Predicting buckling of carbon fiber composite cylindrical shells based on backpropagation neural network improved by sparrow search algorithm
作者机构：School of Mechanical EngineeringJiangsu University of Science and TechnologyZhenjiang212003JiangsuChina
出 版 物：《Journal of Iron and Steel Research International》 (国际钢铁研究杂志)
年 卷 期：2023年第30卷第12期
页 面：2459-2470页
核心收录：
学科分类：08[工学] 080102[工学-固体力学] 0801[工学-力学（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China(Grant No.52271277) the Natural Science Foundation of Jiangsu Province(Grant.No.BK20211343) the State Key Laboratory of Ocean Engineering(Shanghai Jiao Tong University)(Grant.No.GKZD010081) Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant.No.SJCX22_1906)
主 题：Composite cylindrical shell:Carbon fiber Backpropagation neural network Sparrow search algorithm Buckling
摘 要：The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and *** buckling behavior of CF-CCSs was analyzed by finite element and ***,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element *** this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was *** results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,*** buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.
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