| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing: Processing Effects on Microstructure and Material Performance
|
| Presentation Title |
A-144: Residual Stress Mitigation in Selective Laser Melting Through Laser Scan Strategy Optimization Using Machine Learning |
| Author(s) |
Kahraman G. Demir, Charles Yang, Adi Ben-Artzy, Jack Peterson, Grace X. Gu, Peter Hosemann |
| On-Site Speaker (Planned) |
Kahraman G. Demir |
| Abstract Scope |
Selective laser melting has enabled for the fabrication of geometries that would otherwise be impossible or impractical to fabricate with conventional technologies. However, high thermal gradients, resulting from rapid localized heating and cooling, cause the development of intense residual stress fields within the material of the fabricated part which leads to poor fracture properties, poor dimensional tolerances, inconsistent material properties and fabrication failures. Among many process parameters, laser scan strategy significantly influences the development of residual stresses in the fabricated part. In this work, a macroscale thermomechanical finite difference model is used to predict the residual stresses in single layers subjected to different laser scan patterns. These pattern parameters are then coupled with values quantifying the intensity of the residual stress fields and analyzed using machine learning to investigate potential correlations and to gain new insights on residual stress mitigation techniques when generating scan trajectories. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |