About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
In-Situ Monitoring and Control of Solidification & Deformation Processes in Metal Additive Manufacturing
|
| Presentation Title |
Machine Learning Guided Exploration of Process-Structure-Property Relationships in Metal Additive Manufacturing |
| Author(s) |
Samrat Choudhury |
| On-Site Speaker (Planned) |
Samrat Choudhury |
| Abstract Scope |
In this work we present machine learning (ML) guided optimization of metal additive manufacturing (AM) processes including directed energy deposition (DED) using both wire and powder feedstocks, and laser powder-bed fusion (L-PBF). Symbolic regression was applied to predict the fraction of ferrite in steel manufactured with L-PBF across a range of compositions and processing conditions which were later verified experimentally. Our feature importance analysis shows that the nickel to chromium ratio in steel plays a dominant factor in determining the ferrite content in AM steel, while laser power and scan speed plays relatively minor role. In DED, a combination of simulated and experimental data was used to train ML tools to predict bead geometry, including height, width, and depth, for a given set of processing conditions of laser power and wire feed rate. Our results enable improved control, part quality, and the development of closed-loop feedback systems for metal AM. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Machine Learning, Solidification |