Junling Hou, Mengfan Zhao, Yujie Chen, Qun Li, Chunguang Wang
{"title":"使用广义回归神经网络方法预测开孔复合材料层压板的力学性能","authors":"Junling Hou, Mengfan Zhao, Yujie Chen, Qun Li, Chunguang Wang","doi":"10.1007/s00707-024-04025-7","DOIUrl":null,"url":null,"abstract":"<div><p>Mechanical connection is a common method used for joining composite materials, but it is bound to open holes in the composite material structure. These open holes may cause stress concentration at the hole edge, impacting the overall mechanical properties of the component. In this paper, a machine learning-based method for predicting the mechanical properties of open-hole composite laminates is proposed based on generalized regression neural network. In detail, by using the Hashin failure criterion, the finite element models of composite laminates with single holes of different diameters have been established. Their load–displacement curves, maximum failure stresses and maximum failure strains are calculated numerically. Then, the different hole diameters and corresponding load–displacements can be used as the input and output variables of the generalized regression neural network to train the neural network model. Based on the optimal generalized regression neural network model, the mechanical properties of the composite laminates with a certain single hole diameter can be predicted. Compared with the uniaxial tensile experiment of open-hole composite laminates, the effectiveness of this machine learning method is verified. Furthermore, the changes in mechanical properties of double-hole composite laminates under different hole diameters and positions are analyzed. This study holds significant practical implications for enhancing the understanding of the mechanical properties of composite materials and the influence of defects on their performance.</p></div>","PeriodicalId":456,"journal":{"name":"Acta Mechanica","volume":"235 12","pages":"7553 - 7568"},"PeriodicalIF":2.3000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of mechanical property of open-hole composite laminates using generalized regression neural network method\",\"authors\":\"Junling Hou, Mengfan Zhao, Yujie Chen, Qun Li, Chunguang Wang\",\"doi\":\"10.1007/s00707-024-04025-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Mechanical connection is a common method used for joining composite materials, but it is bound to open holes in the composite material structure. These open holes may cause stress concentration at the hole edge, impacting the overall mechanical properties of the component. In this paper, a machine learning-based method for predicting the mechanical properties of open-hole composite laminates is proposed based on generalized regression neural network. In detail, by using the Hashin failure criterion, the finite element models of composite laminates with single holes of different diameters have been established. Their load–displacement curves, maximum failure stresses and maximum failure strains are calculated numerically. Then, the different hole diameters and corresponding load–displacements can be used as the input and output variables of the generalized regression neural network to train the neural network model. Based on the optimal generalized regression neural network model, the mechanical properties of the composite laminates with a certain single hole diameter can be predicted. Compared with the uniaxial tensile experiment of open-hole composite laminates, the effectiveness of this machine learning method is verified. Furthermore, the changes in mechanical properties of double-hole composite laminates under different hole diameters and positions are analyzed. This study holds significant practical implications for enhancing the understanding of the mechanical properties of composite materials and the influence of defects on their performance.</p></div>\",\"PeriodicalId\":456,\"journal\":{\"name\":\"Acta Mechanica\",\"volume\":\"235 12\",\"pages\":\"7553 - 7568\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Mechanica\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00707-024-04025-7\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Mechanica","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s00707-024-04025-7","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MECHANICS","Score":null,"Total":0}
Prediction of mechanical property of open-hole composite laminates using generalized regression neural network method
Mechanical connection is a common method used for joining composite materials, but it is bound to open holes in the composite material structure. These open holes may cause stress concentration at the hole edge, impacting the overall mechanical properties of the component. In this paper, a machine learning-based method for predicting the mechanical properties of open-hole composite laminates is proposed based on generalized regression neural network. In detail, by using the Hashin failure criterion, the finite element models of composite laminates with single holes of different diameters have been established. Their load–displacement curves, maximum failure stresses and maximum failure strains are calculated numerically. Then, the different hole diameters and corresponding load–displacements can be used as the input and output variables of the generalized regression neural network to train the neural network model. Based on the optimal generalized regression neural network model, the mechanical properties of the composite laminates with a certain single hole diameter can be predicted. Compared with the uniaxial tensile experiment of open-hole composite laminates, the effectiveness of this machine learning method is verified. Furthermore, the changes in mechanical properties of double-hole composite laminates under different hole diameters and positions are analyzed. This study holds significant practical implications for enhancing the understanding of the mechanical properties of composite materials and the influence of defects on their performance.
期刊介绍:
Since 1965, the international journal Acta Mechanica has been among the leading journals in the field of theoretical and applied mechanics. In addition to the classical fields such as elasticity, plasticity, vibrations, rigid body dynamics, hydrodynamics, and gasdynamics, it also gives special attention to recently developed areas such as non-Newtonian fluid dynamics, micro/nano mechanics, smart materials and structures, and issues at the interface of mechanics and materials. The journal further publishes papers in such related fields as rheology, thermodynamics, and electromagnetic interactions with fluids and solids. In addition, articles in applied mathematics dealing with significant mechanics problems are also welcome.