| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Quantifying Microstructure Heterogeneity for Qualification of Additively Manufactured Materials
|
| Presentation Title |
Predicting Crystallographic Texture in Laser Powder Bed Fusion via a Machine Learning Approach |
| Author(s) |
Gregory D. Wong, Elizabeth A. Holm, Anthony D. Rollett, Gregory S. Rohrer |
| On-Site Speaker (Planned) |
Gregory D. Wong |
| Abstract Scope |
Metal parts produced using Laser Powder Bed Fusion (LPBF) have crystallographic texture that has been shown through experiments and computational modeling to depend on the parameters of the LPBF build and resulting solidification. This work shows a machine learning approach to predict the as-printed orientation distribution functions of LPBF parts through a method of reconstruction via linear fitting using commonly known texture components as fitting variables. Training and testing data consist of experimental EBSD data that has been fitted to standard texture components (e.g. cube, Goss, etc.) to produce a ground truth output. These data are used to simultaneously train and test a densely connected neural network and random forest model both with an output vector mirroring that of the ground truth data. This talk will discuss the input data fitting method, model structures, and compare the results of the two models relative to each other and traditional computational methods. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Modeling and Simulation, Machine Learning |