Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/4124
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dc.contributor.authorSerin, Gökberk-
dc.contributor.authorŞener, Batıhan-
dc.contributor.authorGüdelek, M. Uğur-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorÜnver, Hakkı Özgür-
dc.date.accessioned2021-01-27T13:45:03Z-
dc.date.available2021-01-27T13:45:03Z-
dc.date.issued2020-
dc.identifier.citationSerin, G., Sener, B., Gudelek, M. U., Ozbayoglu, A. M., and Unver, H. O. (2020). Deep Multi-Layer Perceptron based Prediction of Energy Efficiency and Surface Quality for Milling in The Era of Sustainability and Big Data. Procedia Manufacturing, 51, 1166-1177.en_US
dc.identifier.issn2351-9789-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/4124-
dc.identifier.urihttps://doi.org/10.1016/j.promfg.2020.10.164-
dc.description.abstractIn advanced industries such as aerospace, medical and automotive, high precision machining is increasingly required for many parts made by difficult-to-cut alloys. Machine tool manufacturers respond to this demand by developing more advanced machine tools that have advanced sub-systems for attaining high-precision and wide flexibility, with the expense of energy efficiency. Unfortunately, worldwide primary energy resources continue to run out. Furthermore, GHG emissions mostly related to energy, remain to be a global issue with the ever-increasing economic expansion in many developed and developing economies. In the meantime, increasing use of sensors and Internet of Things (IoT) technologies in shop-floors set off a data explosion, warranting the use of emerging Deep Learning techniques to cope with “Big Data” reality of manufacturing. Therefore, in this study a Deep Multi-Layer Perceptron (DMLP) based algorithm for predicting surface roughness and specific cutting energy - major measures of precision and energy efficiency-, has been developed for slot milling of AL7075. Design of Experiment is used to collect the required data and train DMLP based model. The finalized prediction algorithm estimated quality and energy efficiency with 91.5% and 90.7% accuracy rates in slot milling, verified by additional machining and data collection.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofProcedia Manufacturingen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSustainabilityen_US
dc.subjectBig Dataen_US
dc.subjectDeep learningen_US
dc.subjectMachiningen_US
dc.subjectEnergy-efficiencyen_US
dc.titleDeep Multi-Layer Perceptron Based Prediction of Energy Efficiency and Surface Quality for Milling in the Era of Sustainability and Big Dataen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümütr_TR
dc.identifier.volume51-
dc.identifier.startpage1166-
dc.identifier.endpage1177-
dc.authorid0000-0001-7998-5735-
dc.authorid0000-0002-4632-3505-
dc.identifier.wosWOS:000863680700163en_US
dc.identifier.scopus2-s2.0-85099828504en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.institutionauthorÜnver Hakkı Özgür-
dc.identifier.doi10.1016/j.promfg.2020.10.164-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
crisitem.author.dept02.7. Department of Mechanical Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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