Objective Read World Uncertainty Analysis CMSC 2003 July Tim Nielsen Scott Sandwith

Introduction  Confidently optimize production processes against their requirements  Inputs vs. Outputs  Need to simulate process performance to optimize accuracy, speed, and costs  Need reliable (easy to understand) uncertainty estimates for complex 3D measurements on the factory floor  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

Process Description  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

Background: Uncertainty  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 Taylor and Kuyatt, 1994: NIST TN/1297

Background: Uncertainty  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

Monte Carlo  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

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

Wing to Body Join Application  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: 2   Features: 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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Acknowledgements  John Palmateer (Boeing)  Dr. Joe Calkins (New River Kinematics)

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