Inverse Identification of Residual Stress Distribution in Aluminium Alloy Components Based on Deep Learning

Residual stress within a structural component can significantly affect the mechanical performance and stability of a structure. Therefore, it is crucial to find a way to determine the residual stress distribution to maintain the normal working of structures. Conventional methods for residual stress determination primarily include experimental testing, finite element simulations and inverse identification. However, these methods suffer from disadvantages of high testing costs, long calculation time and low inverse efficiency. To avoid these shortcomings, this study developed a high-performance method based on a deep learning technique. In this method, an artificial neural network was used to replace the finite element calculation in the finite element model updating (FEMU) technique and the residual stress distribution of structural components was inversely obtained based on the measured residual stresses of a finite number of measuring points. Compared with the conventional FEMU technique, the calculation efficiency of the proposed method was considerably improved. Furthermore, the accuracy and efficiency of the method were verified by simulated four-point bending experiments considering an elastic-plastic material.

Automated summary

Residual stress distribution is crucial for maintaining the normal working of structures, and conventional methods suffer from disadvantages. This study developed a high-performance method based on deep learning that effectively overcomes these drawbacks, improving calculation efficiency, and verifying accuracy and efficiency through experiments.