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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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    1. Damien McParland & Szymon Baron & Sarah O’Rourke & Denis Dowling & Eamonn Ahearne & Andrew Parnell, 2019. "Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1259-1270, March.
    2. Dragan Rodić & Milenko Sekulić & Marin Gostimirović & Vladimir Pucovsky & Davorin Kramar, 2021. "Fuzzy logic and sub-clustering approaches to predict main cutting force in high-pressure jet assisted turning," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 21-36, January.
    3. Changqing Liu & Yingguang Li & Guanyan Zhou & Weiming Shen, 2018. "A sensor fusion and support vector machine based approach for recognition of complex machining conditions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1739-1752, December.
    4. Andres Bustillo & Danil Yu. Pimenov & Mozammel Mia & Wojciech Kapłonek, 2021. "Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 895-912, March.
    5. K. Venkata Rao & P. B. G. S. N. Murthy, 2018. "Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1533-1543, October.
    6. Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
    7. D. Yu. Pimenov & A. Bustillo & T. Mikolajczyk, 2018. "Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1045-1061, June.
    8. Doriana M. D’Addona & A. M. M. Sharif Ullah & D. Matarazzo, 2017. "Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1285-1301, August.
    9. X. Ajay Vasanth & P. Sam Paul & A. S. Varadarajan, 2020. "A neural network model to predict surface roughness during turning of hardened SS410 steel," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 704-715, June.
    10. Emel Kuram & Babur Ozcelik, 2016. "Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 817-830, August.
    11. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    12. James M. Griffin, 2018. "The prediction of profile deviations from multi process machining of complex geometrical features using combined evolutionary and neural network algorithms with embedded simulation," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1171-1189, August.
    13. Pedro Santos & Jesús Maudes & Andres Bustillo, 2018. "Identifying maximum imbalance in datasets for fault diagnosis of gearboxes," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 333-351, February.
    14. Guofeng Wang & Yanchao Zhang & Chang Liu & Qinglu Xie & Yonggang Xu, 2019. "A new tool wear monitoring method based on multi-scale PCA," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 113-122, January.
    15. Kamran Javed & Rafael Gouriveau & Xiang Li & Noureddine Zerhouni, 2018. "Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1873-1890, December.
    16. Amit Kumar Jain & Bhupesh Kumar Lad, 2019. "A novel integrated tool condition monitoring system," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1423-1436, March.
    17. Lucas Costa Brito & Márcio Bacci Silva & Marcus Antonio Viana Duarte, 2021. "Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 127-140, January.
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