Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1993
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dc.contributor.authorSerin, Gökberk-
dc.contributor.authorGüdelek, M. Uğur-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorÜnver, Hakkı Özgür-
dc.date.accessioned2019-07-10T14:42:44Z
dc.date.available2019-07-10T14:42:44Z
dc.date.issued2017
dc.identifier.citationSerin, 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.isbn978-1-5386-2715-0
dc.identifier.issn2639-1589
dc.identifier.urihttps://ieeexplore.ieee.org/document/8258158-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/1993-
dc.descriptionIEEE International Conference on Big Data (IEEE Big Data) (2017 : Boston, MA)
dc.description.abstractPredictive 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.isoenen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectfree-form machiningen_US
dc.subjectmanufacturingen_US
dc.subjectdeep neural networksen_US
dc.subjectbig dataen_US
dc.subjectmachine learningen_US
dc.titleEstimation of parameters for the free-form machining with deep neural networken_US
dc.typeConference Objecten_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.startpage2102
dc.identifier.endpage2111
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000428073702012en_US
dc.identifier.scopus2-s2.0-85047798323en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.institutionauthorÜnver, Hakkı Özgür-
dc.identifier.doi10.1109/BigData.2017.8258158-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
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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