Scratches Channel 1
Time (S)
0 0.1 0.2 0.3 0.4-0.2
-0.1
0
0.1
0.2Test 212 SG Scratches Channel 2
Time (S)
Am
plit
ud
e (
V)
0 0.1 0.2 0.3 0.4-0.2
-0.1
0
0.1
0.2Test 212 SG Scratches Channel 1
Time (S)
Am
plit
ud
e
(V)
0 0.1 0.2 0.3 0.4-0.2
-0.1
0
0.1
0.2Test 212 SG Scratches Channel 2
Time (S)
0 0.2 0.4 0.6 0.8 1 1.2
x 10-3
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
Time (S)
Am
plit
ude (
V)
SG4 Experiment Test 212 Hit 2 (normalised)
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Single grit scratch testvv
Scratch
Stroke towards
page
Grit Tip
Steel plate
0 5 10
x 105
0
1
2
3
4 Cutting FFT
Norm
alis
ed A
mplit
ude
0 5 10
x 105
0
0.5
1
1.5 P loughing FFT
Frequency (Hz)0 5 10
x 105
0
0.1
0.2
0.3
0.4 Rubbing FFT
10 20 30-3
-2
-1
0
1Cutting P rofile
depth
of
cut
(um
)
0 20-1
-0.5
0
0.5
1 P loughing profile
c ross sec tion length (um )
0 20 40-0.1
-0.05
0
0.05
0.1 Rubbinging profile
401µm
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Grinding mechanism classification using Neural Networks
0 10 20 30 40 50 600.5
1
1.5
2
2.5
3
3.5
NN Input Vectors Verification Test Set
1:
Cutt
ing,
2:
Plo
ugh
ing
and 3
: ru
bb
ing
NN Results for STFT SG4 AE Signals
x : classification
x : misclassification
x & x : Unseen Data
Network classification: 92%
Unseen Data classification: 83%
0 10 20 30 40 50 600.5
1
1.5
2
2.5
3
3.5
NN INput Vectors Test Set
1:
Cutt
ing,
2:
Plo
ugh
ing
and 3
: ru
bb
ing
NN Results for STFT SG4 AE Signals
x : classification
x : misclassification
x & x : Unseen Data
Network classification: 93%
Unseen Data classification: 87%
Wheel rotational speed = 4000 rpm, Feedrate = 4 m/s.
Workpiece materials: CMSX4
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Feature extraction of AE in grinding
Wheel rotational speed = 4000 rpm,
Feedrate = 4 m/s,
Workpiece materials: CMSX4.
0 10 20 30 40 50 600
0.5
1
1.5
2
2.5
3
3.5
NN Input Vectors (STFT of Hit 14)
1:
Cutt
ing,
2:
Plo
ughin
g a
nd 3
: R
ubbin
g
Hit 14 of 1um grinding cut with VIPER 60 Wheel
0 10 20 30 40 50 60 700
0.5
1
1.5
2
2.5
3
3.5
NN INput Vectors (STFT of Hit 15)
1:
Cutt
ing,
2:
Plo
ughin
g a
nd 3
: R
ubbin
g
Hit 15 of 1um cut with VIPER 60 Wheel
0 10 20 30 40 50 60 700.5
1
1.5
2
2.5
3
NN Input Vectors (STFT of Hit 20)
1: C
utt
ing,
2: P
loughin
g,
3: R
ubbin
g
Hit 20 of 0.1mm grinding cut with VIPER 60 Wheel
C% P% R%
1 µm cut 45 29 26
1 µm cut 45 47 8
0.1 mm cut 57 40 3
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Laser irradiation imitating grinding thermal behaviour
Laser machine Lumonics:JK704 Nd:YAGWave length 1.06 ηm
Pulse energy 1.36J
Maximum peak power 2.5kW
Laser irradiation time 0.06 ms
Focal length 120 mmLight beam diameter 12 mm
Off-focal length 34~46 mm
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Features of thermal stress induced AE signals
Centre for Precision Technologies
Comparison of AE signals generated by laser and grinding
0 1 2 3 4 5 6 7 8 9 10
x 105
0
0.1
0.2
0.3
0.4
0.5
Frequency(Hz)
No
rma
lize
d E
nerg
y
Test No.: Laser23H8
Pulse Time: 0.6msPulse Energy: 3.4J
Off-focal: 15mmT
m=781°C
Thermocouple: K-type
867KHz
835KHz
820KHz
508KHz
461KHz
336KHz
0 1 2 3 4 5 6 7 8 9 10
x 105
0
0.1
0.2
0.3
0.4
0.5
0.6
Frequency(Hz)
Norm
alized E
nerg
y
Pulse Time: 0.6ms
Pulse Energy: 3.5J
Temperature: 108°COff-focal: 30mm
Thermocouple: K-type
Test No.: Laser37
F1=266KHz
F2=258KHz
F3=141KHz
F4=391KHz
F5=836KHz
0 1 2 3 4 5 6 7 8 9 10
x 105
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Frequency(Hz)
Norm
aliz
ed E
ne
rgy
Test No:Fd2-4
Wheel: XA60F13VRP
Material:CMSX4vs=40 m/s
vw
=0.5 m/min
ap=0.074
Direction: down
Coolant: NoT
m=784°C
258
141
391 766
0 1 2 3 4 5 6 7 8 9 10
x 105
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Frequency(Hz)
Norm
alis
ed e
nerg
y
Test No:F155010
Wheel:XA60F13VRP
Material:CMSX4vs=50 m/s
vw
=1 m/min
ap=1.3 mm
Direction: down
Coolant:48barT
m=611°C
391 141
406
156
438
Laser
Grinding
Low temperature High temperature16
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Grinding thermal behaviour monitoring
by using thermal AE signatures
The NN created using AE signatures from laser irradiation
Grinding burn identification using the NN from thermal AE signatures
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Pattern recognition for
grinding defects
A Real Grinding Process (input)
Sensors (AE signal)
Impedance match & Amplifier
Data digitized & logging
Quantification, Normalization, & Denoise etc
Feature extraction: joint-time-frequency domain (wavelet packet)
Feature optimization
Non-burn
Burn
Signal condition
Signal processing
Pattern recognition
Fuzzy classifier
Minimum distance Fuzzy c-mean cluster
Feature extraction by wavelet packetand Joint Time-Frequency Analysis
-Feature calibration
-Transitive closure calculation
-Feature equalization
-Feature normalization
More than
512 features
Similarity
measure &clustering
Less than
10 features
=
mnmjm2m1
iniji2i1
2n2j22
n1j12
x ..., x ..., x x
x ..., x ..., x x
x ..., x x x
x x..., ,x x
n x mX
,,,
,,,
,...,,,
...,,,
)(
21
111
MM
MM
=
nnnjn2n1
iniji2i1
2n2j22
n1j12
K
r ..., r ..., r r
r ..., r ..., r r
r ..., r r r
r r..., ,r r
R
~,~,~,~
~,~,~,~
~,~...,,~,~
~...,,~~,~
21
111
MM
MM
Selection &
Optimisation
Training &
Classification
0
1
2
3
4
5
6
0 5 10 15 20 25 30 35
Burn status classification
Fuzzy recognition procedure
Fuzzy recognition block diagraph
• Success rate 92.3%
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Support
vector
machine
classificationMaximum margin
Optimal Hyperplane
Class I
Class II
Support vectors
1-5
me
asu
rem
en
ts a
cro
ss w
ork
pie
ce
2 4 6 8 10 12 14 16 18 20 22 24
0
2
4
6
Ra measurements for each cut (3 Trials separated by lines)
Test number
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SVM classification
�Small amount of training data required �Small amount of classifying time
Benefits of SVM:
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SVM Classification
Machine: Makino A55 CNC machine centre. Workpiece: Inconel 718.
Wheel: VIPER wheel.
Depth of cut: 1 mm;
Grinding speed: 35 m/s;
Workspeed: 1000 mm/min.
Machine: Makino A55 CNC machine centre. Workpiece: Inconel 718.
Wheel: VIPER wheel.
Depth of cut: 1 mm;
Grinding speed: 55 m/s;
Workspeed: 1000 mm/min.
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+
+*
Y X0.707 1.5
*
+*
Y Y
0.121
*X
X0.707Y + X – 1.5 XY (Y+0.121X)
+
+
X 1.5
*
*
YX
Y+0.121X+ X + 1.5XY(0.707Y)
*
Y0.70
7
+
Y
0.121
*
X
5A
7
A
6A
1
A5B
7B
9
B
8
B
6B
1B
2B
3B 4B
2
A3A 4A
X20
X8
X10
X14
X8
X10
X8
X10
X14
X14
X8
X13 X20
mydivide
plus
mydivide
mydivide
plus
plus
plusX20
mydivide
plus
mydivide
plus
plus
mydivide
-400 -350 -300 -250 -200 -150 -100 -50 0 50 100-100
-50
0
50
100
150
200
250
300
350
400
Distance
Dis
tance
Burn data & cluster centre
Chatter data & cluster centre
Normal grinding data & cluster centre
-400 -350 -300 -250 -200 -150 -100 -50 0 50 100-150
-100
-50
0
50
100
150
Distance
Dis
tance
Burn data & cluster centre
Chatter data & cluster centre
Normal grinding data & cluster centre
MultiMulti--classification of normal grinding, grinding chatter and burn using the GPclassification of normal grinding, grinding chatter and burn using the GP
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Classification of grinding anomalies using genetic programming
No. GP fitness function Data set Function Nodes Test Score Accuracy %
1 sum diff fitness ICA Chatter and burn +,' -, '/, '* 32/40 80
2 sum diff fitness *reduction: burn and no burn +,' -, '/, '* 36/40 90
3 sum diff fitness *reduction: burn and no burn =<, '=>, if 36/40 90
4 sum diff fitness *reduction: burn and chatter +,' -, '/, '* 36/40 90
5 sum diff fitness *reduction: burn and chatter =<, '=>, if 38/40 95
6 classes overlap ICA Burn and no burn +,' -, '/, '* 33/40 82.5
7 classes overlap ICA chatter and no chatter +,' -, '/, '* 32/40 80
8 classes overlap ICA chatter and no chatter +,' -, '/, '* 40/40 100
9 sum diff fitness ICA chatter and no chatter +,' -, '/, '* 36/40 90
10 classes overlap ICA Burn and no burn +,' -, '/, '* 40/40 100
11 classes overlap *reduction: burn and no burn +,' -, '/, '* 40/40 100
12 classes overlap *reduction: chatter & no chatter +,' -, '/, '* 40/40 100
*reduction: is based on the statistical window n-dimensional reduction technique
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Micro and nano scale corrective abrasive machining
Process modelling
Two-body and three-body errosions
Grinding Lapping Polishing
Models Suitable conditions On machine probing
GrolishingGrolishing
Process monitoring24
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Grolishing Process Tools
Centre for Precision Technologies
Models of material removal
rp pvCdt
dz=Preston material removal rate model
Archard material removal volume model
H
sFKV n
w =
v
pHf
pfVS
)( +=
X. Wu, Y. Kita, K. Ikoku (2007) 26
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Grolishing material removalα
Hertzian contact theory
Real result
Modelling of Zeeko grolishing toolsSimulation of Deformations and Stresses
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Taguchi test resultsLevels
Representation Control Factors 1 2
A Head speed (rev/min) 1000 2000
B Tool Angle (o) 0o 10o
C Grit Size (µm) 7 25
Test
No.
Control Factors Mean
Sa(av)
S/N
ratioA B C
1 A1 B1 C1 0.350 9.12
2 A1 B2 C2 0.470 6.45
3 A2 B1 C2 0.350 9.12
4 A2 B2 C1 0.221 13.12
The best result is A2, B2, C1
Further improvement could be achieved by using different pad.
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
0.500
0 1 2 3 4 5
Su
rfa
ce R
ou
gh
ness
µm
No. of Inconel 718 test workpiece
Mean Sa(av)
Mean Ra(av)
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Error compensation On-Machine Metrology enhanced
Machine partwith DIFFSYS® 3DX,Y,Z program
Gather data from X,Y,Z by point-to-point analysis or by scanning Output data file
to DIFFSYS®Auto-correct to desired tool path
Re-machine 3D part to desired profile
Output data file
to 3rd party softwareQuality inspection
2D and 3D Metrology
Utilizing UltraComp’s™ LVDT, 2D and 3D data
files of the part profile can be gathered including
the machine axes
DIFFSYS® MC3 performs data import, graphically
displays and auto-corrects the 3-dimensional part
profiles from the LVDT measurement data to the
desired part profile.
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AE detection
of contact
Voltage(mV) vs Time(us) <2>
0 2000000 4000000 6000000 8000000 10000000 12000000
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
120
140
160
180
Before contact After contact
Contact AE signals
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Manufacturing Knowledge Warehouse Development
Framework of the warehouse
• date Input module
• database module
• problem solving module
• knowledge discovery module
• knowledge warehouse
• knowledge analysis module.
Database
Decision support Questions & answers Statistical Analysis Query/Reporting
Knowledge Acquisition
Knowledge Analysis
Cases Collection
Knowledge Warehouse
(Storage)
Learning Knowledge
Discovery
External sources
Grinding Cases
Cases collection interface
Knowledge engineers
CoPs
Knowledge
Acquisition
Interface
Problem-solving
Knowledge Discovery
Contents of communication in CoP
• Questions and answers.
• Passing documents or web links.
• Calling for events or conference.
• Sending news box.
• Others.
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Opportunities of further developmentOpportunities of further development
� Ultra precision corrective machining at nanometre scale (micro material removal).
� Intelligent machining monitoring and optimisation.
� Precision machining knowledge support system.
Thanks for listeningThanks for listening
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