Pitfalls of Measurement System...
Transcript of Pitfalls of Measurement System...
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Pitfalls of Measurement System Analysis
MSA+DFSS
Tom Early, GE Global Research
QPRC2013
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June 7, 2013
Abstract
Measurement system analysis (MSA) will be
discussed in an industrial environment. MSA is
more than just gauge R&R. Using a “top down”
approach, MSA will be discussed at a system-
level view of the measurement process. Often
misunderstood sub-systems like gauge metrics
and gauge error analysis will be discussed.
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Gauge Requirements
Accurate calibration
Consistent stable, reproducible
Sensitive S/N, repeatable
Linear resolution, discrimination
Useful JIT, traceable to the customer
Inexpensive non-destructive, alpha vs. beta errors
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Gauge Performance Required: measurement context
Is a part within spec? If we ship it, will it work?
What is our process capability? Is the gauge good enough to understand sub-grouping?
Is the process changing or drifting? Can we do SPC to monitor the factory?
Can the process be improved?
Is the gauge good enough to use in an improvement project?
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A Process to be Measured…
http://www.islandone.org/MMSG/aasm/AASM53.html
Inputs Process Outputs
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Where Does the Measurement Begin?
Measurement
System
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Where Does the Measurement Begin?
Measurement
System
Right here!
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The Measurement System is a Process!
Inputs
Measurement
Process
Measurement Data
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The Measurement System as Process
Inputs
Measurement
Process
Measurement Data
Selection Preparation Mount Read
22222 ... ReadingMountingnPreperatioSelectiontSystemMeasuremen
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Measurement Sub-systems
• Measurement subsystems can be very diverse.
• Different failure mechanisms.
• Subsystems may have their own
specifications.
• Can be destructive, even when the
“measurement” is not.
• Might involve different operators.
• Ideally, variability between subsystems does
not correlate.
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Complications from Destructive Tests
• If the sample is destroyed in the measurement
process, sample variability exists in all
estimable characteristics of the study.
• Therefore, use hand-selected identical parts!
• You can’t get at operator*part interactions
in a nested experiment anyway!
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One problem, two kinds of errors.
LSL USL
T
Process
Gauge
Failed Good Units
Gauge
Passed Bad Units
It’s a good idea to track PB and FG separately, even though the exact same mechanism is responsible for both.
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Measurement Error
Input Test Pass
Fail
Repair/Scrap/Retest
Submitdp PBEscape dpdp
FGSubmitPBObserve dpdpdpdp
Observedp
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Gauge Noise Performance Metric A: against a tolerance
Some inflated value of the gauge noise needs to be a small fraction of the process tolerance.
Good for the “end-game” but…
%𝐺𝑅𝑅 = 100% ×𝑓 × 𝜎
𝑈𝑆𝐿 − 𝐿𝑆𝐿
USL LSL
Gauge Error Distribution
f
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Gauge Noise Performance Metric B: against process noise.
A 60% GRR used to measure a Z = 3 process has the same relative contribution to the observed process
variability that a 30% gauge contributes to the observed process variability when measuring a Z = 6 process.
MeasureObserve
Process
cGaugeMetriMeasure
2
Process 2
For a gauge to be useful, it just needs to see the process noise.
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Gauge Noise Performance
Gage R&R
%Contribution
Source VarComp (of VarComp)
Total Gage R&R 12.226 5.62
Repeatability 9.332 4.29
Reproducibility 2.895 1.33
Oper(i) 2.895 1.33
Part-To-Part 205.483 94.38
Total Variation 217.709 100.00
Process tolerance = 20
Study Var %Study Var %Tolerance
Source StdDev (SD) (5.15 * SD) (%SV) (SV/Toler)
Total Gage R&R 3.4966 18.0077 23.70 90.04
Repeatability 3.0548 15.7321 20.70 78.66
Reproducibility 1.7014 8.7624 11.53 43.81
Oper(i) 1.7014 8.7624 11.53 43.81
Part-To-Part 14.3347 73.8236 97.15 369.12
Total Variation 14.7550 75.9881 100.00 379.94
Number of Distinct Categories = 5
A
B
Here, a measurement system that is noisy compared to the tolerance (A) is much less noisy than the process being measured (B).
4966.3
3347.142floor
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Product Process Signal Attenuation
GaugeStudy
Parts
2
2
1
Study
PartsPPSI
D.J. Wheeler, “An Honest Gauge R&R Study”, 2006 ASQ/ASA Fall
Technical Conference.
Interclass Correlation Coefficient
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The Point of a GR&R Study (Random Effects!)
Measure-
ments …
…
Operators P
art
s
But sometimes we
aren’t interested in
the distribution!
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Single, or Limited Operator(s)
If all operators are in the GR&R Study:
• The GR&R experiment is no longer a full
random effect model.
• Each operator can have his own calibration
curve, potential eliminating all operator
contributions to the gauge error.
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Using Calibration Regression Effectively
xy
m N M
x x
x xi
1 1 2
2
( )
( )
Where: dx = Error associated with back calculating the most likely x value from y measured
dy = Standard error of the regression fit
N = Number of replicates of unknown samples
M = Number of calibration samples
m = Calibration slope
x = Back calculated (predicted) value of the unknown
= Average value of calibration samples
xi = Individual calibration samples (1 to M)
x
0
10
20
30
40
50
60
0 20 40 60 80 100 120
Input
Re
spo
nse
Fit
Standards
Error
2 Unknowns
Prediction intervals provide error estimates from a “single” measurement!
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Calibration Pitfalls
• Frequently, calibration standards and process
samples are measured using a different
process.
• Typically, calibration standards require less
preparation.
• As a result, calibration regression residual
error doesn’t capture all sources of sample
measurement variability.
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Summary
• Be sure to treat a measurement process as a complete system.
• Know the measurement process boundaries.
• “Hand select” pseudo-random parts for a gauge R&R study.
• Use more operators than parts.
• Calibration regression is an effective tool to control gauge variability, but, again, know the measurement process boundaries.