基于深度学习的权函数法应力强度因子求解

doi:10.7527/S1000-6893.2023.28367

Abstract

Abstract:

A deep-learning based multiple reference states weight function method is proposed to address the problems in traditional multiple reference states weight function method for complex geometric configurations. Comparison with the Green's functions at corresponding points of the crack surface shows that the deviation between the Green’s functions of unilateral penetrating crack at the edge of the finite width plate obtained by this method and the Wu-Carlsson analytical weight function method is within 0.4%， verifying the accuracy of the weight function obtained by this method and proving the feasibility of this method. Using this method， we derive the weight function with some geometric dimensions for the edge crack at the off-center hole on the finite plate common in the aerospace field， and compare it with the Green’s function value obtained by the finite element method. The results show that most of the deviations are smaller than 1.5%， with the maximum deviation of 4.8%， further verifying the accuracy of the weight function solution obtained by this method. The proposed method has the potential to be applied to complex geometric crack bodies.

Key words: weight function method, stress intensity factor, deep learning, crack at edge of off-center hole, Green’s function

CLC Number:

V215.6

Yunhe ZHAO, Shengnan WANG. Solution to stress intensity factor by weight function method based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(19): 228367-228367.

Figures/Tables 7

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References 25

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Solution to stress intensity factor by weight function method based on deep learning

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