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Utilizing Several JMP Platforms To Conduct Measurement System Analysis in DMAIC Black Belt Project (2022-US-30MP-1106)

The goal of this Six Sigma BB Project was to improve the Pin Gauge Measurement Capability of the diffuse hole size, which is critical to the Production Failure Analysis. Several JMP Analysis objectives are in the Measure Phase. First, compare Excel Xbar-R method vs. JMP ANOVA cross method. Second, choose GRR criteria between Precision to Tolerance (P/T) ratio and Precision to Total Variance (P/TV) ratio. Third, use SPC Control Chart to monitor the GRR stability, determine the process long term sigma for calculating the GRR P/V ratio, and identify the process rational subgrouping. Fourth, evaluate the Pin Gage wear risk to determine whether the Pin Gage measurement is destructive test and use the GRR Nested model. Finally, use GRR Misclassification to assess the risk of both the Alpha risk and Beta risk on the production yield/cost. JMP platforms have helped us execute this Six Sigma BB project effectively.

Hello, everyone.

My name is Kemp.

Come from Taiwan.

I work in a Prime Material for global continuing improvement.

And other author is Wayne also from Taiwan.

He work in Prime Material for customer quality engineer.

Today we will present how to utilize SIPOC JMP platform

to combat measurement system errors

throughout your purchase.

The project is measurement system errors between supplier A and supplier B.

I will go through for the two

topic.

For the introduction and supplier A measurement system.

Then Wayne we will go through the next two topics.

For the introductions,

I will go through the background, SIPOC and in and out scope analysis,

correlations of the MSA CTQ.

Supplier A measurement system,

we will cover the Excel Xbar-R vs JMP ANOVA crossed method.

Gauge R&R

criteria on P/T and P/TV ratio,

variability chart and Gauge R&R mean plots,

Gauge R&R mis-verification.

Supplier B measurement process,

we will cover CE diagram,

Fit Y by X contingency platform.

One simple t and sample power t-test,

Gauge R&R.

The final conclusion will be show

SPC control chart for Gauge R&R, and a summary of analysis.

The rest is the takeaway learnings from the JMP.

First topic we will go through for the introductions.

The project was based on the problem statement.

We understand the VOC.

The purpose of this project is

both supplier A and supplier B measured the same parts.

Supplier B has got a much worse result

unexpectedly than results of the supplier A.

Customer has requested to find out the gap

and validate the measurement system of supplier A and B.

Then we go to for the SIPOC to understand our CTQ.

They are three CTQ, we find out.

C T Q1,

standardize supplier B measurements,

which we need to meet for at the certain criteria,

resolution need to be less than 10 %.

C T Q2, verify the gaps between supplier A and Supplier B.

So we need to meet... The bias need to be zero.

CTQ3, supplier B Gauge R&R

which we need to meet the criteria for the CTQ ratio less than 30 %.

This is the SIPOC structure.

We start with the customer voice

and back to the output CTQ, which we'll find out.

A nd back to the process items,

all process they're bound the measurement system.

With these four sequences, states for calibration to operation.

And the pin gauge calibrations.

Then sample for the diffuser, hole measurement.

Finally, debate hypothesis through the same way like PQP Excel file.

As you can see,

this is all about the MSA.

On the following slide, we will go into picture

of the relation about the SIPOC and the VOP and the customer VOC.

There are two measurement output for the supplier A and the supplier B.

In our goal s need to measure our measurement system,

supplier A and supplier B need to be the same.

On the measurement process variation, there are two parts.

One is the accuracy, the other one, the position.

For the accuracy, is due for the bias

linearity.

And the solution for that,

stability is not in our scope.

For these three is indicate for our CTQ1 and the CTQ2.

For the CTQ3 is ou tput for the

precision problem for the repeatabilty and reproducibility's.

We will go through the second topic, supplier A measurement system.

In our Gauge R& R errors,

we use the continued data,

crossed way

and don't form

for two best.

What is the Xbar-R method?

And otherwise, ANOVA method?

In Xbar-R method is for the PQP Excel file.

They have two disadvantage,

the first one, it could not detect parts- to- operator interaction.

Second,

it is made with the Gauge R&R, with the normal distribution.

So you will be impact by any outlier.

Also, is not good for the sample data is more than five data points.

In ANOVA method,

it not only can detect parts-to-operator interaction,

but also use standard deviation directly .

So it don't need to be considered for the normal distributions.

In our project the interaction is high

so w e must need to be use the ANOVA method for our Gauge R&R analysis.

First, we chose the low data funds for a PQP file.

They use the 10 data points

for

the study.

Actually, it is not good for more than five data point,

X-bar analysis, as I mentioned for this slide.

Anyway,

we still use the same data into JMP ANOVA as well .

The first somewhere PQP is no file

is only just useless file errors.

Y ou tell us there are two ratio for Gauge R&R,

P/TV is 24 %,

is measured [inaudible 00:06:52].

P/T is 9 %, which is pretty good.

What is the difference between P/TV and P/T ?

W e should know there are three variations.

One, equipment variations, which is called repeatability.

Another is the operator variation, which is called the reproducibility.

The last one is the part variations,

which depend on simple sessions.

Now,

P/ TV definition is

P is the process

equal to EV

plus AV.

And TV is total variance equal to EV plus AV plus PV.

There will be a risk when gage sample range will highly impact by

P/TV ratio result.

In P/T definitions,

P is tolerance is Parts Spec. R ange.

When spec is well defined, P/T ratio is our prefered to MSA

for doing Gauge R&R success criteria.

For the second [inaudible 00:08:13]

I'm sorry.

For the second [inaudible 00:08:17] JMP has no Xbar-R errors,

only ANOVA.

There are two model.

One is the main effect model, and other one is crossed model.

Main effect model has no parts-to-operator component.

Will be most assigned to repeatability and is caused by machine issues.

On the other hand,

crossed model can correctly derive

the parts-to-operator interaction component,

which will tell us there is the issue happen on the Gauge R&R,

on the process issues.

We prefer to choose the crossed model rather than main effect model.

On the Gauge R&R variability chart,

on the left side of the graph

measurement mean.

Here we need to be see is there 50 %,

of point, is outside of the control maybe.

It indicate the case is capable

to detect the [inaudible 00:09:32] .

Our case is a 100 % outside of the control, which is good.

In the standard deviation chart,

on the operator B, on the parts A has repeatability issues.

There are 25 % of the data is on the zero ,

which indicate measurement result is not attainable.

On the Gauge R&R, mean plot for the measurement operator.

We can see for the operator C, is

higher than operator A and B

on measurement by parts.

You can see the data is up and down,

it indicate large different between parts.

Also, the sample range is 1.2 equal to 20% of the tolerance range ,

which is too small,

resulting in higher P/T ratio.

The last one is the parts- to- operator interactions.

On the parts 7 to A,

there is the crossed line between part operator B

with the operator A and C.

It indicate interaction between appraiser and the part.

We finally going to the last part of the Gauge R&R.

The type one and type two error.

The type one error

is often rejected alpha risk.

Maybe in manufacturer side parts [inaudible 00:11:04] is good

but we reject.

And otherwise,

type two error

is often accepted, beta risk,

which means is customer side part [inaudible 00:11:16] is bad.

But we said,

and this side,

we can see there is nothing.

We can see

that alpha is zero and the beta is 39 %.

If you are still not clear ,

we can show you the example.

In manufacturer side, produces 300 parts.

There are 200 parts, is good parts

and the 100 parts, is bad parts.

If the beta risk is 39 %,

which we'll deliver

39 bad parts to the customer site.

We conclude 39 % of the parts,

will be delivered to the customer,

which is not acceptable.

We need to consider for the spec limit

for the adjust our offer and the beta risk.

In the data

Gauge R&R samples are all good, 100 %.

And only beta

risk have been observed.

Here are the Gauge R&R summary.

We recommend P/T ratio instead of the P/TV ratio.

We recommend use the JMP crossed method

for allocate your Gauge R&R errors.

Even P/T is best,

we still have to watch the operator-to- parts interaction

the misclassification

for our parts quality efforts.

Okay, I finished my part.

So the next two parts

will be show by Wayne.

Wayne is your turn.

Hello.

This is Wayne speaking.

Let me uncover the last two session

for the supplier B measurement process.

This is

C&E diagram.

Fish bone, to identify potential cost

across standard

procedure and the standard

supplier B

and supplier A.

After last discussion with engineering,

we conclude five potential

good cause

to standardize and validate

the first item is pin

measurement sequence.

Supplier B adapted from larger pin to smaller pin by standard procedure.

[inaudible 00:13:59] both way,

supplier A adopt smaller to larger diversity.

The second item is precheck by calibration.

Supplier B

didn't do the action

by a standard procedure and supplier A did.

The third

action is a pin gauge resolution.

Supplier B used the larger pin gauge increment,

50 % in resolution,

while standard and supplier A take 10 % in resolution.

The fourth item is whether to check the pin

before entering the diffuser hole or not.

The last item about the pin holder weight.

Supplier B adopted behaviour, than standard procedure

and supplier A.

Now we will

do one by one,

the hypothesis test and

valid data with high score contingency.

The first hypothesis we have to do is verify

if sequence one is different from sequence two or not.

Sequence one pin go from smaller size to larger size.

Sequence two is just on the contrary, reverse pin.

Here is the major result for the sequence one and sequence two.

Let's do the chi-squared test to see

the difference between [inaudible 00:15:25] or not .

In contingency table, the results shows p-value less than 5 %.

So we rejected a null hypothesis,

which means sequence one is rather different from sequence two.

Therefore we must watch out this key factor on pin gauge measurement.

The second hypothesis

is the way that you do precheck pin size by calibration tool.

Learn your calibre before the measurement.

Here is the diffuser, whole size data.

The assumption is that we mistake 90 for 90.6 without precheck.

There will be 11 zones

become no- go.

Based on contingency table.

In chi-squared test, we reject null hypothesis.

This means calibration before measurement is significant,

important.

The third hypothesis test is to compare all the measurement tool,

20 % resolution

with new two or 10 % resolution on the measurement variation.

By using

the similar concept,

we can know why we used the higher resolution

on the measurement.

The same on the item four, shaking the pin before entering the hole.

We use the specific 48 holes

with and without shaking to see the go/no-go effect.

The results shows null hypothesis is rejected.

That means shaking pin is important on measurement.

However,

regarding the item five pin vise

all this weight...

Chi-square p-value more than 5 %,

which means we cannot reject the null hypothesis.

That tell us there's no difference on the measurement between the unit

and five times unit in weight.

Because we have varied data all of five key measurement items above.

FMEA can further estimated RPN

before and after the improvement of our recommend action.

Based on severity, probability and detectivity.

For [inaudible 00:17:55]

pin gauge measurement sequence, severity is high because the wrong go.

Past probability is also high because dis location to hole center,

W e use the sequence one because the high detec tivity.

After we change to sequence two,

the probability and detectivity will drop to the half, so RPN reduced to 54

As y ou can see

all the score are under 100 meeting our forecast.

Okay,

let's move on the CT Q2 about bias between supplier B and supplier A.

From the FMEA, we are confident

of the diffuser pin hole size is around 90 to 90.6.

However, supplier A, FA report shows all the pin hole points 96.

We take distribution mean test,

the p-value suggest the gap between supplier B and supplier A

actual standardized is really something different.

Therefore we have last communication with the supplier A.

We conclude the two potential cause.

One potential cause might be some hole point 93 some are 90.6.

In current symbolic method

parts

straight over into 24 wrong.

Sample one

randomly in 24 wrong.

Is it enough to find out a larger size?

So we confirm with the sample size and power test.

The other possible cause could be the hole enlarged during the measurement.

W e validated by repeating the pin gauge measurement.

All right, let's see the result .

Here you the sample size and power test result.

In exact test binominal,

power is equal to 99.7,

that's more than 90 %.

In normal approximation, power is

97.6.

That's more than 90 % as well.

In other word for power, more than 90 % will just require 18 points to major.

Therefore, current stratified

random sampling is good detectivity.

It is a fat chance that we cannot catch 96 in diameter

and current 24 straight by wrong.

In parts do have larger hole size.

Now, after the gap on was found

we further check if our CTQ3 Guage R&R  is qualified or not.

F rom left table, you can see the hole diameter was increased by 4 % only

compared to tolerance.

The resolution is less than 10 %.

We can assume nondestructive crossed method ,

to qualify this measurement capability.

Now,

although the

operator- to- parts interaction accounts for 13.6%.

P/T ratio is still 18 %,

which would meet Gauge R&R criteria,  less than 30%

Later we going to address more

about interaction here regarding the P/TV ratio.

Sigma and variance also are high and cannot

be...

Quantified because the same hole size selection

is not wide enough.

Layers for the reference only.

Okay,

for our conclusion

and about the SPC control chart to monitor the Gauge R&R stability.

We use the Levey Jenning chart

to get the process long-term sigma for Gauge R&R P/V ratio calculation.

Phase before in supplier B,

with measurement resolution 50 % shows larger sigma

and wider control limit according.

After, in supplier B,

with measurement resolution 10 %, shows smaller sigma and narrow control limit.

It got improved on the measurement precision

for a common cause.

Here is the summary for a CT Q1

standardized supplier B measurement with the resolution less than 10 %.

Only pin holder weight is not significant

the other four measurement item are all significant.

For CTQ2 , sample size is good

and the hole enlargement issue during measurement was found.

For the CTQ3 , supplier B

provide its qualified measurement capability

Gauge R&R less than 30 %.

Where the repeatability less than 10 %, and

re producibility less than 20 %.

For take away learning, we use the last JMP tool.

Like C&E diagram, Fit Y by X contingency table,

sample power test, distribution mean test

ANOVA crossed Gauge R&R to standardize our pin gauge measurement.

That's all about the JMP practicing.

Thank you for listening.

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