Most major companies invest heavily in advanced measurement and data collection technology for manufacturing quality assurance. One of the most promising measurement technologies that is broadly applicable in discrete parts manufacturing is noncontact dimensional metrology using laser and/or vision systems. Such optical coordinate measuring machines (OCMMs) produce large volumes of profile, point cloud, and high resolution image data that represent parametric and nonparametric surface geometry characteristics. OCMM technology is well developed, and accuracy and throughput are now at levels that render it suitable for quality control of parts with reasonably high precision. There is a large body of existing work on analyzing OCMM data that pertains to fitting specific geometric features for individual parts (e.g., fitting a circle to the perimeter of a drilled hole) for the purpose of verifying the dimensional integrity of the individual parts. However, although there is also a wealth of information on the nature of part-to-part variation buried in the rich and complex structure of the spatially dense OCMM data, there is currently no comprehensive and generic approach for uncovering this information. To fill this void, this research will develop a paradigm for identifying and visualizing complex part-to-part variation patterns in high-dimensional, spatially dense OCMM data, which will provide a powerful tool to facilitate the discovery and elimination of major root causes of manufacturing variation.
|Effective start/end date||8/15/13 → 7/31/17|
- National Science Foundation (CMMI-1265709)
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