Role of length-scale in machine learning based image analysis of ductile fracture surfaces

Abstract

Recent advancements in machine learning (ML) techniques have opened up new opportunities for using image analysis to solve materials science and engineering problems. In this work, we have used an ML-based workflow to classify the fracture surfaces of dual-phase steels subjected to different stress states. This task is not straightforward, as the ductile fracture surfaces of many metallic materials exhibit similar features, such as dimples. The ML-based workflow uses a pre-trained convolution neural network in unsupervised mode to extract image features, which are then reduced in dimensionality using principal component analysis. Next, images are clustered and classified using K-Means and K-Nearest Neighbors algorithms, respectively. Our results show that the accuracy of the ML-based technique is sensitive to the length-scales of the fracture surface images, and the critical length-scale corresponding to the maximum accuracy depends on the typological categories being classified. A physical interpretation of the critical length-scales associated with the fracture surface images is provided through quantitative fracture surface roughness analysis. Our work demonstrates the potential of using unsupervised ML-based techniques for fractography of ductile materials, especially for typological classification. More importantly, it emphasizes the importance of length-scales in image analysis in materials science and engineering.