Event Date/Time
Location
Room 222
Series/Event Type
Current research efforts at my manufacturing group are rooted in advancing new flexible manufacturing processes using the hybrid physics-based data-driven approaches. In this talk, I will post the manufacturing challenges that we are facing and use two flexible processes, i.e., metal powder-based additive manufacturing and rapid dieless forming for producing three-dimensional parts without geometry-specific tooling, as demonstration cases. 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. Furthermore, I will show how we use machine learning for active sensing with the goal of effective in-situ local process control. 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.