About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
Prediction of Defects, Microstructure, and Properties in Laser Powder Bed Fusion Using Physics-Aware Deep Learning |
| Author(s) |
Prahalada K. Rao, Antonio Carrington, Reagan Orth, Kaustubh Deshmukh |
| On-Site Speaker (Planned) |
Prahalada K. Rao |
| Abstract Scope |
Accurate prediction of the solidified microstructure in laser powder bed fusion (LPBF)-processed components is critical because heterogeneity and spatial anisotropy in the solidified microstructure lead to variation in functional properties. This work presents a physics-aware, real-time, data-integrated machine learning approach for predicting the solidified microstructure of LPBF-processed Inconel 718 parts.
This work establishes a graybox modeling approach that combines temperature fields predicted by a physics-based thermal model with real-time data acquired from in situ infrared thermal imaging and optical tomography sensors. The graybox model is trained to predict the following microstructural aspects of LPBF-processed Inconel 718 parts: melt pool depth; primary dendritic arm spacing; crystallographic texture, orientation, and grain aspect ratio; and microhardness. The graybox model predicted the solidified microstructure with an accuracy approaching 95% (Rē). |