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

• Published in: International journal of advanced manufacturing technology 2020-07, Vol.109 (3-4), p.953-974
• Main Authors: Serin, G., Sener, B., Ozbayoglu, A. M., Unver, H. O.
• Format: Article
• Language: English
• Subjects: Acoustic emission; Artificial intelligence; Artificial neural networks; CAE) and Design; Chatter; Computer-Aided Engineering (CAD; Cutting tools; Cutting wear; Deep learning; Engineering; Identification methods; Industrial and Production Engineering; Machine learning; Machine tools; Machinery condition monitoring; Machining; Manufacturing; Mechanical Engineering; Media Management; Multilayers; Original Article; Pollution monitoring; Surface properties; Temperature sensors; Tool life; Tool wear; Vibration monitoring
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-020-05449-w

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.