Msa la
-
Upload
paul-robere -
Category
Technology
-
view
361 -
download
3
description
Transcript of Msa la
Measurement Systems AnalysisISO TS 16949:2002 Lead Auditor Course
2
Course Objectives
• By the end of the course the participant should be able to;– Understand how to Audit the
requirements of MSA Identify what constitutes a
Measurement Systems Analysis Complete and understand all types of
Measurement Systems Analysis
3
Measurement Systems Analysis
ISO TS 16949 requires a Measurement Systems Analysis be conducted on all inspection, measuring and test devices
denoted on the Control plan.
4
Measurement Systems Analysis
• What is Measurement Systems Analysis (MSA)?– A Measurement System Analysis (MSA)
determines the error in the measuring device in comparison to the tolerance.
5
Measurement Systems Analysis
• Measurement Systems Analysis (MSA) consists of?
– Gauge Repeatability– Gauge Reproducibility– Bias – Linearity– Stability
6
Measurement Systems Analysis
• So what is Gauge R&R?Gauge R&R is an acronym
for Gauge
Repeatability and
Reproducibility
7
Measurement Systems Analysis
• Definition of Gauge Repeatability– Repeatability
• The ability of a measurement device to repeat its reading when used several times by the same operator to measure the same characteristic. Generally this is
referred to as Equipment variation.
– Repeatability = Equipment Variation
8
Measurement Systems Analysis
• Definition of Gauge Reproducibility– Reproducibility
• The variation between the averages of the measurements taken by different operators using the same measurement device and measuring the same characteristic. Generally this is referred to as Operator Variation
Reproducibility = Operator Variation
9
Measurement Systems Analysis
• There are three types of Gauge R&R studies– Variable - Short Method (Range method)
– Variable - Long Method (Average & Range method)
– Attribute Gauge study
10
Measurement Systems AnalysisVariable - Short Method (Range method)
• Step 1– Obtain 2 operators and 5 parts for this study
• Step 2– Each operator is to measure the product once
and record their findings e.g.
Part # Operator A Operator B1 1.75 1.702 1.75 1.653 1.65 1.654 1.70 1.705 1.70 1.65
11
Measurement Systems Analysis Variable - Short Method (Range method)
• Step 3– Calculate the range e.g.
Part # Operator A Operator B Range1 1.75 1.70 0.052 1.75 1.65 0.103 1.65 1.65 0.004 1.70 1.70 0.005 1.70 1.65 0.05
12
Measurement Systems Analysis Variable - Short Method (Range method)
• Step 4– Determine the average range and calculate the
% Gauge R&R e.g.
A v e r a g e R a n g e ( R ) = R i / 5 = 0 . 2 0 / 5 = 0 . 0 4
T h e f o r m u l a t o c a l c u l a t e t h e % R & R i s ;
% R & R = 1 0 0 [ R & R / T o l e r a n c e ]
w h e r e R & R = 4 . 3 3 ( R ) = 4 . 3 3 ( 0 . 0 4 ) = 0 . 1 7 3 2
a s s u m i n g t h a t t h e t o l e r a n c e = 0 . 5 u n i t s
% R & R = 1 0 0 [ 0 . 1 7 3 2 / 0 . 5 ] = 3 4 . 6 %
13
Measurement Systems Analysis Variable - Short Method (Range method)
• Step 5– Interpret the result
• The acceptance criteria for variable Gauge R&R studies is that the % R&R is below 30%
• Based on the results obtained the measurement error is to large and we therefore must review the measurement device and techniques employed.
• Measurement device is unsatisfactory
14
Measurement Systems Analysis Variable - Long Method (Range method)
• Step 1– Record all preliminary information onto the form
e.g.Part Name: Engine mount Characteristic: Hardness Tolerance: 10 unitsPart Number: 92045612 Gauge Name/Number: QA1234 Date: 27 September 1995Calculated by: John Adamek Operator names: Operator A, Operator B, Operator C
Operator A Operator B Operator CSample Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range
12345678910
Total
Measurement Systems Analysis Variable - Long Method (Range method)
• Step 2– Choose 2 or 3 operators and have each operator
measure 10 parts randomly 2 or 3 times - Enter these results on to the form
Part Name: Engine mount Characteristic: Hardness Tolerance: 10 unitsPart Number: 92045612 Gauge Number: QA 1234 Date: 27 September 1995Calculated by: John Adamek Operator names: Operator A, Operator B, Operator C
Operator A Operator B Operator CSample Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range
1 75 75 74 76 76 75 76 75 752 73 74 76 76 75 75 75 76 763 74 75 76 76 75 76 74 76 764 74 75 74 75 75 74 74 74 745 75 74 74 74 74 76 76 75 746 76 75 75 74 74 76 76 76 767 74 77 75 76 75 74 75 75 748 75 74 75 75 74 74 75 74 769 76 77 77 74 76 76 74 74 7610 77 77 76 76 74 75 75 76 74
Total
16
Measurement Systems Analysis Variable - Long Method (Range method)
• Step 3– Calculate the ranges and the averages e.g.
Part Name: Engine mount Characteristic: Hardness Tolerance: 10 unitsPart Number: 92045612 Gauge Number: QA 1234 Date: 27 September 1995Calculated by: John Adamek Operator names: Operator A, Operator B, Operator C
Operator A Operator B Operator CSample Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range
1 75 75 74 1 76 76 75 1 76 75 75 12 73 74 76 3 76 75 75 1 75 76 76 13 74 75 76 2 76 75 76 1 74 76 76 24 74 75 74 1 75 75 74 1 74 74 74 05 75 74 74 1 74 74 76 2 76 75 74 26 76 75 75 1 74 74 76 2 76 76 76 07 74 77 75 3 76 75 74 2 75 75 74 18 75 74 75 1 75 74 74 1 75 74 76 29 76 77 77 1 74 76 76 2 74 74 76 210 77 77 76 1 76 74 75 2 75 76 74 2
Average 74.9 75.3 75.2 75.2 74.8 75.1 75.0 75.1 75.1
17
Measurement Systems Analysis Variable - Long Method (Range method)
• Step 4– Calculate the average of the averages then determine the
maximum difference and then determine the average of the average ranges e.g..
Operator A Operator B Operator CSample Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range1 75 75 74 1 76 76 75 1 76 75 75 12 73 74 76 3 76 75 75 1 75 76 76 13 74 75 76 2 76 75 76 1 74 76 76 24 74 75 74 1 75 75 74 1 74 74 74 05 75 74 74 1 74 74 76 2 76 75 74 26 76 75 75 1 74 74 76 2 76 76 76 07 74 77 75 3 76 75 74 2 75 75 74 18 75 74 75 1 75 74 74 1 75 74 76 29 76 77 77 1 74 76 76 2 74 74 76 210 77 77 76 1 76 74 75 2 75 76 74 2
Average 74.9 75.3 75.2 1.5 75.2 74.8 75.1 1.5 75.0 75.1 75.1 1.3
X
X
X
X
A
B
C
diff
= ( 74.9+75.3 +75.2)/3 = 75.1 R=average of the average ranges
= ( 75.2+74.8+75.1)/3 = 75.0 R = (1.5+1.5+1.3)/3 = 1.43
= ( 75.0+75.1+75.1)/3 = 75.1
= Xmax -Xmin = 75.1-75.0 = 0.1
18
Measurement Systems Analysis Variable - Long Method (Range method)
• Step 5– Calculate the UCLR and discard or repeat any readings
with values greater than the UCLR – Since there are no values greeter than 3.70, continue
* RUCL = R x D4 = 1.43 x 2.58 = 3.70
19
Measurement Systems Analysis Variable - Long Method (Range method)
• Step 6– Calculate the equipment variation using the following
formula;
R e p e a t a b i l i t y - E q u i p m e n t V a r i a t i o n ( E . V . )
E . V . = R x K % E . V . = 1 0 0 [ ( E . V . ) / ( T O L ) ]
E . V . = 1 . 4 3 x 3 . 0 5 % E . V . = 1 0 0 [ ( 4 . 3 6 ) / ( 1 0 ) ]
E . V . = 4 . 3 6 % E . V . = 4 3 . 6 %
1
T r i a l s 2 3K 1 4 . 5 6 3 . 0 5
20
Measurement Systems Analysis Variable - Long Method (Range method)
• Step 7– Calculate the Operator Variation using the following
formula;
Reproducibilty - Operator Variation (O. V. )
O. V. = (X x K E. V) N x R)] %O. V. = 100[(O. V. ) / (TOL)]
O. V. = (0.1 x 2.7) - [(4.36) / (10 x 3)] %O. V. = 100 [(0.0) / (10)]
O. V. = 0 %O. V. = 0.0%
diff 2
2 2
22) [( / (
# Operators 2 3K2 3.65 2.70
21
Measurement Systems Analysis Variable - Long Method (Range method)
• Step 8– Calculate the Repeatability and Reproducibility using the
following formula;
R e p e a t a b i l i t y a n d R e p r o d u c i b i l i t y ( R & R )
R & R = ( E . V . ) + ( A . V . ) % R & R = 1 0 0 [ ( R & R ) / ( T O L ) ]
R & R = ( 4 . 3 6 ) + ( 0 . 0 ) % R & R = 1 0 0 [ ( 4 . 3 6 ) / ( 1 0 ) ]
R & R = 4 . 3 6 % R & R = 4 3 . 6 %
2 2
2 2
22
Measurement Systems Analysis Variable - Long Method (Range method)
• Step 9– Interpret the results;
• The gauge %R&R result is greater than 30% therefore it is unacceptable
• The operator variation is zero and therefore we can conclude that the error due to operators is insignificant
• The focus on achieving an acceptable % Gauge R&R must be on the equipment
23
Measurement Systems Analysis Attributes Gauge study
• The purpose of any gauge is to detect nonconforming product. If it is able to detect nonconforming product it is acceptable, otherwise, the gauge is unacceptable
• An attributes Gauge study cannot quantify how “good” the gauge is, but only whether the gauge is acceptable or not.
24
Measurement Systems Analysis Attributes Gauge study
• Methodology - Step1– Select 20 parts. When selecting these
parts ensure that a sample (say 2-6) are slightly below or above the specification.
• Step 2– Number them. Preferably in a area that is
not noticeable to the operator, if this is possible.
25
Measurement Systems Analysis Attributes Gauge study
• Step 3– Two operators measure the parts twice.
Ensure the parts are randomised to prevent bias.
• Step 4– Record the results
• Step 5 – Assess capability of gauge
26
Measurement Systems Analysis Attributes Gauge study
• Acceptance criteria– The gage is acceptable if all
measurement decisions agree i.e. all four measurements must be the same
Refer to example on next page
27
Measurement Systems Analysis Attributes Gauge study - Example
Part Name: Rubber Hose I.D. Gauge Name/ID: Go/No-Go GaugePart number: 92015623 Date: 3 October 1995
Operator A Operator BTrial 1 Trial 2 Trial 1 Trial 2
1 G G G G
2 NG NG NG NG
3 NG NG G G
4 G G G G
5 G G G G
6 NG NG NG NG
7 NG G G NG
8 G G G G
9 G G G G
10 G G G G
11 G G G G
12 NG NG NG G
13 G G NG G
14 G G G G
15 G G G G
16 G G G G
17 G G G G
18 G G G G
19 G G G G
20 NG NG NG NG
Result: Acceptable/Unacceptable
Interpretation of results
1. Assume parts 2,3,6,12 and 20 were the nonconforming parts.
2. The gauge detected part #2 as nonconforming.
3. Although part #3 is also nonconforming Operator B did not detect this. Therefore the gauge is unacceptable
4. Part #6 was nonconforming. this was detected by both operators.
5. Part #7 was acceptable but it was found to be nonconforming using the gauge by both operators once.
6.
28
Measurement Systems Analysis Bias
Definition of Bias
Bias is defined as the
difference
between the average measured value
and
the true value.
29
Measurement Systems Analysis Bias
• Bias is related to accuracy, in that, if the average measured value is the same or approximately the same, there is said to be zero bias and therefore the gauge being used is “accurate”.
30
Measurement Systems Analysis Linearity
• Definition of Linearity
Linearity is defined as the difference in the bias values of a gauge
through the expected operating range of the gauge.
31
Measurement Systems AnalysisStability
• Definition of Stability
Stability is defined as the difference in process variation over a period of
time.
32
Sample calculations
• For– Bias– Linearity– Stability
33
Measurement Systems AnalysisDetermining the amount of Bias with an example
Step 1. Obtain 50 or more measurements
Example: A micrometer is used to measure the diameter of a pin produced by an automatic machining process. The true value of the pin is 1 inch. The resolution of the micrometer is 0.0050 inches. All of the readings in table 1 are deviations from the standard value in 0.0010 increments
Ref: Pyzdeks guide to SPC. Vol 2
34
Measurement Systems Analysis Determining the amount of Bias with an example
Table 1
-50 -50 0 50 -50 -100 0 -50 -150 0
50 -100 -50 0 0 0 100 -100 -100 -50
-50 -100 0 -50 50 0 0 0 -100 0
0 -100 -100 -50 -100 -50 0 0 -50 -100
100 50 50 -50 0 -50 -50 0 -50 0
35
Measurement Systems Analysis Determining the amount of Bias with an example
Step 2.
If all of the readings are equal to the true value, then there is no bias and the gauge is accurate. If all of the reading are identical but are not the same as the true value, then bias exist, to identify the level of bias and whether it is acceptable we continue.
36
Measurement Systems Analysis Determining the amount of Bias with an example
Step 3. Determine the moving ranges based on the data from table 1.
None 150 50 0 0 100 50 0 150 50
100 50 50 50 50 100 100 50 50 50
100 0 50 50 50 0 100 100 0 50
50 0 100 0 150 50 0 0 50 100
100 150 150 0 100 0 50 0 50 100
0
37
Measurement Systems Analysis Determining the amount of Bias with an example
Step 4 Prepare a frequency tally for the moving ranges. In this example each gauge increment will equal one cell i.e.
Range Frequency Cum. Freq. Cum. Freq %
0 14 14 28.6%
50 18 32 65.3
100 12 44 89.8
150 5 49 100.0
38
Measurement Systems Analysis Determining the amount of Bias with an example
Step 5. Determine the “cut off” point using the following equation;
cut off = value of cell that put count above 50% + value of next cell
2.0
cut off = (50 + 100)/2 = 75.0
39
Measurement Systems Analysis Determining the amount of Bias with an example
• Step 6. Calculate the cut off portion using the following equation;
17.0667.98
167.17
32
49) x (2
61
17
32
count x total2
61
count remaining portion offcut
40
Measurement Systems Analysis Determining the amount of Bias with an example
• Step 7. Determine the Equivalent Gaussian Deviate (EGD) that corresponds to the cut off portion.
From Statistical tables, the EGD = 0.95
41
Measurement Systems Analysis Determining the amount of Bias with an example
• Step 8. Determine the estimated standard deviation;
8.55 95.02
75
2 ˆ
EGD
cutoff
42
Measurement Systems Analysis Determining the amount of Bias with an example
• Step 9. Calculate the Control Lines
only. deviations shows data
recorded thesince zero, is value trueThe :Note
4.1678.5530ˆ3
4.1678.5530ˆ3
truevalueUCL
truevalueLCL
bias
bias
43
Measurement Systems Analysis Determining the amount of Bias with an example
• Step 10. Plot the chart - Individual & Moving Range.
Refer to chart
44
Measurement Systems Analysis Determining the amount of Bias with an example
• Step 11. Interpret the chart.
• If all of the points fall within the Control lines we conclude that the gauge is accurate and the bias that does exist has no effect
45
Measurement Systems Analysis Determining the amount of Bias with an example
• Step 11. Interpret the chart cont..
If points were found outside of the control lines it could be concluded that their exists a “special” cause which may be the source of variation
CONTROL CHART INDIVIDUALS & MOVING RANGE (X-MR) – Bias Example
200UCL
5
0.0
-100
LCL-200
Moving Range readings
150
100
50
DATETIME1 -50 50 -50 0 100 -50 -100 -100 -100 50 0 -50 0 -100 50 50 0 -50 -50 -50 -50 0 50 -100 0 -100 0 0
Moving range 100 100 50 100 150 50 0 0 150 50 50 50 100 150 0 50 50 0 0 0 50 50 150 100 100 100 0
* For sample sizes of less than seven, there is no lower control limit for ranges.
47
Measurement Systems Analysis Linearity
• Definition of Linearity
Linearity is defined as the difference in the bias values of a gauge
through the expected operating range of the gauge.
48
Measurement Systems AnalysisExample of how to determine Linearity
• Linearity Example:
• An Engineer was interested in determining the linearity of a measurement system. The operating range of the gauge ranged from 2.0 mm to 10.0 mm.
49
Measurement Systems AnalysisExample of how to determine Linearity
• Step 1
• Select a minimum of 5 parts to be measured at least 10 times each. For this example we will select 5 parts and measure each part 12 times.
• Refer to the following page for data.
50
Measurement Systems AnalysisExample of how to determine Linearity
• Part 1 Part 2 Part 3 Part 4 Part 5
Ref. value 2.00 4.00 6.00 8.00 10.00Meas. 1 2.70 5.10 5.80 7.60 9.10Meas. 2 2.50 3.90 5.70 7.70 9.30Meas. 3 2.40 4.20 5.90 7.80 9.50Meas. 4 2.50 5.00 5.90 7.70 9.30Meas. 5 2.70 3.80 6.00 7.80 9.40Meas. 6 2.30 3.90 6.10 7.80 9.50Meas. 7 2.50 3.90 6.00 7.80 9.50Meas. 8 2.50 3.90 6.10 7.70 9.50Meas. 9 2.40 3.90 6.40 7.80 9.60Meas. 10 2.40 4.00 6.30 7.50 9.20Meas. 11 2.60 4.10 6.00 7.60 9.30Meas. 12 2.40 3.80 6.10 7.70 9.40
51
Measurement Systems AnalysisExample of how to determine Linearity
• Step 2
• Calculate the;– Part Average– Bias– Range
Refer to the following page
52
Measurement Systems AnalysisExample of how to determine Linearity
Part 1 Part 2 Part 3 Part 4 Part 5Ref. value 2.00 4.00 6.00 8.00 10.00Meas. 1 2.70 5.10 5.80 7.60 9.10Meas. 2 2.50 3.90 5.70 7.70 9.30Meas. 3 2.40 4.20 5.90 7.80 9.50Meas. 4 2.50 5.00 5.90 7.70 9.30Meas. 5 2.70 3.80 6.00 7.80 9.40Meas. 6 2.30 3.90 6.10 7.80 9.50Meas. 7 2.50 3.90 6.00 7.80 9.50Meas. 8 2.50 3.90 6.10 7.70 9.50Meas. 9 2.40 3.90 6.40 7.80 9.60Meas. 10 2.40 4.00 6.30 7.50 9.20Meas. 11 2.60 4.10 6.00 7.60 9.30Meas. 12 2.40 3.80 6.10 7.70 9.40Average 2.49 4.13 6.03 7.71 9.38Bias +0.49 +0.13 +0.03 -0.29 -0.62Range 0.4 1.3 0.7 0.3 0.5
53
Measurement Systems AnalysisExample of how to determine Linearity
• Step 3
Plot the bias vs Reference value
refer to next page..
54
Measurement Systems AnalysisExample of how to determine Linearity
Linearity Plot
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
2 4 6 8 10
Reference Value
Bia
s
55
Measurement Systems AnalysisExample of how to determine Linearity
• Step 4. Determine from the graph whether a linear relationship exists between the bias and reference values. If a “good” linear relationship exists then the % linearity can be calculated. If a linear relationship does not exist, then we must look at other sources of variation.
56
Measurement Systems AnalysisExample of how to determine Linearity
• Step 5 Calculate the Linearity, using;
%17.13 variationprocess
Linearity100 %linearity
0.79 6.000.1317 var linearity
98.0 fit of goodness
7367.0n
y b 0.1317-
1
.,, where;
2
2
2
2
2
2
22
iationprocessslope
n
yy
n
xx
n
yxxy
R
n
xaslope
xn
x
n
yxxy
a
valuerefxslopeabiasyaxby
57
Measurement Systems AnalysisStability
• Definition of Stability
Stability is defined as the difference in process variation over a period of
time.
58
Measurement Systems AnalysisStability
• To calculate stability use the following steps;
• Step 1.
Obtain a master sample and establish its reference value(s)
• Step 2
On a periodic basis measure the master sample five times.
59
Measurement Systems AnalysisStability
• Step 3Plot the data on an Xbar and R chart• Step 4Calculate the Control limits and
evaluate for any out of control conditions
• Step 5If out of control conditions exist, the
measurement system is not stable.
60
Auditing MSA
1. Does the organisation conduct an MSA on all IMTE denoted in the Control Plan
2. Is the acceptance criteria for Gauge R&R met?
3. Where it is not met, what actions have taken place?
4. Have these been communicated to the customer?
5. What mechanism is in place to ensure all new IMTE undergoes a MSA study?
6. Does the organisation conduct attribute Gauge studies on subjective characteristics?
61
Auditing MSA
7. Verify that the calculations are correct for a number of Gauge R&R studies
8. Ensure the correct tolerance is used for the algorithm
9. Does the organisation consider the capability of the existing IMTE during APQP and any new IMTE for new parts/projects?