Wei Zhang, Surong Qu, Li Li, Qinglin Miao, Changyuan Song
{"title":"人工神经网络在导电材料测厚中的应用","authors":"Wei Zhang, Surong Qu, Li Li, Qinglin Miao, Changyuan Song","doi":"10.1109/AICI.2009.468","DOIUrl":null,"url":null,"abstract":"Eddy current testing (ECT) is becoming a widely used inspection technique, particularly in the aircraft, power and nuclear industries. Many factors may affect the eddy current response. Inverse problems to determine the thickness from ECT signals of multilayer conductors have been a challenge for a certain degree. The objectives of this study are to introduce a method based on improved Back Propagation neural network (BPNN) to identify the multilayer thickness from their ECT signals. The simulation study and an experimental validation carried out on a number of specimens with different known thickness confirmed the suitability of the proposed approach for multilayer thickness measuring.","PeriodicalId":289808,"journal":{"name":"2009 International Conference on Artificial Intelligence and Computational Intelligence","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of ANN in the Thickness Measuring of Conductive Materials\",\"authors\":\"Wei Zhang, Surong Qu, Li Li, Qinglin Miao, Changyuan Song\",\"doi\":\"10.1109/AICI.2009.468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Eddy current testing (ECT) is becoming a widely used inspection technique, particularly in the aircraft, power and nuclear industries. Many factors may affect the eddy current response. Inverse problems to determine the thickness from ECT signals of multilayer conductors have been a challenge for a certain degree. The objectives of this study are to introduce a method based on improved Back Propagation neural network (BPNN) to identify the multilayer thickness from their ECT signals. The simulation study and an experimental validation carried out on a number of specimens with different known thickness confirmed the suitability of the proposed approach for multilayer thickness measuring.\",\"PeriodicalId\":289808,\"journal\":{\"name\":\"2009 International Conference on Artificial Intelligence and Computational Intelligence\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Artificial Intelligence and Computational Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICI.2009.468\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Artificial Intelligence and Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICI.2009.468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of ANN in the Thickness Measuring of Conductive Materials
Eddy current testing (ECT) is becoming a widely used inspection technique, particularly in the aircraft, power and nuclear industries. Many factors may affect the eddy current response. Inverse problems to determine the thickness from ECT signals of multilayer conductors have been a challenge for a certain degree. The objectives of this study are to introduce a method based on improved Back Propagation neural network (BPNN) to identify the multilayer thickness from their ECT signals. The simulation study and an experimental validation carried out on a number of specimens with different known thickness confirmed the suitability of the proposed approach for multilayer thickness measuring.