MAE Seminar – Applications of AI/ML to Advanced Materials and Structures Manufacturing

Details

MAE Seminar – Applications of AI/ML to Advanced Materials and Structures Manufacturing

Thursday, April 18, 2024, at 12:50pm, Location: MAE-A 303

Chuck Zhang, H. Milton Stewart School of Industrial & Systems Engineering, George W. Woodruff School of Mechanical Engineering, Georgia Tech Manufacturing Institute, Georgia Institute of Technology, Atlanta, GA

Abstract
The transformation of artificial intelligence (AI) and machine learning (ML) from computer science theory into real-world technologies is a key enabler for the fourth industrial revolution. The AI/ML technology has been increasingly used in advanced manufacturing including product design, digital twins, process monitoring and control, predictive maintenance, quality control, and supply chain management, etc. This seminar presents two research studies on applications of AI/ML to manufacturing of advanced materials and structures. The first one involves personalized heart surgery planning and optimization with integration of advanced materials design, multi-material 3D printing, and machine learning techniques. In this study, a meta-material design approach together with ML was first developed to create a structure that can mimic mechanical behavior of human aortic valves. The tissue-mimicking heart valves were then fabricated using a multi-material 3D printing process. The 3D printed heart valves can be used for AI-driven pre-surgery planning of heart disease treatment and intervention. The second study focuses on the development of a physics-inform ML model for quality prediction of adhesive joints in composite structures. In this work, a novel framework of Physics-Informed Neural Ordinary differential equation with Heterogeneous control Input (PINOHI) is proposed, which links the heterogeneous manufacturing parameters to the final bonding quality of composite joints. The model structure is heavily motivated by engineering knowledge, with incorporation of a calibrated mathematical physics model into the Neural ODE framework, which can significantly reduce the number of data samples required from costly experiments while maintaining high prediction accuracy.

Biography

Dr. Chuck Zhang is Eugene C. Gwaltney, Jr. Chair and Professor in the H. Milton Stewart School of Industrial & Systems Engineering and an adjunct professor at the G.W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. He serves as the Director of the three-university NSF Industry/University Cooperative Research Center (IUCRC) for Composite and Hybrid Materials Interfacing (CHMI). Prior to joining Georgia Tech, he served as Professor and Chair in the Department of Industrial & Manufacturing Engineering at the Florida A&M University-Florida State University College of Engineering. Dr. Zhang received his Ph.D. degree in Industrial Engineering from the University of Iowa. His current research interests include additive manufacturing, advanced composite/nanocomposite manufacturing, composite structures inspection, repair and maintenance, scalable nano-/bio-manufacturing, and application of AI/ML to manufacturing. Dr. Zhang’s research has been sponsored by federal agencies including NSF, NIST, DoD, FDA and VA, as well as numerous industrial companies such as Delta Air Lines, Lockheed Martin, Siemens, and Solvay. He has published over 220 refereed journal articles. He also holds 27 U.S. patents. Dr. Zhang is a Fellow of the Institute of Industrial and Systems Engineers (IISE).

MAE Faculty Host: Dr. Yong Huang