Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/3832
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Serin, G. | - |
dc.contributor.author | Şener, B. | - |
dc.contributor.author | Özbayoğlu, Ahmet Murat | - |
dc.contributor.author | Ünver, Hakkı Özgür | - |
dc.date.accessioned | 2020-10-21T10:05:17Z | - |
dc.date.available | 2020-10-21T10:05:17Z | - |
dc.date.issued | 2020-07 | - |
dc.identifier.citation | Serin, G., Sener, B., Ozbayoglu, A. M., and Unver, H. O. (2020). Review of tool condition monitoring in machining and opportunities for deep learning. The International Journal of Advanced Manufacturing Technology, 1-22. | en_US |
dc.identifier.issn | 1433-3015 | - |
dc.identifier.issn | 0268-3768 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/3832 | - |
dc.identifier.uri | https://link.springer.com/article/10.1007/s00170-020-05449-w | - |
dc.description.abstract | Tool condition monitoring and machine tool diagnostics are performed using advanced sensors and computational intelligence to predict and avoid adverse conditions for cutting tools and machinery. Undesirable conditions during machining cause chatter, tool wear, and tool breakage, directly affecting the tool life and consequently the surface quality, dimensional accuracy of the machined parts, and tool costs. Tool condition monitoring is, therefore, extremely important for manufacturing efficiency and economics. Acoustic emission, vibration, power, and temperature sensors monitor the stability and efficiency of the machining process, collecting large amounts of data to detect tool wear, breakage, and chatter. Studies on monitoring the vibrations and acoustic emissions from machine tools have provided information and data regarding the detection of undesirable conditions. Herein, studies on tool condition monitoring are reviewed and classified. As Industry 4.0 penetrates all manufacturing sectors, the amount of manufacturing data generated has reached the level of big data, and classical artificial intelligence analyses are no longer adequate. Nevertheless, recent advances in deep learning methods have achieved revolutionary success in numerous industries. Deep multi-layer perceptron (DMLP), long-short-term memory (LSTM), convolutional neural network (CNN), and deep reinforcement learning (DRL) are among the most preferred methods of deep learning in recent years. As data size increases, these methods have shown promising performance improvement in prediction and learning, compared to classical artificial intelligence methods. This paper summarizes tool condition monitoring first, then presents the underlying theory of some of the most recent deep learning methods, and finally, attempts to identify new opportunities in tool condition monitoring, toward the realization of Industry 4.0. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | International Journal of Advanced Manufacturing Technology | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Tool condition monitoring | en_US |
dc.subject | Machining | en_US |
dc.subject | Industry 4 | en_US |
dc.subject | Deep multi-layer perceptron | en_US |
dc.subject | Long-short-term memory | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Reinforcement learning | en_US |
dc.title | Review of Tool Condition Monitoring in Machining and Opportunities for Deep Learning | en_US |
dc.type | Review | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Mechanical Engineering | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Artificial Intelligence Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümü | tr_TR |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.department | Fakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 109 | - |
dc.identifier.startpage | 953 | - |
dc.identifier.endpage | 974 | - |
dc.relation.tubitak | info:eu-repo/grantAgreement/TÜBİTAK/MAG/118M414 | en_US |
dc.authorid | 0000-0002-4632-3505 | - |
dc.authorid | 0000-0001-7998-5735 | - |
dc.identifier.wos | WOS:000547231800002 | en_US |
dc.identifier.scopus | 2-s2.0-85087708373 | en_US |
dc.institutionauthor | Özbayoglu, Ahmet Murat | - |
dc.institutionauthor | Ünver, Hakkı Özgür | - |
dc.identifier.doi | 10.1007/s00170-020-05449-w | - |
dc.relation.publicationcategory | Diğer | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Review | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.1. Department of Artificial Intelligence Engineering | - |
crisitem.author.dept | 02.7. 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 |
CORE Recommender
SCOPUSTM
Citations
29
checked on Dec 21, 2024
WEB OF SCIENCETM
Citations
196
checked on Dec 21, 2024
Page view(s)
326
checked on Dec 23, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.