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dc.contributor.authorLin, HLen_US
dc.contributor.authorChou, CPen_US
dc.date.accessioned2014-12-08T15:17:30Z-
dc.date.available2014-12-08T15:17:30Z-
dc.date.issued2006-02-01en_US
dc.identifier.issn1362-1718en_US
dc.identifier.urihttp://dx.doi.org/10.1179/174329306X84328en_US
dc.identifier.urihttp://hdl.handle.net/11536/12684-
dc.description.abstractThere are many parameters that affect gas tungsten arc (GTA) welding process quality. Conventionally, the Taguchi method has been widely used in engineering; however, with this method the desired results can only be obtained with the use of very discrete control factors, thus leading to uncertainty about the real optimum. In this study, the Taguchi method was used for the initial optimisation of the GTA welding process parameters. A neural network was then used to construct the relationships between the welding process parameters and weld pool geometry of each weldment. The optimal parameters of the GTA welding process were determined by simulating significant process parameters obtained by the Taguchi method. Experimental results are provided to illustrate the proposed approach.en_US
dc.language.isoen_USen_US
dc.subjectgas tungsten arc weldingen_US
dc.subjectneural networksen_US
dc.subjectTaguchi methoden_US
dc.titleOptimisation of the GTA welding process using the Taguchi method and a neural networken_US
dc.typeArticleen_US
dc.identifier.doi10.1179/174329306X84328en_US
dc.identifier.journalSCIENCE AND TECHNOLOGY OF WELDING AND JOININGen_US
dc.citation.volume11en_US
dc.citation.issue1en_US
dc.citation.spage120en_US
dc.citation.epage126en_US
dc.contributor.department機械工程學系zh_TW
dc.contributor.departmentDepartment of Mechanical Engineeringen_US
dc.identifier.wosnumberWOS:000236940600015-
dc.citation.woscount12-
Appears in Collections:Articles