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

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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.

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Funding

The authors would like to thank for their financial support to the Polısh Natıonal Agency For Academıc Exchange (NAWA) (No. PPN/ULM/2020/1/00121), the Polish National Science Centre (NCN) (Project No. UMO-2020/37/K/ST8/02795), the Spanish Ministry of Science and Innovation (project No. PID2020-119894 GB-I00) and the Junta de Castilla y León (project No BU055P20!), the two last of them cofinanced with European Union FEDER funds.

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Authors and Affiliations

  1. Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, 454080, Chelyabinsk, Russia

    Danil Yu Pimenov

  2. Department of Civil Engineering, Universidad de Burgos, Avda Cantabria s/n, 09006, Burgos, Spain

    Andres Bustillo

  3. Faculty of Mechanical Engineering, Poznan University of Technology, 60-965, Poznan, Poland

    Szymon Wojciechowski

  4. School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Johannesburg, South Africa

    Vishal S. Sharma

  5. Faculty of Mechanical Engineering, Opole University of Technology, 45-758, Opole, Poland

    Munish K. Gupta

  6. Technology Faculty, Mechanical Engineering Department, Selcuk University, Konya, 42130, Turkey

    Mustafa Kuntoğlu

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  1. Danil Yu Pimenov
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  4. Vishal S. Sharma
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  5. Munish K. Gupta
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  6. Mustafa Kuntoğlu
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Correspondence to Danil Yu Pimenov, Szymon Wojciechowski, Vishal S. Sharma or Munish K. Gupta.

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Pimenov, D.Y., Bustillo, A., Wojciechowski, S. et al. Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review. J Intell Manuf 34, 2079–2121 (2023). https://doi.org/10.1007/s10845-022-01923-2

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