Statistical analysis of WEDM machining parameters of Ti-6Al-4V alloy using taguchi-based grey relational analysis and artificial neural network

dc.authorid0000-0002-1628-1316en_US
dc.contributor.authorKarataş, Meltem Altın
dc.contributor.authorBiberci, Mehmet Ali
dc.date.accessioned2024-01-18T08:36:38Z
dc.date.available2024-01-18T08:36:38Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Gerede Meslek Yüksekokulu, Makine ve Metal Teknolojileri Bölümüen_US
dc.description.abstractIn this present study, the effect of processing parameters on cutting width (kerf), material removal rate (MRR), Ra (arithmetic mean deviation), Rq (root mean square deviation) and Rz (maximum height) values as a result of wire electrical discharge machining (WEDM) of Ti-6Al-4V alloy was investigated. It is aimed to determine the optimum values of the cutting parameters to obtain the highest MRR value with the lowest kerf, Ra, Rq, Rz. Cutting experiments were carried out using three different voltages (46, 56, 66 V), three different dielectric fluid pressures (10, 12, 14 kg/cm(2)) and three different wire feed rates (8, 10, 12 m/min). The parameters used in the experiments were designed according to the Taguchi L-9 (3(3)) orthogonal array in order to reduce the experimental cost. Gray Relational Analysis (GRA), one of the multi-criteria decision-making methods, has been applied to optimize the machining parameters in the cutting process with the wire erosion machine. Analysis of variance (ANOVA) was used to determine the effect percentages of the processing parameters. By using the data obtained from the experiments, the prediction study of the experimental data was carried out with the Artificial Neural Networks (ANN) model. High correlation coefficients were obtained in the regression model created using the ANN technique, and it was observed that both models were suitable and usable to predict the answers. As a result of GRA, the most ideal sequence was determined as VG(1)LQ(1)WS(1). Ideal conditions were determined as 46 V voltage, 10 kg/cm(2) dielectric fluid pressure and 8 m/min wire feed rate. Using the optimum machining parameters, an improvement of 4.22%, 54.65%, 28.77%, 31.94% and 35.24% was obtained for kerf, MRR, Ra, Rq, and Rz, respectively. As for the results obtained from ANOVA the contribution rate of the voltage was 72.18%. However, the effect of wire feed rate and dielectric fluid pressure was not statistically significant.en_US
dc.identifier.citationAltin Karataş, M., & Biberci, M. A. (2023). Statistical Analysis of WEDM Machining Parameters of Ti-6Al-4V Alloy Using Taguchi-Based Grey Relational Analysis and Artificial Neural Network. Experimental Techniques, 47(4), 851-870.en_US
dc.identifier.doi10.1007/s40799-022-00601-5
dc.identifier.endpage870en_US
dc.identifier.issn0732-8818
dc.identifier.issn1747-1567
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85134660238en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage851en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s40799-022-00601-5
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11958
dc.identifier.volume47en_US
dc.identifier.wosWOS:000829709000001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKarataş, Meltem Altın
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofExperimental Techniquesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTi-6Al-4V; WEDMen_US
dc.subjectMaterial Removal Rateen_US
dc.subjectKerfen_US
dc.subjectSurface Roughnessen_US
dc.subjectTaguchi Methoden_US
dc.subjectGray Relational Analysisen_US
dc.titleStatistical analysis of WEDM machining parameters of Ti-6Al-4V alloy using taguchi-based grey relational analysis and artificial neural networken_US
dc.typeArticleen_US

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