Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX

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dc.authorscopusid56784270900
dc.authorscopusid57194059788
dc.authorscopusid57205041215
dc.authorscopusid56509529900
dc.authorscopusid36644031300
dc.contributor.authorManoj, I.V.
dc.contributor.authorSoni, Hargovind
dc.contributor.authorNarendranath, S.
dc.contributor.authorMashinini, P.M.
dc.contributor.authorKara, Fuat
dc.date.accessioned2023-07-26T11:53:55Z
dc.date.available2023-07-26T11:53:55Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractThe Altemp HX is a nickel-based superalloy having many applications in chemical, nuclear, aerospace, and marine industries. Machining such superalloys is challenging as it may cause both tool and surface damage. WEDM, a non-contact machining technique, can be employed in the machining of such alloys. In the present study, different input parameters which include pulse on time, wire span, and servo gap voltage were investigated. The cutting velocity, surface roughness, recast layer, and microhardness variations were examined on the WEDMed surface. The genetic algorithm was used to optimize the cutting velocity and surface roughness, thereby improving the overall quality of the product. The highest recast layer values were recorded as 25.8 ?m, and the lowest microhardness was 170 HV. Response surface methodology and artificial neural network were employed for the prediction of cutting velocity and surface roughness. Artificial neural network prediction technique was the most efficient method for the prediction of response parameters as it predicted an error percentage lesser than 6%. © 2022 I. V. Manoj et al.en_US
dc.identifier.doi10.1155/2022/5192981
dc.identifier.issn1687-8434
dc.identifier.scopus2-s2.0-85124029778en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1155/2022/5192981
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12656
dc.identifier.volume2022en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKara, Fuat
dc.language.isoenen_US
dc.publisherHindawi Limiteden_US
dc.relation.ispartofAdvances in Materials Science and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectForecastingen_US
dc.subjectGenetic algorithmsen_US
dc.subjectMarine applicationsen_US
dc.subjectMarine industryen_US
dc.subjectMicrohardnessen_US
dc.subjectNeural networksen_US
dc.subjectNickel alloysen_US
dc.subjectSuperalloysen_US
dc.subjectVelocityen_US
dc.subjectCutting velocityen_US
dc.subjectGap voltageen_US
dc.subjectInput parameteren_US
dc.subjectMachining parametersen_US
dc.subjectMachining techniquesen_US
dc.subjectNickel-based superalloysen_US
dc.subjectNon-contacten_US
dc.subjectPulse on-timeen_US
dc.subjectRecast layeren_US
dc.subjectSurface damagesen_US
dc.subjectSurface roughnessen_US
dc.titleExamination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HXen_US
dc.typeArticleen_US

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