Sparse Representation of Acoustic Emission Signals for Identifying Tensile Process of Aluminum Alloy Sheets

doi:10.3901/JME.2024.02.001

Abstract

Abstract: Aiming at the need of quality monitoring in sheet metal forming process, an automatic identification method of aluminum alloy sheet drawing process based on sparse representation of acoustic emission signals was proposed. The method to monitor the process of sheet metal drawing of the acoustic emission signal is non-negative matrix factorization, and extract the mapping on the low-dimensional subspace characteristic coefficient of dictionary is used to construct the training and testing samples, and the use of l₁ norm for sparse solution and the signal reconstruction, thus realize the tensile elasticity, plasticity, yield, hardening, and in the process of necking five different stress-strain state of automatic identification. At the same time, the influence of data types of acoustic emission signals on the recognition accuracy of different stress-strain states is studied. The results show that the proposed sparse representation method based on acoustic emission signals can well realize the identification of five different stress-strain states in the tensile process of aluminum alloy. Compared with Fast Fourier Transform data, the identification accuracy of Short-Time Fourier Transform data is higher. The research provides a feasible solution for the quality control of sheet metal forming.

Key words: tensile process, sparse representation, acoustic emission, stress-strain state, non-negative matrix factorization

CLC Number:

TM154

JIAO Jingpin, SUN Yandong, LI Guanghai, ZHAO Pengjing, WU Bin, HE Cunfu. Sparse Representation of Acoustic Emission Signals for Identifying Tensile Process of Aluminum Alloy Sheets[J]. Journal of Mechanical Engineering, 2024, 60(2): 1-9.

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