Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

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dc.authoridGupta, Munish/0000-0002-0777-1559
dc.authoridM, BELSAM JEBA ANANTH/0000-0003-4799-018X
dc.authoridMashinini, Madindwa/0000-0001-8614-1610
dc.authoridKORKMAZ, Mehmet Erdi/0000-0002-0481-6002
dc.authoridnag, akash/0000-0003-0400-7739
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorMashinini, Peter Madindwa
dc.contributor.authorMishra, Priyanka
dc.contributor.authorAnanth, M. Belsam Jeba
dc.contributor.authorMustafa, Sithara Mohamed
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.date.accessioned2024-09-29T15:57:43Z
dc.date.available2024-09-29T15:57:43Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractSelective laser melting (SLM) can produce complex metal components with high densities, thereby surpassing the limitations of traditional machining methods. However, achieving accurate dimensions, geometries, and acceptable surface states in parts fabricated through SLM remains a concern as they often fall short compared to traditionally machined components. As a solution, a hybrid additive-subtractive manufacturing (HASM) method was developed to effectively utilize the advantages of both techniques. In this study, SLM-made 316 L stainless steel was machined under distinct cooling conditions to investigate the effects of roughness and tool wear. After a thorough investigation, the dual-MQL strategy was evaluated and compared with dry and MQL cutting strategies. The findings showed that the dual-MQL condition led to a significant reduction in flank wear by 54-56% and 29-34%, respectively, associated with dry and MQL cutting techniques, making it a highly promising key for machining SLM-made steel components. Machine learning techniques are potential tools for prediction and classification capabilities in machining processes. For milling SLM-made 316 L SS, multilayer perceptron (MLP) proved to be the most effective prediction model and for classification MLP and Random forest performed better.en_US
dc.identifier.doi10.1016/j.jmrt.2024.06.183
dc.identifier.endpage1852en_US
dc.identifier.issn2238-7854
dc.identifier.issn2214-0697
dc.identifier.scopus2-s2.0-85197101595en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1837en_US
dc.identifier.urihttps://doi.org/10.1016/j.jmrt.2024.06.183
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4980
dc.identifier.volume31en_US
dc.identifier.wosWOS:001262824300001en_US
dc.identifier.wosqualityN/Aen_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.subjectSLMen_US
dc.subjectHASMen_US
dc.subjectDual-MQLen_US
dc.subjectSurface finishen_US
dc.subjectMLPen_US
dc.titleEnhancing surface quality and tool life in SLM-machined components with Dual-MQL approachen_US
dc.typeArticleen_US

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