{"title":"利用有限元分析和人工神经网络对混合复合材料悬臂梁进行基于振动的损伤检测","authors":"Ashok Ravichandran","doi":"10.1177/09574565231222617","DOIUrl":null,"url":null,"abstract":"Damage in mechanical structure causes a change in its physical properties and it will affect the real-time application. Cracks in a structure that distressed in model parameters like mode shape, and natural frequency. Its need to the identification of damage early to avoid catastrophic failure and increase the life of the mechanical structure. In this paper, a model for natural fundamental vibration analysis of cantilever beam with the inclined crack with different depths and different locations has been presented. The natural frequency of crack reduces by an increase in crack depth. The performances of results have been verified with finite element analyses software ANSYS, Experimental analysis and finally compare results with a neural network.","PeriodicalId":508830,"journal":{"name":"Noise & Vibration Worldwide","volume":"25 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Vibration based damage detection for hybrid composite cantilever beam using finite element analysis and artificial neural network\",\"authors\":\"Ashok Ravichandran\",\"doi\":\"10.1177/09574565231222617\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Damage in mechanical structure causes a change in its physical properties and it will affect the real-time application. Cracks in a structure that distressed in model parameters like mode shape, and natural frequency. Its need to the identification of damage early to avoid catastrophic failure and increase the life of the mechanical structure. In this paper, a model for natural fundamental vibration analysis of cantilever beam with the inclined crack with different depths and different locations has been presented. The natural frequency of crack reduces by an increase in crack depth. The performances of results have been verified with finite element analyses software ANSYS, Experimental analysis and finally compare results with a neural network.\",\"PeriodicalId\":508830,\"journal\":{\"name\":\"Noise & Vibration Worldwide\",\"volume\":\"25 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Noise & Vibration Worldwide\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/09574565231222617\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Noise & Vibration Worldwide","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/09574565231222617","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vibration based damage detection for hybrid composite cantilever beam using finite element analysis and artificial neural network
Damage in mechanical structure causes a change in its physical properties and it will affect the real-time application. Cracks in a structure that distressed in model parameters like mode shape, and natural frequency. Its need to the identification of damage early to avoid catastrophic failure and increase the life of the mechanical structure. In this paper, a model for natural fundamental vibration analysis of cantilever beam with the inclined crack with different depths and different locations has been presented. The natural frequency of crack reduces by an increase in crack depth. The performances of results have been verified with finite element analyses software ANSYS, Experimental analysis and finally compare results with a neural network.