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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 the lenses 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 system Note: We want most of the variation coming from the Product/Part and very little coming from the Measuring

System

Total 

2  Process/ Part/

Service 

2  2  Measurement System 

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How might measurement variation affect these decisions?

What if the amount of measurement variation is

unknown?

Process

Measurement

Process

Measurement

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

Why Worry About Measurement

Variation? Consider the reasons why we measure:

Verify product/process conformity to specifications

 Assist in continuous improvement activities

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Components of Measurement Error The sources of variability for the measurement system containing continuous data are:

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Measurement Properties to Study

•  

The two most common key measures associated with a measurement 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 be accurate and precise, but! 

 –  

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

 –  

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

 –  

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

applications

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Desired

Desired

Current

Current

USL

USLLSL

LSL

Target

Target

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 Accuracy and Bias

•  

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 Accurate vs. Inaccurate

Mean Mean

True Value

True Value

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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. Bias effects include:  –

Operator Bias – Different operators get detectable different averages for the same value

 –  

Instrument Bias – Different instruments get detectable different averages for the same measurement

 –  

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

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Mean

Bias

True Value

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•  

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

Precision Precision is the extent to which 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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Ruler Caliper

Micrometer

.28

.279

.2794

.28

.282

.2822

.28

.282

.2819

.28

.279

.2791

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, the instrument 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 see determines linearity. If the bias is constant over the range of measurements, then linearity is good

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

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

concern

Measurement Scale

Low End

High End

Measurement  Variation

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Basic Model of Precision

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rpd rpt MS    !  !  !     +=

The Measurement System Variation is equal to the variation due to Repeatability 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 the measurement 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 the measurements 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 Variat