Predictive modelling and optimization for machinability indicators in cleaner milling of PH13-8Mo using sustainable cutting environments

Basit Öğe Kaydı
dc.authoridGUNAY, MUSTAFA/0000-0002-1281-1359
dc.contributor.authorYurtkuran, Hakan
dc.contributor.authorGunay, Mustafa
dc.date.accessioned2024-09-29T15:54:47Z
dc.date.available2024-09-29T15:54:47Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis study aims to optimize and model the resultant cutting force (Fr) and surface roughness (Ra) in the cleaner milling of PH13-8Mo stainless steel, which is extremely difficult to machine due to its high technical properties. The impacts of dry, minimal quantity lubrication (MQL) and cryogenic (Cryo) environments on the Fr and Ra were investigated in the up-milling of PH13-8Mo. The experiments were done using TiAlN-coated inserts at varying cutting speeds and feed rates. Control factors were optimized simultaneously with Taguchi-based grey relational analysis (TGRA) to minimize Fr and Ra. Predictive models of Fr and Ra were developed by the response surface method. An average of 20.98% and 19.86% improvement in Ra was achieved in the MQL and cryo environments, respectively. Increased sticking, chipping and microcracks in the insert due to cryogenic cooling increased Fr and Ra. Optimum factors were found as an MQL environment, a 60 m/min cutting speed and a 0.04 mm/rev feed rate with TGRA. The high correlations of the developed mathematical models showed that the models were reliable. Thus, significant support will be provided to sustainable machining with the industrial use of data obtained for machinability indicators in milling PH13-8Mo steel.en_US
dc.description.sponsorshipKarabuek University Scientific Research Projects Unit; [KBUBAP-22-DR-073]en_US
dc.description.sponsorshipThe authors thank the Karabuek University Scientific Research Projects Unit for providing financial support for this study under the project code KBUBAP-22-DR-073.en_US
dc.identifier.doi10.1007/s40430-024-04897-9
dc.identifier.issn1678-5878
dc.identifier.issn1806-3691
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85191748889en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s40430-024-04897-9
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4287
dc.identifier.volume46en_US
dc.identifier.wosWOS:001209836900003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofJournal of the Brazilian Society of Mechanical Sciences and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPH13-8Moen_US
dc.subjectCleaner machiningen_US
dc.subjectMQLen_US
dc.subjectCryogenicen_US
dc.subjectOptimizationen_US
dc.subjectModellingen_US
dc.titlePredictive modelling and optimization for machinability indicators in cleaner milling of PH13-8Mo using sustainable cutting environmentsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu