Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy

Basit Öğe Kaydı
dc.authorid0009-0009-7332-8166
dc.authorid0000-0002-8594-1234
dc.contributor.authorTuran, İbrahim
dc.contributor.authorÖzlü, Barış
dc.contributor.authorUlaş, Hasan Basri
dc.contributor.authorDemir, Halil
dc.date.accessioned2025-07-14T05:38:57Z
dc.date.available2025-07-14T05:38:57Z
dc.date.issued2025
dc.departmentTeknik Bilimler Meslek Yüksekokulu
dc.description.abstractIn this study, the drilling of an Al 6082-T6 alloy and the effects of cutting tool coating and cutting parameters on surface roughness, cutting temperature, hole diameter, circularity, and cylindrical variations was investigated. In addition, the prediction accuracy of Taguchi, artificial neural networks (ANNs), and adaptive neuro-fuzzy inference system (ANFIS) methods was compared using both experimental results and Signal/Noise (S/N) ratios derived from the experimental results. The experimental design was prepared according to Taguchi L27 orthogonal indexing. As a result, it was observed that increasing the cutting speed and feed rate increases the cutting temperature hole error, circularity error and cylindricity error. Increasing the cutting speed positively affected the surface roughness, while increasing the feed rate led to an increase in the surface roughness. The lowest surface roughness, cutting temperature, hole diameter error and hole circularity error values were measured for the uncoated cutting tool. The minimum cylindricity variation was measured for drilling with TiAlN-coated cutting tools. The optimum cutting parameters were A1B1C3 (Uncoated, 0.11 mm/rev, 200 m/min) for surface roughness, A1B1C1 (Uncoated, 0.11 mm/rev, 120 m/min) for cutting temperature, hole error, circularity error and cylindricity error. In the estimation of the output parameters with Taguchi, ANNs and ANFIS, it was observed that the estimates made by converting the experimental values into S/N ratios were more accurate than the estimates made with the experimental results. The reliability coefficient and prediction ability of the ANN model were found to be higher than Taguchi and ANFIS models in estimating the output parameters.
dc.identifier.doi10.3390/jmmp9030092
dc.identifier.issn25044494
dc.identifier.issue3
dc.identifier.scopus105001249115
dc.identifier.urihttps://dx.doi.org/10.3390/jmmp9030092
dc.identifier.urihttps://hdl.handle.net/20.500.12451/13277
dc.identifier.volume9
dc.identifier.wosWOS:001453165000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorÖzlü, Barış
dc.institutionauthorid0000-0002-8594-1234
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofJournal of Manufacturing and Materials Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAl 6082-T6 Alloy
dc.subjectANFIS
dc.subjectANN
dc.subjectDrilling
dc.subjectTaguchi
dc.titlePrediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
turan-ibrahim-2025.pdf
Boyut:
7.11 MB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu