MSA Guide for guidance at manufacturing_

MSA_Guide for guidance at manufacturing

1. ©2/2001 Cayman Systems Revision M
[phone] -- 16949.com Slide 1, Printed 08/02/24Measurement Systems Analysis
Measurement
Systems
Analysis

2. ©2/2001 Cayman Systems Revision M
[phone] -- 16949.com Slide 2, Printed 08/02/24Measurement Systems Analysis
Don’t Let This Happen To YOU!

3. ©2/2001 Cayman Systems Revision M
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Variation
Think of Measurement
as a Process

4. ©2/2001 Cayman Systems Revision M
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Definition
Measurement
The assignment of numbers to material
things to represent the relationships
among them with respect to particular
properties.
C. Eisenhart (1963)

5. ©2/2001 Cayman Systems Revision M
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Measurement Systems Analysis
•Basic Concepts of Measurement Systems
A Process
•Statistics and the Analysis of Measurement
Systems
•Conducting a Measurement Systems Analysis
•ISO - TC 69 is the Statistics Group
•Ensures high ‘Data Quality’ (Think of Bias)

6. ©2/2001 Cayman Systems Revision M
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Course Focus & Flow
Measurement as a Process
•Mechanical Aspects (vs Destructive)
Piece part
Continuous (fabric)
•Features of a Measurement System
•Methods of Analysis
•Gauge R&R Studies
•Special Gauging Situations
Go/No-Go
Destructive Tests

7. ©2/2001 Cayman Systems Revision M
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Place Timeline Here

8. ©2/2001 Cayman Systems Revision M
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The Target & Goal
Prototype
Pre-Launch
Production
USLLSL
Continuous Improvement

9. ©2/2001 Cayman Systems Revision M
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Key Words
•Discrimination
Ability to tell things apart
•Bias [per AIAG] (Accuracy)
•Repeatability [per AIAG] (Precision)
•Reproducibility
•Linearity
•Stability

10. ©2/2001 Cayman Systems Revision M
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Terminology
•Error ≠ Mistake
•Error ≠ Uncertainty
•Percentage Error ≠ Percentage Uncertainty
•Accuracy ≠ Precision

11. ©2/2001 Cayman Systems Revision M
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Measurement Uncertainty
•Different conventions are used to report
measurement uncertainty.
•What does ±5 mean in m = 75 ±5?
Estimated Standard Deviation: 
Estimated Standard Error: 
m
= /√N
Expanded Uncertainty of ± 2 or 3
Sometimes ± 1 (Why?)
95% or 99% Confidence Interval
Standard Uncertainty: u
Combined Standard Uncertainty: u
c

12. ©2/2001 Cayman Systems Revision M
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Measurement Uncertainty
•Typical Reports
•Physici

13. ©2/2001 Cayman Systems Revision M
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Measurement as a Process
Basic Concepts
•Components of the Measurement System
•Requirements of a Measurement System
•Factors Affecting a Measurement System
•Characteristics of a Measurement System
Features (Qualities) of a Measurement Number
•Units (Scale)
•Accuracy
•Precision (Consistency or Repeatability)
•Resolution (Reproducibility)

14. ©2/2001 Cayman Systems Revision M
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Measurement Related Systems
Typical Experiences with
Measurement Systems

15. ©2/2001 Cayman Systems Revision M
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Basic Concepts
•Every Process Produces a “Product”
•Every Product Possesses Qualities (Features)
•Every Quality Feature Can Be Measured
•Total Variation
= Product Variation + Measurement Variation
•Some Variation Inherent in System Design
•Some Variation is Due to a Faulty Performance of
the System(s)

16. ©2/2001 Cayman Systems Revision M
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The Measurement Process
What is the ‘Product’ of the Measurement Process?
What are the Features or Qualities of this Product?
How Can We Measure Those Features?

17. ©2/2001 Cayman Systems Revision M
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Measurement Systems Components
•Material to be Inspected
Piece
Continuous
•Characteristic to be Measured
•Collecting and Preparing Specimens
•Type and Scale of Measurement
•Instrument or Test Set
•Inspector or Technician
AIAG calls these ‘Appraiser’
•Conditions of Use

18. ©2/2001 Cayman Systems Revision M
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Where Does It Start?
During the Design (APQP) Stage:
The engineer responsible for determining inspections
and tests, and for specifying appropriate equipment
should be well versed in measurement systems. The
Calibration folks should be part of the process as a
part of a cross-functional team.
Variability chosen instrument must be small when
compared with:
Process Variability
Specification Limits

19. ©2/2001 Cayman Systems Revision M
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Typical Progression
Determine ‘Critical’
Characteristic
Determine What
Equipment is Already
Available
Determine Required
Resolution
Consideration of the Entire
Measurement System for
the Characteristic
(Variables)
Cross-Functional
Product Engineer
Product Engineer
Metrology
How will the data
be used?

20. ©2/2001 Cayman Systems Revision M
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Measurement Systems Variables
Measurement
Instrument Environment
Material Inspector Methods
Sample
Preparation
Sample
Collection
Parallax
Reproducibility
Training
Practice
Ergonomics
Test Method
Workmanship
Samples
Standards
Discrimination
Repeatability
Bias
Calibration
Linearity
Vibration
Lighting
Temperature
Humidity
These are some of the variables in a measurement
system. What others can you think of?
Fixture
Eyesight
Air Pressure
Air Movement
Fatigue

21. ©2/2001 Cayman Systems Revision M
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Determining What To Measure
•Voice of the Customer
You Must Convert to Technical Features
•Technical Features
•Failure Modes Analysis
•Control Plan
Convert To
External
Requirements
Internal
Requirements

22. ©2/2001 Cayman Systems Revision M
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Voice of the Customer
•External and
Internal
Customers
•Stated vs Real
and Perceived
Needs
•Cultural Needs
•Unintended
Uses
•Functional
Needs vs.
Technical
Features
Customer
may
specify
causes
rather than
output

23. ©2/2001 Cayman Systems Revision M
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Convert to Technical Features
•Agreed upon
Measure(s)
•Related to
Functional Needs
•Understandable
•Uniform
Interpretation
•Broad Application
•Economical
•Compatible
•Basis for Decisions
Y
Z
Technical Feature
Functional Need

24. ©2/2001 Cayman Systems Revision M
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Failure Modes Analysis
•Design
FMEA
•Process
FMEA
•Identify
Key
Features
•Identify
Control
Needs
Critical Features are Defined Here!

25. ©2/2001 Cayman Systems Revision M
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Automotive FMEA
Process Failure Mode And Effects Analysis Low - High
Process: Outside Suppliers Affected: Engineer: 1 - 10
Primary Process Responsibility: Model Year/Vehicle(s): Part Number:
Other Div. Or People Involved: Scheduled Production Released: PFMEA Date: Rev.
Approvals: Quality Assurance Manager Quality Assurance Engineer
Operations Manager Senior Advisor
Part Name
Operation
Number Process Function
Potential Failure
Mode
Potential Effects Of
Failure Potential Cause Of Failure Current Controls OccuredSeverityDetectionRPN
Recommended
Actions And
Status
Actions
Taken OccuredSeverityDetectionRPN
Responsible
Activity
SIR Take TPPE Wrong MaterialFragmented Container Insufficient Supplier ControlMaterial Certification
19218
Container Material Held In Unpredictable Deployment Improper Handling Required With Each
1 Storage Area Misidentified Material Shipment
Release Verification
Out Of SpecFragmented Container Supplier Process Control Periodic Audit Of 310390
Material Unpredictable Deployment Supplier Material
ContaminatedFragmented Container Open Boxes Visual Inspection 19763
Material Unpredictable Deployment
Material Fragmented Container Engineering Change Release Verification 110770
CompositionUnpredictable Deployment Supplier Change Green "OK" Tag
Change Customer Notification
2 Move To UnreleasedFragmentation Untrained LTO Check For Green "OK"
510150
Approved Untrained Personnel Tag At Press
Storage Trace Card
Check List
Training
Leading to MSA. Critical features are determined by the FMEA
(RPN indicators) and put into the Control Plan.

26. ©2/2001 Cayman Systems Revision M
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Control Plan / Flow Diagram
•Inspection Points
•Inspection
Frequency
•Instrument
•Measurement Scale
•Sample Preparation
•Inspection/Test
Method
•Inspector (who?)
•Method of Analysis

27. ©2/2001 Cayman Systems Revision M
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GM Process Flow Chart
Process Flow Diagram Approved By:
Part Number: Date:4/5/93 QA Manager
Part Description: Rev. :C Operations Manager
Prepared By: Senior Advisor
QA Engineer
Step FabricationMoveStoreInspectOperation Description Item #Key Product CharacteristicItem # Key Control Characteristic
1 Move "OK" Vinyl Material 1.0Material Specs 1.0Material Certification Tag
From Storage Area and
Load Into Press.
2 Auto Injection Mold Cover 2.0Tearstrip In Cover 2.1Tool Setup
In Tool # 2.2Machine Setup
3.0Hole Diameter In Cover 2.1Tool Setup
2.2Machine Setup
4.0Flange Thickness In Cover 2.1Tool Setup
2.2Machine Setup
5.0Pressure Control Protrusions 2.1Tool Setup
Height 2.2Machine Setup
3 Visually Inspect Cover 6.0Pressure Control Protrusions 2.1Tool Setup
Filled Out 2.2Machine Setup

28. ©2/2001 Cayman Systems Revision M
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Standard Control Plan Example
Control Plan Number Key Contact / Phone Date (Orig.) Date (Rev.)
Part No./ Latest Change No. Core Team Customer Engineering Approval/Date
Part Name/Description Supplier/Plant Apoproval/Date Customer Quality Approval/Date
Supplier/Plant Supplier Code Other Approval/date (If Req'd) Other Approval/date (If Req'd)
Characteristics Methods
Part/
Process
Number
Process Name/
Operation
Description
Machine,
Device,
Jig, Tools
for Mfg.No.ProductProcess
Special
Char.
Class
Product/
Process
Spec/
Tolerance
Evaluation
Measurement
Technique Size
Frequ-
ency
Control
Method
Reaction
Plan
This form is on course disk

29. ©2/2001 Cayman Systems Revision M
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Ford’s Dimensional Control Plan (DCP)

30. ©2/2001 Cayman Systems Revision M
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Measurement as a System
•Choosing the Right Instrument
Instrument Calibration Needs
Standards or Masters Needed
Accuracy and Precision
•Measurement Practices
Where
How Many Places
•Reported Figures
Significant Figures Rule
2 Action Figures
Rule of 10
Individuals, Averages, High-Lows

31. ©2/2001 Cayman Systems Revision M
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Measurement Error
Measured Value (y)
=
True Value (x) + Measurement Error
Deming says there is
no such thing as a
‘True’ Value.
Consistent (linear)?

32. ©2/2001 Cayman Systems Revision M
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Sources of Measurement Error
•Sensitivity (Threshold)
Chemical Indicators
•Discrimination
•Precision (Repeatability)
•Accuracy (Bias)
•Damage
•Differences in use by Inspector (Reproducibility)
Training Issues
•Differences Among Instruments and Fixtures
•Differences Among Methods of Use
•Differences Due to Environment

33. ©2/2001 Cayman Systems Revision M
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Types of Measurement Scales
•Variables
Can be measured on a continuous scale
Defined, standard Units of Measurement
•Attributes
No scale
Derived ‘Unit of Measurement’
Can be observed or counted
Either present or not
Needs large sample size because of low information content

34. ©2/2001 Cayman Systems Revision M
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How We Get Data
•Inspection
•Measurement
•Test
Includes Sensory (e.g..: Beer)
Magnitude of Quality

35. ©2/2001 Cayman Systems Revision M
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Operational Definitions
•Is the container Round?
•Is your software Accurate?
•Is the computer screen Clean?
•Is the truck On Time?

36. ©2/2001 Cayman Systems Revision M
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Different Method = Different Results
In SpecOut of Spec
Method 1
Method 2

37. ©2/2001 Cayman Systems Revision M
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Measurement System Variability
•Small with respect to Process Variation
•Small with respect to Specified
Requirements
•Must be in Statistical Control
Measurement IS a Process!
Free of Assignable Causes of variation

38. ©2/2001 Cayman Systems Revision M
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Studying the Measurement System
•Environmental Factors
•Human Factors
•System Features
•Measurement Studies

39. ©2/2001 Cayman Systems Revision M
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Standards
•National
In the US - Kept or Tracked by NIST
•Primary
Copied directly from National Standard using ‘State-of-the-
Art’ Equipment
•Secondary
Transferred from Primary Standard
•Working
Used to calibrate laboratory and shop instruments

40. ©2/2001 Cayman Systems Revision M
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Environmental Factors
•Temperature
•Humidity
•Vibration
•Lighting
•Corrosion
•Wear
•Contaminants
Oil & Grease
Aerosols
Where is the study performed?
1. Lab?
2. Where used?
3. Both?

41. ©2/2001 Cayman Systems Revision M
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Human Factors
•Training
•Skills
•Fatigue
•Boredom
•Eyesight
•Comfort
•Complexity of Part
•Speed of Inspection (parts per hour)
•Misunderstood Instructions

42. ©2/2001 Cayman Systems Revision M
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•Sources of Errors
Inadvertent Errors
•Attentiveness
•Random
•Good Mistake-Proofing Target
Technique Errors
•Consistent
Wilful Errors (Bad mood)
•Error Types (Can be machine or human)
Type I - Alpha Errors [ risk]
Type II - Beta Errors [ risk]
Human Measurement Errors
Accept
Reject
Good Bad
OK!
OK!alpha
beta
Training
Issue
Process in control,
but needs
adjustment, False
alarm
Unaware of
problem

43. ©2/2001 Cayman Systems Revision M
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Measurement System Features
•Discrimination
Ability to tell things apart
•Bias [per AIAG] (Accuracy)
•Repeatability [per AIAG] (Precision)
•Reproducibility
•Linearity
•Stability

44. ©2/2001 Cayman Systems Revision M
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Discrimination
•Readable Increments of Scale
•If Unit of Measure is too course: Process
variation will be lost in Rounding Off
•The “Rule of Ten”: Ten possible values
between limits is ideal
Five Possible Values: Marginally useful
Four or Less: Inadequate Discrimination

45. ©2/2001 Cayman Systems Revision M
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Discrimination

46. ©2/2001 Cayman Systems Revision M
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Range Charts & Discrimination
Indicates
Poor
Precision

47. ©2/2001 Cayman Systems Revision M
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Bias and Repeatability
Precise Imprecise
Accurate
Inaccurate
Bias
You can correct for Bias
You can NOT correct for Imprecision

48. ©2/2001 Cayman Systems Revision M
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Bias
•Difference between
average of measurements
and an Agreed Upon
standard value
•Known as Accuracy
•Cannot be evaluated
without a Standard
•Adds a Consistent “Bias
Factor” to ALL
measurements
•Affects all measurements
in the same way
Standard
Value
Measurement Scale
Bias

49. ©2/2001 Cayman Systems Revision M
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Causes of Bias
•Error in Master
•Worn components
•Instrument improperly calibrated
•Instrument damaged
•Instrument improperly used
•Instrument read incorrectly
•Part set incorrectly (wrong datum)

50. ©2/2001 Cayman Systems Revision M
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Bias and QS9000
Bias - The difference between the observed
Average of measurements and the master
Average of the same parts using precision
instruments. (MSA Manual Glossary)
The auditor may want evidence that the concept of
bias is understood. Remember that bias is basically
an offset from ‘zero’. Bias is linked to Stability in the
sense that an instrument may be ‘zeroed’ during
calibration verification. Knowing this we deduce that
the bias changes with instrument use. This is in part
the concept of Drift.

51. ©2/2001 Cayman Systems Revision M
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Bias
•I choose a caliper (resolution 0.01) for
the measurement. I measure a set of
parts and derive the average.
•I take the same parts and measure
them with a micrometer (resolution
0.001). I then derive the average.
•I compare the two averages. The
difference is the Bias.

52. ©2/2001 Cayman Systems Revision M
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Repeatability
•Variation among
repeated
measurements
•Known as Precision
•Standard NOT
required
•May add or subtract
from a given
measurement
•Affects each
measurement
randomly
Measurement Scale
Repeatability
Margin of Error
Doesn’t address Bias
5.15 = 99%

53. ©2/2001 Cayman Systems Revision M
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Repeatability Issues
•Measurement Steps
Sample preparation
Setting up the instrument
Locating on the part
•How much of the measurement process
should we repeat?

54. ©2/2001 Cayman Systems Revision M
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Using Shewhart Charts I
Repeatability

55. ©2/2001 Cayman Systems Revision M
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Using Shewhart Charts II

56. ©2/2001 Cayman Systems Revision M
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•Same appraiser, Same part, Same instrument
•Multiple readings (n≥10 with 20 to 40 better)
•Analysis
Average minus Standard Value = Bias
5.15* Standard Deviation = Repeatability
or +/- 2.575  [99% repeatability]
or +/- 2  [95% repeatability]
•Histogram
•Probability
Evaluating Bias & Repeatability
AIAG
True

57. ©2/2001 Cayman Systems Revision M
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Repeatability Issues
•Making a measurement may involve
numerous steps
Sample preparation
Setting up the instrument
Locating the part, etc.
•How much of the measurement process
should we repeat? How far do we go?

58. ©2/2001 Cayman Systems Revision M
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Bias & Repeatability Histogram
Never include assignable cause errors

59. ©2/2001 Cayman Systems Revision M
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Linearity
•The difference in the Bias or Repeatability across
the expected operating range of the instrument.

60. ©2/2001 Cayman Systems Revision M
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Plot Biases vs. Ref. Values
Linearity = |Slope| * Process Variation = 0.1317*6.00 = 0.79
% Linearity = 100 * |Slope| = 13.17%

61. ©2/2001 Cayman Systems Revision M
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Causes of Poor Linearity
•Instrument not properly calibrated at
both Upper and Lower extremes
•Error in the minimum or maximum
Master
•Worn Instrument
•Instrument design characteristics

62. ©2/2001 Cayman Systems Revision M
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Reproducibility
•Variation in the
averages among
different
appraisers
repeatedly
measuring the
same part
characteristic
•Concept can also
apply to variation
among different
instruments
Includes repeatability which must be accounted for.

63. ©2/2001 Cayman Systems Revision M
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Reproducibility Example

64. ©2/2001 Cayman Systems Revision M
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Calculating Reproducibility (I)
•Find the range of the appraiser averages (R0)
•Convert to Standard Deviation using d2*
(m=# of appraisers; g=# of ranges used = 1)
•Multiply by 5.15
•Subtract the portion of this due to
repeatability

65. ©2/2001 Cayman Systems Revision M
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Calculating Reproducibility
People variance
Trials
Times done

66. ©2/2001 Cayman Systems Revision M
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Stability
•Variation in
measurements of a
single
characteristic
•On the same
master
•Over an extended
period of time
•Evaluate using
Shewhart charts

67. ©2/2001 Cayman Systems Revision M
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Evaluate Stability with Run Charts

68. ©2/2001 Cayman Systems Revision M
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Stability
Both gages are stable, but.....

69. ©2/2001 Cayman Systems Revision M
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Importance of Stability
•Statistical stability, combined with subject-
matter knowledge, allows predictions of
process performance
•Action based on analysis of Unstable systems
may increase Variation due to ‘Tampering’
•A statistically unstable measurement system
cannot provide reliable data on the process

70. ©2/2001 Cayman Systems Revision M
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Methods of Analysis

71. ©2/2001 Cayman Systems Revision M
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Analysis Tools
•Calculations of Average and Standard
Deviation
•Correlation Charts
•Multi-Vari Charts
•Box-and-Whisker Plots
•Run charts
•Shewhart charts

72. ©2/2001 Cayman Systems Revision M
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Average and Standard Deviation

73. ©2/2001 Cayman Systems Revision M
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Correlation Charts
Describe Relationships
•Substitute measurement for desired
measurement
•Actual measurement to reference value
•Inexpensive gaging method versus
Expensive gaging method
•Appraiser A with appraiser B

74. ©2/2001 Cayman Systems Revision M
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Substitute Measurements
•Cannot directly
measure quality
•Correlate
substitute measure
•Measure substitute
•Convert to desired
quality

75. ©2/2001 Cayman Systems Revision M
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Comparing Two Methods
•Two
methods
•Measure
parts using
both
•Correlate
the two
•Compare to
“Line of No
Bias”
•Investigate
differences
Magnetic
Stripping
Line of Perfect Agreement
Line of Correlation

76. ©2/2001 Cayman Systems Revision M
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Measurements vs. Reference Data

77. ©2/2001 Cayman Systems Revision M
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Measurements vs. Reference Correlation
Disparity

78. ©2/2001 Cayman Systems Revision M
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Comparing Two Appraisers

79. ©2/2001 Cayman Systems Revision M
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Run Charts Examine Stability

80. ©2/2001 Cayman Systems Revision M
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Multiple Run Charts
More than 3 appraisers confuses things...

81. ©2/2001 Cayman Systems Revision M
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Multi-Vari Charts
High Reading
Low Reading
Average Reading
•Displays 3 points
•Length of bar; bar-to-bar;
Bar cluster to cluster
•Plot High and Low
readings as Length of bar
•Each appraiser on a
separate bar
•Each piece in a separate
bar cluster

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Multi-Vari Type I
•Bar lengths
are long
•Appraiser
differences
small in
comparison
•Piece-to-
piece hard to
detect
•Problem is
repeatability

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Multi-Vari Type II
•Appraiser
differences are
biggest source
of variation
•Bar length is
small in
comparison
•Piece-to-piece
hard to detect
•Problem is
reproducibility

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Multi-Vari Type III
•Piece-to-piece
variation is the
biggest source of
variation
•Bar length
(repeatability) is small
in comparison
•Appraiser differences
(bar-to-bar) is small
in comparison
•Ideal Pattern

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Multi-Vari Chart Example
Normalized Data

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Multi-Vari Chart, Joined
Look for similar pattern

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Using Shewhart Charts
•Subgroup = Repeated measurements,, same piece
•Different Subgroups = Different pieces and/or
appraisers
•Range chart shows precision (repeatability)
•Average chart “In Control” shows reproducibility
If subgroups are different appraisers
•Average chart shows discriminating power
If subgroups are different pieces
(“In Control” is BAD!)

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Shewhart Charts
This is not a good way to plot this data
Too many lines

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Shewhart Chart of Instrument

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Gage R&R Studies

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Gauge R&R Studies
•Developed by Jack Gantt
•Originally plotted on probability paper
•Revived as purely numerical
calculations
•Worksheets developed by AIAG
•Renewed awareness of Measurement
Systems as ‘Part of the Process’
Consider Numerical vs. Graphical Data Evaluations

92. ©2/2001 Cayman Systems Revision M
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Terms Used in R&R (I)
•n = Number of Parts [2 to 10]
Parts represent total range of process variation
Need not be “good” parts. Do NOT use consecutive pieces.
Screen for size
•a = Number of Appraisers
Each appraiser measures each part r times
Study must be by those actually using
•R - Number of trials
–Also called “m” in AIAG manual
•g = r*a [Used to find d2* in table 2, p. 29 AIAG manual]
1 Outside Low/High
1 Inside Low/High
Target
Minimum of 5.
2 to 10 To accommodate
worksheet factors
12
3
45

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•R-barA = Average range for appraiser A, etc.
•R-double bar = Average of R-barA, R-barB
•Rp = Range of part averages
•XDIFF = Difference between High & Low appraiser
averages
Also a range, but “R” is not used to avoid confusion
•EV = 5.15 = Equipment variation (repeatability)
•EV = 5.15 = Equipment variation (reproducibility)
•PV = Part variation
•TV = Total variation
Terms Used in R&R (II)
Process Variation

94. ©2/2001 Cayman Systems Revision M
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R&R Calculations
Measurement
System Variation
Product Process
Variation
Left over
Repeatability
Remember -
Nonconsecutive
Pieces
Left over
Repeatability

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Accumulation of Variances

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Evaluating R&R
•%R&R=100*[R&R/TV] (Process Control)
•%R&R=100*[R&R/Tolerance] (Inspection)
•Under 10%: Measurement System Acceptable
•10% to 30%: Possibly acceptable,
depending upon use, cost, etc.
•Over 30%: Needs serious improvement

97. ©2/2001 Cayman Systems Revision M
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Analysis of Variance I
•Mean squares and Sums of squares
•Ratio of variances versus expected F-ratio
•Advantages
Any experimental layout
Estimate interaction effects
•Disadvantages
Must use computer
Non-intuitive interpretation

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Analysis of Variance II
•The n*r measurements must be done in
random sequence [a good idea anyway]
•Assumes that EV [repeatability] is normal
and that EV is not proportional to
measurement [normally a fairly good
assumption]
•Details beyond scope of this course

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Special Gauging Situations
•Go/No-Go
•Destructive Testing

100. ©2/2001 Cayman Systems Revision M
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If Gauges were Perfect

101. ©2/2001 Cayman Systems Revision M
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But Repeatability Means We Never
Know The Precise Value

102. ©2/2001 Cayman Systems Revision M
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So - Actual Part Acceptance Will Look Like
This:

103. ©2/2001 Cayman Systems Revision M
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The Effect of Bias on Part Acceptance

104. ©2/2001 Cayman Systems Revision M
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Go/No-Go gauges
•Treat variables like attributes
•Provide less information on the process,
but...
•Are fast and inexpensive
•Cannot use for Process Control
•Can be used for Sorting purposes

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“Short” Go/No-Go Study
•Collect 20 parts covering the entire
process range
•Use two inspectors
•Gage each part twice
•Accept gauge if there is agreement on
each of the 20 parts
* May reject a good measuring system

106. ©2/2001 Cayman Systems Revision M
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Destructive Tests
•Cannot make true duplicate tests
•Use interpenetrating samples
•Compare 3 averages
•Adjust using √n

107. ©2/2001 Cayman Systems Revision M
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Destructive Tests: Interpreting Samples
AIAG does not address

108. ©2/2001 Cayman Systems Revision M
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Summary

109. ©2/2001 Cayman Systems Revision M
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Measurement Variation
•Observed variation is a combination of
the production process PLUS the
measurement process
•The contribution of the measurement
system is often overlooked

110. ©2/2001 Cayman Systems Revision M
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Types of Measurement Variation
•Bias (Inaccuracy)
•Repeatability (Imprecision)
•Discrimination
•Linearity
•Stability

111. ©2/2001 Cayman Systems Revision M
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Measurement Systems
•Material
•Characteristic
•Sampling and Preparation
•Operational Definition of Measurement
•Instrument
•Appraiser
•Environment and Ergonomics

112. ©2/2001 Cayman Systems Revision M
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Measurement Systems Evaluation Tools
•Histograms
•Probability paper
•Run Charts
•Scatter diagrams
•Multi-Vari Charts
•Gantt “R&R” analysis
•Analysis of Variance (ANOVA)
•Shewhart “Control” Charts

113. ©2/2001 Cayman Systems Revision M
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Shewhart Charts
•Range chart shows repeatability
•X-bar limits show discriminating power
•X-double bar shows bias
(if a known standard exists)
•Average chart shows stability
(sub-groups overtime)
•Average chart shows reproducibility
(sub-groups over technicians/instruments)

114. ©2/2001 Cayman Systems Revision M
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Conclusion
•Rule of Ten
•Operating Characteristic Curve
•Special Problems
Go/No-Go Gages
Attribute Inspection
Destructive Testing