ANN Assisted Prediction of Weld Bead Geometry in Gas Tungsten Arc Welding of HSLA Steels

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dc.contributor.authorIqbal, Asif
dc.contributor.authorKhan, Saeed M.
dc.contributor.authorSahir, Mukhtar H.
dc.date.accessioned2026-02-06T18:29:08Z
dc.date.issued2011
dc.departmentDoğu Akdeniz Üniversitesi
dc.descriptionWorld Congress on Engineering (WCE 2011) -- JUL 06-08, 2011 -- Imperial Coll, London, UNITED KINGDOM
dc.description.abstractWeld bead geometry (front bead width and height, and back bead width and height) is a significant physical characteristic of a weldment. Several welding parameters such as welding speed, weld current, voltage, and shielding gas flow rate affect the weld bead geometry. Traditionally, an expert welder from his experience of trial and error selects a set of parameters that may yield fairly good results. However, the trial and error can be avoided, if a suitable automation tool can be developed, which could forecast the output from a set of desired parameters. Artificial neural net (ANN) was applied to predict the weld bead geometry in gas tungsten arc (GTA) welding of high strength low alloy (HSLA) steel. Back-propagation neural network algorithm has been followed to associate the welding process parameters with the weld bead geometry.
dc.description.sponsorshipInt Assoc Engineers,IAENG, Soc Artificial Intelligence,IAENG, Soc Bioinformat,IAENG, Soc Computer Sci,IAENG, Soc Data Min,IAENG, Soc Elect Engn,IAENG, Soc Imagl Engn,IAENG, Soc Ind Engn,IAENG, Soc Informat Syst Engn,IAENG, Soc Internet Comput & Web Serv,IAENG, Soc Mech Engn,IAENG, Soc Operat Res,IAENG, Soc Sci Comput,IAENG, Soc Software Engn,IAENG, Soc Wireless Engn
dc.identifier.endpage821
dc.identifier.isbn978-988-18210-6-5
dc.identifier.issn2078-0958
dc.identifier.orcid0000-0002-4372-8179
dc.identifier.orcid0000-0003-4891-3442
dc.identifier.scopus2-s2.0-80755174542
dc.identifier.scopusqualityQ4
dc.identifier.startpage818
dc.identifier.urihttps://hdl.handle.net/11129/11276
dc.identifier.wosWOS:000393011100162
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInt Assoc Engineers-Iaeng
dc.relation.ispartofWorld Congress on Engineering, Wce 2011, Vol I
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260204
dc.subjectneural nets
dc.subjectlearning
dc.subjectback propagation
dc.subjectwelding parameters
dc.titleANN Assisted Prediction of Weld Bead Geometry in Gas Tungsten Arc Welding of HSLA Steels
dc.typeConference Object

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