利用人工神经网络对复合材料修补结构进行基于可靠性的多目标优化设计

IF 6.3 2区材料科学 Q1 MATERIALS SCIENCE, COMPOSITES

Composite Structures Pub Date : 2024-11-05 DOI:10.1016/j.compstruct.2024.118692

Yubo Zhao , Shanyong Xuan , Yuan Wang , Yongbin Li , Xuefeng Yao

{"title":"利用人工神经网络对复合材料修补结构进行基于可靠性的多目标优化设计","authors":"Yubo Zhao , Shanyong Xuan , Yuan Wang , Yongbin Li , Xuefeng Yao","doi":"10.1016/j.compstruct.2024.118692","DOIUrl":null,"url":null,"abstract":"<div><div>Due to the parameter differences and the existence of random factors, the performance of composite repair structure shows a large dispersion. In order to obtain stronger, lighter and more reliable repair structure, multi-objective optimization design of carbon fiber reinforced polymers (CFRP) laminate repair structure is investigated by combining the reliability theory, artificial neural networks and the genetic algorithm. First, a 3D simulation model of the composite laminate patch repair structures is established using the 3D Hashin criterion and the cohesive region model. Then, the Latin Hypercube sampling (LHS) method is used to realize the random sampling, and the strength proxy model of the repair structure is established by using Back-propagation artificial neural network, further a multi-objective optimization model with tensile strength, reliability and weight as objective functions is built considering the design parameters and the random parameters. Finally, NSGAⅡalgorithm is used to solve the multi-objective optimization problem, and a set of solutions on the Pareto front surface are obtained, also the optimal design parameters of the composite repair structure meets the requirements is obtained.</div></div>","PeriodicalId":281,"journal":{"name":"Composite Structures","volume":"352 ","pages":"Article 118692"},"PeriodicalIF":6.3000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reliability-based multi-objective optimization design of composite patch repair structure using artificial neural networks\",\"authors\":\"Yubo Zhao , Shanyong Xuan , Yuan Wang , Yongbin Li , Xuefeng Yao\",\"doi\":\"10.1016/j.compstruct.2024.118692\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Due to the parameter differences and the existence of random factors, the performance of composite repair structure shows a large dispersion. In order to obtain stronger, lighter and more reliable repair structure, multi-objective optimization design of carbon fiber reinforced polymers (CFRP) laminate repair structure is investigated by combining the reliability theory, artificial neural networks and the genetic algorithm. First, a 3D simulation model of the composite laminate patch repair structures is established using the 3D Hashin criterion and the cohesive region model. Then, the Latin Hypercube sampling (LHS) method is used to realize the random sampling, and the strength proxy model of the repair structure is established by using Back-propagation artificial neural network, further a multi-objective optimization model with tensile strength, reliability and weight as objective functions is built considering the design parameters and the random parameters. Finally, NSGAⅡalgorithm is used to solve the multi-objective optimization problem, and a set of solutions on the Pareto front surface are obtained, also the optimal design parameters of the composite repair structure meets the requirements is obtained.</div></div>\",\"PeriodicalId\":281,\"journal\":{\"name\":\"Composite Structures\",\"volume\":\"352 \",\"pages\":\"Article 118692\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Composite Structures\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0263822324008201\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, COMPOSITES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Composite Structures","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0263822324008201","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, COMPOSITES","Score":null,"Total":0}

引用次数: 0

摘要

由于参数差异和随机因素的存在，复合材料修复结构的性能表现出较大的离散性。为了获得更强、更轻、更可靠的修复结构，结合可靠性理论、人工神经网络和遗传算法，研究了碳纤维增强聚合物（CFRP）层状修复结构的多目标优化设计。首先，利用三维哈欣准则和内聚区域模型建立了复合材料层压板修补结构的三维仿真模型。然后，利用拉丁超立方采样（LHS）方法实现随机抽样，并利用反向传播人工神经网络建立修复结构的强度代理模型，进一步考虑设计参数和随机参数，建立以抗拉强度、可靠性和重量为目标函数的多目标优化模型。最后，利用 NSGAⅡ算法求解多目标优化问题，得到帕累托前沿面上的一组解，并得到复合材料修复结构的最优设计参数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Reliability-based multi-objective optimization design of composite patch repair structure using artificial neural networks

Due to the parameter differences and the existence of random factors, the performance of composite repair structure shows a large dispersion. In order to obtain stronger, lighter and more reliable repair structure, multi-objective optimization design of carbon fiber reinforced polymers (CFRP) laminate repair structure is investigated by combining the reliability theory, artificial neural networks and the genetic algorithm. First, a 3D simulation model of the composite laminate patch repair structures is established using the 3D Hashin criterion and the cohesive region model. Then, the Latin Hypercube sampling (LHS) method is used to realize the random sampling, and the strength proxy model of the repair structure is established by using Back-propagation artificial neural network, further a multi-objective optimization model with tensile strength, reliability and weight as objective functions is built considering the design parameters and the random parameters. Finally, NSGAⅡalgorithm is used to solve the multi-objective optimization problem, and a set of solutions on the Pareto front surface are obtained, also the optimal design parameters of the composite repair structure meets the requirements is obtained.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Composite Structures 工程技术-材料科学：复合

CiteScore

12.00

自引率

12.70%

发文量

1246

审稿时长

78 days

期刊介绍： The past few decades have seen outstanding advances in the use of composite materials in structural applications. There can be little doubt that, within engineering circles, composites have revolutionised traditional design concepts and made possible an unparalleled range of new and exciting possibilities as viable materials for construction. Composite Structures, an International Journal, disseminates knowledge between users, manufacturers, designers and researchers involved in structures or structural components manufactured using composite materials. The journal publishes papers which contribute to knowledge in the use of composite materials in engineering structures. Papers deal with design, research and development studies, experimental investigations, theoretical analysis and fabrication techniques relevant to the application of composites in load-bearing components for assemblies, ranging from individual components such as plates and shells to complete composite structures.