Xiaojian Han, Kai Huang, Tao Zheng, Jindi Zhou, Hongsen Liu, Zhixing Li, Li Zhang, Licheng Guo
{"title":"基于 ANN 的分层纤维增强复合材料并发多尺度损伤演变模型","authors":"Xiaojian Han, Kai Huang, Tao Zheng, Jindi Zhou, Hongsen Liu, Zhixing Li, Li Zhang, Licheng Guo","doi":"10.1016/j.compscitech.2024.110910","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, an ANN-based concurrent multiscale damage evolution model is proposed, which is able to investigate the complex failure behaviors of hierarchical fiber-reinforced composites. In the framework of the proposed model, yarn damage evolution laws at the mesoscale are indirectly derived from the microscale representative volume element (RVE), using artificial neural networks (ANNs) as a surrogate model. A homogenized characterization method is proposed to derive the homogenized damage variables. The homogenized strain and damage variables of the microscale RVE are taken as inputs and outputs in ANNs, respectively. The dataset is generated by combining clustering with the finite element simulation. A typical kind of plain-woven composite is adopted as a benchmark material for numerical implementation and experimental verification. The numerical predictions, including the tensile properties and damage evolution, are consistent with the results from quasi-static tension experiments.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"259 ","pages":"Article 110910"},"PeriodicalIF":8.3000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An ANN-based concurrent multiscale damage evolution model for hierarchical fiber-reinforced composites\",\"authors\":\"Xiaojian Han, Kai Huang, Tao Zheng, Jindi Zhou, Hongsen Liu, Zhixing Li, Li Zhang, Licheng Guo\",\"doi\":\"10.1016/j.compscitech.2024.110910\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this paper, an ANN-based concurrent multiscale damage evolution model is proposed, which is able to investigate the complex failure behaviors of hierarchical fiber-reinforced composites. In the framework of the proposed model, yarn damage evolution laws at the mesoscale are indirectly derived from the microscale representative volume element (RVE), using artificial neural networks (ANNs) as a surrogate model. A homogenized characterization method is proposed to derive the homogenized damage variables. The homogenized strain and damage variables of the microscale RVE are taken as inputs and outputs in ANNs, respectively. The dataset is generated by combining clustering with the finite element simulation. A typical kind of plain-woven composite is adopted as a benchmark material for numerical implementation and experimental verification. The numerical predictions, including the tensile properties and damage evolution, are consistent with the results from quasi-static tension experiments.</div></div>\",\"PeriodicalId\":283,\"journal\":{\"name\":\"Composites Science and Technology\",\"volume\":\"259 \",\"pages\":\"Article 110910\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Composites Science and Technology\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0266353824004809\",\"RegionNum\":1,\"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":"Composites Science and Technology","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0266353824004809","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, COMPOSITES","Score":null,"Total":0}
An ANN-based concurrent multiscale damage evolution model for hierarchical fiber-reinforced composites
In this paper, an ANN-based concurrent multiscale damage evolution model is proposed, which is able to investigate the complex failure behaviors of hierarchical fiber-reinforced composites. In the framework of the proposed model, yarn damage evolution laws at the mesoscale are indirectly derived from the microscale representative volume element (RVE), using artificial neural networks (ANNs) as a surrogate model. A homogenized characterization method is proposed to derive the homogenized damage variables. The homogenized strain and damage variables of the microscale RVE are taken as inputs and outputs in ANNs, respectively. The dataset is generated by combining clustering with the finite element simulation. A typical kind of plain-woven composite is adopted as a benchmark material for numerical implementation and experimental verification. The numerical predictions, including the tensile properties and damage evolution, are consistent with the results from quasi-static tension experiments.
期刊介绍:
Composites Science and Technology publishes refereed original articles on the fundamental and applied science of engineering composites. The focus of this journal is on polymeric matrix composites with reinforcements/fillers ranging from nano- to macro-scale. CSTE encourages manuscripts reporting unique, innovative contributions to the physics, chemistry, materials science and applied mechanics aspects of advanced composites.
Besides traditional fiber reinforced composites, novel composites with significant potential for engineering applications are encouraged.