A Data Driven Approach to Attaining 100% Automatic Quality Assurance David Kazmer Univ. Mass. Lowell...

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#Operators#Machines #Eng/Mgt Energy Use

#Operators#Machines #Eng/Mgt Energy Use

“Lights Out” Molder

“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

Mold CommissioningLights Out Molding

Automatic Quality Assurance

• Process monitored

• Data features, E:– Verify process

settings, X– Estimate part quality, Y

BarrelHeaters

Reciprocating Screw

Check valveInjectionCylinder

ClampingCylinder

Operator Interface

Stationary PlatenMoving PlatenMold

Pellets

PolymerMelt

Process ControllerHydraulic

Power Supply

Clamping Unit Injection Unit

Tie Rods

BarrelHeaters

Reciprocating Screw


ClampingCylinder

Operator Interface


Pellets

PolymerMelt


Power Supply


Tie Rods

X: Machine Settings

Y: Part

Qualities

E: Data

Features

Agenda

• Introduction

• Experimental




• 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

• Four stages– Initial delay– Filling stage– Packing stage– Cooling stage

• Auto-ID– Mold closed– 1% of ram displacement– Maximum filling pressure– Maximum ram displacement– Mold opening

0 5 10 15 20 2510

20

30

40

50

60

70

80

90

100

Scr

ew P

ositi

on (

mm

)

Time (s)D

EL

AY

FIL

LIN

G

PA

CK

ING

CO

OL

ING

& P

LA

ST

ICA

TIO

N

MO

LD

OP

EN

Ram Velocity Trace

• Derivative ofram position

• Filtering– Filtered forward

& backward intime domain

– 3rd orderButterworth filter

• 100 Hz cut-off frequency

0 5 10 15 20 25-30

-20

-10

0

10

20

30

40

Scr

ew V

eloc

ity (

mm

/s)

Time (s)

5321.0929.1374.2

0029.00087.00087.00029.0~

23

23

sss

ssss

sX

sV

Pressure Traces

• Locations– Machine nozzle– Runner (bottom

of sprue)– Gates of

• Impact bar• Tensile specimen• Stepped plaque

• Purpose– Melt arrival, velocity,

viscosity, and shrinkage

0 5 10 15 20 250

10

20

30

40

50

60

70

80

90

Pre

ssur

e (M

Pa)

Time (s)

Nozzle

Tensile

RunnerStepped

Impact

Cavity Temperature Traces

• Unshieldedthermocouples

• End of cavities:– Impact bar– Tensile specimen

• Purpose:– Mold temperature– Arrival of melt– Melt temperature

0 5 10 15 20 2530

35

40

45

50

55

Tem

pera

ture

(C

)

Time (s)

Tensile

Impact

Nozzle Temperature Traces

• Type JThermocouple

• Locations– Nozzle steel– 1/8” into

3/8” melt channel

• Purpose:– Melt temperature entering mold

0 5 10 15 20 25205

210

215

220

225

230

235

240

245

Tem

pera

ture

(C

)

Time (s)

Steel

Melt

Additional Traces

• Velocity vs. position– Total shot size– Velocity averages/steps– Kick back

• Estimated clamp tonnage– – Possible mold opening

0 5 10 15 20 250

5

10

15

20

25

30

35

40

Cla

mp

Ton

nage

(m

Ton

s))

Time (s)10 20 30 40 50 60 70 80 90 100 110

-20

0

20

40

60

80

100

120

Scr

ew V

eloc

ity (

mm

/s))

Screw Position (mm)

A cavdAP

Agenda

• Introduction

• Experimental




• 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 pointsacross cycleper channel

sdt

dtdss

max

~14 featuresacross 3 stages

per channel

Data Feature 1: Average

s

tFILLING PACKING RECOVERY

11s

• Simple aggregate measure of process signal

Data Feature 2: Maximum

• Measure of peak signal

s


21s

Data Feature 3: Minimum

• Measure of minimum signal

s


31s

Data Feature 4: Range

• Measure of total change in signal

s


41s

Data Feature 5: Derivative w.r.t. time, ds/dt

• Measure of average rate of change with respect to time

s


51s

Data Feature 6: Derivative w.r.t. position, ds/dx

• Measure of average rate of change with respect to injected volume of plastic

s


61s

Data Feature 7: Integral w.r.t. time, s dt

• Measure of cumulative “energy” of signal with respect to time

s


71s

Data Feature 8: Integral w.r.t. position, s dx

• Measure of cumulative “energy” of signal with respect to position

s

xFILLING PACKING RECOVERY

81s

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


91s

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


101s

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


111s

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


121s

Data Feature 13:Curvature w.r.t. time, d2s/dt2

• Measure of changing process dynamics with respect to time across stage

s


131s

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


141s

Data Feature Summary• Number of data features per 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

cycle

featuressignals

cycle

stages

signalstage

features54613

1

3

1

14

Agenda

• Introduction

• Experimental


• Data Feature Selection– Multi-Variate Data Analysis


• Conclusions

Approach:Multi-Variate Data Analysis (MVDA)• Relate

– Independent variables (X, machine settings)

• To– Dependent variables

(E, data features)

• And Relate– Independent variables

(E, data features)

• To– Dependent variables

(Y, part qualities)

Mold Commissioning

X: Machine Settings

Lights Out Molding

Y: Part

Qualities

E: Data

Features

Design of Experiments

• Investigate significant machine settings– Velocity– Shot Size– Pack Pressure– Barrel Temp– Mold Temp– Screw RPM– Back Pressure– Pack Time– Cooling Time– Mold Delay– Material

• Resulting DOE– 211-6 Partial factorial design– Resolution IV design

• Main effects not confounded with second order interactions

– 10 parts/run– 340 moldings

• Quality measurements– Thicknesses– Lengths– Mass– Short shot– Flash

BarrelHeaters

Reciprocating Screw


ClampingCylinder

Operator Interface


Pellets

PolymerMelt


Power Supply


Tie Rods

BarrelHeaters

Reciprocating Screw


ClampingCylinder

Operator Interface


Pellets

PolymerMelt


Power Supply


Tie Rods

Run Velocity Shot SizePack

Pressure Barrel Temp

Mold Temp

Screw RPM

Back Pressure Pack Time

Cooling Time

Mold Delay Material

Units mm/sec mm Bar °C °C RPM Bar Sec Sec Sec MFI1 40 97 425 200 50 180 50 7 12 0 A2 120 97 425 200 50 220 50 7 32 10 A3 40 97 475 200 50 220 100 15 32 0 A4 120 97 475 200 50 180 100 15 12 10 A5 40 103 425 200 70 220 100 15 12 10 A6 120 103 425 200 70 180 100 15 32 0 A7 40 103 475 200 70 180 50 7 32 10 A8 120 103 475 200 70 220 50 7 12 0 A9 40 103 425 200 50 220 100 7 12 0 B

10 120 103 425 200 50 180 100 7 32 10 B11 40 103 475 200 50 180 50 15 32 0 B12 120 103 475 200 50 220 50 15 12 10 B13 40 97 425 200 70 180 50 15 12 10 B14 120 97 425 200 70 220 50 15 32 0 B15 40 97 475 200 70 220 100 7 32 10 B16 120 97 475 200 70 180 100 7 12 0 B17 80 100 450 210 60 200 75 11 22 5 A19 80 100 450 210 60 200 75 11 22 5 B20 40 103 425 220 50 220 50 15 32 10 A21 120 103 425 220 50 180 50 15 12 0 A22 40 103 475 220 50 180 100 7 12 10 A23 120 103 475 220 50 220 100 7 32 0 A24 40 97 425 220 70 180 100 7 32 0 A25 120 97 425 220 70 220 100 7 12 10 A26 40 97 475 220 70 220 50 15 12 0 A27 120 97 475 220 70 180 50 15 32 10 A28 40 97 425 220 50 180 100 15 32 10 B29 120 97 425 220 50 220 100 15 12 0 B30 40 97 475 220 50 220 50 7 12 10 B31 120 97 475 220 50 180 50 7 32 0 B32 40 103 425 220 70 220 50 7 32 0 B33 120 103 425 220 70 180 50 7 12 10 B34 40 103 475 220 70 180 100 15 12 0 B35 120 103 475 220 70 220 100 15 32 10 B

MVDA: XEExample: Ram Velocity

• Regress data featuresto ram velocity– Identify features

with highest correlation

MVDA: XEExample: Ram Velocity

• F1-3-2: Maximum ram velocity during filling stage– Excellent correlation, R2=0.9999

0 50 100 150 200 250 300 35030

40

50

60

70

80

90

100

110

120

Cycle

Vel

ocity

X1

F1-3-2

40 50 60 70 80 90 100 110 12030

40

50

60

70

80

90

100

110

120

X1

F1-

3-2

MVDA: XEExample: Melt Temperature

• Regress data featuresto melt temperature– Identify features

with highest correlation

MVDA: XEExample: Melt Temperature

• F1-7-3: Minimum melt thermocouple temperature during filling

• Varies with barrel temp, plast pressure, RPM, velocity

200 202 204 206 208 210 212 214 216 218 220215

220

225

230

235

240

245

X4

F1-

7-3

0 50 100 150 200 250 300 350190

200

210

220

230

240

250

Cycle

Mel

t T

emp

X4

F1-7-3

MVDA Question:Which Is Better?

• Y as a function of X?– Conduct DOE & use X

as independent variables– Machine is in control– If machine settings are

known, quality can be predicted

• Y as a function of E?– Conduct DOE & use E as

independent variables– Machine is not perfect– Quality may be better

predicted with data closer to the mold

X: Machine Settings

Y: Part

Qualities

E: Data

Features

Y: Part

Qualities

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Mass H_Tens H_Flex H_Flash H_Plaq1 H_Plaq2 H_Plaq3 L_Short

Quality Attribute

Reg

ress

ion

Co

effi

cien

t

DOE

Data Features

MVDA: Model Comparison• R2: fraction of behavior captured by model

• DOE based models are not as predictive as data feature based models

SensorsAdd

Value

MVDA: Y=f(X)Predict Quality from Machine Settings• Effect of machine settings on flash

-0.2

-0.1

-0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Vel

ocity

Sho

t_S

ize

Pol

y_M

FI

Pac

k_P

res

Bac

k_P

res

Mol

d_D

elay

Pac

k_T

ime

Scr

ew_R

PM

Mel

t_T

emp

Coo

l_T

ime

Mol

d_T

emp

Coe

ffCS

[4](

H_F

lash

)

Var ID (Primary)

FinalFeatsDOEwPredictions.M25 (PLS), X vs All YCoeffCS[Last comp.](H_Flash)

SIMCA-P+ 11 - 4/3/2006 11:10:56 AM

MVDA: Y=f(E)Predict Quality from Data Features • Effect of data features on flash

-0.3

-0.2

-0.1

-0.0

0.1

0.2

0.3

X_P

last

PI_

Max

Ton

_Max

PT

_Max

X_F

illV

_Fill

X_P

ack

T1_

Init

Tn_

DT

dtt_

star

tt_

cool

T2_

Init

V_P

ack

Pn_

IntR

t_U

pMT

2t_

cycl

et_

UpM

T1

PT

_Int

gP

S_M

axt_

plas

tP

n_P

las

Pn_

Pac

kT

p_D

Tdt

Tn_

Max

Tn_

Init

V_P

last

Tp_

Init

E_R

PM

Tp_

Max

Ton

_Int

t_pa

ckP

n_F

illT

1_M

axP

I_In

tgt_

Err

VP

E_V

isc1

T1_

Intg

PS

_Int

gT

2_M

axT

2_In

tgE

_Vis

c2t_

fill

Pn_

IntF

V_A

ftVP

Coe

ffCS

[5](

H_F

lash

)

Var ID (Primary)

FinalFeatsDOEwPredictions.M18 (PLS), FY-FlashCoeffCS[Last comp.](H_Flash)

SIMCA-P+ 11 - 4/3/2006 11:14:45 AM

MVDA: Y=f(E)Importance of Data Features

• Many different sensors are important– Need to gain ‘observability’ of process

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

E_R

PM

E_V

isc1

E_V

isc2

PI_

Intg

PI_

Max

Pn_

Fill

Pn_

IntF

Pn_

IntR

Pn_

Pac

kP

n_P

las

PS

_Int

gP

S_M

axP

T_I

ntg

PT

_Max

T1_

Init

T1_

Intg

T1_

Max

T2_

Init

T2_

Intg

T2_

Max

t_co

olt_

cycl

et_

Err

VP

t_fil

lt_

pack

t_pl

ast

t_st

art

t_U

pMT

1t_

UpM

T2

Tn_

DT

dtT

n_In

itT

n_M

axT

on_I

ntT

on_M

axT

p_D

Tdt

Tp_

Init

Tp_

Max

V_A

ftVP

V_F

illV

_Pac

kV

_Pla

stX

_Fill

X_P

ack

X_P

last

VIP

[9]

Var ID (Primary)

FinalFeatsDOEwPredictions.M24 (PLS), F vs All YVIP[Last comp.]

SIMCA-P+ 11 - 4/3/2006 11:17:40 AM

Estimators

Pressures

Mold

Temp’sTim

es

Machine

Temp’sVelocit

ies

Positions

Agenda

• Introduction

• Experimental




• Conclusions

“Blind” Validation

• Ten molding trials conducted underrandom 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

Parameter Actual PredictedRam Velocity (mm/s) 65 66

Shot Size (mm) 102 100Pack Pressure (bar) 465 460

Barrel Temperature (C) 206 201Mold Coolant Temperature (C) 65 62

Screw RPM 190 193Plastication Pressure (bar) 60 56

Pack Time (s) 12 10Cooling Time (s) 30 30

Mold Open Delay (s) 8 2Material (MFI) 0.38 0.41

Parameter Actual PredictedRam Velocity (mm/s) 75 76

Shot Size (mm) 99 98Pack Pressure (bar) 455 464

Barrel Temperature (C) 212 206Mold Coolant Temperature (C) 59 61

Screw RPM 195 206Plastication Pressure (bar) 79 68

Pack Time (s) 9 7Cooling Time (s) 21 21

Mold Open Delay (s) 4 0Material (MFI) 0.59 0.69

Trial 2, Part 7 Trial 9, Part 7 Observed PredictedChange Change15.4% 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– Very good correlation– Good velocity control

• Material MFI– Good correlation– Prediction doesn’t include

effect of melt temperature

R2 = 0.9503

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Material MFI (Constant Melt Temperature)

Pre

dic

ted

MF

I

R2 = 0.9999

40

50

60

70

80

90

100

110

120

40 50 60 70 80 90 100 110 120

Set Ram Velocity (mm/sec)

Pre

dic

ted

Ra

m V

elo

city

(m

m/s

ec) T

mel

t

Predictions:Part Quality from Data

Quality Attribute Actual PredictedMass (g) 28.27 27.02

Tensile Bar Thickness (mm) 3.23 3.47Flex Bar Thickness (mm) 3.17 2.99

Flash Thickness (mm) 0.06 -0.14Plaque Thickness 1 (mm) 5.03 4.94Plaque Thickness 2 (mm) 3.24 3.13Plaque Thickness 3 (mm) 1.60 1.49

Short Shot Length (g) 0.00 -20.27

Quality Attribute Actual PredictedMass (g) 29.44 28.46

Tensile Bar Thickness (mm) 3.33 3.54Flex Bar Thickness (mm) 3.29 3.11

Flash Thickness (mm) 0.15 -0.04Plaque Thickness 1 (mm) 4.98 4.90Plaque Thickness 2 (mm) 3.26 3.18Plaque Thickness 3 (mm) 1.63 1.54

Short Shot Length (g) 0.00 -19.17

Lightest MoldingTrial 1, Run 7

Heaviest MoldingTrial 7, Run 7 Observed Predicted

Change Change4.1% 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– Good correlation

• Quantitative offset• Qualitatively correct

• Flash thickness– Fair correlation

• Quantitative offset error• Qualitatively useful

Tm

elt

R2 = 0.9239

26.8

27

27.2

27.4

27.6

27.8

28

28.2

28.4

28.6

28.2 28.4 28.6 28.8 29 29.2 29.4 29.6

Observed Mass (g)

Pre

dict

ed M

ass

(g)

R2 = 0.8114

-0.2

-0.18

-0.16

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18

Observed Flash (mm)

Pre

dict

ed F

lash

(m

m)

Agenda

• Introduction

• Experimental




• 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 settings• Part qualities

• Blind validation shown:– Excellent prediction of process settings– Good prediction of part qualities

Process SPCbetter than

Machine SPC

ANNOUNCEMENT:ANTEC 2006

Business Meeting May 9th at 10:30AM

A Data Driven Approach to Attaining 100% Automatic Quality Assurance David Kazmer Univ. Mass. Lowell...

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