Four Innovative Methods to Evaluate Attribute Measurement ... · Four Innovative Methods to...

48
Copyright Autoliv Inc., All Rights Reserved Saving More Lives Four Innovative Methods to Evaluate Attribute Measurement Systems Thomas Rust Reliability Engineer/Trainer Sept. 2016

Transcript of Four Innovative Methods to Evaluate Attribute Measurement ... · Four Innovative Methods to...

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Copyright Autoliv Inc., All Rights ReservedSaving More Lives

Four Innovative Methods to Evaluate Attribute Measurement Systems

Thomas Rust – Reliability Engineer/Trainer

Sept. 2016

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31%

35%

17%

7%

10%

Autoliv

RoA

Japan

Europe

Americas

ChinaSales

2015

Global Footprint

Sales and technology leader

Global Company

Sales to all major vehicle manufacturers

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Autoliv Products

Inflatable curtain airbag

Passenger airbag

Steering wheel

Driver airbag

Side airbag

Seatbelt systems

Vision system

Electronic control unit

Night driving assist

Driver assist radar

Blind spot radar

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Our Guiding Principles

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Our Strategy to Stay AheadRelentless focus on Operational Excellence

Zero Defects by flawless

execution

One Product One Process to improve

cost effectiveness and robustness

Innovation to lead industry

in Real Life Safety

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Measurements

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The Gray Areas

Area I

Parts are measured bad

Area II

Parts are sometimes

measured good and

sometimes bad

Due to random variation in the

measuring system

Area III

Parts are measured good

I II II IIII

LSL USL

Target

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AIAG Attribute MSA Methods

Cross-Tab Method

Kappa

Confidence Intervals on

Percent Agreement

Signal Detection

Approach

Analytic Method

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The Attribute Paradigm

All we can do is count

the number of

agreements /

disagreements

All attribute data are the

same

It always takes a lot of

samples to evaluate and

attribute measurement

system

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Breaking The Attribute Paradigm

There are four types of Attribute Measurement systems

1. Underlying variable measurement

2. Attribute measurement of a variable product

3. Variable measurement of an attribute product

4. Attribute measurement of a attribute product

Think

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1- Underlying Variable Measurement

The characteristic is measured with variable data (or could be), but only the binary result is reported

ExamplesMeasuring height and

classifying as good or bad

Measuring force but only recording if above or below a limit

Operator could classify ‘how good/bad’ but only classifies as pass/fail

92

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Underlying Variable MeasurementMethod

MethodRecord the variable

measurement

Treat the same as other variable measurements Gage R&R

Have operators rate the ‘how’ good or bad each part is even if they don’t normally

A rating system (1-10) can be established1 = Best

4 = Barely passes

5 = Barely fails

10 = Worst

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Sprocket Inspection

A sprocket is supplied to a company to be included in an assembly

The end customer will interface with the sprocket so the appearance is important as well as the dimensions

The acceptance criteria is based on: Color

Surface Roughness

Paint Quality (no chipping)

Parts are inspected as pass or fail at the supplier, a sorting company, and the assembly company

All examples and data in

this presentation are

plausible fabrications

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Sprocket InspectionSet-Up and Method

43 Parts were selected

Both good and bad parts are found for different criteria Acceptable Color (AC) – 9

Bad Color (BC) – 13

High Roughness (RO) – 7

Chipped Paint (CP) – 9

Good Parts (GP) – 5

Three judges were selected as the experts to identify the standard rating of each partOne from the assembly

company (Customer)

One from the sorting company

One from the supplier

Each Part was inspected three times by each judge Parts were presented in a

random order

Part identity was hidden from each judge

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Initial Study - Roughness

Study Var %Study Var

Source StdDev (SD) (6 × SD) (%SV)

Total Gage R&R 2.11386 12.6831 95.30

Repeatability 1.71194 10.2716 77.18

Reproducibility 1.24003 7.4402 55.90

Judge 1.24003 7.4402 55.90

Part-To-Part 0.67238 4.0343 30.31

Total Variation 2.21822 13.3093 100.00

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Second Study - Roughness

Study Var %Study Var

Source StdDev (SD) (6 × SD) (%SV)

Total Gage R&R 1.69965 10.1979 84.39

Repeatability 1.37447 8.2468 68.24

Reproducibility 0.99981 5.9989 49.64

Judge 0.99981 5.9989 49.64

Part-To-Part 1.08074 6.4845 53.66

Total Variation 2.01415 12.0849 100.00

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Initial Study - Color

Study Var %Study Var

Source StdDev (SD) (6 × SD) (%SV)

Total Gage R&R 2.24318 13.4591 76.98

Repeatability 1.98639 11.9183 68.17

Reproducibility 1.04218 6.2531 35.77

Judge 0.79825 4.7895 27.39

Judge*Part 0.67003 4.0202 22.99

Part-To-Part 1.85980 11.1588 63.83

Total Variation 2.91389 17.4833 100.00

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Second Study - Color

Study Var %Study Var

Source StdDev (SD) (6 × SD) (%SV)

Total Gage R&R 2.22694 13.3616 83.22

Repeatability 0.86923 5.2154 32.48

Reproducibility 2.05029 12.3018 76.62

Judge 1.57253 9.4352 58.77

Judge*Part 1.31562 7.8937 49.17

Part-To-Part 1.48366 8.9019 55.44

Total Variation 2.67591 16.0555 100.00

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Initial Study – Chipped Paint

Study Var %Study Var

Source StdDev (SD) (6 × SD) (%SV)

Total Gage R&R 2.64770 15.8862 90.70

Repeatability 1.77333 10.6400 60.74

Reproducibility 1.96612 11.7967 67.35

Judge 1.32016 7.9209 45.22

Judge*Part 1.45699 8.7419 49.91

Part-To-Part 1.22964 7.3778 42.12

Total Variation 2.91931 17.5158 100.00

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Second Study – Chipped Paint

Study Var %Study Var

Source StdDev (SD) (6 × SD) (%SV)

Total Gage R&R 1.48823 8.9294 44.20

Repeatability 0.69921 4.1952 20.76

Reproducibility 1.31375 7.8825 39.02

Judge 0.65389 3.9233 19.42

Judge*Part 1.13945 6.8367 33.84

Part-To-Part 3.02051 18.1230 89.70

Total Variation 3.36724 20.2034 100.00

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2- Attribute measurement of a variable product

The characteristic could

be measured with

variable data but the

measurement system

being used can only

classify the results in

binary

Examples

Height Pass/Fail Gage

Diameter Go/No Go Gage

AIAG’s Signal Detection Approach and

Analytic Method apply to this type of system

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Logistic Regressionwith Normit Link Function

Parts across the range

from consistently good

to consistently bad

Measure multiple times

Quantify percent pass

Model and S-curve

Normit Link Function52.552.051.551.050.550.049.549.0

1 .0

0.8

0.6

0.4

0.2

0.0

Diameter

Pro

ba

bil

i ty

of

Pa

ss

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Go / No-Go Example

A Go / No-Go gage is used to verify that a hole is not too small

Parts are created that span the range from bad to good with some very close to the limit

Each part was measured carefully with a CMM

The parts where evaluated with the Go / No-Go gage Using actual operators

Each part was measured 10 times

Parts were evaluated in a random order

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Go / No-Go ExampleResults

Normit Link Function

P = Φ( α + βx )

μ = -α / β

σ = | 1 / β |

Example Calculations

μ = -(-136.7) / 2.674 = 51.1

σ = | 1 / (-136.7)| = 0.374

Bias = 51.1 – 50 = 1.1mm

%GRRTol = 6σ/Tol =

(6*0.374)/18 = 12.47%Specification Limits => 50-68mm

Binary Logistic Regression with Normit Link

Function

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Residual Analysis

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Go / No-Go ResultsMinitab Macro

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AIAG Example

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Logit ErrorCal ErrorSDA Error

1 0

0

-1 0

-20

-30

Data

Interval Plot of SDA Error, Cal Error, Logit Error95% CI for the Mean

The pooled standard deviation is used to calculate the intervals.

Gage R&R and Attribute MSA Simulation

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3 - Variable Measurement of an Attribute Product

Count of Paper Sheets by weight

Proximity Sensor verifies a

component is present

Camera detects a correct

configuration

Optical Sensor

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Attribute MSAVariable measurement of an attribute product

MethodRecord sensor output for

‘good’ and ‘bad’ conditions

Voltage, output, etc.

Create distribution curves

Estimate Alpha and Beta Risks

Include Lower Bound of Confidence Interval

Measurement ‘System’ includes the gage variation and the product variation

900.0

50.0

01.0

51.0

02.0

04 05 06 07 08 0

48.83 3.833 45

85.88 2.1 78 1 00

Mean StDev N

D

87

ytisn

eD

ata

M

elbairaV

traP dooG

traP gnissi

H lamroN

traP dooG ,traP gnissiM fo margotsi

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Part Presence Sensor Example

A camera system is used to detect if a washer is present

The camera is ‘taught’ what a good condition is

When connected to a computer, the camera reports the percent pixels that match the good condition

A threshold (limit) is set to define good from bad at 78%

Multiple good and bad conditions are measured and the percentages of pixel match are recorded

Good

Bad

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Histograms with Limit

900.0

50.0

01.0

51.0

02.0

04 05 06 07 08 0

48.83 3.833 45

85.88 2.1 78 1 00

Mean StDev N

D

87

ytisn

eD

ata

M

elbairaV

traP dooG

traP gnissi

H lamroN

traP dooG ,traP gnissiM fo margotsi

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Good ConditionsCapability Analysis

A capability analysis in Minitab can estimate the z-score of the threshold (limit)

A lower bound of the z-score can also be reported The lower bound takes into

account the sample size and the uncertainty in the estimate

Non-normal distributions can be used when applicable

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Alpha RiskProbability of Failing a Good Part

Using the Z-Score and a

normal distribution plot,

the alpha risk can be

calculated

The upper bound of the

alpha risk can be

calculated from the

lower bound Z-score

0.005

0.004

0.003

0.002

0.001

0.000

Percent Pixel Match

Den

sity

-3.62

0.0001473

Normal, Mean=0, StDev=1

Distribution Plot - Alpha Risk

0.005

0.004

0.003

0.002

0.001

0.000

Percent Pixel Match

Den

sity

-3.16

0.0007888

Normal, Mean=0, StDev=1

Distribution Plot - Alpha Risk Lower Bound

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Missing Part AnalysisBeta Risk

1 .0000E-1 2

8.0000E-1 3

6.0000E-1 3

4.0000E-1 3

2.0000E-1 3

0.0000E+00

Percent Pixel Match

Den

sity

7.61

1.3656E-14

Normal, Mean=0, StDev=1

Distribution Plot - Beta Risk

1 .0000E-09

8.0000E-1 0

6.0000E-1 0

4.0000E-1 0

2.0000E-1 0

0.0000E+00

Percent Pixel Match

Den

sity

6.25

2.0523E-10

Normal, Mean=0, StDev=1

Distribution Plot - Beta Risk Lower Bound

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Reporting Methods

Risk Alpha Risk = 0.0147%

Alpha LB = 0.0789%

Beta Risk = 1.37E-12%

Beta LB = 2.05E-8%

PPM Alpha Risk = 147 PPM

Alpha LB = 789 PPM

Beta Risk = 1.37E-8 PPM

Beta LB = 2.05E-4 PPM

Measurement Capability PpkGood = 1.21

LB for PpkGood = 1.05

PpkBad = 2.54

LB for PpkBad = 2.08

Reliability Pass a Good Part

99.985%

3 Nines

99.922% (LB)

3 Nines (LB)

Fail a Bad Part ~100%

13 Nines

99.99999998% (LB)

9 Nines (LB)

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Part Presence SensorMinitab Macro

800.0

50.0

01.0

51.0

02.0

04 84 65 46 27 08 8

85.88 2.1 78 1 00

48.83 3.833 45

Mean StDev N

P

87

ytisn

eD

hctaM lexiP tnecre

P

elbairaV

liaF

ssa

A

)seniN 9(08999999999.0

daB a liaF

)seniN 3(22999.0

dooG a ssaPdnuoB rewoL %59

527548.0-daB a liaF

73726.4dooG a ssaP

rotcaF ytefaS elitnauQ

00000000000.1

daB a liaF

58999.0dooG a ssaP

detamitsE

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Part Presence SensorMinitab Macro - Residuals

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Other Examples

900.0

50.0

01.0

51.0

02.0

4.26 2.76 0.27 8.67 6.18 4.68 2.1

86.33 2.376 1 00

69.42 3.471 45

Mean StDev N

P

08

ytisn

eD

hctaM tnecreP lexi

P

elbairaV

liaF

ssa

A

)seniN 2(0399.0

daB a liaF

)seniN 1(099.0

dooG a ssaPdnuoB rewoL %59

621817.0-daB a liaF

00475.3dooG a ssaP

rotcaF ytefaS elitnauQ

8899.0

daB a liaF

699.0dooG a ssaP

detamitsE

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:eman egaG

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traP gnissiM .sv traP dooG - yrammuS ASM .tt

900.0

20.0

40.0

60.0

80.0

01.0

21.0

41.0

61.0

81.0

41 82 24 65 07 48 8

92.27 2.583 1 00

21 .1 6 3.881 45

Mean StDev N

S

56

ytisn

eD

egatloV rosne

P

elbairaV

liaF

ssa

A

)seniN 02>(000000000000.1

daB a liaF

)seniN 02>(000000000000.1

dooG a ssaPdnuoB rewoL %59

997256.0-daB a liaF

9204.71dooG a ssaP

rotcaF ytefaS elitnauQ

000000000000.1

daB a liaF

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detamitsE

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traP gnissiM .sv traP dooG - yrammuS ASM .tt

900.0

50.0

01.0

51.0

02.0

04 05 06 07 08 0

85.88 2.1 78 1 00

48.83 3.833 45

Mean StDev N

P

47

ytisn

eD

hctaM tnecreP lexi

P

elbairaV

liaF

ssa

A

)seniN 7(469999999.0

daB a liaF

)seniN 6(81999999.0

dooG a ssaPdnuoB rewoL %59

999927.0-daB a liaF

97219.6dooG a ssaP

rotcaF ytefaS elitnauQ

000000000.1

daB a liaF

89999999.0dooG a ssaP

detamitsE

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6102 naJ 61eoD nhoJ

rosneS ecneserP traP

:yduts fo etaD:yb detropeR

:eman egaG

detubirtsid yllamron eb ot demussa era ataD

traP gnissiM .sv traP dooG - yrammuS ASM .tt

900.0

20.0

40.0

60.0

80.0

01.0

21.0

41.0

0 41 82 24 65 07 48 8

89.64 3.432 1 00

42.27 20.42 45

Mean StDev N

S

87

ytisn

eD

egatloV rosne

P

elbairaV

liaF

ssa

A

)seniN 1(319.0

daB a liaF

)seniN 2(5899.0

dooG a ssaPdnuoB rewoL %59

613487.0-daB a liaF

22815.7dooG a ssaP

rotcaF ytefaS elitnauQ

069.0

daB a liaF

7999.0dooG a ssaP

detamitsE

ytilibaborPnoitacifissalC

6102 naJ 61eoD nhoJ

rosneS ecneserP traP

:yduts fo etaD:yb detropeR

:eman egaG

detubirtsid yllamron eb ot demussa era ataD

traP gnissiM .sv traP dooG - yrammuS ASM .tt

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Evaluation over Time

454137332925211 71 3951

60

40

20

Observation

Ind

ivid

ua

l Va

lue

_X=42.27

UCL=53.44

LCL=31.10

454137332925211 71 3951

30

20

1 0

0

Observation

Mo

vin

g R

an

ge

__MR=4.20

UCL=13.73

LCL=0

111

1

11

11

11

1111

1

1

1

1

11111

11

111

1

111

1

11

1

11

111

1111

1

Worksheet: Worksheet 2

I-MR Chart of Good Parts

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Risks and Concerns

Non – normal

Multiple failure modes

‘Like catching Terrorists’

No variation

Difficult to collect data

Changes over time

Variation in the process

vs. measurement

system

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4 - Attribute Measurement of an Attribute Product

Judgement of a Coin

toss

Mechanical detection of

a part present

Binary response of

correct set-up

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Attribute Measurement of an Attribute Product

Method

Attribute Agreement

Analysis with Lower-Bound

Confidence Interval

Caution needs to be taken

on selection of good and

bad parts

A minimum of 50 total parts

with at least 150

observations should be used

for a reliable analysis

Selecting parts

At least 25%

of parts will

be in all

categories?

Randomly select parts

from the entire process

A significant

sample of

parts from

each

category is

available?

Cautiously create at

least 10 parts for each

category that will

represent the process

Randomly select at least

10 parts from each

category

no

yes

yes

no

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Examples of Attribute Agreement Analysis

CBA

95

90

85

80

75

70

65

Appraiser

Perc

en

t

95.0% CI

Percent

CBA

95

90

85

80

75

70

65

Appraiser

Perc

en

t

95.0% CI

Percent

Date of study:

Reported by: John Doe

Name of product: Sprocket

Misc: Chipped Paint

Assessment Agreement

Worksheet: All Data Attribute Example

Within Appraisers Appraiser vs Standard

Date of study:

Reported by: John Doe

Name of product: Sprocket

Misc: Roughness

SupplierSorting CoCustomer

1 00

80

60

40

20

0

AppraiserP

erc

en

t

95.0% CI

Percent

Assessment Agreement

Within Appraisers

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Attribute Agreement AnalysisImpact of Parts Range

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Best Practices

Mission

Possible

The New Approach

To Attribute MSA

Historical

Approach

to Attribute

MSA

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Every year, Autoliv’s products

save over 30,000 lives

and prevent ten times

as many severe injuries

Thank You!

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References

MEASUREMENT SYSTEMS ANALYSIS Reference Manual

Fourth Edition

AIAG

Copyright 2010, Chrysler Group LLC, Ford Motor Company, General

Motors Corporation

EMP III, Evaluating the Measurement Process & Using

Imperfect Data

Donald J. Wheeler

Copyright 2006 SPC Press