Jian Caos manufacturing group aims to advance the capability to co-design materials and manufacturing processes using hybrid physics-based and data-driven approaches. In this talk, Cao will demonstrate their work in the development of differentiable simulation tools, sensing and process control to achieve effective and efficient predictions and control of a materials mechanical behaviour in metal additive manufacturing processes. Furthermore, Cao will show how to use machine learning to accelerate the physics-based simulations and to realize active sensing with the goal of effective in situ local process control. Caos solutions particularly target three notoriously challenging aspects of the process: long history-dependent properties, complex geometric features and the high dimensionality of their design space. The approaches are applicable to other manufacturing processes as well, such as flexible incremental forming.
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