| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing: Processing Effects on Microstructure and Material Performance
|
| Presentation Title |
Selection of Process Parameters for Controlling Microstructural Properties in Additive Manufacturing: A Machine Learning Based Approach |
| Author(s) |
Sudeepta Mondal, Daniel Gwynn, Nandana Menon, Asok Ray, Amrita Basak |
| On-Site Speaker (Planned) |
Sudeepta Mondal |
| Abstract Scope |
During material consolidation in additive manufacturing (AM), the melt pool dimensions play a critical role in determining the final grain structure and thus the resulting mechanical properties. However, maintaining desired melt-pool properties are challenging due to the inherent layer-by-layer fabrication process resulting in cyclic heating and cooling as well as thermal gain of the component being built. As a possible solution to this problem, a physics-informed machine learning (ML) assisted modeling and optimization framework was explored in this work. An analytical heat transfer model is employed for predicting the thermal distribution in a directed energy deposition process for faster computation. Thereafter, a surrogate-assisted statistical learning and optimization architecture involving Gaussian Process-based modeling and Bayesian Optimization is employed for finding the optimal set of process parameters as the scan progresses, subject to the constraint of maintaining a desired percentage of columnar growth during the build. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |