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Estimation of parameters for the free-form machining with deep neural network

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dc.contributor.author Serin, Gokberk
dc.contributor.author Gudelek, M. Ugur
dc.contributor.author Özbayoglu, Ahmet Murat
dc.contributor.author Ünver, Hakkı Özgür
dc.date.accessioned 2019-07-10T14:42:44Z
dc.date.available 2019-07-10T14:42:44Z
dc.date.issued 2017
dc.identifier.citation Serin, G., Gudelek, M. U., Ozbayoglu, A. M., & Unver, H. O. (2017, December). Estimation of parameters for the free-form machining with deep neural network. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 2102-2111). IEEE. en_US
dc.identifier.isbn 978-1-5386-2715-0
dc.identifier.issn 2639-1589
dc.identifier.uri https://ieeexplore.ieee.org/document/8258158
dc.identifier.uri http://hdl.handle.net/20.500.11851/1993
dc.description IEEE International Conference on Big Data (IEEE Big Data) (2017 : Boston, MA)
dc.description.abstract Predictive Analytics is a crucial part of a Big Data application. Lately, developers have turned their attention to deep learning models due to their huge success in various implementations. Meanwhile, there is lack of deep learning implementations in manufacturing applications due to insufficient data. This phenomenon has been slowly shifting due to the application of IoT and Industry 4.0 concept within the manufacturing industry. Streaming and batch data producing sources are becoming more and more common in the machining industry. In this paper, we propose a deep learning predictive analytics model based on the data generated by a particular machining process. The results indicate that using such a model can make very accurate predictions and can be used as part of a real-time decision-making process in the manufacturing industry. In this study, the prediction models of three crucial metrics of machining such as quality, performance and energy consumption have been developed by utilizing artificial neural networks and deep learning methods. Specific measures of quality, performance and energy consumption refer to material removal rate (MRR), surface roughness (Ra) and specific energy consumption (SEC) respectively. The control parameters of machining are selected as stepover (a(e)), depth of cut (a(p)), feed per tooth (f(z)) and cutting speed (V-c). In addition, variance analysis (ANOVA) has been used to examine the effects of the input parameters on the output parameters. en_US
dc.language.iso eng en_US
dc.publisher IEEE en_US
dc.rights info:eu-repo/semantics/closedAccess
dc.subject free-form machining en_US
dc.subject manufacturing en_US
dc.subject deep neural networks en_US
dc.subject big data en_US
dc.subject machine learning en_US
dc.title Estimation of parameters for the free-form machining with deep neural network en_US
dc.type conferenceObject en_US
dc.contributor.department TOBB ETU, Faculty of Engineering, Department of Computer Engineering en_US
dc.contributor.department TOBB ETU, Faculty of Engineering, Department of Mechanical Engineering en_US
dc.contributor.department TOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR
dc.contributor.department TOBB ETÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü tr_TR
dc.identifier.startpage 2102
dc.identifier.endpage 2111
dc.contributor.orcid https://orcid.org/0000-0001-7998-5735
dc.identifier.wos WOS:000428073702012
dc.identifier.scopus 2-s2.0-85047798323
dc.contributor.tobbetuauthor Özbayoğlu, Ahmet Murat
dc.contributor.tobbetuauthor Ünver, Hakkı Özgür
dc.contributor.YOKid Özbayoğlu, Ahmet Murat/142991
dc.contributor.YOKid Ünver, Hakkı Özgür/180394
dc.identifier.doi 10.1109/BigData.2017.8258158
dc.contributor.wosresearcherID H-2328-2011
dc.contributor.ScopusAuthorID 6505999525
dc.contributor.ScopusAuthorID 6603873269
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı tr_TR


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