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Advanced manufacturing processes and industrial autonomy provide new opportunities for the manufacturing industry by providing a data-rich environment that can facilitate the connection between the physical, digital, and biological worlds. However, it becomes difficult to model and make inferences from this data that is often characterized by complex structures. The increasing need for high-precision measurements, fast and robust decision-making, and high levels of automation make this even more complicated. In this talk, we first focus on the modeling of the three-dimensional point cloud, which is ubiquitous in reverse engineering (RE) and additive manufacturing (AM). In order to add “intelligence” to the process, a novel statistical data analysis framework, volumetric data analysis (VDA), is developed to extract process knowledge of RE and AM from the 3D point cloud samples, with the goal of bridging the gap between complex closed-shape 3D data structures and advanced multivariate statistical learning methodologies. The essential technical foundation and major algorithms of the VDA framework will be provided and then applied to solve process planning, variation modeling, and tolerancing problems in reverse engineering and geometric metrology. Furthermore, the VDA framework is applied for precision analysis and quality improvement in freeform additive manufacturing. Finally, we will detail our recent work on incorporating human knowledge and AI, such as knowledge graphs, computer vision, and robotics, to increase the intelligence for advanced manufacturing systems.
Bio: Zhaohui Geng is currently an Assistant Professor in the Department of Manufacturing and Industrial Engineering at The University of Texas Rio Grande Valley. He leads the Cyber Manufacturing and Smart, Connected Systems division at the UTRGV Center for Advanced Manufacturing Innovation and Cyber Systems. He is also a core member of the Smart Manufacturing and Cyber Systems concentration in the Mathematics and Statistics with Interdisciplinary Applications program at UTRGV. His research interests focus on the intersection of data science, artificial intelligence, and optimization, with applications in reverse engineering, additive manufacturing, metrology, and production systems. Zhaohui received his Ph.D. in Industrial Engineering (2021), M.A. in Statistics (2018), and M.S. in Industrial Engineering (2016) from the University of Pittsburgh, and Bachelor of Engineering in Electronic Science and Technology from Nankai University, China.