Examples of use of Design of Experiments within · Sigma level and performance (with ± 1,5 σshift) 1) Number of standard deviations to both sides within tolerance area 2) M.J. Harry,

ISPE Nordic PAT COP 2009.03.25

Winner of the ISPE awardCompany of the Year 2008

Examples of use of Design of Experiments within manufacturing of Medical Devices“When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot your knowledge is of a unsatisfactory kind”Lord Kelvin

“A theory should be as simple as possible, but no simpler”“Perfection of means and confusion of goals seem to characterize our age”Albert Einstein

Per Vase



Agenda

• Why DoE? Reduce Cost of Poor Quality• Health Authority view• Case 1: Ultrasonic welding• Case 2: Foam coating• Case 3: Injection moulding• Conclusions



Off-line inspection. Low Yield, Bad Quality and Long Cycling Time waiting for QC approval

In-line SPC. High Yield, Good Quality and Short Cycling Time with continuous release

In-line inspection, analysis, control (adjustment if appropriate) and release used for many years in e.g. Automotive Industry



that lead tothat lead to

What is a Process?

C U S T O M E R S

OutputsProcess

Steps

or

Actions

Series of

that will satisfythat will satisfy

S U P P L I ER S

Inputs

withwith



Where does Variation Occur?

C U S T O M E R S

OutputsProcess

Steps

or

Actions

Series of

S U P P L I ER S

Inputs

Next, Process Capability…



0 2σ 4σ 6σ−2σ−4σ−6σ

Six sigma requires process variation small enough that there is room for ±6σ within spec limits

LowerSpecLimit

UpperSpecLimit

1.5σ1.5σ 4.5σ4.5σ

3.4 ppm3.4 ppm

When producing 200 units, 6σ seems to be too strict demands, but…

… when producing thousands of units

… and knowing that it is impossible to keep processes on target, 6σ ensures acceptable failure rates below 3.4 ppm



Keeping the process on target by adjustmentImportant for both yield and quality

Taguchi loss function



The cost of poor quality (as a % of revenue)

Customer Returns

Inspection Costs

Recalls

Waste

Rejects

Rework

Testing Costs

Customer AllowancesIncorrectly Completed Sales Order

Excess InventoryIncorrect orders shipped

Time with Dissatisfied CustomerLate Paperwork

Expediting Costs

Planning DelaysExcessive Overtime

Premium Freight CostsExcessive Field Service Costs

Pricing or Billing Errors

Complaint Handling

Overdue Receivables

Employee TurnoverDevelopment Cost of Failed Products




Expediting Costs

Planning Delays




Expediting Costs

Planning DelaysExcessive Overtime



Complaint Handling

Overdue Receivables


Excessive Overtime



Complaint Handling

Overdue Receivables


15 -25%

Unused Capacity

Lost goodwill

Delays

15 -25%

Unused Capacity

Lost goodwill

Delays

2-3%



Capabilities of Inputs: Cp

•Defined as:

•USL-LSL represents the width of the spec•6S represents width of process (±3σ)

−=p S6

LSLUSLC



Capabilities of Inputs: Cp

LSL

USL

Cp = 1/2 Cp = 1 Cp = 2Cp = 1.5



Sigma level and performance(with ± 1,5 σ shift)

1) Number of standard deviations to both sides within tolerance area2) M.J. Harry, Quality Progress, May 1998, p 60-64.3) T.J. Clark, Success Through Quality, Quality Press, ISBN0-87389-441-3, www.successthroughquality.com4) http://www-1.ibm.com/services/us/imc/pdf/ge510-4034-metamorphosis-of-manufacturing.pdf

SIGMALEVEL

(1)

YIELD 1

STEP %

YIELD 10

STEP %

YIELD 100

STEP %

YIELD 1000

STEP %

CP BEFORE

SORTING

SYSTEM DOWNTIME

PR YEAR (DAYS)

COPQ (2)

% OF SALES

COPQ (3)

% OF SALES

1 30 0 0 0 0,33 255 >40 >70 Non competitive

2 69 0,02 0 0 0,67 112 30-40 >40 Non competitive

3 93 50 0,1 0 1,00 24 20-30 25-40 Average Pharma (4) (Sigma = 5 after sorting)

4 99,4 94,0 54 0,20 1,33 2,27 15-20 15-25 Average Industry

5 99,98 99,8 97,7 79 1,67 0,085 10-15 5-15 6 100 99,997 99,966 99,66 2,00 0,0012 <10 <1 World Class

Pharma 7 100 100 100 99,998 2,33 0,000069 ? ? 8 100 100 100 100 2,67 0 ? ? World Class

Industry



Quality Function Deployment.Relating Customer Attributes to Critical Process Parameters (CPP)

CTS = Critical to Satisfaction”Design for Six Sigma - A Roadmap for Product Development”, Yang, Kai et al, McGraw-Hill 2003

House ofQuality

#1Cus

tom

erA

ttrib

utes

WH

ATs

CTSs

PrioritizedCTSs

HOWs

House ofQuality

#1Cus

tom

erA

ttrib

utes

WH

ATs

CTSs

PrioritizedCTSs

HOWs

House ofQuality

#2CTS

s

WH

ATs

FunctionalRequirements

PrioritizedFunctions

HOWs

House ofQuality

#2CTS

s

WH

ATs

FunctionalRequirements

PrioritizedFunctions

HOWs

House ofQuality

#3Func

tiona

lR

equi

rem

ents

WH

ATs

DesignParameters

PrioritizedDesign

Parameters

HOWs

House ofQuality

#3Func

tiona

lR

equi

rem

ents

WH

ATs

DesignParameters

PrioritizedDesign

Parameters

HOWs

House ofQuality

#4Des

ign

Para

met

ers

WH

ATs

ProcessParameters

Criticalsources

of Variability

House ofQuality

#4Des

ign

Para

met

ers

WH

ATs

ProcessParameters

Criticalsources

of Variability

DoE

FDA PAT Guidance:“A process is generally considered well understood when:

• all critical sources of variability are identified and explained;• variability is managed by the process.”



Agenda

••• Why DoE? Reduce Cost of Poor QualityWhy DoE? Reduce Cost of Poor QualityWhy DoE? Reduce Cost of Poor Quality• Health Authority view• Case 1: Ultrasonic welding• Case 2: Foam coating• Case 3: Injection moulding• Conclusions



GHTF Process Validation Guidance.http://www.ghtf.org/documents/sg3/sg3_fd_n99-10_edition2.pdf



FDA PAT Guidance

”The Agency considers PAT to be a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes, with the goal of ensuring final product quality””The health of our citizens depends on the availability of safe, effective, and affordable medicines.””For processes that are well understood, opportunities exist to develop less restrictive regulatory approaches to manage change”.”Quality decisions should be based on process understanding and the prediction and control of relevant process/product attributes””In a PAT framework, validation can be demonstrated through continuous quality assurance where a process is continually monitored, evaluated, and adjusted using validated in-process measurements, tests, controls, and process end points.””Reducing production cycle times by using on-, in-, and/or at-line measurements and controls””A process is generally considered well understood when

• all critical sources of variability are identified and explained;• variability is managed by the process”



You can get warning letters now on lack of process understanding/use of DoE



PAT and QbDBenefits of submission

• PAT & QbD can be used much more powerfully – a new way of submitting to the authorities (Q8, Q9 and Q10 guidance's)

Development Facility Submission Operations

xx

xxxx

x x x

•Low process knowledge•Difficult scale up

•Long ramp up•Mass validation•Huge resource use

•Huge paper exercise•Process fixed using

3 validation batches

x 10000

•Process fixed•Scrap 5-10%•Capacity 30%•Limited continuous

improvement

Development Facility Submission Operations

•Smart experiments•High process knowledge•Ease of scale up

•Short ramp up•Risk based validation•Efficient resource

•Paper ‘lite’ submission•Demonstrates scientific knowledge•Gives regulatory relief

x 100

•Robust & adjustable process•Scrap < 1%•Capacity > 70%•Full continuous improvement

Traditional approach

PAT & QbD approach



Agenda

••• Why DoE? Reduce Cost of Poor QualityWhy DoE? Reduce Cost of Poor QualityWhy DoE? Reduce Cost of Poor Quality

••• Health Authority viewHealth Authority viewHealth Authority view• Case 1: Ultrasonic welding• Case 2: Foam coating• Case 3: Injection moulding• Conclusions



Design of Experiments (DoE) example

Ultrasonic welding of housing for rotary lockSeverity S4

Housing upper part

Rotary lock

Housing lower part

External CTQ StrengthInternal CTQ Height difference



Ishikawa Diagram for CTQ

External CTQ Strength Internal CTQ Height Difference

Environment

Humidity

TemperatureMethod

Recipe

Machine

Fixture

Holding time

Pressure

Energy

Trigger

AmplitudeMaterial

Upper Part

ToleranceMoisture

Rotary lock

Lower part

ToleranceMoisture

Man

Maintenance

Calibration

Measurement

Calibration

Fixture

Cause and Effect Diagram



Quality function Deployment, ultrasonic welder

Costumerattributes

ProcessUnderstanding

In-lineProcess control

Process Requirements

Weld

ing

stren

gth

No

weld

ing

flush

Heig

ht

Orie

nta

tion

No

dam

ag

e

Rank 10 8 7 9 5Internal CQA

Heigth reduction x xHeight beforeHeight after xLock height xWelding time x x x

Factors Score MitigationEnergy 10 10 2 0 5 219 DoEPressure 10 10 2 0 5 219 DoETrigger 5 5 1 0 0 97 DoEAmplitude 5 5 1 0 0 97 DoETime 2 2 1 0 0 43 DoEInput components 5 6 10 5 0 168 QCEquipment wear 6 4 2 8 8 146 SOPCalibration 6 5 1 0 0 107 SOPCustomer AttributesUsage safe FMECA_1 FMECA_2 FMECA_3Usage possible FMECA_4 FMECA_5 FMECA_6

External CQA



DoE plans made in SAS.JMP



-0,35

-0,3

-0,25

-0,2

-0,15

-0,1

-0,05

0

0,05

Høj

de d

iffer

ens

Act

ual

-0,35 -0,25 -0,15 -0,05 0,00 0,05Højde differens Predicted P<,0001RSq=0,99 RMSE=0,0137

Actual by Predicted Plot

The change in predicted response as you vary one factor at a time, holdingthe other factors at their current values. Click in the graph to change thecurrent values of the factors.

Høj

dedi

ffere

ns

0,045

-0,339

-0,2558±0,0186

Arbejdstryk(bar)

0,75

1,251

Energi (Ws)1,

6

2,4

2

Trigger

1

1,6

1,3

Amplitude

3 7

5

Dorn

01 02 03 04 05 06 07 08 09 10 11 12

Prediction Profiler

Høj

de d

iffer

ens

-0,35

-0,34

-0,33

-0,32

-0,31

-0,3

-0,29

-0,28

-0,27

-0,26

01 02 03 04 05 06 07 08 09 10 11 12

Dorn

Oneway Analysis of Højde differens By Dorn

Analysis of DoE



Transfer function from DoE with one controlling factor to be used in SPC

• If height difference too low increase Energy, • If height difference too high decrease Energy


Dif

0,338

0,125

0,306794

±0,0082

Energi

2 7

5,4

Prediction Profiler



0

50

100

150

200

250

300

Aftr

ækn

ings

kraf

t

-0,35 -0,3 -0,25 -0,2 -0,15 -0,1 -0,05 0 0,05Højde differens

Polynomial Fit Degree=2

Bivariate Fit of Aftrækningskraft By Højde differensS

kæg

0,00

0,25

0,50

0,75

1,00

-0,35 -0,3 -0,25 -0,2 -0,15 -0,1 -0,05 0 0,05

Højde differens

0

1

2

3

Logistic Fit of Skæg By Højde differens

100

150

200

250

mea

n kr

aft

0,15 0,2 0,25 0,3 0,35mean dif

Linear Fit Name("Tryk (N)")==40Linear Fit Name("Tryk (N)")==60Linear Fit Name("Tryk (N)")==80

Bivariate Fit of mean kraft By mean dif

-0,5

0

0,5

1

1,5

2

2,5

Skæ

g

0,15 0,2 0,25 0,3 0,35mean dif

Linear Fit Name("Tryk (N)")==40Linear Fit Name("Tryk (N)")==60Linear Fit Name("Tryk (N)")==80

Bivariate Fit of Skæg By mean dif

Correlate external CQA (strength and flush) to on-line measurable internal CQA (height difference)



Before and After DoE

LSL USLLSL USL

0,2 0,3 0,4 0,5

Lower Spec LimitUpper Spec LimitSpec Target

Specification0,20,4

.

Value Below LSLAbove USLTotal Outside

Portion0,00002,80002,8000

% Actual

LSL USL

-3s +3sMean

0,2 0,3 0,4 0,5

PpPpkCPMPPLPPU

Capability1,0500,636

.1,4640,636

Index1,0040,601

.1,3960,601

Lower CI1,0960,670

.1,5310,670

Upper CI

Below LSLAbove USLTotal Outside

Portion0,00062,82862,8291

Percent5,6308

28285,66528291,296

PPM5,8913,4073,407

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z1,9074,3911,907

Index

Overall, Sigma = 0,03176

Capability Analysis

Pred Formula Height Dif

LSL USLLSL USL

0,2 0,3 0,4 0,5


Specification0,20,4

.


Portion0,00000,00000,0000

% Actual

LSL USL

-3s +3sMean

0,2 0,3 0,4

PpPpkCPMPPLPPU


.2,6852,515

Index2,4862,403

.2,5652,403

Lower CI2,7142,627

.2,8042,627

Upper CI


Portion0,00000,00000,0000

Percent0,00000,00000,0000

PPM9,5419,0459,042

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z7,5428,0547,545

Index


Capability Analysis

Pred Formula Height Dif

25Amplitude

0,10,3Holding Time

5,41,6Energy

40100Pressure

028291Scrap (ppm)

2,520,64Ppk

2,601,05Pp

AfterBefore

In-line release instead of QC tests

• Pressure and Energy have large influence on product quality.

• Holding Time has a large influence on cycle time and low influence on product quality.



SPC system Proficy Shop Floor SPC from GE Fanuc

SQL-db(stores all data)

iFIX

Tekst fil

Report

SPC parameters put on specificPLC addresses = Raw data

Raw data imported by VisualSPCand shown on clients

Raw data exported from VisualSPCclients to SQL database

SAS.JMP gets data from database

PLCPLCPLCPLCPLCPLC

Visual SPC client

Visual SPC clientVisual SPC client



PLC

All gan get access todata from corporate

network

iFIX server PC

Production machines

SIX driver Visual SPC client

client part

Locan network

Corp. network

SQL andVisualSPCserver PC

SAS.JMP

Raw data written to a text file



Shopfloor screen, information to operator

Six SigmaQuality



Operator reaction

MonitorX-R charts Dif OOC Time OOC

CleanMeasuring

system

Dif OOC

Edgeof design

space

ServiceAdjust Energyusing transfer

function

Manual EPCCompensate

cause

SPCRemovecause

SPCRemovecause



Agenda


••• Health Authority viewHealth Authority viewHealth Authority view••• Case 1: Ultrasonic weldingCase 1: Ultrasonic weldingCase 1: Ultrasonic welding• Case 2: Foam coating• Case 3: Injection moulding• Conclusions



Continuous foam coating process

CoaterOvenFoam winder

Tankfarm

Foam webrewinding Drying

Casting andcuring of foam

web

Raw materialssupply



Starting point:Historical data showed performance very low but dependent on settings.Data set incomplete and correlated only multivariate modeling possible.

LSL USLTarget

50 60 70 80 90 100 120 140

Y9

60

70

80

60 70 80

YV

arP

S(Y

9)

YPredPS[4](Y9)

historicaldatanew.M1 (PLS), PS-historicaldatanewYPredPS[Last comp.](Y9)/YVarPS(Y9)

SIMCA-P+ 11.5 - 15-09-2008 16:16:32



Product QFD finding CPP

Dark green factors chosen for further DoE investigation

DP

1

DP

2

DP

3 DP

4

DP

5

DP

6

DP

7

DP

8

DP

9

DP

10

DP

11

DP

12

DP

13

DP

14

DP

15

DP

16

DP

17

DP

18

DP

19

Technical importance rating 105 40 ## 192 0 21 138 40 88 63 39 17 36 99 85 90 81 63 45

Score Factors 0,1,3,9Adjustability

0,1,3,9Score*Adju

st PriorityRaw materials

4302 Batch Component A 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 12906 2

4302 Batch component B 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 12906 2

4302 Batch Component C 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 12906 2

0 Batch component D 0

0Raw material storage

0 Temp A & B container 0 0

2373 Temp Component C 3 3 9 9 3 3 7119 1

0 0Mixing

0 CPP1 1 0 1

5247 Concentration A, B, C and D 9 9 9 9 9 0 0

0 CPP2 0 0

0 CPP3 0 0

2655 CPP4 9 9 9 9 23895 3

0 CPP5 9 0 2

4212 CPP6 9 9 9 9 9 9 37908 10 0

Additives

810 A or B 9 3 2430 3

0 0Coater

2583 CPP1 9 9 9 9 0 0

1242 CPP2 9 9 11178 1

1242 CPP3 9 3 3726 2

4410 CPP4 9 3 9 9 9 9 3 3 13230 1

4410 CPP5 9 3 9 9 9 9 3 3 13230 1

0 0Environment

0 Temp 0 3

1638 Humidity 3 3 3 3 3 3 0 1

0 Pressure 0 3

0 0Importance Customer attributes 0,1,3,9 QC Cover

9 CTS 1 3 3 9 9 1 9 1 1 367 CTS 2 1 9 10

7 CTS 3 3 1 4

5 CTS 4 3 9 12

6 CTS 5 1 3 3 1 8

6 CTS 6 1 1 3 3 3 3 14

8 CTS 7 9 9 9 27

4 CTS 8 1 3 3 1 8

8 CTS 9 3 1 1 9 14

9 CTS 10 9 9

4 CTS 11 3 9 3 1 16

9 CTS 12 1 3 3 1 9 9 26

9 CTS 13 9 9

Process Parameters

Customer

attributes

Continuous foam coating

Design Parameters



10 factors were chosen for a 19 run screening experiment

By using Whole Plots the hard to change factors CPP4, CPP5 and CPP8 does not have to be changed as often as the other factors. They are kept constant within a Whole Plot.

This makes the experiment more practical to run and the analysis will corrected for lack of fully randomization by introducing the Whole Plot factor.



We turned the right buttons.


0,8

1,21,6

22,4

Y 1,65

5289

±0,0

8749

22 26 30 34

28,5CPP1

0,85

0,90

0,95

1,00

1,05

1,10

1,15

1CPP2

35 38 41 44 47

41,0509

CPP3

1A 2M 3B

1APosition

Prediction Profiler

Y

1,0

1,5

2,0

2,5

7,51012,51517,52022,52527,530

CPP1

1A 2M 3B 1A 2M 3B 1A 2M 3B 1A 2M 3B 1A 2M 3B 1A 2M 3B Position

34 48 40 48 34 48 CPP30,85 1,00 1,15 CPP2

Variability Chart for AUPR (g/cm3)

Variability Gauge

Major effects: • CPP1: Not a factor in previous DoE´s because “it

had to be high to allow high production speed”.

• CPP2 and CPP3: Not factors in previous DoE´s because of “regulatory issues”

• CPP5: No influence, not a CPP. Process step can be removed.

• At CPP1 and high CPP2 and CPP3 and high B we reach our target for Y.



1,4

1,6

1,8

2,0

2,2

2,4

2,6

2,8

3,0

Y

8 16 24 32 40 48 56 64 72 80 88 96

Sample

Avg=2,071

LCL=1,734

UCL=2,407

Individual Measurement of Y

,01

,05,10

,25

,50

,75

,90,95

,99

-3

-2

-1

0

1

2

3

Nor

mal

Qua

ntile

Plo

t

LSL USLTarget

1,5 2 2,5 3

Lower Spec LimitSpec TargetUpper Spec Limit

Specification1,52,22,9


Portion0,00000,00000,0000

% Actual

LSL USLTarget

-3s +3sMean

1 2 3

Sigma = 0,11219

CPCPKCPMCPLCPU

Capability2,0801,6951,3621,6952,464

Index1,7901,4501,2001,4502,115

Lower CI2,3691,9401,5231,9392,813

Upper CI


Portion0,00000,00000,0000

Percent0,18360,00000,1836

PPM6,5858,8936,585

SigmaQuality

Control Chart Sigma

Capability Analysis

Y

Distributions

Control Chart

Verification experiment

Now the process is capable and stable



CToo High/Low Reduce/Increase CPP1 or Increase/Reduce CPP2

1,020

1,030

1,040

1,050

1,060

1,070

1,080

1,090

C

B

A

C

B

A 5 5

5

2008

-01-

23T0

7:58

2008

-01-

23T0

9:19

2008

-01-

23T0

9:55

2008

-01-

23T1

0:42

2008

-01-

23T1

1:29

2008

-01-

23T1

2:16

2008

-01-

23T1

3:01

2008

-01-

23T1

3:48

2008

-01-

23T1

4:36

TIME

µ0=1,05400

LCL=1,02650

UCL=1,08150

CQA =CTQ1 *CTQ2

Control ChartToo High/Low Reduce/Increase CPP1

0,270

0,275

0,280

0,285

C

B

A

C

B

A

65

5

2008

-01-

23T0

7:58

2008

-01-

23T0

9:19

2008

-01-

23T0

9:55

2008

-01-

23T1

0:42

2008

-01-

23T1

1:29

2008

-01-

23T1

2:16

2008

-01-

23T1

3:01

2008

-01-

23T1

3:48

2008

-01-

23T1

4:36

TIME

µ0=0,27943

LCL=0,27214

UCL=0,28672

CTQ1/CTQ3

Control ChartToo High/Low Reduce/Increase CPP3

-0,040

-0,030

-0,020-0,010

0,000

0,010

0,020

0,030

0,040

C

B

A

C

B

A

2008

-01-

23T0

7:58

2008

-01-

23T0

9:19

2008

-01-

23T0

9:55

2008

-01-

23T1

0:42

2008

-01-

23T1

1:29

2008

-01-

23T1

2:16

2008

-01-

23T1

3:01

2008

-01-

23T1

3:48

2008

-01-

23T1

4:36

TIME

µ0=0,00000

LCL=-0,03600

UCL=0,03600

CTQ1(M)-(CTQ1(A)+CTQ(B))/2

Control Chart

Too High/Low Increase/Reduce CPP4

2,0

3,0

4,0

5,0

6,0

CBA

CBA

1

6

6

2008

-01-

23T0

7:58

2008

-01-

23T0

9:19

2008

-01-

23T0

9:55

2008

-01-

23T1

0:42

2008

-01-

23T1

1:29

2008

-01-

23T1

2:16

2008

-01-

23T1

3:01

2008

-01-

23T1

3:48

2008

-01-

23T1

4:36

TIME

µ0=3,500

LCL=1,800

UCL=5,200

CTQ4

Control ChartToo High/Low Increase/Reduce CPP2

365

370

375

380

385

390

395

400

405

C

B

A

C

B

A

5 5 5

5 6 6

2008

-01-

23T0

7:58

2008

-01-

23T0

9:19

2008

-01-

23T0

9:55

2008

-01-

23T1

0:42

2008

-01-

23T1

1:29

2008

-01-

23T1

2:16

2008

-01-

23T1

3:01

2008

-01-

23T1

3:48

2008

-01-

23T1

4:36

TIME

µ0=385,00

LCL=370,00

UCL=400,00

CTQ3

Control Chart

1 to 1 correspondence between selected internal CTQ formulas and CPP´s. Easy for the operator to act on the control charts.

Control strategy: Shop floor SPC.

Control with CPP1 Control with CPP3

Control with CPP4 Control with CPP2

Monitor CQA



Agenda


••• Health Authority viewHealth Authority viewHealth Authority view••• Case 1: Ultrasonic weldingCase 1: Ultrasonic weldingCase 1: Ultrasonic welding

••• Case 2: Foam coatingCase 2: Foam coatingCase 2: Foam coating• Case 3: Injection moulding• Conclusions



Evolutionary Operations:keep Design Space updated

• Evolutionary Operation EVOP What is it? Introduced in the 1950s by George Box, EVOP is an ongoing mode of using an operating full-scale process, so that information on how to improve the process is generated from a simple experimental design while production is underway. To avoid appreciable changes in the characteristics of the product, only small changes are made in the levels of the process variables.

• Why is it important? Classic experimental design are methodologies which help us to establish an optimum combination of the design factors and their levels for the manufacturing process. They are very powerful tools; but they cost money, time, manpower. In most cases, they interrupt production.

• When to use it?The basic idea is to replace the static operation of a process by a continuous and systematic scheme of slight perturbations in the control variables. The effect of these perturbations is evaluated and the process is shifted in the direction of improvement.



EVOP Injection Molding

• Pressure and Temperature varied slightly around previously established optimum

• Target value 0, LSL=-4.5, USL=4.5

-1,5

-1

-0,5

0

0,5

1

1,5Te

mpe

ratu

re

-1,5 -1 -0,5 0 0,5 1 1,5Pressure

Overlay Plot



EVOP round 1No significant influence on dimensions

All batches too short, especially at center point.Increasing Pressure and Temperature might be better, but not significant.No change in settings.More data needed to conclude = New EVOP round

-5-4-3-2-1012345

Dat

a A

ctua

l

1, -1 -1, 1-1, -11, 1

0, 0

-3,0 -2,5 -2,0 -1,5 -1,0 -0,5Data Predicted P=0,9847RSq=0,11 RMSE=1,5183

Actual by Predicted PlotContinuous factors centered by mean, scaled by range/2

InterceptTemperaturePressureTemperature*Pressure

Term-1,192

-0,01750,17750,1925

Scaled Estimate0,679

0,7591450,7591450,759145

Std Error-1,76-0,020,230,25

t Ratio0,32960,98530,85380,8419

Prob>|t|

Scaled Estimates



EVOP round 1+2No significant influence on dimensions.

All batches except one (High pressure and temperature) too short.Pressure close to have a significant influence.No change in settings.More data needed to conclude = New EVOP round

-5-4-3-2-1012345

Dat

a A

ctua

l

1, -1 -1, 1-1, -1 1, 1

0, 01, -1

-1, 1-1, -1

1, 1

0, 0

-4 -3 -2 -1 0 1Data Predicted P=0,2635RSq=0,46 RMSE=0,8937


Continuous factors centered by mean, scaled by range/2


Term-1,433

0,101250,578750,40875

Scaled Estimate0,2826120,315970,315970,31597

Std Error-5,070,321,831,29

t Ratio0,0023*0,75950,11670,2434

Prob>|t|

Scaled Estimates



EVOP round 1+2+3 Significant influence on dimensions.

Continuous factors centered by mean, scaled by range/2


Term-1,1006670,04666670,7033333

0,58

Scaled Estimate0,2402630,2686220,2686220,268622

Std Error-4,580,172,622,16

t Ratio0,0008*0,86520,0239*0,0538

Prob>|t|

Scaled Estimates


-5

-3

-1

1

3

5

Dat

a-1

,44e

-6±0

,962

594

0,00

0,25

0,50

0,75

1,00

Des

irabi

lity

1

-1

-0,5 0

0,5 1

0,681579Temperature

-1

-0,5 0

0,5 1

0,972884Pressure

0,00

0,25

0,50

0,75

1,00

Desirability

Prediction Profiler

One batch at center point above target.On average high temperature and pressure (1,1) give best results.Pressure have significant influence.Temperature*Pressure close to having significant influence at 5% level.Change in settings for both Temperature and Pressure.New optimum found.All batches within specifications = DoE for free!!!

-5-4-3-2-1012345

Dat

a A

ctua

l

1, -1 -1, 1-1, -1 1, 1

0, 01, -1

-1, 1-1, -1

1, 1

0, 01, -1

-1, 1-1, -1

1, 1

0, 0

-4 -3 -2 -1 0 1 2Data Predicted P=0,0417RSq=0,51 RMSE=0,9305




Before and after EVOP

By changing settings Ppkestimate increased from 1,19 to 1,49.

Confidence intervals still overlap, might be too early to conclude.

Based on clients existing process understanding increasing pressure and temperature should increase dimensions.

Pp unchanged.

.01

.05

.10

.25

.50

.75

.90

.95

.99

-3

-2

-1

0

1

2

3

Nor

mal

Qua

ntile

Plo

t

LSL USLLSL USL

-5 -4 -3 -2 -1 0 1 2 3 4 5


Specification-4,54,5

.


Portion0,00000,00000,0000

% Actual

LSL USL

-3s +3sMean

-6 -4 -2 0 2 4 6

PpPpkCPMPPLPPU


.1,4921,515

Index1,2071,183

.1,1831,201

Lower CI1,8001,802

.1,8001,827

Upper CI


Portion0,00040,00030,0007

Percent3,78742,74046,5277

PPM5,9776,0455,859

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z4,3594,4774,545

Index


Capability AnalysisData

Distributions

LSL USLLSL USL

-5 -4 -3 -2 -1 0 1 2 3 4 5


Specification-4,54,5

.


Portion0,00000,00000,0000

% Actual

LSL USL

-3s +3sMean

-6 -4 -2 0 2 4 6

PpPpkCPMPPLPPU


.1,1921,699

Index1,1600,938

.0,9381,350

Lower CI1,7301,445

.1,4432,046

Upper CI


Portion0,01750,00000,0175

Percent174,9538

0,1732175,1271

PPM5,0756,5965,075

Sigma Quality

Z BenchZ LSLZ USL

Benchmark Z3,5753,5755,096

Index


Capability AnalysisData

Distributions



Agenda


••• Health Authority viewHealth Authority viewHealth Authority view••• Case 1: Ultrasonic weldingCase 1: Ultrasonic weldingCase 1: Ultrasonic welding

••• Case 2: Foam coatingCase 2: Foam coatingCase 2: Foam coating••• Case 3: Injection mouldingCase 3: Injection mouldingCase 3: Injection moulding• Conclusions



When and where to use DoE and SPC

DoE establishes transfer function between control variables and internal CTQ´s. From transfer function optimal setting of control (adjustment) variables can be found that gives the right mean value and low variation.

SPC monitors internal CTQ´s and give alert if CTQ deviates from previous accepted experience. In case of alert transfer function is being used to get back on track.

Input ProcessY

55,04

-20,498

-0,62125±3,7396

X1

L1 L2

X2

L1 L2

X3

L1 L2

X4

L1 L2

X5

L1 L2

X6

L1 L2

X7

L1 L2

X8

L1 L2

X9

L1 L2

X10

L1 L2

Prediction Profiler

Diff

eren

s 0,5

0,053

0,399983±0,0186

Des

irabi

lity 1

0

1

Energi [Ws]

2 6

3,75394

Arbejdstryk [N]

150

250

199,993

Amplitude

5 9

6,94282

Trigger

1

2,4

1,41123

Desirability

0 1

Prediction Profiler

0,390

0,395

0,400

0,405

0,410

0,415

0,420

0,425

Diff

ere

ns

2 4 6 8 10 12 14 16 18 20 22 24 26 28

Samp le

Avg=0,40760

LCL=0,39264

UCL=0,42256

Individual Measurement of Differens

Process Diagram

DoE Screening

DoE Model

SPC

Output

Settings

Noise



Overview

Documents

Examples of use of Design of Experiments within · Sigma level and performance (with ± 1,5 σshift) 1) Number of standard deviations to both sides within tolerance area 2) M.J. Harry,