|About this Abstract
||MS&T21: Materials Science & Technology
||Additive Manufacturing Modeling and Simulation: Microstructure, Mechanics, and Process
||Deep Learning Prediction of Stress Fields in Additively Manufactured Metals with Intricate Defect Networks
||Brendan P. Croom, Michael Berkson, Bobby Mueller, Michael Presley, Steven M Storck
|On-Site Speaker (Planned)
||Brendan P. Croom
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.