2012 SQC Parrillo M

Michael ParrilloASQ; CQE, CRE, CSSGB, CSSBB, CMQ/OE, CQA

http://www.linkedin.com/in/parrilloasq


WHAT IS MSA? A means to determine the percent of variation

between operator, gauge, and process in a measuring procedure.

WHY? Variation exists in all measuring processes.

Operator performance will vary from day to day and operator to operator

Gauge may or may not be adequate for it’s intended use

Process may or may not be adequate for it’s intended use

FISHBONE EXERCISE SWIPE

Standard

Workpiece

Instrument

Person/Process

Environment

FISHBONE EXERCISE

BASICS Definition Uncertainty

Discrimination

Linearity

Stability

True Value

Bias

Reference Value

Repeatability

Reproducibility

UNCERTAINTY Uncertainty is a quantified expression of measurement

reliability that describes the range of a measurement result within a level of confidence.

Estimating uncertainties is a vast subject which in itself can take quite a bit of effort to master. Suggested reading on this subject is ANSI/NCSL Z540.3 (appendix A is particularly helpful), and ISO/IEC Guide to the Expression of Uncertainty in Measurement (GUM). Also, the following documents can be found on the internet free of charge; NIST Technical Note 1297, EA 4/02, and M3003.

TYPE “A” AND TYPE “B” Type “A” uncertainties are quantified by statistic and include

studies such as: Accuracy Linearity Repeatability Reproducibility

Type “B” uncertainties can not be evaluated by statistic and include

Temperature Errors Fixture variation Calibration

UNCERTAINTY BUDGETSource % Uncertainty *Divisor X X²

Repeatability 1.3 1 1.3 1.69

Reproducibility 4.02 1 4.02 16.1604

DUT Certification .03 2 .015 .000225

Total 17.8506

RSS 4.225

**Expanded (K=2) 8.45%

DISCRIMINATION (RESOLUTION) Smallest scale unit of measure for an instrument

10 to 1 rule +/- .005 inch?

.005/10 = .0005 inch

.005/4 = .00125 inch

Requirement varies based on application

Cost of gage must be considered and weighed against ROI (Micrometer-$200, or optical comparator-$15,000)

Disposable toothbrushes – Airplane tires

DISCRIMINATION FOUND ON SPC RANGE CHART

Not recommended If:3 or less values are displayed on the chart orMore that ¼ of the values are 0

NUMBER OF DISTINCT CATAGORIES This number represents the ability of your measurement

device to segment the total range of values.

AIAG recommends a minimum of 5 ndc

If you require more ndc than the study determined Run the study with more parts that represent the

entire range Improve the measurement tool to deliver more

precision

NUMBER OF DISTINCT CATAGORIES

There is a mathematic relation between ndc and

% Total Variation

You will need less than approximately 27% Total Variation to have a minimum of 5 ndc

LINEARITY Collective variation over the range of measurement

If gage gains .1 inch per foot and you measure 6 inches than variation in linearity may be .05 of your range

This “may” be acceptable if tolerance is +/- 1 one inch and total length measured is 6 inches.

This would not be acceptable if total length is over 10 feet

.1 x 10 = 1”

LINEARITY STUDY Choose 5 parts representing the full range of values

Determine each parts reference value

Have best operator randomly measure each part 12 times

Determine bias of each part

Enter data in software to create linearity plot

LINEARITY PLOT

LINEARITY RESULTS Result is a linearity problem

R-Sq is only 71.4% (0 to 100% - Larger better)

Bias line intercepted by regression line (Unacceptable if significantly different than “0”)

Distribution is bimodal (7 data points at value 4 & 6)

STABILITY (Drift) The change in the difference between measurement

value and reference value over time

Electronic instruments may change over time due to the drifting of values

Operators may deviate in methods over time

Temperature may vary over time (or per day)

DETERMINING STABILITY Obtain a part to use as a master reference value

Can be done by averaging repetitive measurements of a master over time

Measure this master 5 times per shift over four weeks until at least 20 subgroups are obtained

Plot the data on Xbar/R Chart

Monitor using standard SPC analysis for special cause

DETERMINING STABILITY

REPEATABILITY Variation in measurements obtained with one

measuring instrument when used several times by an operator while measuring the identical characteristic on the same part (AIAG).

Commonly referred to as Equipment Variation (E.V.)

Variation in the gage

POOR REPEATABILITY Part – Surface, position, consistency of part

Instrument – Repair, wear, fixture, maintenance

Standard – Quality, wear

Method - Variation, technique

Appraiser – Technique, experience, fatigue

Environment – Temperature, humidity, vibration, lighting, cleanliness

Assumptions – Stable

REPRODUCABILITY Variation in the average of the measurements made by

different operators using the same gage when measuring a characteristic on one part

Commonly referred to as Appraiser variation (A.V.)

POOR REPRODUCABILITY Part – Between part variation

Instrument – Between instrument variation

Standard – Influence of different settings

Method – Holding & clamping methods, zeroing

Appraisers – Between appraiser variation, training, skill, experience

Environment – Environmental cycles

Assumptions – Stability of process

R & R ACCEPTABILITY Under 10% - Acceptable

10 – 30% - May be acceptable based on application

Over 30% - Not acceptable

BIAS (ACCURACY) Bias is the difference between observed measurement

and reference value (AIAG)

True value is the actual value of the artifact

Unknown and unknowable

Reference Value is the accepted value of an artifact

Artifacts or reference materials can be used to calibrate instruments or to validate measurement methods.

BIAS (ACCURACY) Possible causes

Out of calibration

Worn or damaged fixture, equipment, instrument

Wrong gage

Environmental conditions

Operator skill level, performed wrong method

http://thequalityportal.com/q_forms.htm

Gage R & R

Study

Worksheet

Note - We received two complaints regarding the calculation used in this form - please refer to the comment in Cell i43. Date:

Gage Number: Part Number:

Gage Cert. Level: Part Name:

Gage Cert. Date: Characteristic:

Gage Build Source: Engineering Level:

Operator: A B C

No Operators: 3 Tolerance: 1.02

Number of Trials: 3 Number of Parts: 10

Trial Part

Operator Number 1 2 3 4 5 6 7 8 9 10 Average

A 1 0.29 -0.56 1.34 0.47 -0.80 0.02 0.59 -0.31 2.26 -1.36 0.19

2 0.41 -0.68 1.17 0.50 -0.92 -0.11 0.75 -0.20 1.99 -1.25 0.17

3 0.64 -0.58 1.27 0.64 -0.84 -0.21 0.66 -0.17 2.01 -1.31 0.21

Average 0.45 -0.61 1.26 0.54 -0.85 -0.10 0.67 -0.23 2.09 -1.31 X-bar 0.19

Range 0.35 0.12 0.17 0.17 0.12 0.23 0.16 0.14 0.27 0.11 R-bar 0.18

Trial Part


B 1 0.08 -0.47 1.19 0.01 -0.56 -0.20 0.47 -0.63 1.80 -1.68 0.00

2 0.25 -1.22 0.94 1.03 -1.20 0.22 0.55 0.08 2.12 -1.62 0.12

3 0.07 -0.68 1.34 0.20 -1.28 0.06 0.83 -0.34 2.19 -1.50 0.09

Average 0.13 -0.79 1.16 0.41 -1.01 0.03 0.62 -0.30 2.04 -1.60 X-bar 0.07

Range 0.18 0.75 0.40 1.02 0.72 0.42 0.36 0.71 0.39 0.18 R-bar 0.51

Trial Part


C 1 0.04 -1.38 0.88 0.14 -1.46 -0.29 0.02 -0.46 1.77 -1.49 -0.22

2 -0.11 -1.13 1.09 0.20 -1.07 -0.67 0.01 -0.56 1.45 -1.77 -0.26

3 -0.15 -0.96 0.67 0.11 -1.45 -0.49 0.21 -0.49 1.87 -2.16 -0.28

Average -0.07 -1.16 0.88 0.15 -1.33 -0.48 0.08 -0.50 1.70 -1.81 X-bar -0.25

Range 0.19 0.42 0.42 0.09 0.39 0.38 0.20 0.10 0.42 0.67 R-bar 0.33

Part

Average0.17 -0.85 1.10 0.37 -1.06 -0.19 0.45 -0.34 1.94 -1.57 Rp 3.51

R & R 0.30577 %EV 19.8% %EV-TV 17.61% R-Bar 0.3417

Equipment Variation: 0.20186 Part Var: 1.10460 %AV 22.5% %AV-TV 20.04% X-Dif 0.4447

Appraiser Variation: 0.22967 Total Var: 1.14613 %RR 30.0% %RR-TV 26.68% UCLr 0.8815

repeatability 0.04 R&R 0.05937Notes %PV-TV 96.38% LCLr 0.00

reproducibility 0.04 TV 0.22255 ndc 5 Min %RR 26.68% Max Range 1.0200

Criteria < 30% Pass/Fail Pass Stable? No



Per

cent

Part-to-PartReprodRepeatGage R&R

100

50

0

% Contribution

% Study Var

Sam

ple

Ran

ge

0.002

0.001

0.000

_R=0.000533

UCL=0.001743

LCL=0

Harish Pankaj Pramod

Sam

ple

Mea

n

0.08

0.06

0.04

__X=0.04627UCL=0.04727LCL=0.04526

Harish Pankaj Pramod

OperatorPart

PramodPankajHarish151413121110987654321

0.08

0.06

0.04

OperatorPramodPankajHarish

0.08

0.06

0.04

Gage name: V ision Microscope

Date of study : 12-20-06

Reported by: Mike Parrillo

Tolerance: +/- .001 inch

Misc: Accuracy .0001 inch

Components of Variation

R Chart by Operator

Xbar Chart by Operator

Response By Part ( Operator )

Response by Operator

Jacket Line Measurement Analysis, ANOVA

EXAMPLE

EXAMPLEP

erce

nt

Part-to-PartReprodRepeatGage R&R

100

50

0

% Contribution

% Study Var

Sam

ple

Ran

ge

0.0010

0.0005

0.0000

_R=0.0004

UCL=0.001307

LCL=0

Jay Kirte Nick

Sam

ple

Mea

n 0.020

0.016

0.012

__X=0.01593UCL=0.01669

LCL=0.01518

Jay Kirte Nick

OperatorPart

NickKirteJay151413121154321109876

0.020

0.015

0.010

OperatorNickKirteJay

0.020

0.015

0.010

Gage name: V ision Microscope

Date of study : 12-20-06

Reported by: Mike Parrillo

Tolerance: +/- .001 inch

Misc: Accuracy .0001 inch

Components of Variation

R Chart by Operator

Xbar Chart by Operator

Response By Part ( Operator )

Response by Operator

Primary Line Measurement Analysis, ANOVA

ATTRIBUTE STUDY A study comparing categories as opposed to

measurements.

Go/No Go gages, classifications such as good, fair, poor, unacceptable.

Involves human judgment which may vary

ATTRIBUTE STUDY Collect minimum of 30 samples that span the entire

range

Determine reference value of each sample

Have three operators measure each part 3x (Unaware of reference value, or part number). Total inspected 90

ATTRIBUTE STUDY Determine

How often did each appraiser pass the same part

How often did each appraiser fail the same part

How often did appraiser “A” pass and “B” fail

How often did appraiser “B” pass and “A” fail

Compare all appraisers in combinations (A &B, A & C, B & C)

Determine expected CT/GT*RT (CT-Column Total, GT- Grand Total, RT- Row Total)

ATTRIBUTE STUDYAppraiser “A”

Appraiser “B”

Pass Fail Total

Pass CountExpected

248.4

419.6

2828

Fail CountExpected

318.6

5943.4

6290

Total CountExpected

2727

6363

9090

Cohen’s Kappa P observed – p expected/1-p expected

From 0 to 1

0 - no agreement

1 - complete agreement

.80 – 1 Very good

.60 - .80 Good

.40 - .60 Moderate

.20 - .40 Fair

> .20 Poor

Cohen’s Kappa P observed (Agreement) = (24 + 59)/90 = .922

P expected (Agreement) = (8.4 + 43.9)/90 = .576

.922 - .576/1 - .576 = .82

Calculate tables for each combination of appraisers.

Calculate Cohen’s Kappa for each combination of appraisers.

Review results and act appropriately

ATTRIBUTE STUDY If Cohen’s Kappa is under minimum requirement:

Appraiser – Interpretation, eyesight, skill level, method

Organization – Procedure, training, peer/management pressure, fatigue

Bibliography Automotive Industry Action Group (AIAG) (2002).

Measurement Systems Analysis Reference Manual, 3rd edition. Chrysler, Ford, General Motors Supplier Quality Requirements Task Force.

Minitab - Understanding "Number of distinct categories" in Gage R&R output - ID 276


Michael ParrilloASQ; CQE, CRE, CSSGB, CSSBB, CMQ/OE, CQA



2012 SQC Parrillo M

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