Trustworthy Contextual Neural Networks for Deciphering Fracture in Metals

Abstract

A novel approach was proposed and implemented to assess the confidence of the individual class predictions made by convolutional neural networks trained to identify the type of fracture in metals. This approach involves utilizing contextual evidence in the form of contextual fracture images and contextual scores, which serve as indicators for determining the certainty of the predictions. This approach was first tested on both shallow and deep convolutional neural networks employing four publicly available image datasets: MNIST, EMNIST, FMNIST, and CIFAR10, and subsequently validated on an in-house steel fracture dataset—FRAC, containing ductile and brittle fracture images. The effectiveness of the method is validated by producing contextual images and scores for the fracture image data and other image datasets to assess the confidence of selected predictions from the datasets. The CIFAR-10 dataset yielded the lowest mean contextual score of 78 for the shallow model, with over 50% of representative test instances receiving a score below 90, indicating lower confidence in the model's predictions. In contrast, the CNN model used for the fracture dataset achieved a mean contextual score of 99, with 0% of representative test instances receiving a score below 90, suggesting a high level of confidence in its predictions. This approach enhances the interpretability of trained convolutional neural networks and provides greater insight into the confidence of their outputs.