Prediction of Hardness and Fracture Toughness for WC-FeAl from Its Microstructural Images via Convolutional Neural Network

Abstract

The mechanical properties, such as Vickers hardness and crack length, of various WC-FeAl hard materials were predicted using deep learning via a convolutional neural network (CNN) trained on microstructures. The accuracy of the predictions was verified using gradient-weighted class activation mapping (Grad-CAM), which is a kind of image visualization technology that identifies important structural features for AI classification based on mechanical properties. The accuracies, expressed as coefficients of determination for unknown samples (test data), were found to be 0.89 and 0.75 at most for Vickers hardness and crack length, respectively. The AI correctly recognized microstructural quality and determined classes that represented differences in mechanical properties as evidenced by the feature maps obtained, indicating that CNN prediction was a powerful tool for analyzing WC-FeAl hard materials.