{"title":"基于声发射监测的复合材料环失效载荷预测","authors":"V. Arumugam, G. Vaidyanathan, A. Stanley","doi":"10.15866/IREPHY.V9I1.6157","DOIUrl":null,"url":null,"abstract":"The Aim of this Study is to investigate the progressive failure of ring specimens cut from a Filament wound Glass-Epoxy pipes and predict the failure load using Acoustic Emission Technique. Defects like Delamination and fibercut are being artificially introduced in the specimen during the winding process. The split disk fixture is fabricated and Rings are tested in Universal testing machine under Acoustic Emission Monitoring. Acoustic Emission Results obtained with specimen having artificial induced defect is compared with specimen without any induced defect. The AE parameters are obtained for number of specimens and they are given as input to Neural Network. AE data of new specimen can be stimulated in the Neural Network for predicting the failure load.","PeriodicalId":448231,"journal":{"name":"International Review of Physics","volume":"90 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Failure Load Prediction of Composite Rings Using Acoustic Emission Monitoring\",\"authors\":\"V. Arumugam, G. Vaidyanathan, A. Stanley\",\"doi\":\"10.15866/IREPHY.V9I1.6157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Aim of this Study is to investigate the progressive failure of ring specimens cut from a Filament wound Glass-Epoxy pipes and predict the failure load using Acoustic Emission Technique. Defects like Delamination and fibercut are being artificially introduced in the specimen during the winding process. The split disk fixture is fabricated and Rings are tested in Universal testing machine under Acoustic Emission Monitoring. Acoustic Emission Results obtained with specimen having artificial induced defect is compared with specimen without any induced defect. The AE parameters are obtained for number of specimens and they are given as input to Neural Network. AE data of new specimen can be stimulated in the Neural Network for predicting the failure load.\",\"PeriodicalId\":448231,\"journal\":{\"name\":\"International Review of Physics\",\"volume\":\"90 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Review of Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15866/IREPHY.V9I1.6157\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Review of Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15866/IREPHY.V9I1.6157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Failure Load Prediction of Composite Rings Using Acoustic Emission Monitoring
The Aim of this Study is to investigate the progressive failure of ring specimens cut from a Filament wound Glass-Epoxy pipes and predict the failure load using Acoustic Emission Technique. Defects like Delamination and fibercut are being artificially introduced in the specimen during the winding process. The split disk fixture is fabricated and Rings are tested in Universal testing machine under Acoustic Emission Monitoring. Acoustic Emission Results obtained with specimen having artificial induced defect is compared with specimen without any induced defect. The AE parameters are obtained for number of specimens and they are given as input to Neural Network. AE data of new specimen can be stimulated in the Neural Network for predicting the failure load.