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Carsten Lund & EPSILON•Injection Moulding•Process analysis•Six Sigma Projects•GMP assistance and process validation.•Associate partner RISMA (http://rismasystems.com/)

Background: 15+ years in medical device•Injection moulding optimizations and validation•Project management•Six Sigma and other applied statistics•Optimization and documentation for work flow•Operational management; Quality, Moulding, Metrology

Who is Epsilon?

www.epsilonplus.dk

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CHALLENGES !

The process to the left does not provide a guarantee that all bad (red) parts are removed.The process on the right can only “produce” good (green) parts – it may reject some good parts but it will never approve bad parts.

Injection Moulding is like the process to the leftIt has a lot of variables; process parameters, material variation, wear, ambient

temperature…. You name it

MUST VALIDATE MUST NOT

Validation

Challenge the process to establish evidence

Process window

Just a few variables are controlable

But the output; Variation, can be “forced” to max by those few

Process window

Var = √ dT2 + ds2 + d2 +…..

Var = √ dT2+ ds2 + d2 +…..

= Planning by applying statistical methods

Design Of Experiment

DOE

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DOE

PLANNING

What is the purpose?

Screening

DetailsWhat kind of output

Select type and extent of the (first) experiment

Any ”specials” to consider?

Two days, two shifts, three machines,

Process knowledge

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DOE OUPUT

1

2

3

4

5

6

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A Pareto diagram tellswhat paremeters aresignificant, but just as interesting:

– which one areINsignificant

DOE

1,21,00,80,60,40,2

25,49

25,48

25,47

25,46

Packing time

Q-M

ea

su

re

90,087,585,082,580,077,575,0

25,49

25,48

25,47

25,46

Mold temp.

Q-M

ea

su

re

Scatterplot of Q-Measure vs Packing time

Scatterplot of Q-Measure vs Mold temp.

Linearity or notCheck the Data

Samples are just that: samples

Statistical analysis accepts a level of chance called confidence

USLLSL

Based on a sample we can say; this is the result we got and this is whatexactly these 15 parts are like

But if we tried it again we could get another result

I am reasonably (80%) sure that wewould get something in this intervalBut I am very certain (99%) that it

will not fall outside this range

CONFIDENCE

Signal to noise ratio. The signal is the “answer” searched forand the noise is what “hides” the answer

Sample size

The measurement system (not only in the CMM) will add variation to the data

NOTICE it is “variation” not ”error”

Will cause doubt about quality

MSA: Measurement System Analysis

MSA

LSL USL

REJECT REJECT

DOUBTDOUBT

ACCEPT

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Gauge R&R

What difference do we need to detect?

the parts in the study.

measurement system. The process variation is estimated from

60,6% of all process variation can be attributed to the

100%30%10%0%

NoYes

60,6%

Measurement system variation equals 6,5% of the tolerance.

100%30%10%0%

NoYes

6,5%

ReproducibilityRepeatabilityTotal Gage

60

40

20

0

30

10

% of Process

% of Tolerance

of the total variation in the process.

accounts for 81,4% of the measurement variation. It is 49,3%

occurs when different people measure the same item. This

-- Operator component (Reproducibility): The variation that

is 35,2% of the total variation in the process.

times. This accounts for 58,2% of the measurement variation. It

occurs when the same person measures the same item multiple

-- Test-Retest component (Repeatability): The variation that

and use this information to guide improvements:

Examine the bar chart showing the component contributions,

>30%: unacceptable

10% - 30%: marginal

<10%: acceptable

General rules used to determine the capability of the system:

Number of parts in study 5

Number of operators in study 2

Number of replicates 3

Study Information

Variation Breakdown

reproducibility?

Is there a problem with repeatability or

(Replicates: Number of times each operator measured each part)

Comments

Gage R&R Study for Pos.2

Summary Report

Can you adequately assess process performance?

Can you sort good parts from bad?

estimate the process variation.

measurement system. A historical standard deviation is used to

7,0% of all process variation can be attributed to the

100%30%10%0%

NoYes

7,0%

Measurement system variation equals 6,5% of the tolerance.

100%30%10%0%

NoYes

6,5%

ReproducibilityRepeatabilityTotal Gage

45

30

15

0

30

10

% of Process

% of Tolerance

the total variation in the process.

accounts for 81,4% of the measurement variation. It is 5,7% of

occurs when different people measure the same item. This

-- Operator component (Reproducibility): The variation that

is 4,1% of the total variation in the process.

times. This accounts for 58,2% of the measurement variation. It

occurs when the same person measures the same item multiple

-- Test-Retest component (Repeatability): The variation that

and use this information to guide improvements:

Examine the bar chart showing the component contributions,

>30%: unacceptable

10% - 30%: marginal

<10%: acceptable

General rules used to determine the capability of the system:

Number of parts in study 5

Number of operators in study 2

Number of replicates 3

Study Information

Variation Breakdown

reproducibility?

Is there a problem with repeatability or

(Replicates: Number of times each operator measured each part)

Comments

Gage R&R Study for Pos.2

Summary Report

Can you adequately assess process performance?

Can you sort good parts from bad?

Small or large part-varitation?

Apply historical Standard deviation:

σ = TOLERANCE / 8

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CAPABILITY

What Cpk do you need?

0.2% x 0.2% = 4 PPM

(Two parts interfacing each with Cpk = 1)

Capability PPM

1,00 2025

1,33 50

1,66 0,5

2,00 0,001

3,00 0,0000

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CAPABILITY

Samples size do not need to be big !

0

1

2

3

4

5

6

7

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Cap

abili

ty in

de

ks

Sample size

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

5,00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Cap

abili

ty in

de

ks

Sample size

It needs to be adequate !

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CAPABILITY

Applying Confidence

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

1,60

1,80

2,00

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930310,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031

Any mistake is now on the safe side

AND

About 10-15 samples is adequate for both

90% small variation

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031

80% small

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031

70% small

90% large variation

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WRAP UP

CONSIDER THE RISK AND FOCUS ON THE PRODUCT

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WRAP UP

WHAT ARE WE LOOKING FOR?

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WRAP UP

DO NOT BLINDLY JUMP TO CONCLUSIONS