| About this Abstract |
| Meeting |
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Symposium
|
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Presentation Title |
Bridging High-Fidelity and Macroscopic Simulations of the Laser Powder Bed Fusion Processes |
| Author(s) |
Raeita Mehraban Teymouri, Chinnapat Panwisawas, Bahram Ravani |
| On-Site Speaker (Planned) |
Raeita Mehraban Teymouri |
| Abstract Scope |
Porosity, undesired microstructure, and residual stresses are among the major challenges in Laser Powder Bed Fusion (LPBF) processes that can be potentially mitigated by optimization of process parameters. Reduced Order Models (ROMs) can be used to study these defects while developing a link between micro-scale and macro-scale behavior of the materials. In this work, a ROM of the multi-layer deposition process is developed to model the thermal field during the LPBF process while accounting for lower-length scale phenomena. To improve the accuracy of modeling predictions, temperature-dependency of the thermo-physical properties of the powder materials is considered. The ROM can be used to develop process maps for specific materials in metal AM. Furthermore, it can be used to develop a Machine Learning (ML) tool with the benefit of allowing for near real-time analysis that can ultimately be used as a building block of a digital twin for the LPBF process. |
| Proceedings Inclusion? |
Definite: Other |