Predicting failure characteristics of structural materials via deep learning based on nondestructive void topology (2205.09075v1)

Abstract: Accurate predictions of the failure progression of structural materials is critical for preventing failure-induced accidents. Despite considerable mechanics modeling-based efforts, accurate prediction remains a challenging task in real-world environments due to unexpected damage factors and defect evolutions. Here, we report a novel method for predicting material failure characteristics that uniquely combines nondestructive X-ray computed tomography (X-CT), persistent homology (PH), and deep multimodal learning (DML). The combined method exploits the microstructural defect state at the time of material 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 a representative structural material. The method achieves a mean absolute error (MAE) of 0.09 in predicting the local strain with the tensile dataset and an MAE of 0.14 in predicting the fracture progress with the fatigue dataset. These high accuracies are mainly due to PH processing of the X-CT images, which transforms complex and noisy three-dimensional X-CT images into compact two-dimensional persistence diagrams that preserve key topological features such as the internal void size, density, and distribution. The combined PH and DML processing of 3D X-CT data is our unique approach enabling reliable failure predictions at the time of material examination based on void topology progressions, and the method can be extended to various nondestructive failure tests for practical use.