Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1993
Title: Estimation of parameters for the free-form machining with deep neural network
Authors: Serin, Gökberk
Güdelek, M. Uğur
Özbayoğlu, Ahmet Murat
Ünver, Hakkı Özgür
Ünver, Hakkı Özgür/180394
Keywords: free-form machining
manufacturing
deep neural networks
big data
machine learning
Issue Date: 2017
Publisher: IEEE
Source: 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.
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.
Description: IEEE International Conference on Big Data (IEEE Big Data) (2017 : Boston, MA)
URI: https://ieeexplore.ieee.org/document/8258158
https://hdl.handle.net/20.500.11851/1993
ISBN: 978-1-5386-2715-0
ISSN: 2639-1589
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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