Predicting High Stress Regions in a Microstructure using Convolutional Neural Networks

Predicting High Stress Regions in a Microstructure using Convolutional Neural Networks

Abstract

Origins of failure are often driven by localizations in material response due to the applied stress/strain state. These stress “hot spots” intuitively represent regions that accumulate higher damage than their surroundings, serving as prime locations for crack nucleation. There are a number of microstructural factors that contribute to whether a given neighborhood is prone to forming a hot spot, including the morphology of local features, their relative crystallographic misorientations and elastic anisotropy, and their preferred orientations with respect to the load state. Given the large number of features that may influence the formation of stress hot spots, it is advantageous to develop techniques that attempt to predict their formation based solely on an image of the underlying microstructure. We describe such a method based on a convolutional neural network (CNN). The CNN is trained by cutting local patches out of a synthetic microstructure, created in DREAM.3D, that had its elastic response modeled using a spectral technique based on fast Fourier transforms. The resulting trained CNN is able to predict which regions of a microstructure are susceptible to forming hot spots, based only on an image description and features [

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