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Quality Tools - Yanez

Jul 07, 2018

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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

    Current

    Current

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    LSL

    Target

    Target

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     Accuracy and Bias

    •  

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     Accurate vs. Inaccurate

    Mean Mean

    True Value

    True Value

    J3,/

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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

    1+2)3/345

    •  

    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

    222

    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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    K<

    % Repeatability and Reproducibility

    Observed Part/Process Variat