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) Mg 85 Zn 6 Y 9 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.

Reduction in the Sensor Effect on Acoustic Emission Data to Create a Generalizable Library by Data Merging

“…A challenge in AE monitoring is to establish a clear link between the recorded AE signals and the corresponding source. The possibility of identifying the signatures of damage mechanisms is a well-established field [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Most of the time, the analysis of AE data is established through empirical correlations between the damage mechanism and the recorded signal.…”

“…The attribution of each class to a specific damage mechanism is mainly based on empirical approaches, and the validation of this labeling remains difficult and is still a challenge. For the supervised approach [ 13 , 14 ], the quality of the library is crucial. A robust dataset from the AE experiments is necessary to improve the accuracy of machine learning [ 15 , 16 ].…”