About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
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Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
Presentation Title |
Prediction of Microstructure Formation Under Rapid Solidification Using a Deep Learning Approach |
Author(s) |
Anindya Bhaduri, Chen Shen, Alex Kitt, Lee Kerwin, Siyeong Ju, Luke Mohr, Yang Jiao, Marissa Brennan, Shenyan Huang, Sreekar Karnati, Monica Soare, Arushi Dhakad, Hamedreza Seyyedhosseinzadeh, Liping Wang, Changjie Sun, Lang Yuan |
On-Site Speaker (Planned) |
Anindya Bhaduri |
Abstract Scope |
In this study, a cellular automaton-based solidification model was considered to predict the grain structures in single tracks during laser scanning under additive manufacturing conditions. The model directly utilized the thermal history calculated via Rosenthal solution under a wide range of process parameters. The challenge here is that the simulation involves a complex spatiotemporal stochastic solution and thus very expensive to solve. To make the predictions fast with sufficient accuracy, the goal is to develop a machine learning framework to efficiently map the process parameters to the final grain structure. Specifically, a probabilistic deep learning model is developed that can successfully tackle the issues of efficiency, accuracy, and stochasticity. |