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Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review

Author

Listed:
  • Danil Yu Pimenov

    (South Ural State University)

  • Andres Bustillo

    (Universidad de Burgos)

  • Szymon Wojciechowski

    (Poznan University of Technology)

  • Vishal S. Sharma

    (University of the Witwatersrand)

  • Munish K. Gupta

    (Opole University of Technology)

  • Mustafa Kuntoğlu

    (Selcuk University)

Abstract

The wear of cutting tools, cutting force determination, surface roughness variations and other machining responses are of keen interest to latest researchers. The variations of these machining responses results in change in dimensional accuracy and productivity upto great extent. In addition, an excessive increase in wear leads to catastrophic consequences, exceeding the tool breakage. Therefore, this article discusses the online trend of modern approaches in tool condition monitoring while different machining operations. For this purpose, the effective use of new sensors and artificial intelligence (AI) is considered and followed during this holistic review work. The sensor systems used for monitoring tool wear are dynamometers, accelerometers, acoustic emission sensors, current and power sensors, image sensors, other sensors. These systems allow to solve the problem of automation and modeling of technological parameters of the main types of cutting, such as turning, milling, drilling and grinding. The modern artificial intelligence methods are considered, such as: Neural networks, Image recognition, Fuzzy logic, Adaptive neuro-fuzzy inference systems, Bayesian Networks, Support vector machine, Ensembles, Decision and regression trees, k-nearest neighbors, Artificial Neural Network, Markov model, Singular Spectrum Analysis, Genetic algorithms. Discussions also includes the main advantages, disadvantages and prospects of using various AI methods for tool wear monitoring. Moreover, the problems and future directions of the main processing methods using AI models are also highlighted.

Suggested Citation

  • Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01923-2
    DOI: 10.1007/s10845-022-01923-2
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    References listed on IDEAS

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    Cited by:

    1. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    2. Lei Guo & Zhengcong Duan & Wanjin Guo & Kai Ding & Chul-Hee Lee & Felix T. S. Chan, 2024. "Machine vision-based recognition of elastic abrasive tool wear and its influence on machining performance," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4201-4216, December.
    3. Reza Teimouri & Sebastian Skoczypiec, 2024. "Predictive modeling of roughness change in multistep machining," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3577-3598, October.

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