Robust detection of ductile fracture by acoustic emission data-driven unsupervised learning

Abstract

The quantitative assessment of the formability of bulk metallic materials remains challenging, primarily due to considerable difficulties in accurately identifying the initiation of fractures. This study introduces a novel methodology for detecting the onset of ductile fracture by using acoustic emission signals. Unlike traditional methods that rely on standard characteristics, this approach utilizes acoustic emission signals that are depicted through vivid and direct feature images. These images represent short-term and relative energies, which are termed as energy allocation maps. These maps are generated utilizing a novel signal processing technique that employs the maximal overlap discrete wavelet transform for multi-resolution analysis. In this study, a parameter known as accumulative relative energy is developed to illustrate the global energy distribution across resolutions, while another parameter, normalized short-term energy, is designed to capture the local energy distribution of resolutions. The natural neighbor interpolation technique is subsequently employed to derive the energy allocation map. A set of stacked autoencoders is accordingly configured to transform this map into a singular value that represents the condition of the material. Consequently, a ductile fracture indicator is proposed to identify the onset of fracture. The accuracy and efficacy of the proposed method are validated through partial experiments. The study examines four bulk metallic materials, providing insights into their deformation capabilities under compression.