Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting

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dc.authoridNAS, Engin/0000-0002-4828-9240
dc.authorwosidNAS, Engin/V-8276-2017
dc.contributor.authorKara, Fuat
dc.contributor.authorKarabatak, Mustafa
dc.contributor.authorAyyildiz, Mustafa
dc.contributor.authorNas, Engin
dc.date.accessioned2021-12-01T18:47:53Z
dc.date.available2021-12-01T18:47:53Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractThis study examined the hard turning of AISI D2 cold work tool steel subjected to deep cryogenic processing and tempering and investigated the effects on surface roughness and tool wear. In addition, the effects of the deep cryogenic processes on mechanical properties (macro and micro hardness) and microstructure were investigated. Three groups of test samples were evaluated: conventional heat treatment (CHT), deep cryogenic treatment (DCT-36) and deep cryogenic treatment with tempering (DCTT-36). The samples in the first group were subjected to only CHT to 62 HRc hardness. The second group (DCT-36) underwent processing for 36 h at -145 degrees C after conventional heat treatment. The latter group (DCTT-36) had been subjected to both conventional heat treatment and deep cryogenic treatment followed by 2 h of tempering at 200 degrees C. In the experiments, Al2O3 + TiC matrix-based untreated mixed alumina ceramic (AB30) and Al2O3 + TiC matrix-based TiN-coated ceramic (AB2010) cutting tools were used. The artificial intelligence method known as artificial neural networks (ANNs) was used to estimate the surface roughness based on cutting speed, cutting tool, workpiece, depth of cut and feed rate. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. Three different cutting speeds (50, 100 and 150 m/min), three different feed rates (0.08, 0.16 and 0.24 mm/rev) and three different cutting depths (0.25, 0.50 and 0.75 mm) were selected. Tool wear experiments were carried out at a cutting speed of 150 m/min, a feed rate of 0.08 mm/rev and a cutting depth of 0.6 mm. As a result of the experiments, the best results for both surface roughness and tool wear were obtained with the DCTT-36 sample. When cutting tools were compared, the best results for surface roughness and tool wear were obtained with the coated ceramic tool (AB2010). The macroscopic and micro hardness values were highest for the DCT-36. From the microstructural point of view, the DCTT-36 sample showed the best results with homogeneous and thinner secondary carbide formations. (C) 2019 The Authors. Published by Elsevier B.V.en_US
dc.description.sponsorshipDuzce UniversityDuzce University [2015.07.04.388]en_US
dc.description.sponsorshipThe authors would like to thank Duzce University Scientific Research Projects Coordinator for supporting this study with BAP project 2015.07.04.388.en_US
dc.identifier.doi10.1016/j.jmrt.2019.11.037
dc.identifier.endpage983en_US
dc.identifier.issn2238-7854
dc.identifier.issn2214-0697
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85076572438en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage969en_US
dc.identifier.urihttps://doi.org/10.1016/j.jmrt.2019.11.037
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10403
dc.identifier.volume9en_US
dc.identifier.wosWOS:000509333300095en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal Of Materials Research And Technology-Jmr&Ten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectANNen_US
dc.subjectMachinabilityen_US
dc.subjectDeep cryogenicen_US
dc.subjectMicrostructureen_US
dc.subjectHardnessen_US
dc.subjectArtificial Neural-Networksen_US
dc.subject1.2080 Tool Steelen_US
dc.subjectSurface-Roughnessen_US
dc.subjectWear Behavioren_US
dc.subjectTribological Behavioren_US
dc.subjectFlank Wearen_US
dc.subjectTemperatureen_US
dc.subjectPredictionen_US
dc.subjectOptimizationen_US
dc.subjectRegressionen_US
dc.titleEffect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cuttingen_US
dc.typeArticleen_US

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