The objective of this award is to develop a paradigm for identifying and visualizing complex part-to-part variation patterns in high-dimensional, spatially dense optical coordinate measuring machine (OCMM) data. OCMMs for noncontact dimensional metrology using laser and/or vision systems are one of the most promising emerging measurement technologies for quality control in discrete parts manufacturing. They produce large volumes of profile, point cloud, and high resolution image data that represent parametric and nonparametric surface geometry characteristics. The emphasis of this award on part-to-part variation is fundamentally different than the emphasis of current OCMM data analysis software, which fits geometric features separately to individual parts. To create the proposed paradigm for understanding variation, a manifold learning framework for identifying and visualizing the variation patterns will be developed, addressing challenges that include simultaneously identifying nonparametric and parametric variation patterns, handling measurement noise structure that differs from what is typically assumed in manifold learning, transforming OCMM data to reduce the extent of nonlinearity in the patterns, and handling heterogeneous data types obtained at different process stages.
If successful, the results of this research will provide a powerful tool to facilitate the discovery and elimination of major root causes of manufacturing variation. Many discrete parts manufacturing industries invest heavily in OCMM equipment but lack knowledge discovery tools for fully utilizing the technology to understand part-to-part variation. This research will fill a critical void by creating a methodology for more effectively analyzing high-dimensional, spatially dense OCMM data. It will provide more sophisticated, badly needed variation reduction tools suitable for manufacturing six-sigma programs that employ modern OCMM technology, which will increase the competitiveness of US manufacturers. It will also allow for a greater return on investment in OCMM technology, which is expected to increase demand for the hardware/software systems and spark further advances in the technology.
