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Accurate predictions of the failure progression of structural materials iscritical for preventing failure-induced accidents. Despite considerablemechanics modeling-based efforts, accurate prediction remains a challengingtask in real-world environments due to unexpected damage factors and defectevolutions. Here, we report a novel method for predicting material failurecharacteristics that uniquely combines nondestructive X-ray computed tomography(X-CT), persistent homology (PH), and deep multimodal learning (DML). Thecombined method exploits the microstructural defect state at the time ofmaterial examination as an input, and outputs the failure-related properties.Our method is demonstrated to be effective using two types of fracture datasets(tensile and fatigue datasets) with ferritic low alloy steel as arepresentative structural material. The method achieves a mean absolute error(MAE) of 0.09 in predicting the local strain with the tensile dataset and anMAE of 0.14 in predicting the fracture progress with the fatigue dataset. Thesehigh accuracies are mainly due to PH processing of the X-CT images, whichtransforms complex and noisy three-dimensional X-CT images into compacttwo-dimensional persistence diagrams that preserve key topological featuressuch as the internal void size, density, and distribution. The combined PH andDML processing of 3D X-CT data is our unique approach enabling reliable failurepredictions at the time of material examination based on void topologyprogressions, and the method can be extended to various nondestructive failuretests for practical use.

Predicting failure characteristics of structural materials via deep learning based on nondestructive void topology