{"title":"Shimming Analysis of Carbon-Fiber Composite Materials with Eddy Current Testing","authors":"Gianni D’Angelo, G. Cavaccini, S. Rampone","doi":"10.1109/METROAEROSPACE.2018.8453579","DOIUrl":null,"url":null,"abstract":"This paper investigates the use of the Eddy Current Testing (ECT) for detecting gaps between carbon-fiber composite materials, caused by overlapping of assembly parts with geometrical variations. To this purpose, we use two overlapped carbon-fiber reinforced plastic (CFRP) tapes, while an increasing number of PVC sheets are placed between these tapes to vary the thickness of the gaps. Several experiments are carried out. For everyone, and for each considered gap, three ECT response parameters are extracted. The obtained data are used to train different machine learning-based classifiers to distinguish the gaps. Their validation, assessed through the 10-fold cross validation technique, proves the effectiveness of ECT as gaps detector for CFRP materials.","PeriodicalId":142603,"journal":{"name":"2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace)","volume":"17 22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/METROAEROSPACE.2018.8453579","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
This paper investigates the use of the Eddy Current Testing (ECT) for detecting gaps between carbon-fiber composite materials, caused by overlapping of assembly parts with geometrical variations. To this purpose, we use two overlapped carbon-fiber reinforced plastic (CFRP) tapes, while an increasing number of PVC sheets are placed between these tapes to vary the thickness of the gaps. Several experiments are carried out. For everyone, and for each considered gap, three ECT response parameters are extracted. The obtained data are used to train different machine learning-based classifiers to distinguish the gaps. Their validation, assessed through the 10-fold cross validation technique, proves the effectiveness of ECT as gaps detector for CFRP materials.