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In context of the universal presence of defects in additively manufactured(AM) metals, efficient computational tools are required to rapidly screen AMmicrostructures for mechanical integrity. To this end, a deep learning approachis used to predict the elastic stress fields in images of defect-containingmetal microstructures. A large dataset consisting of the stress response of100,000 random microstructure images is generated using high-resolution FastFourier Transform-based finite element (FFT-FE) calculations, which is thenused to train a modified U-Net style convolutional neural network (CNN) model.The trained U-Net model more accurately predicted the stress response comparedto alternative CNN architectures, exceeded the accuracy of low-resolutionFFT-FE calculations, and was generalizable to microstructures with complexdefect geometries. The model was applied to images of real AM microstructureswith severe lack of fusion defects, and predicted a strong linear increase ofmaximum stress as a function of pore fraction. Together, the proposed CNNoffers an efficient and accurate way to predict the structural response ofdefect-containing AM microstructures.

Deep learning prediction of stress fields in additively manufactured metals with intricate defect networks