The research objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) collaborative research award is to establish a series of quality control methodologies on modeling, monitoring, and diagnosis of spatial point patterns. A spatial point pattern is a set of locations randomly distributed within a designated region. It is a natural way to model many critical quality characteristics in various manufacturing processes, such as the surface defects on steel bars, slabs and semiconductor wafer, and the distribution of reinforce particles in composite materials. The research approach is in three focus areas: (i) Modeling and monitoring of replicated spatial point patterns by integrating the deterministic point pattern alignment methods and spatial statistics techniques; (ii) Identification of the impacts of covariates on replicated point patterns based on a nonparametric functional regression model; and (iii) Geometric and three-dimension point pattern detection by bringing the Hough Transform method, an interesting computer vision method, into the quality control area.
If successful, the results of this research will provide a novel set of quality control tools to various relevant industries such as steel rolling, semiconductor manufacturing, and composite fabrication. These tools will take advantage of the increasingly available spatial point data and help to significantly improve the process productivity and quality. The close collaboration with OG Technologies in this Grant Opportunities for Academic Liaison with Industry project will lead to a realistic testbed and quick dissemination of research results among practitioners, as well as initiation of technology transfer. The interdisciplinary nature of this project can provide students the unique opportunity to obtain training in spatial statistics, manufacturing quality control, and computer vision.
