Abstract
Machining processes are of considerable significance to industries such as aviation, power generation, oil, and gas since a significant part of the industrial mechanical components went through a machining process during its manufacturing. Therefore, using worn cutting tools can lead to operational interruptions, accidents, and potential economic losses during these processes. Concerning these consequences, real-time monitoring can result in cost reduction, along with productivity and safety increase. This paper aims to discuss an autonomous model based on the self-organized direction-aware data partitioning algorithm and machine learning techniques, including time series feature extraction based on scalable hypothesis tests, to solve this problem. The model proposed in this work was tested in a data set recorded in a real machining system at the Manufacturing Processes Laboratory of the Federal University of Juiz de Fora in collaboration with the Laboratory of Industrial Automation and Computational Intelligence. This model can identify the patterns that distinguish the cutting tool operations as adequate or inadequate, achieving satisfactory performances in all cases presented in this work and potentially allowing to prevent faulty pieces fabrication.
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Acknowledgements
The authors would like to acknowledge the Coordination for the Improvement of Higher Education Personnel—Brazil (CAPES)—Financing Code 001, National Council for Scientific and Technological Development—CNPq (process 433389/2018-4), Minas Gerais State Research Support Foundation—FAPEMIG (APQ-02922-18) and Federal University of Juiz de Fora—UFJF for financial support. An early version of paper was presented at XXIII Congresso Brasileiro de Automática (CBA 2020).
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Appendix: Critical Values for the Kolmogorov–Smirnov test
Appendix: Critical Values for the Kolmogorov–Smirnov test
See Table 5.
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Fernandes, T.E., Ferreira, M.A.M., Miranda, G.P.C.d. et al. Classification of Lathe’s Cutting Tool Wear Based on an Autonomous Machine Learning Model. J Control Autom Electr Syst 33, 167–182 (2022). https://doi.org/10.1007/s40313-021-00819-5
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DOI: https://doi.org/10.1007/s40313-021-00819-5