Weld defect categorization from welding current using principle component analysis

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dc.contributor.authorArabaci, Hayri.
dc.contributor.authorLaving, Salman.
dc.date.accessioned2020-03-26T20:19:45Z
dc.date.available2020-03-26T20:19:45Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractReal time welding quality control still remains a challenging task due to the dynamic characteristic of welding. Welding current of gas metal arc welding possess valuable information that can be analyzed for weld quality assessment purposes. On-line monitoring of motor current can be provided information about the welding. In this study, current signals obtained during welding in the short- circuit metal transfer mode were used for real-time categorization of deliberately induced weld defects and good welds. A hall-effect current sensor was employed on the ground wiring of the welding machine to acquire the welding current signals during the welding process. Vector reduction of the current signals in time domain was achieved by principle component analysis. The reduced vector was then classified by various classification techniques such as support vector machines, decision trees and nearest neighbor to categorize the arc weld defects or pass it as a good weld. The proposed technique has proved to be successful with accurate classification of the welding categories using all three classifiers. The classification technique is fast enough so it can be used for real time weld quality control as all the signal processing is carried out in the time domain.en_US
dc.identifier.citationArabaci, H., Laving, S. (2019). Weld Defect Categorization from Welding Current using Principle Component Analysis. International Journal of Advanced Computer Science and Applications, 10(6), 204-211.
dc.identifier.endpage211en_US
dc.identifier.issn2158-107Xen_US
dc.identifier.issn2156-5570en_US
dc.identifier.issue6en_US
dc.identifier.pmid#YOKen_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage204en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/38387
dc.identifier.volume10en_US
dc.identifier.wosWOS:000476620800029en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArabaci, Hayri.
dc.institutionauthorLaving, Salman.
dc.language.isoenen_US
dc.publisherSCIENCE & INFORMATION SAI ORGANIZATION LTDen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArc weld defectsen_US
dc.subjectfeature extractionen_US
dc.subjectPCAen_US
dc.subjectclassification techniquesen_US
dc.subjecton-line monitoringen_US
dc.titleWeld defect categorization from welding current using principle component analysisen_US
dc.typeArticleen_US

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