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dc.contributor.authorKurt, Halil Ibrahim
dc.contributor.authorOduncuoglu, Murat
dc.contributor.authorYilmaz, Necip Fazil
dc.contributor.authorErgul, Engin
dc.contributor.authorAsmatulu, Ramazan
dc.date.accessioned2018-07-09T18:23:58Z
dc.date.available2018-07-09T18:23:58Z
dc.date.issued2018-05-08
dc.identifier.citationKurt, H.I.; Oduncuoglu, M.; Yilmaz, N.F.; Ergul, E.; Asmatulu, R. A Comparative Study on the Effect of Welding Parameters of Austenitic Stainless Steels Using Artificial Neural Network and Taguchi Approaches with ANOVA Analysis. Metals 2018, 8, 326en_US
dc.identifier.issn2075-4701
dc.identifier.otherWOS:000435109300036
dc.identifier.urihttps://doi.org/10.3390/met8050326
dc.identifier.urihttp://hdl.handle.net/10057/15368
dc.descriptionThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).en_US
dc.description.abstractIn order to investigate the structure of welds, austenitic stainless steel ( SS) studs with a diameter of 6 mm were welded to austenitic SS plates with a thickness of 5 mm using an arc stud welding (ASW) method. The effects of the welding current, welding time, and tip volume of the stud on the microstructure and ultimate tensile strength (UTS) of the welded samples were investigated in detail. The formation of delta-ferrites was detected in the weld zone because of the higher heat generated during the welding process. Higher welding current and time adversely affected the stud and significantly reduced the UTS of the samples. The UTS of the joints was also estimated using artificial neural network (ANN) and Taguchi approaches. The mathematical formulations for these two approaches were given in explicit form. Experimental results showed that the neural network results are more consistent with experimental results than those of the Taguchi method. Overall, it can be concluded that in order to achieve good welding joints and high strength values, ASW parameters should be investigated properly to determine the optimum conditions for each metal.en_US
dc.language.isoen_USen_US
dc.publisherMDPI AG, Basel, Switzerlanden_US
dc.relation.ispartofseriesMetals;v.8:no.5
dc.subjectStud weldingen_US
dc.subjectMechanical propertiesen_US
dc.subjectANNen_US
dc.subjectTaguchien_US
dc.subjectANOVAen_US
dc.titleA comparative study on the effect of welding parameters of austenitic stainless steels using artificial neural network and Taguchi approaches with ANOVA analysisen_US
dc.typeArticleen_US
dc.rights.holder© 1996-2018 MDPI (Basel, Switzerland) unless otherwise stateden_US


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