1. No category

Gaining Control of Your
Measurement Process
Using PolyWorks®
www.mvsgroup.com
Discussion Topics
 Motivation
 Extracting a Measured Primitive
 A Computer Experiment
 Experimental Results
 Conclusions and Recommendations
 Questions…
2
MOTIVATION…
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Source: Downloaded from: http://westlife.northcoastnow.com/mccall-bursts-on-scene-for-north-ridgeville/NR-footbal-McCall.jpg, Sept. 8, 2013
Source: Convergence, 1952 by Jackson Pollock Downloaded from: http://www.jackson-pollock.org/convergence.jsp
Upon further
reflection…
“With great power comes
great responsibility.”
– Stan Lee
…or was it Spiderman’s Uncle Ben?...
CREATING A MEASURED
PRIMITIVE
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Extracting a Measured Primitive
1. Collect Data Points
2. Filter the Data Points
3. Fit the Data Points
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COLLECT – Fit Zone
 Max Distance to Nominal Primitive…
RAW DATA
LARGE DISTANCE TO NOMINAL PRIMITIVE
RESULT OF “MAX” FIT
12
COLLECT – Fit Zone
 Max Distance to Nominal Primitive…
LARGE DISTANCE TO NOMINAL PRIMITIVE
RAW DATA
RESULT OF “BEST” FIT
13
COLLECT – Fit Zone
 Max Distance to Nominal Primitive…
RAW DATA
SMALL DISTANCE TO NOMINAL PRIMITIVE
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COLLECT – Large Deviation to Nominal Primitive
 Search Distance
Anything suspicious here?
15
COLLECT – Boundary Exclusion Distance
LOW BOUNDARY EXCLUSION
HIGH BOUNDARY EXCLUSION
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FILTER – Subsampling Step
Computing GD&T Measurements…
18
FILTER – Max Angle
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FILTER – Reject Outliers
 Empirical Rule:
– 68% within 1σ
– 95.5% within 2σ
– 99.73% within 3σ
Outside Std Dev Factor
100% = 64,283
Percentage of Points
Filter at 1σ = 43,097
(or about 67%)
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A COMPUTER EXPERIMENT
22
Computer Experiment
 Computers are deterministic
– replications not required
 4 different subjects (the minions…)
 100 runs per subject
– 50 using “Best-Fit”
– 50 using “Max Fit”
 Modeled 16 different parameters
– 9 continuous variables
– 5 binary variables
– 2 categorical variables
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The Subjects
1. cylinder with form error but
essentially at nominal
2. same cylinder with rotation
error introduced
3. same cylinder with location
error introduced (no rotation)
4. same cylinder with both
location rotation error
– not the same errors as above
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Parameter
Possible
Values
Default
Values
Experimental
Values
greater than 0
On/Off
greater than 0
greater than 0
4mm
Off
4mm
0.5mm
.5mm to 6mm
On/Off
1mm to 8mm
0 to 5mm
On/Off
greater than 0
On/Off
0 to 180
On/Off
0 to 100%
Off
1
On
15 degrees
On
2.5 sigma (~98.75%)
On/Off
0, 0.2, 0.5, 1, 2mm
On/Off*
0 to 180
On/Off*
0 to 31.73%
"Best-Fit", "Min", "Max"
On/Off
On/Off
greater than 0
0 to 180 degrees
greater than 0
"Best-fit"
Off
Off
1mm
15 degrees
50% of radius
"Best-Fit", "Max"
On/Off
On/Off
6mm
0 to 180 degrees
0 to 100% of radius
Collect:
Fit Zone
Large deviation to nominal primitive
Large deviation to nominal primitive - Search Distance
Boundary Exclusion Distance
Filter:
Subsampling Step
Subsampling Step
Max Angle
Max Angle
Reject Outliers
Reject Outliers
Fit:
Fit Type
Constraining plane
Constraint Radius
Constraint Radius Size
Min/Max Fit - Max Angle Deviation
Min/Max Fit - Max Positional Deviation
Latin Hypercube Sampling
180-
6 mm -
0.5 mm -
0-
3.25 mm -
90-
Experiment Macro
 Approximately 100 lines of code
 Read text files containing parameter values from MINITAB®
 Same 50 runs were repeated 8 times
– a single run consisted of unique values for 16 different parameters
 Wrote text files of the results using array variables into
directory to be read back into MINITAB® for analysis
EXPERIMENTAL RESULTS
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BAD FORM
DEFAULT FIT STD DEV: 0.310
MANUAL FIT STD DEV: 0.341
DEFAULT
MANUAL
ROTATED
DEFAULT FIT STD DEV: 0.270
MANUAL FIT STD DEV: 0.341
DEFAULT
MANUAL
OFF LOCATION
DEFAULT FIT STD DEV: 0.280
MANUAL FIT STD DEV: 0.341
DEFAULT
MANUAL
OFF LOCATION AND ROTATED
DEFAULT FIT STD DEV: 0.343
MANUAL FIT STD DEV: 0.341
DEFAULT
MANUAL
6
5
4
3
2
1
0
Fit Type
SAMPLE
BEST MAX
1_BAD FORM
BEST MAX
2_ROTATED
BEST MAX
3_LOCATION
BEST MAX
4_LOC_ROT
6
5
4
3
2
1
0
Planar Const
Fit Type
SAMPLE
0 1
0 1
BEST
MAX
1_BAD FORM
0 1
0 1
BEST
MAX
2_ROTATED
0 1
0 1
BEST
MAX
3_LOCATION
0 1
0 1
BEST
MAX
4_LOC_ROT
CONCLUSIONS AND
RECOMMENDATIONS
37
Visual Interrogation of Measured Data Points
 Select >> Data Points >> Measured Data Points
 Ctrl+M
Other Recommendations
 Consider creating alignments for each individual
“problem” feature
– Select data points using feature primitive
– “Best-Fit” using selected elements
– May require 2 repetitions
 Use Large Deviation
– ensure distance exceeds expected location variation of feature
(at least exceeds tolerance zone)
– ensure fit zone exceeds expected size, form, orientation
variation of feature
 Use sensitivity analysis
– Change parameter settings to see how much the results change
Tools >> Macro Scripts >> Create Play Inspection Macro Script
Thank You!
Questions?