摘要
Bistable curved shells have become a promising low-cost application in energy absorption fields owing to recentadvances in material and technology.Significant research has been conducted to improve their energy absorptioneffect through forward prediction and single-objective optimization.However,these approaches may not fully explore their functional potential.In this study,we propose a multi-objective optimization framework based on theprinciple of main objective optimization that combines neural networks and genetic algorithms.The energy absorption effect and backward snapping force of the bistable curved shell are improved synchronously.Meanwhile,a reverse design algorithm is developed to generate the preset load-displacement curve,which further expandsthe application of machine learning methods in the field of multi-objective optimization.The combination ofmachine learning and multi-objective optimization is highly effective for building meta-structures with specificperformance requirements and has potential applications in solving complex optimization tasks in various fields.
基金
supported by the National Natural Science Foundation of China(Grant Nos.12102143,12172151,and 12172149)
National funded postdoctoral researcher program(Grant No.GZC20230962)
Guangdong Basic and Applied Basic Research Foundation(Grant No.2024A1515010379)
Natural Science Foundation of Guangzhou City(Grant Nos.202201010217,202201020539,and 2024A04J3401)
Young Talent Support Project of Guangzhou Association for Science and Technology,the Fellowship of China Postdoctoral Science Foundation(Grant No.2022M711333)
the Fundamental Research Funds for the Central Universities(Grant No.21623332)
the High Performance Public Computing Service Platform of Jinan University.
