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The International Journal of Advanced Manufacturing Technology
Erschienen in: The International Journal of Advanced Manufacturing Technology 3-4/2020

10.07.2020 | ORIGINAL ARTICLE

Review of tool condition monitoring in machining and opportunities for deep learning

verfasst von: G. Serin, B. Sener, A. M. Ozbayoglu, H. O. Unver

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 3-4/2020

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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.
Vorheriger Artikel Tool performance on micro-abrasive post-treatment coated carbide
Nächster Artikel Characterization of microstructure, mechanical properties and corrosion response of aluminium-based composites fabricated via casting—a review

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Metadaten
Titel
Review of tool condition monitoring in machining and opportunities for deep learning
verfasst von
G. Serin
B. Sener
A. M. Ozbayoglu
H. O. Unver
Publikationsdatum
10.07.2020
Verlag
Springer London
Erschienen in
The International Journal of Advanced Manufacturing Technology / Ausgabe 3-4/2020
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-020-05449-w

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