Statistical analysis of WEDM machining parameters of Ti-6Al-4V alloy using taguchi-based grey relational analysis and artificial neural network
dc.authorid | 0000-0002-1628-1316 | en_US |
dc.contributor.author | Karataş, Meltem Altın | |
dc.contributor.author | Biberci, Mehmet Ali | |
dc.date.accessioned | 2024-01-18T08:36:38Z | |
dc.date.available | 2024-01-18T08:36:38Z | |
dc.date.issued | 2023 | en_US |
dc.department | BAİBÜ, Gerede Meslek Yüksekokulu, Makine ve Metal Teknolojileri Bölümü | en_US |
dc.description.abstract | In 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.citation | Altin 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.doi | 10.1007/s40799-022-00601-5 | |
dc.identifier.endpage | 870 | en_US |
dc.identifier.issn | 0732-8818 | |
dc.identifier.issn | 1747-1567 | |
dc.identifier.issue | 4 | en_US |
dc.identifier.scopus | 2-s2.0-85134660238 | en_US |
dc.identifier.scopusquality | Q2 | en_US |
dc.identifier.startpage | 851 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1007/s40799-022-00601-5 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12491/11958 | |
dc.identifier.volume | 47 | en_US |
dc.identifier.wos | WOS:000829709000001 | en_US |
dc.identifier.wosquality | Q3 | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.institutionauthor | Karataş, Meltem Altın | |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Experimental Techniques | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Ti-6Al-4V; WEDM | en_US |
dc.subject | Material Removal Rate | en_US |
dc.subject | Kerf | en_US |
dc.subject | Surface Roughness | en_US |
dc.subject | Taguchi Method | en_US |
dc.subject | Gray Relational Analysis | en_US |
dc.title | Statistical analysis of WEDM machining parameters of Ti-6Al-4V alloy using taguchi-based grey relational analysis and artificial neural network | en_US |
dc.type | Article | en_US |