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Meeting MS&T22: Materials Science & Technology
Symposium Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
Organizer(s) Jing Zhang, Indiana University – Purdue University Indianapolis
Brandon A. McWilliams, US Army Research Laboratory
Li Ma, Johns Hopkins University Applied Physics Laboratory
Yeongil Jung, Changwon National University
Scope This symposium will provide a platform to exchange the latest information in additive manufacturing (AM) modeling, simulation, and machine learning. Although there are extensive advances in AM field, challenges in predictive theoretical and computational approaches still hinder the widespread adoption of AM. The symposium is interested in receiving contributions in the following non-exclusive areas: In particular, the following topics, but not limited to, are of interest:

1.Modeling of microstructure evolution, phase transformation, and defect formation in AM parts
2.Modeling of residual stress, distortion, plasticity/damage, creep, and fatigue in AM parts
3.AM process monitoring and defect quantification
4.Machine learning (ML) and artificial intelligence (AI) applications to AM
5.Efficient computational methods using reduced order models or fast emulators for process control
6.Multiscale/multiphysics modeling strategies, including any or all of the scales associated with the spatial, temporal, and/or material domains

Abstracts Due 03/15/2022
Proceedings Plan Planned: At-meeting proceedings
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