| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Materials Processing Fundamentals
|
| Presentation Title |
A High-fidelity Numerical Model Informed Machine Learning Framework for Melt Pool Prediction in Laser Additive Manufacturing |
| Author(s) |
Shashank Sharma, Mohammad Parsazadeh, Zhaochen Gu, Narendra B Dahotre, Song Fu |
| On-Site Speaker (Planned) |
Shashank Sharma |
| Abstract Scope |
The recent implementation of machine learning (ML) and artificial intelligence (AI) in metal additive manufacturing (AM) has proven to be a significant step toward the realization of its (AM) digital twin. However, the major bottleneck faced in the implementation of ML in AM is the need for an unprecedented amount of data-set (“big data”), which can be expensive if obtained using experiments. In this work, a physics-informed machine learning framework is proposed for laser-based additive manufacturing, in which, a high-fidelity Multiphysics single-track melt pool simulation is used to provide a sufficient set of input data-set for supervised machine learning models. The model accurately predicts significant process attributes such as melt pool geometry, and its transition from conduction to keyhole regime. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Modeling and Simulation, Machine Learning |