Transcript On The Analysis & Evaluation of Data Features

A Data Driven Approach to
Attaining 100% Automatic
Quality Assurance
David Kazmer
Univ. Mass. Lowell
06-Apr-06
Agenda
•
•
•
•
•
•
Introduction
Experimental
Data Feature Generation
Data Feature Selection
Data Feature Validation
Conclusions
Modern Manufacturing
Standard Molder
#Machines
#Operators
#Eng/Mgt
Energy Use
“Lights Out” Molder
#Machines
#Operators
#Eng/Mgt
Energy Use
“Lights Out” Molding Methodology
• Mold commissioning
 – Process characterization
 – Process capability validation
 – Process optimization
• Lights out injection molding
 – Automatic materials/parts handling
 – Automatic process monitoring
 – Automatic quality assurance
 – Automatic process correction & model
adaptation
Automatic Quality Assurance
Stationary Platen
Moving Platen
• Process monitored
• Data features, E:
Clamping
Cylinder
– Verify process
settings, X
– Estimate part quality, Y
Mold
Tie Rods
Operator Interface
Check valve
Pellets
Injection
Cylinder
Reciprocating
Screw
Polymer
Melt
Barrel
Heaters
Process Controller
Hydraulic
Power Supply
Injection Unit
Clamping Unit
Mold Commissioning
Lights Out Molding
X:
Machine
Settings
E:
Data
Features
Y:
Part
Qualities
Agenda
•
•
•
•
•
•
Introduction
Experimental
Data Feature Generation
Data Feature Selection
Data Feature Validation
Conclusions
Molding Machine & Instrumentation
• Electra 50Ton Machine
• Machine sensors
– Ram position/velocity
– Nozzle pressure
– Nozzle steel thermocouple
– Intrusive melt thermocouple
• Mold sensors
– 4 Piezoelectric pressure sensors
– 2 Unshielded cavity thermocouples
• Priamus EDaq data acquisition
Experimentation
• Electra injection molding
machine
• Instrumented mold
– 2 temperature sensors
– 4 pressure transducers
• Priamus eDAQ data
acquisition system
Ram Position Trace
100
• Four stages
50
40
30
20
Mold closed
10
0
1% of ram displacement
Maximum filling pressure
Maximum ram displacement
Mold opening
5
10
15
Time (s)
20
MOLD OPEN
COOLING
& PLASTICATION
–
–
–
–
–
60
PACKING
• Auto-ID
70
DELAY
FILLING
Initial delay
Filling stage
Packing stage
Cooling stage
80
Screw Position (mm)
–
–
–
–
90
25
Ram Velocity Trace
40
– Filtered forward
& backward in
time domain
– 3rd order
Butterworth filter
30
20
Screw Velocity (mm/s)
• Derivative of
ram position
• Filtering
10
0
-10
-20
-30
0
• 100 Hz cut-off frequency

5
10
15
20
Time (s)

~
V s  s 0.0029s 3  0.0087s 2  0.0087s  0.0029

X s 
s 3  2.374s 2  1.929s  0.5321
25
Pressure Traces
90
• Locations
• Impact bar
• Tensile specimen
• Stepped plaque
• Purpose
– Melt arrival, velocity,
viscosity, and
shrinkage
80
70
60
Pressure (MPa)
– Machine nozzle
– Runner (bottom
of sprue)
– Gates of
Nozzle
Tensile
Runner
Stepped
Impact
50
40
30
20
10
0
0
5
10
15
Time (s)
20
25
Cavity Temperature Traces
55
– Impact bar
– Tensile specimen
50
Temperature (C)
• Unshielded
thermocouples
• End of cavities:
Tensile
Impact
• Purpose:
– Mold temperature
– Arrival of melt
– Melt temperature
45
40
35
30
0
5
10
15
Time (s)
20
25
Nozzle Temperature Traces
– Nozzle steel
– 1/8” into
3/8” melt channel
• Purpose:
Steel
Melt
240
235
Temperature (C)
• Type J
Thermocouple
• Locations
245
230
225
220
215
210
205
0
5
10
– Melt temperature entering mold
15
Time (s)
20
25
Additional Traces
• Velocity vs. position
– Total shot size
– Velocity averages/steps
– Kick back
• Estimated clamp
tonnage
– A Pcav dA
– Possible mold opening
120
40
100
35
30
Clamp Tonnage (mTons))
Screw Velocity (mm/s))
80
60
40
20
25
20
15
10
0
-20
10
5
20
30
40
50
60
70
Screw Position (mm)
80
90
100
110
0
0
5
10
15
Time (s)
20
25
Agenda
•
•
•
•
•
•
Introduction
Experimental
Data Feature Generation
Data Feature Selection
Data Feature Validation
Conclusions
Data Feature Generation
• Purpose:
– Condense time-varying traces into a set of
representative single point data
– One set of data features per cycle
s
t
10,000 data points
across cycle
per channel
 smax 
ds 
 dt
  


  sdt 


~14 features
across 3 stages
per channel
Data Feature 1: Average
• Simple aggregate measure of process
signal
s
s11
FILLING
PACKING
t
RECOVERY
Data Feature 2: Maximum
• Measure of peak signal
2
1
s
s
FILLING
PACKING
t
RECOVERY
Data Feature 3: Minimum
• Measure of minimum signal
s
3
1
s
FILLING
PACKING
t
RECOVERY
Data Feature 4: Range
• Measure of total change in signal
s
4
1
s
FILLING
PACKING
t
RECOVERY
Data Feature 5:
Derivative w.r.t. time, ds/dt
• Measure of average rate of change with
respect to time
s
5
1
s
FILLING
PACKING
t
RECOVERY
Data Feature 6:
Derivative w.r.t. position, ds/dx
• Measure of average rate of change with
respect to injected volume of plastic
s
6
1
s
FILLING
PACKING
t
RECOVERY
Data Feature 7:
Integral w.r.t. time, s dt
• Measure of cumulative “energy” of signal
with respect to time
s
7
1
s
FILLING
PACKING
t
RECOVERY
Data Feature 8:
Integral w.r.t. position, s dx
• Measure of cumulative “energy” of signal
with respect to position
s
8
1
s
FILLING
PACKING
x
RECOVERY
Data Feature 9:
Slope at start w.r.t. time, ds/dt
• Measure of process dynamic with respect
to time at start of stage
s
9
1
s
FILLING
PACKING
t
RECOVERY
Data Feature 10:
Slope at start w.r.t. position, ds/dx
• Measure of process dynamic with respect
to injected volume of material at start of
stage
s
10
1
s
FILLING
PACKING
x
RECOVERY
Data Feature 11:
Slope at end w.r.t. time, ds/dt
• Measure of process dynamic with respect
to time at end of stage
s
11
1
s
FILLING
PACKING
t
RECOVERY
Data Feature 12:
Slope at end w.r.t. position, ds/dx
• Measure of process dynamic with respect
to injected volume of material at end of
stage
s
12
1
s
FILLING
PACKING
x
RECOVERY
Data Feature 13:
Curvature w.r.t. time, d2s/dt2
• Measure of changing process dynamics
with respect to time across stage
s
13
1
s
FILLING
PACKING
t
RECOVERY
Data Feature 14:
Curvature w.r.t. position, d2s/dx2
• Measure of changing process dynamics
with respect to injected volume of material
across stage
s
14
1
s
FILLING
PACKING
x
RECOVERY
Data Feature Summary
• Number of data features per cycle:
14 features 3 stages
features

13signals  546
1 stage signal 1cycle
cycle
• But given this multitude of data features:
– Which are statistically significant?
– Which are indicative of part quality?
– What set provides “good” observability of:
•
•
•
•
Machine settings
Material properties
Environmental conditions
Mold/machine states
Agenda
•
•
•
•
Introduction
Experimental
Data Feature Generation
Data Feature Selection
– Multi-Variate Data Analysis
• Data Feature Validation
• Conclusions
Approach:
Multi-Variate Data Analysis (MVDA)
• Relate
• And Relate
– Independent variables
(X, machine settings)
• To
– Independent variables
(E, data features)
• To
– Dependent variables
(E, data features)
– Dependent variables
(Y, part qualities)
Mold Commissioning
Lights Out Molding
X:
Machine
Settings
E:
Data
Features
Y:
Part
Qualities
Design of Experiments
Stationary Platen
Moving Platen
• Investigate significant
machine settings
–
–
–
–
–
–
–
–
–
–
–
Velocity
Shot Size
Pack Pressure
Barrel Temp
Mold Temp
Screw RPM
Back Pressure
Pack Time
Cooling Time
Mold Delay
Material
Clamping
Cylinder
Operator Interface
Mold
Tie Rods
• Resulting DOE
Check valve
Pellets
Injection
Cylinder
Reciprocating
Screw
– 211-6 Partial factorial design
– Resolution IV design
Polymer
Melt
Barrel
Heaters
• Main effects not
confounded with
second order interactions
– 10 parts/run
– 340 moldings
Process Controller
Hydraulic
Power Supply
• Quality measurements
Clamping Unit
–
–
–
–
–
Thicknesses
Lengths
Mass
Short shot
Flash
Injection Unit
Run
Units
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Velocity
mm/sec
40
120
40
120
40
120
40
120
40
120
40
120
40
120
40
120
80
80
40
120
40
120
40
120
40
120
40
120
40
120
40
120
40
120
Pack
Shot Size Pressure
mm
Bar
97
425
97
425
97
475
97
475
103
425
103
425
103
475
103
475
103
425
103
425
103
475
103
475
97
425
97
425
97
475
97
475
100
450
100
450
103
425
103
425
103
475
103
475
97
425
97
425
97
475
97
475
97
425
97
425
97
475
97
475
103
425
103
425
103
475
103
475
Barrel
Temp
°C
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
210
210
220
220
220
220
220
220
220
220
220
220
220
220
220
220
220
220
Mold
Temp
°C
50
50
50
50
70
70
70
70
50
50
50
50
70
70
70
70
60
60
50
50
50
50
70
70
70
70
50
50
50
50
70
70
70
70
Screw
RPM
RPM
180
220
220
180
220
180
180
220
220
180
180
220
180
220
220
180
200
200
220
180
180
220
180
220
220
180
180
220
220
180
220
180
180
220
Back
Pressure Pack Time
Bar
Sec
50
7
50
7
100
15
100
15
100
15
100
15
50
7
50
7
100
7
100
7
50
15
50
15
50
15
50
15
100
7
100
7
75
11
75
11
50
15
50
15
100
7
100
7
100
7
100
7
50
15
50
15
100
15
100
15
50
7
50
7
50
7
50
7
100
15
100
15
Cooling
Time
Sec
12
32
32
12
12
32
32
12
12
32
32
12
12
32
32
12
22
22
32
12
12
32
32
12
12
32
32
12
12
32
32
12
12
32
Mold
Delay
Sec
0
10
0
10
10
0
10
0
0
10
0
10
10
0
10
0
5
5
10
0
10
0
0
10
0
10
10
0
10
0
0
10
0
10
Material
MFI
A
A
A
A
A
A
A
A
B
B
B
B
B
B
B
B
A
B
A
A
A
A
A
A
A
A
B
B
B
B
B
B
B
B
MVDA: XE
Example: Ram Velocity
• Regress data features
to ram velocity
– Identify features
with highest correlation
MVDA: XE
Example: Ram Velocity
• F1-3-2: Maximum ram velocity during
filling stage
– Excellent correlation, R2=0.9999
120
120
110
110
X1
F1-3-2
100
90
90
80
80
F1-3-2
Velocity
100
70
70
60
60
50
50
40
40
30
0
50
100
150
200
Cycle
250
300
350
30
40
50
60
70
80
X1
90
100
110
120
MVDA: XE
Example: Melt Temperature
• Regress data features
to melt temperature
– Identify features
with highest correlation
MVDA: XE
Example: Melt Temperature
• F1-7-3: Minimum melt thermocouple
temperature during filling
250
245
240
240
230
235
F1-7-3
Melt Temp
• Varies with barrel temp, plast pressure, RPM, velocity
220
230
225
210
X4
F1-7-3
220
200
190
0
50
100
150
200
Cycle
250
300
350
215
200
202
204
206
208
210
X4
212
214
216
218
220
MVDA Question:
Which Is Better?
• Y as a function of X? • Y as a function of E?
– Conduct DOE & use X
as independent variables
– Machine is in control
– If machine settings are
known, quality can be
predicted
X:
Machine
Settings
Y:
Part
Qualities
– Conduct DOE & use E as
independent variables
– Machine is not perfect
– Quality may be better
predicted with data closer
to the mold
E:
Data
Features
Y:
Part
Qualities
MVDA: Model Comparison
• R2: fraction of behavior captured by model
1
0.9
Regression Coefficient
0.8
0.7
Sensors
Add
Value
0.6
0.5
0.4
DOE
Data Features
0.3
0.2
0.1
0
Mass
H_Tens
H_Flex
H_Flash
H_Plaq1
H_Plaq2
H_Plaq3
L_Short
Quality Attribute
• DOE based models are not as predictive
as data feature based models
MVDA: Y=f(X)
Predict Quality from Machine Settings
• Effect of machine settings on flash
MVDA: Y=f(E)
Predict Quality from Data Features
• Effect of data features on flash
MVDA: Y=f(E)
Importance of Data Features
• Many different sensors are important
– Need to gain ‘observability’ of process
Agenda
•
•
•
•
•
•
Introduction
Experimental
Data Feature Generation
Data Feature Selection
Data Feature Validation
Conclusions
“Blind” Validation
• Ten molding trials conducted under
random conditions
– Velocity, Shot Size, Pack Pressure, Barrel Temp,
Mold Temp, Screw RPM, Back Pressure, Pack
Time, Cooling Time, Mold Open Delay
– Materials
• Low MFI
• High MFI
• Mixed Low & High MFI
These process conditions and resulting moldings were
not known prior to the following predictions:
Predictions:
Machine Settings from Data
Trial 2, Part 7
Parameter
Ram Velocity (mm/s)
Shot Size (mm)
Pack Pressure (bar)
Barrel Temperature (C)
Mold Coolant Temperature (C)
Screw RPM
Plastication Pressure (bar)
Pack Time (s)
Cooling Time (s)
Mold Open Delay (s)
Material (MFI)
Actual
Predicted
Parameter
65
66 Velocity (mm/s)
Ram
102
100 Shot Size (mm)
465
460
Pack Pressure (bar)
206
201Temperature (C)
Barrel
65
62 Temperature (C)
Mold Coolant
190
193
Screw RPM
60 Plastication
56 Pressure (bar)
12
10
Pack Time (s)
30
30 Cooling Time (s)
8
2 Open Delay (s)
Mold
0.38
0.41 Material (MFI)
Trial 9, Part 7
Actual
75
99
455
212
59
195
79
9
21
4
0.59
Predicted
76
98
464
206
61
206
68
7
21
0
0.69
Observed Predicted
Change
Change
15.4%
15.4%
-2.9%
-2.2%
-2.2%
0.9%
2.9%
2.4%
-9.2%
-1.6%
2.6%
7.0%
31.7%
20.9%
-25.0%
-28.1%
-30.0%
-31.0%
-50.0%
-100.0%
55.3%
69.6%
Predictions:
Machine Settings from Data
• Ram velocity
• Material MFI
– Very good correlation
– Good velocity control
– Good correlation
– Prediction doesn’t include
effect of melt temperature
120
1
R2 = 0.9999
0.9
100
0.8
90
Predicted MFI
Predicted Ram Velocity (mm/sec)
DTmelt
R2 = 0.9503
110
80
0.7
0.6
70
0.5
60
0.4
50
40
0.3
40
50
60
70
80
90
Set Ram Velocity (mm/sec)
100
110
120
0.3
0.4
0.5
0.6
0.7
0.8
Material MFI (Constant Melt Temperature)
0.9
1
Predictions:
Part Quality from Data
Lightest Molding
Trial 1, Run 7
Quality Attribute
Mass (g)
Tensile Bar Thickness (mm)
Flex Bar Thickness (mm)
Flash Thickness (mm)
Plaque Thickness 1 (mm)
Plaque Thickness 2 (mm)
Plaque Thickness 3 (mm)
Short Shot Length (g)
Heaviest Molding
Trial 7, Run 7
Actual
Predicted
Quality Attribute
28.27
27.02
Mass (g)
3.23
Tensile Bar
3.47
Thickness (mm)
3.17Flex Bar
2.99
Thickness (mm)
0.06 Flash
-0.14
Thickness (mm)
5.03Plaque Thickness
4.94
1 (mm)
3.24Plaque Thickness
3.13
2 (mm)
1.60Plaque Thickness
1.49
3 (mm)
0.00
Short
-20.27
Shot Length (g)
Actual
29.44
3.33
3.29
0.15
4.98
3.26
1.63
0.00
Predicted
28.46
3.54
3.11
-0.04
4.90
3.18
1.54
-19.17
Observed Predicted
Change
Change
4.1%
5.3%
3.1%
2.0%
3.7%
4.0%
155.9%
71.9%
-1.0%
-0.8%
0.7%
1.6%
2.1%
3.5%
0.0%
5.4%
Predictions:
Part Quality from Data
• Part mass
• Flash thickness
– Good correlation
– Fair correlation
• Quantitative offset
• Qualitatively correct
• Quantitative offset error
• Qualitatively useful
28.6
-0.02
28.2
-0.04
R2 = 0.8114
-0.06
28
Predicted Flash (mm)
Predicted Mass (g)
28.4
DTmelt
0
R2 = 0.9239
27.8
27.6
27.4
-0.08
-0.1
-0.12
-0.14
27.2
-0.16
27
26.8
28.2
-0.18
-0.2
28.4
28.6
28.8
29
Observed Mass (g)
29.2
29.4
29.6
0
0.02
0.04
0.06
0.08
0.1
Observed Flash (mm)
0.12
0.14
0.16
0.18
Agenda
•
•
•
•
•
•
Introduction
Experimental
Data Feature Generation
Data Feature Selection
Data Feature Validation
Conclusions
Conclusions:
Current State of Molding Technology
• “Scientific molding” is increasingly common,
but not fully leveraged
• Processes are not fully optimized
– Trade-off between multiple qualities & cost
• Automatic quality control is not yet realized
– Simple diagnostics but not
on-line automatic quality assurance
– No automatic diagnosis & correction
Conclusions:
Summary of Presented Work
• Data features defined as state estimators
• Design of experiments & MVDA used to:
– Characterize & optimize process
– Relate data features to:
Process SPC
• Process settings
• Part qualities
better than
Machine SPC
• Blind validation shown:
– Excellent prediction of process settings
– Good prediction of part qualities
ANNOUNCEMENT:
ANTEC 2006
Business Meeting May 9th at 10:30AM