Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3828
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSerin, G.-
dc.contributor.authorGüdelek, M. U.-
dc.contributor.authorŞener, B.-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorÜnver, Hakkı Özgür-
dc.date.accessioned2020-10-21T10:05:16Z-
dc.date.available2020-10-21T10:05:16Z-
dc.date.issued2019-11en_US
dc.identifier.citationSerin, G., Gudelek, M. U., Sener, B., Özbayoglu, A. M. and Ünver, H. Ö. (2019, November). Chatter detection in milling with acoustic emissions and deep learning. In 9th International Congress On Machining, 7 – 9 November 2018, Barut Kemer - Antalya, Turkey. Congress Proceedings (pp. 28-39). Antalya : UTIS.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11851/3828-
dc.description.abstractOne of the main pillars of Industry 4.0 is the use of Machine Learning algorithms to make accurate predictions of the manufacturing environment in order to make informed decisions. Deep Learning algorithm, which is a subfield of machine learning, has been widely used and proven more effective than other classic computational intelligence methods in many fields such as aviation-aerospace, automotive, health, and finance. Deep learning algorithms, which lead to revolutionary new products and applications in these sectors, have also been gaining attention in the field of machining to make machine tools more intelligent to improve manufacturing efficiency. In this study, Convolutional Neural Networks (CNN) which is an architecture of deep learning has been implemented to detect chatter in a slot milling process. Chatter is vibrational phenomena and has severe effects on machining performance. The undesirable vibrations caused by chatter, may cause lower surface qualities and even tool breakages. Since chatter lowers the performance of the machining operations, it is very crucial to detect and take timely action. In this research, an acoustic emission (AE) sensor was mounted to the milling machine and collects acoustic emissions. Captured data was used for training and testing by the estimation model in order to detect chatter.en_US
dc.language.isoenen_US
dc.publisherUTISen_US
dc.relation.ispartof9th International Congress On Machining, Congress Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChatter detectionen_US
dc.subjectMillingen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional Neural Networksen_US
dc.titleChatter Detection in Milling With Acoustic Emissions and Deep Learningen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.authorid0000-0001-7998-5735-
dc.authorid0000-0002-4632-3505-
dc.institutionauthorÖzbayoglu, Ahmet Murat-
dc.institutionauthorÜnver, Hakkı Özgür-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextopen-
item.fulltextWith 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:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Files in This Item:
File Description SizeFormat 
UTIS2019.pdf3.78 MBAdobe PDFThumbnail
View/Open
Show simple item record



CORE Recommender

Page view(s)

370
checked on Dec 23, 2024

Download(s)

184
checked on Dec 23, 2024

Google ScholarTM

Check





Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.