Machine learning-based quantitative analysis of metal ductile fracture surface

Abstract

Quantitative fracture analysis (QFA) can infer mechanical properties and fracture mechanism of materials based on quantitative statistics of various topographical features on the fracture surface. However, it is still a challenge to screen the key fracture surface descriptors for a given property through experimental insight and theory. Obtaining an accurate quantitative relationship is difficult as different properties involve a variety of fracture surface descriptors. Herein, a QFA based on machine learning is proposed. The fracture surface images are first divided into three classes: intergranular, quasi-cleavage and dimple, by an unsupervised classification model based on a convolutional neural network with an accuracy of 0.867. The fracture surface descriptors of the identified dimple images are extracted by image segmentation and used to train separate regression models to predict the values of fracture toughness (KIC), tensile strength (Rm), yield strength (Re), elongation after fracture (A) and fracture shrinkage (Z). These models demonstrate good performance with an R2 in the test set of 0.934, 0.944, 0.840, 0.890, 0.900, respectively. In addition, with KIC and Rm as dependent variables, the explicit relationships between them and key morphological features are established based on symbolic regression. The R2 between the predicted and experimental values of the two formulae reach 0.925 and 0.926, respectively. Such a machine learning-based QFA can assist in the rapid prediction and evaluation of mechanical properties of materials in engineering applications.