Quality Tools - Yanez

8/18/2019 Quality Tools - Yanez

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Measuring System Analysis

Case Study for the Automotive

Industry

Alberto A. Yáñez-Moreno

TMAC/UTA ASQ Houston 2013


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Learning Objectives

• Understand why we measure

• MSA basic concepts

•

Components of measurement errors

•

Understand how to conduct a MSA

• Data analysis theory

•

Automotive case studies


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!

Measurement Systems

•

Measurement systems are like eyeglasses, when thelenses are incorrect, the vision is blurred.

• A measurement system allows us to “see” the process.

When a measurement system is poor, we lose the ability to

make good decisions about how to improve the process• In the Measure Phase of the DMAIC process, the MSA

should be conducted on the “Y” or KPOV

• In the Control Phase of the DMAIC process, the MSA

should be conducted on the critical few “X’s” or KPIV


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Basic Concepts

•

Every process produces a “product” or “service”•

Every product or service possesses requirements

• Every requirement can be measured

• The total observed variation is equal to the real product

variation plus the variation due to the measurement systemNote: We want most of the variation coming from theProduct/Part and very little coming from the Measuring

System

Total

2 Process/Part/

Service

2 2 Measurement System


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How might measurementvariation affect these decisions?

What if the amount ofmeasurement variation is

unknown?

Process

Measurement

Process

Measurement

Measurement variation can make our processcapabilities appear worse/better than they are

Why Worry About Measurement

Variation?Consider the reasons why we measure:

Verify product/process conformity tospecifications

Assist in continuous improvementactivities


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Components of Measurement ErrorThe sources of variability for the measurement systemcontaining continuous data are:


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Measurement Properties to Study

•

The two most common key measures associated with ameasurement system are accuracy and precision

• Accuracy and precision are different, independent

properties. You may encounter a data set that is accurate,yet not precise or precise, yet inaccurate

• Sometimes you may encounter a data set that is neither accurate nor precise. Obviously, we desire to have data

that exhibits both properties


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Measurement Properties to Study

•

Not only do we want our measurement systems to beaccurate and precise, but!

–

We want the measurement system to be able to detect smallchanges to the process (good discrimination)

–

When applied to the same items of interest, the measurementsystem should produce the same results in the future that it did inthe past (stability)

–

We want the system to be linear. Linearity concerns the behaviorof the measurement process across a wide spectrum of

applications


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Desired

Desired

Current

Current

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USLLSL

LSL

Target

Target

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Accuracy and Bias

•

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Accurate vs. Inaccurate

Mean Mean

TrueValue

TrueValue

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Instrument 2

Instrument 1

Potential Bias Problems• Average of measurements are different by a fixed

amount. Consider the manufacture of first-article. Biaseffects include: –

Operator Bias – Different operators get detectable differentaverages for the same value

–

Instrument Bias – Different instruments get detectable differentaverages for the same measurement

–

Others – Day-to-day (environment), fixtures, customer andsupplier (sites)


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Mean

Bias

TrueValue

1+2)3/345

•

In addition to accuracy, it is important for a measurementsystem to be precise

Precision Precision is the extent towhich we are able to get

the same data values

when independentmeasurements are made

on the same entity

Using the mean of

repeated measurements

improves precisionbecause the dispersion of

the averages is always

less than the dispersion of

the individual data points


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RulerCaliper

Micrometer

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.282

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Discrimination• Discrimination is the capability of detecting small changes in the characteristic

•

The instrument may not be appropriate to identify process variation or quantify

individual part characteristic values if the discrimination is unacceptable

• If an instrument does not allow differentiation between common variation in the

process and special cause variation, it is unsatisfactory

• A common cause to MSA failure can be attributed to rounding up or down

measurements


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Time One

Time Two

Stability

•

If measurements do not change or drift over time, theinstrument is considered to be stable


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Linearity• A measure of the difference in bias (or offset) over the range of

the sample characteristic the instrument is expected to seedetermines linearity. If the bias is constant over the range ofmeasurements, then linearity is good

• Over what range of values for a given characteristic can thedevice be used? –

When the measurement equipment is used to measure a wide range of values, linearity is a

concern

Measurement Scale

LowEnd

HighEnd

Measurement Variation


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Basic Model of Precision

222

rpd rpt MS ! ! ! +=

The Measurement System Variation is equal to the variation due toRepeatability plus the variation due to the Reproducibility and represents

the common cause variation in the Measurement System

2 Repeatability

2 2 Reproducibility Measurement

System


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Repeatability (Gage precision)

•

Repeatability is the inherent variability of themeasurement system. Used as an estimate of short term

variation. It is the variation that occurs when successive

measurements are made under the same conditions:

–

Same part –

Same characteristic

–

Same person

–

Same instrument

–

Same set-up

–

Same environmental conditions

Repeatability: It takes one to repeat


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Reproducibility (operators’ precision)

• Reproducibility is the variation in the average of themeasurements made by different operators using the

same measuring instrument when measuring the identical

characteristic on the same part

Operator A

Operator B Operator C

2

rpd

!

Reproducibility: It takes two to reproduce


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% Repeatability and Reproducibility

Observed Part/Process Variation

MeasurementSystem Variation%R&R = 20%

%R&R = 75%

%R&R = 100% Rule of Thumb:We want

%R&R < 30%


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Capability of the Measurement

System

• Once precision has been quantified, we now need toassess if the measurement system is precise enough. Todo this we must compare the measurement variation tothe production process. There are many capability indicesbut we will be focusing on the following two indices: –

% R&R

–

Discrimination Index


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Gage R&R: % R&R

• Assesses what percent of the Total Variation is taken up by

measurement error

•

Includes both repeatability and reproducibility

Care must be taken to use samples representing full, but typical,process variation

100ˆ

ˆ& % !=

Total

MS

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Acceptability Summary

% Contribution

1%

10%

% Study

Variation

10%

30%

% Tolerance

10%

30%

Number of

Distinct

Categories

10

5

Desirable to Have All 4 Indicators Say “Go”

“Product

Control”

“Process

Control”


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Gage R&R•

The objective of a Gage R&R study is to learn as much as possible

about the measurement process in a short amount of time

•

The strategy is to include equipment, operators, parts, and other factors

that will usually be elements of the measurement process

•

Make careful selection of parts (or samples) representing the entire

range of process variation. (Good and Bad to the entire specification)

•

The parts should be labeled in such a way to preclude operator

identification and therefore remove possible operator bias

•

Each part will then be measured multiple times in random sequence byeach operator using the same equipment. This can be replicated for

each equipment set

•

Verify that an on-going calibration, maintenance, and metrology

program exists and is current on the measurement system


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AIAG Gage R&R Standards

•

The Automotive Industry Action Group (AIAG) has tworecognized MSA standards for Gage R&R: – Short Form – Five parts measured two times by two

different operators

– Long Form – Ten parts measured three times each by three

different operators• For additional insights into Gage R&R, go to

www.aiag.org

• Remember that the Measurement System is acceptable if

the Gage R&R variability is small compared to theProcess or Study Variation seen


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Running the Gage R&R

•

Each sample should be measured 2 - 3 times by each operator(2 Times is the Short Test)

• Make sure the parts are marked for ease of data collection butremain “blind” (unidentifiable) to the operators

• Be there for the study. Watch for unplanned influences

• Randomize the parts continuously during the study to precludeoperators influencing the test

• The first time evaluating a given measurement process, let theprocess run as it would normally run

• Enter data in appropriate software like: Minitab, QI Macros,SigmaXL, Engine Room, Jump, etc.


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Minitab Gage R&R “Sixpack”

Let’s look at these sixcharts one at a time


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Gage R&R Relationships• A measurement process is said to be consistent when the results for

operators are Repeatable and the results between operators areReproducible

•

A gage is valid to detect part-to-part variation when the variability of operator

measurements is small relative to process variability or the tolerance range

• The percent of process variation consumed by the measurement (% R&R) is

then determined once the measurement process is consistent and can detectpart-to-part variation


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2. R Chart by Operator•

Repeatability is checked by using a special Range Chart where the

differences in the measurements by each operator on each part ischarted. If the difference between the largest value of a measured part

and the smallest value of the same part does not exceed the UCL,

then that gage and operator are considered to be Repeatable

Repeatability is indicated when virtually all of the range points lieunder the upper control limit on the range chart. Any points that

fall above the limit need to be investigated


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3. Xbar Chart by Operator

•

It is desirable to see plots that consistently go outside the UCL and LCL becauselimits are determined by gage variance and these plots should show that gage

variance is much smaller than variability between the parts

• If the samples chosen do not represent the total variability of the process, the

gage (repeatability) variance may be larger than the part variance and invalidate

the distinct categories calculation

• If the patterns of the operators are not comparable, there may be significant

operator and part interactions (discussed on another slide)

On this chart you want At Least 50% of the points to be

Outside the Control Limits


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4. Response by Part

•

This graph shows the data for all ten parts for all operators plotted

together. It should show plots that vary from the smallest dimensions

for the parts made by the process to the largest dimensions for thesame parts. Parts should be both in tolerance and out of tolerance if the

process makes them

•

If a part shows a large spread, it may be a poor candidate for the test

because the feature may not be clear on that part


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5. Response by Operator

•

This graph shows the data for all ten parts for plotted by each operator.

The line connecting the averages (shown as the circle with crosshairs)

of all 10 parts measured by each operator should be horizontal

•

Any significant slope is an indication that this operator has a general

bias to measure large or small when compared to the other operators


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6. Operator * Part Interaction

• Operator Influence: If the lines connecting the plottedaverage points diverge significantly, then there is a

relationship between the operator making the

measurements and the part that the operator is measuring.

This is not good and needs to be investigated


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!#

Minitab Sixpack

Questions on the graphical output?


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For %Contribution the guidelines are different:=10% : Unacceptable

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Gage R&R: The Numerical Output

The first column represents the source of variation, the second column is an estimateof the actual variation for each source (factor). The third column is the linear % that

each represents of the total variation. It is depicted as the red bar (left-most) on thePareto in the six-pack diagram


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Gage R&R: The Numerical Output

• This tabulation from Minitab builds the % of Study Variation that each source contributes to a calculatedpotential Total Variation seen in the study. The 6.0 * SD is how statistically 99.73% of the Total Variation

is calculated and this is assumed to equal 99.73% of the true process variation unless the Historical

Sigma is input

•

The %’s are used to grade the validity of the measurement system to perform measurement analysisusing %’s already taught. If the process is performing well, the % Tolerance is then important. The sum

of the %’s may add to more than 100% due to the math

• The Number of Distinct Categories represents the number of non-overlapping measurement groups that

this measurement system can reliably distinguish in the Study Variation. We would like that number to be

5 or higher. Four is marginal. Fewer than 4 implies that the measurement system can only work withattribute data

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Case Study # 1•

Selection of parts to be tested

–

10 parts randomly selected – Good, bad & average parts used

• Instructions to operators – Perform tests as normal

– Report variance

•

Tests conducted –

10 parts tested

–

2 operators

– 3 checks on each of the 10 parts

given to the operator in random order


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MSA – Gage R&R for Value

39


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MSA Session - Gage R&R

40


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Case Study # 2

MSA Bolt Torque


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Electronic Torque Wrench

Bolt Torque Wrench Operator


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Minitab Data


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Minitab Data Session Data


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Minitab Data


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Minitab Data


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Minitab Data


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