A safety assessment approach to pressure vessels based on machine learning - Zhang, Xing; Hu, Yifeng; Shi, Junping; Liang, Hao; Xu, Yong; Cao, Xiaoshan (Frontiers in Materials, 2022)
The safety assessment of the pressure vessel with surface crack is an important part of the safety assessment of engineering equipment. However, the existing methods are mostly based on the assumption of plane specimens and the K criterion applicable to brittle fracture, which reduce the feasibility to the fracture problem of actual pressure vessels. In this paper, based on finite element method (FEM) and artificial neural network (ANN), the elastic-plastic three-dimensional J-integral of crack tip in a pressure vessel with axial semi-elliptic crack on the surface under the loading of internal pressure is studied. Firstly, the influence of vessel geometry, crack size and internal pressure on three-dimensional J-integral is analyzed. Secondly, the machine learning data set is constructed based on the results of 1200 cases of FEM calculation, then ANNs are used to learn the potential relationship between multiple parameters and three-dimensional J-integral. The results show that the neural network constructed in this paper can well predict the elastoplastic three-dimensional J-integral of pressure vessel surface crack.