Clustering Analysis of Acoustic Emission Signals during Compression Tests in Mille-Feuille Structure Materials

Abstract

Acoustic emission (AE) methods with supervised and unsupervised machine learning were applied to investigate deformation behaviors of MgYZn alloys and Ti12Mo alloy with mille-feuille-like structure. In the supervised learning process, AE signals received from compression tests with pure magnesium and directionally solidified (DS) Mg85Zn6Y9 alloy with long-period stacking ordered (LPSO) structure were used as the training data to build a classification model for classifying AE sources from -Mg phase and LPSO phase in MgYZn alloys. In the unsupervised learning process, AE signals data from Ti12Mo alloy were divided into two clusters according to the frequency spectrum features, and digital image correlation (DIC) was carried out to study those clusters and deformation behaviors. Deformation behavior of MgYZn alloys and Ti12Mo alloy were compared and discussed, and the method of applying AE with supervised and unsupervised machine learning was evaluated.