| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling and Simulation: Microstructure, Mechanics, and Process
|
| Presentation Title |
Deep Learning Prediction of Stress Fields in Additively Manufactured Metals with Intricate Defect Networks |
| Author(s) |
Brendan P. Croom, Michael Berkson, Bobby Mueller, Michael Presley, Steven M Storck |
| On-Site Speaker (Planned) |
Brendan P. Croom |
| Abstract Scope |
In context of the universal presence of defects in additively manufactured (AM) metals, efficient computational tools are required to rapidly screen AM microstructures for mechanical integrity. To this end, a deep learning approach is used to predict the elastic stress fields in images of defect-containing metal microstructures. A large dataset consisting of the stress response of 100,000 random microstructure images is generated using high-resolution Fast Fourier Transform-based finite element (FFT-FE) calculations, which is then used to train a modified U-Net style convolutional neural network (CNN) model. The trained U-Net model more accurately predicted the stress response compared to previous CNN architectures, and exceeded the accuracy of low-resolution FFT-FE calculations. The model was applied to images of real AM microstructures with severe lack of fusion defects, and predicted a strong linear increase of maximum stress as a function of pore fraction. |