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  • 标题:Prediction Model of Stress Intensity Factor of Circumferential Through Crack in Elbow Based on Neural Network
  • 本地全文:下载
  • 作者:Xiaohong Li ; Xianghui Li ; Bin Chen
  • 期刊名称:Computational Intelligence and Neuroscience
  • 印刷版ISSN:1687-5265
  • 电子版ISSN:1687-5273
  • 出版年度:2022
  • 卷号:2022
  • DOI:10.1155/2022/8395505
  • 语种:English
  • 出版社:Hindawi Publishing Corporation
  • 摘要:Using ANSYS software to establish the finite element model of crack bending tube, the SIF at the tip of the crack is calculated for the difference in the diameter of the pipe, the outer diameter of the elbow, and the bending angle of the bend pipe, and it is used as a neural network to calculate the sample. By using three layers of BP network to establish the prediction model of the SIF of cracked pipe, the simulation of 39 sets of samples proves that the relative error of the BP network model is 0.19% and the mean square error of the network output is 0.0102. The prediction model has high prediction precision and generalization ability and can be used in engineering design and calculation.
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