Uncertainty analysis of milling parameters using Monte Carlo simulation, the Taguchi optimization method and data-driven modeling

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dc.authorid0000-0002-1377-9338en_US
dc.authorid0000-0001-6228-3630
dc.authorid0000-0002-7275-1132
dc.contributor.authorKahraman, Mehmet Faith
dc.contributor.authorBilge, Habibullah
dc.contributor.authorÖztürk, Sabri
dc.date.accessioned2021-06-23T19:51:44Z
dc.date.available2021-06-23T19:51:44Z
dc.date.issued2019
dc.departmentBAİBÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractSurface roughness plays an important role in the performance of finished structures. The surface quality obtained is enormously affected by cutting parameters. Therefore, the purpose of the present study is to examine the surface roughness value of aluminum 7075 workpiece material during milling operation by considering three steps: (1) the multi-nonlinear regression (MNLR) modeling basis of Taguchi design, (2) optimization based on signal to noise ratio (S/N), and (3) probabilistic uncertainty analysis depending on Monte Carlo technique as a result of depth of cut, cutting speed and feed rate. The depth of cut of 0.2 mm, cutting speed of 900 m x min(-1), and feed rate of 0.1 mm x tooth(-1) were determined as Taguchi-optimized conditions with a surface roughness of 0.964 mu m. In order to justify the surface roughness predicted under optimized conditions in relation to the predicted Taguchi method, three repetitive verification experiments were performed and surface roughness of 0.964 mu m +/- 0.3% was achieved. The best-fit MNLR method with an R-pred(2) (predicted regression coefficient) of 98.02 % is useful for calculating the success of estimating the outcome variable. Monte Carlo simulations were found to be quite effective for identifying the uncertainties in surface roughness that could not be captured by means of deterministic methods.en_US
dc.identifier.doi10.3139/120.111344
dc.identifier.endpage483en_US
dc.identifier.issn0025-5300
dc.identifier.issue5en_US
dc.identifier.startpage477en_US
dc.identifier.urihttps://doi.org/10.3139/120.111344
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10038
dc.identifier.volume61en_US
dc.identifier.wosWOS:000466985100012en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorKahraman, Mehmet Faith
dc.institutionauthorBilge, Habibullah
dc.institutionauthorÖztürk, Sabri
dc.language.isoenen_US
dc.publisherCarl Hanser Verlagen_US
dc.relation.ispartofMaterials Testingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSurface Roughnessen_US
dc.subjectAluminum 7075en_US
dc.subjectTaguchi Methoden_US
dc.subjectMulti Non-linear Regressionen_US
dc.subjectMonte Carlo Simulationen_US
dc.titleUncertainty analysis of milling parameters using Monte Carlo simulation, the Taguchi optimization method and data-driven modelingen_US
dc.typeArticleen_US

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