Abstract
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.
Highlights
Residual stress is inevitably introduced into structural components during manufacturing processes such as forging, cutting and shot peeing [1,2]
The inverse method proposed in this study used a convolutional neural network (CNN) to replace the finite element model updating (FEMU) used in the conventional inverse method
By replacing the FEMU technique in the conventional inverse method with a CNN, a novel inverse method is proposed in this paper to determine the residual stress field of structural components
Summary
Residual stress is inevitably introduced into structural components during manufacturing processes such as forging, cutting and shot peeing [1,2]. The inverse identification method combines an experimental test with the FEMU method [16,17] and is expected to become an effective method for residual stress field prediction because its finite element calculation results in the residual stress distribution naturally satisfying the elastic theory. An inverse method to generate a self-balanced residual stress field by replacing the finite element calculation with a convolutional neural network (CNN) [21] is proposed in the paper. By using this method, the residual stress field can be obtained from the residual stresses measured at a finite number of points.
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