Investigation of the effect of hydromechanical deep drawing process parameters on formability of AA5754 sheets metals by using neuro-fuzzy forecasting approach

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dc.contributor.authorTinkir, Mustafa
dc.contributor.authorDilmec, Murat
dc.contributor.authorTurkoz, Mevlut
dc.contributor.authorHalkaci, H. Selcuk
dc.date.accessioned2024-02-23T14:34:44Z
dc.date.available2024-02-23T14:34:44Z
dc.date.issued2015
dc.departmentNEÜen_US
dc.description.abstractAdaptive neural-network based fuzzy logic inference system (ANFIS) is a useful method instead of costly Finite Element Analysis (FEA) in order to reduce investigation cost of forming processes. In this research, the effect of hydromechanical deep drawing (HDD) process parameters on AA5754-O sheet was investigated by FE simulations with analysis of variance (ANOVA) and Adaptive Neuro-Fuzzy Modeling approach. In order to determine the prediction error of the ANFIS model according to FEA, firstly a series of FEA of the HDD process were conducted according to Taguchi's Design of Experiment Method (DOE). The results of the FEA were confirmed by comparing the thickness distributions of the formed cups by experimentally and numerically. Moreover an adaptive neural-network based fuzzy logic inference system (ANFIS) was created according to results of simulation to predict the maximum thinning of AA5754-O sheet without needing FE simulations. The calculation performances of the ANFIS model were determined by comparing the estimated results with the results of the FE simulations. By using the results of the FE simulations which were conducted according to a matrix plan, the effects of the parameters to the thinning of the blank were determined by the analysis of variance (ANOVA) method. ABAQUS and MATLAB/ANFIS/Simulink softwares were used to realize and simulate proposed techniques. Mean error of prediction result of ANFIS is found as 0.89% according to FEA.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [108M516]en_US
dc.description.sponsorshipThis work is supported by The Scientific and Technological Research Council of Turkey (TUBITAK). Project number: 108M516. TUBITAK support is profoundly acknowledged.en_US
dc.identifier.doi10.3233/IFS-141346
dc.identifier.endpage659en_US
dc.identifier.issn1064-1246
dc.identifier.issn1875-8967
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-84921905125en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage647en_US
dc.identifier.urihttps://doi.org/10.3233/IFS-141346
dc.identifier.urihttps://hdl.handle.net/20.500.12452/15732
dc.identifier.volume28en_US
dc.identifier.wosWOS:000348366900015en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIos Pressen_US
dc.relation.ispartofJournal Of Intelligent & Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHydromechanical Deep Drawingen_US
dc.subjectLimiting Drawing Ratioen_US
dc.subjectPredictionen_US
dc.subjectFinite Elementen_US
dc.subjectTaguchi Doeen_US
dc.subjectAdaptive Neuro-Fuzzyen_US
dc.titleInvestigation of the effect of hydromechanical deep drawing process parameters on formability of AA5754 sheets metals by using neuro-fuzzy forecasting approachen_US
dc.typeArticleen_US

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