Tim Nielsen Scott Sandwith
CMSC 2003 July 21-25 2003

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Confidently optimize production processes against their requirements
 Inputs vs. Outputs
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Need to simulate process performance to optimize accuracy, speed, and costs
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Need reliable (easy to understand) uncertainty estimates for complex 3D measurements on the factory floor
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Need to estimate the benefits of combining measurement systems
 Common Type Network (n Trackers)
 Hybrid Type Network (Scanners + Trackers etc.)

Need real-time (easy to understand) feedback on measurement system performance

Need traceable measurement uncertainty for each assembly

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Inputs
 Object Characteristics
(e.g., Volume, Surface)
 Expected Tolerances
 Instrument Types and
Number of Stations
 Cycle Time
 Measurement Constraints
(e.g., line-of-sight, targeting the actual critical features)

Outputs
 GUM Compliant
Uncertainty Estimates of
Feature Measurements
 Measurement Plan

Number of Instruments
(Stations)

Types of Instrument

Instrument Placement

Targeting Requirements
 Network/Orientation
Requirements

Transform vs. Bundle

Number of Common Pts

Closure
 Analysis Dependencies


Guide to Uncertainty in Measurement (GUM)
 ISO way to express uncertainty in measurement
 Error and Uncertainty are not the same

Quantify components of Uncertainty
 Type A vs. B depends on the estimation method

A = Statistical Methods (e.g., Monte Carlo, 1 st -order Partials)

B = Other means (e.g., measurements, experience, specs)
 Random vs. Systematic Effects (e.g., Noise vs. Scale)

Both are components estimated with Type A or B methods
 Uncertainty Estimates can contain Type A & B methods

GUM mandates uncertainty statements in order to provide traceability for measurement results

A measurement result is complete only when accompanied by a quantitative statement of its uncertainty 1
1 - Taylor and Kuyatt, 1994: NIST TN/1297

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Specifications
 Instrument specifications are not representative of the results from actual use of the instrument in a network

3D Measurement Networks
 1 Instrument + References
 1 Instrument in multiple locations + References
 n Instruments (types) + References

Application of 3D Measurement Systems
 Real use  multiple stations and different instruments in the same network

Quantify coordinate data uncertainty fields in a network
 Practical methods to estimate the uncertainty of specific systems
 Combining measurement systems
 Combining measurement uncertainties
 Results need to in an easy to understand and meaningful format


What: Non-linear statistical technique

Why: Difficult problems and expensive to state or solve

When: Consequences are expensive

How:
 List of possible conditions (where the activity being studied is to large or complex to be easily stated)
 Random numbers (from estimates of each measured component)
 Model of Network … interactions
 Large number of solution are run
 Statistical inferences are drawn
Monte Carlo technique was developed during World War II in Los Alamos for the atom bomb project


Modeling
 Instruments

Axes

Angles

Ranging

Offsets

Joins
 Measurements

Angles  ppm

Ranges  ppm + offset
 Confidence


Inputs
 CAD Model includes Features,
Relationships, Tolerances

Sweep, Dihedral, Incidence
 Scanners, Trackers, Local
GPS, Robotics, Gap
Measurement Devices
 Production Measurement +
Analysis < 3 minutes
 Aluminum Surface
 Targeted and Pre-measured
Assembly Interface Features
 Transfer critical object control to continuously visible features

Outputs
 Surface: 0.080” @ 2 
 Features: 0.004” @ 2 
 2 Scanners + Local GPS +
GAP Measurement Tool
 Optimized Instrument Location
 Bundle Local GPS and
Transfer to (11) Common Pts
 Local GPS updates at 2 Hz
 Aerodynamically matched orientation within process uncertainty

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