Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6910
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dc.contributor.authorŞener, Batıhan-
dc.contributor.authorSerin, Gökberk-
dc.contributor.authorGüdelek, M. Uğur-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorÜnver, Hakkı Özgür-
dc.date.accessioned2021-09-11T15:44:13Z-
dc.date.available2021-09-11T15:44:13Z-
dc.date.issued2020en_US
dc.identifier.citation8th IEEE International Conference on Big Data (Big Data) -- DEC 10-13, 2020 -- ELECTR NETWORKen_US
dc.identifier.isbn978-1-7281-6251-5-
dc.identifier.issn2639-1589-
dc.identifier.urihttps://doi.org/10.1109/BigData50022.2020.9378223-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6910-
dc.description.abstractMilling is a highly crucial machining process in the modern industry. With the recent trends of Industry 4.0, it is becoming more common to implement Artificial Intelligence (AI) methods to increase the performance of milling processes. As a significant limitation for the efficiency of the machining processes, chatter detection, and avoidance are critical. In this paper, a chatter detection method based on vibration data features for the slot milling process is proposed. This method benefits from a deep learning method, Deep Multi-Layer Perceptron (DMLP). Vibration data was acquired by attaching an accelerometer to the spindle housing during slot milling operations. Fast Fouries Transform (FFT) was applied to time-domain vibratory data. Frequency domain data achieved by FFT was investigated for labeling the occurrence of chatter. These labels were used to train the DMLP algorithm. Time-domain signal features such as root mean square, clearance factor, skewness, crest factor, and shape factor were selected as inputs for the chatter detection algorithm. Finally, validation cuttings were performed for verifying the results of the DMLP algorithm. The results prove that time-domain features can provide enough information about the chatter occurrence in slot milling operations, and the DMLP algorithm proposed in this research can successfully detect the chatter occurrence.en_US
dc.description.sponsorshipIEEE, IEEE Comp Soc, IBM, Ankuraen_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [118M414]en_US
dc.description.sponsorshipWe are grateful to Prof. Dr. Yusuf Altintas for providing us CutPROT software and technical support of MAL Inc. team, throughout this study. This study is funded by TUBITAK (The Scientific and Technological Research Council of Turkey) through project grant no. 118M414.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2020 IEEE International Conference On Big Data (Big Data)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdeep learningen_US
dc.subjectchatter detectionen_US
dc.subjectdeep multi-layer perceptronen_US
dc.subjectmillingen_US
dc.subjectindustry 4.0en_US
dc.titleIntelligent Chatter Detection in Milling using Vibration Data Features and Deep Multi-Layer Perceptronen_US
dc.typeConference Objecten_US
dc.relation.ispartofseriesIEEE International Conference on Big Dataen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Artificial Intelligence Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümütr_TR
dc.identifier.startpage4759en_US
dc.identifier.endpage4768en_US
dc.identifier.wosWOS:000662554704103en_US
dc.identifier.scopus2-s2.0-85103844256en_US
dc.institutionauthorGüdelek, Mehmet Uğur-
dc.institutionauthorÖzbayoğlu, Aahmet Murat-
dc.institutionauthorÜnver, Hakkı Özgür-
dc.identifier.doi10.1109/BigData50022.2020.9378223-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference8th IEEE International Conference on Big Data (Big Data)en_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
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
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering
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