| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Layer-wise Prediction of Microstructural Evolution in Laser Powder Bed Fusion Additive Manufacturing using Physics-based Machine Learning |
| Author(s) |
Alexander R. Riensche, Ajay Krishnan, Benjamin Bevans, Grant King, Kevin Cole, Prahalada Rao |
| On-Site Speaker (Planned) |
Alexander R. Riensche |
| Abstract Scope |
In this work we developed a framework to predict microstructure formation in the laser powder bed fusion (LPBF) of Inconel 718 parts. The microstructure is predicted as a function of sub-surface cooling rate estimated from a rapid part-level computational thermal model within elementary machine learning models. In this work, the microstructure evolved is quantified layer-by-layer in terms of three aspects: meltpool depth, grain size (primary dendritic arm spacing), and microhardness. The approach predicts the microstructure evolved with statistical fidelity exceeding 85% (R2). This is substantial improvement over existing microstructure prediction which are only able to predict the microstructure of a small region (~1 mm3) and not of the entire part. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |