Attend a seminar on the physics-based and data-driven manufacturing process and product quality control, April 10
About
Robert Gao is the Cady Staley Professor and chair of mechanical and aerospace engineering at Case Western Reserve University in Cleveland, Ohio. Since earning his doctoral degree from the Technical University of Berlin in 1991, he has conducted research in physics-based signal transduction mechanisms, multiresolution signal processing, stochastic modeling, and artificial intelligence and machine learning for manufacturing process and product quality control.
His work has resulted in more than 450 technical publications, including 220 journal articles, 13 patents and two books. He was recognized by Web of Science as a Clarivate “Highly Cited Researcher in the field of engineering” in 2023 and 2024.
Gao is a Fellow of ASME, SME and CIRP, a Life Fellow of IEEE and a Distinguished Fellow of the International Institute of Acoustics and Vibration. His honors include the ASME Milton C. Shaw Manufacturing Research Medal, the ASME Blackall Machine Tool and Gage Award, the SME Gold Medal, the SME Eli Whitney Productivity Award, the IEEE Instrumentation and Measurement Society Technical Award, the IEEE Best Application in Instrumentation and Measurement Award, multiple best paper awards and a National Science Foundation Faculty Early Career Development Program (CAREER) Award.
He has served as chair of the CIRP Collaborative Working Group on AI in Manufacturing, scientific committee chair of the North American Manufacturing Research Institute of SME and is currently NAMRI president-elect. He previously served as a senior editor of the IEEE/ASME Transactions on Mechatronics and as an associate editor for several journals affiliated with ASME, IEEE and IFAC.
Abstract
Rapid advances in data science are creating new opportunities to improve manufacturing process control and quality assurance. This seminar highlights recent research demonstrating the benefits of integrating model-based and data-driven methods for manufacturing applications.
Two case studies illustrate this approach. The first explores the integration of ridgelet transform with a convolutional neural network to characterize machined parts, using noncontact surface roughness evaluation as an example. The second examines Gaussian process-enhanced model predictive control for in-process control of incremental forming of skeletal fixation plates, a key component in craniomaxillofacial reconstructive surgery used to restore patients’ facial structure and function.
Together, these examples demonstrate how convergent research can advance point-of-care manufacturing and improve product quality control.
Physics-Based and Data-Driven Manufacturing Process and Product Quality Control seminar
Friday, April 10, 2026
11 a.m.–noon
Interdisciplinary Science and Technology Building 12 (ITSB12) room 215, Polytechnic campus [map]