Publication:
Effect of Machinability, Microstructure and Hardness of Deep Cryogenic Treatment in Hard Turning of AISI D2 Steel With Ceramic Cutting

dc.authorscopusid36644031300
dc.authorscopusid57212382507
dc.authorscopusid36752294700
dc.authorscopusid56741121800
dc.contributor.authorKara, F.
dc.contributor.authorKarabatak, M.
dc.contributor.authorAyyildiz, M.
dc.contributor.authorNas, E.
dc.date.accessioned2020-06-21T12:19:06Z
dc.date.available2020-06-21T12:19:06Z
dc.date.issued2020
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Kara] Fuat, Department of Mechanical and Manufacturing Engineering, Düzce Üniversitesi, Duzce, Turkey; [Karabatak] Mustafa, Institute of Science and Technology, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Ayyildiz] Mustafa, Department of Mechanical and Manufacturing Engineering, Düzce Üniversitesi, Duzce, Turkey; [Nas] Engin, Dr. Engin PAK Cumayeri Vocational School of Higher Education, Düzce Üniversitesi, Duzce, Turkeyen_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 °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 °C. In the experiments, Al<inf>2</inf>O<inf>3</inf> + TiC matrix-based untreated mixed alumina ceramic (AB30) and Al<inf>2</inf>O<inf>3</inf> + 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. © 2019 The Authors.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-s2.0-85076572438
dc.identifier.scopusqualityQ2
dc.identifier.startpage969en_US
dc.identifier.urihttps://doi.org/10.1016/j.jmrt.2019.11.037
dc.identifier.volume9en_US
dc.identifier.wosWOS:000509333300095
dc.identifier.wosqualityQ1
dc.language.isoenen_US
dc.publisherElsevier Editora Ltdaen_US
dc.relation.ispartofJournal of Materials Research and Technology-JMR&Ten_US
dc.relation.journalJournal 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.subjectDeep Cryogenicen_US
dc.subjectHardnessen_US
dc.subjectMachinabilityen_US
dc.subjectMicrostructureen_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
dspace.entity.typePublication

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