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Physics-Based AI-Assisted Numerical Simulations for Manufacturing Process Design and Control

Jian Cao, Northwestern University

I view manufacturing as an integration platform that translates ideas and resources into products used by societies. I will present our current research efforts in advancing metal powder-based additive manufacturing processes and forming processes using the combination of the mechanics-driven and data-driven approaches. Specifically, I will show how the integration of the fundamental process mechanics, process control, and techniques including machine learning to achieve effective and efficient predictions of material’s mechanical behavior due to or during a manufacturing process. Our solutions particularly target three notoriously challenging aspects of the process, i.e., long history-dependent properties, complex geometric features, and the high dimensionality of their design space.