About this Abstract |
Meeting |
5th International Congress on 3D Materials Science (3DMS 2021)
|
Symposium
|
5th International Congress on 3D Materials Science (3DMS 2021)
|
Presentation Title |
Predicting Microstructure-dependent Mechanical Properties in Additively Manufactured Metals Using Machine- and Deep-learning Methods |
Author(s) |
Carl Herriott, Ashley D. Spear |
On-Site Speaker (Planned) |
Ashley D. Spear |
Abstract Scope |
The efficacy of machine-learning (ML) and deep-learning (DL) models to predict microstructure-sensitive mechanical properties in metal additive manufacturing (AM) is assessed using results from high-fidelity, multi-physics simulations as training data. Build domains exhibiting vastly different microstructures of AM SS316L were generated using the physics-based framework. Microstructural subvolumes and corresponding homogenized yield-strength values (~7700 data points) were then used to train two types of ML models (Ridge regression and XGBoost) and one type of DL model (convolutional neural network, CNN). The ML models require substantial pre-processing to extract volume-averaged microstructural descriptors; whereas, 3D image data comprising basic microstructural information are input to the CNN models. Among all models tested, the CNN models that use crystal orientation as input provide the best predictions, require little pre-processing, and predict spatial-property maps in a matter of seconds. Results demonstrate that suitably trained data-driven models can complement physics-driven modeling by massively expediting structure-property predictions. |
Proceedings Inclusion? |
Definite: Other |