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About this Symposium
Meeting MS&T23: Materials Science & Technology
Symposium Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
Sponsorship TMS: Additive Manufacturing Committee
TMS: Computational Materials Science and Engineering Committee
Organizer(s) Jing Zhang, Indiana University – Purdue University Indianapolis
Li Ma, Johns Hopkins University
Brandon A. McWilliams, US Army Research Laboratory
Yeongil Jung, Changwon National University
Scope This symposium will provide an excellent platform to exchange the latest knowledge in additive manufacturing (AM) modeling, simulation, and machine learning. Despite extensive progress in AM field, there are still many challenges in predictive theoretical and computational approaches that hinder the advance of AM technologies. 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.Machine learning (ML) and artificial intelligence (AI) applications to AM
4.Calibration and validation data sets relevant to models
5.AM process monitoring and defect quantification
6.Efficient computational methods using reduced-order models or fast emulators for process control
7.Multiscale/multiphysics modeling strategies, including any or all of the scales associated with the spatial, temporal, and/or material domains

Abstracts Due 03/15/2023
Proceedings Plan Undecided
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE
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