Machine Learning-Based Non-Destructive Evaluation of Fatigue Damage in Metals
DISCOVER HOW MACHINE LEARNING OF ULTRASOUND MEASUREMENTS PROMOTES EFFECTIVE NDE OF FATIGUE DAMAGE IN METALS
Join REMADE for an exciting project update webinar looking at non-destructive evaluation (NDE) of fatigue damage in end-of-life metals and exploring how NDE is essential to maximizing the benefits of such materials for remanufacturing. NDE not only enables effective materials screening but also provides valuable information for the process optimization and control of downstream remanufacturing processes.
In this webinar, we will present a novel machine learning-based NDE framework that harnesses linear and nonlinear ultrasound techniques to predict the fatigue damage, remaining useful life, and residual stress. Experimental results will be presented to show the effectiveness of our technology.
Dr. Chenhui Shao, University of Illinois at Urbana-Champaign
Dr. Chenhui Shao is an Assistant Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. He received his B.E. degree in Automation from the University of Science and Technology of China; M.S.E. degree in Industrial and Operations Engineering, M.A. degree in Statistics, and Ph.D. degree in Mechanical Engineering, all from the University of Michigan, Ann Arbor. His research interests focus on smart manufacturing, machine learning, statistics, materials joining, and manufacturing systems control and automation. Dr. Shao has received multiple awards including the NSF CAREER Award, SME Barbara M. Fossum Outstanding Young Manufacturing Engineer Award, SME 30 Under 30 Honoree, and multiple best paper awards and feature articles. He is an associate editor for the Journal of Manufacturing Processes.
Dr. Jingjing Li, Penn State University
Dr. Jingjing Li is an Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at Penn State University. Her primary research interest focuses on materials processing and characterization, particularly on in-situ material characterization, mechanical behavior, failure analysis, and the effect of microstructure on macroscopic properties with applications in sheet metal forming, joining of dissimilar materials, additive manufacturing, and composite manufacturing. She is the vice-chair for the ASME Manufacturing Processes Technical Committee, an associate editor of Journal of Manufacturing Science and Engineering, Manufacturing Letters, and Journal of Manufacturing Processes, and a recipient of the ASME Chao and Trigger Young Manufacturing Engineer Award, NSF CAREER Award, and several best paper awards.
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