ACS Applied Computer Science

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MODELING OF OPTIMAL PROBE MEASUREMENT TIME ON A MACHINE TOOL USING MACHINE LEARNING METHODS

Jerzy JÓZWIK, Magdalena ZAWADA- MICHAŁOWSKA, Monika KULISZ, Paweł TOMIŁO, Marcin BARSZCZ, Paweł PIEŚKO, Michał LELEŃ, Kamil CYBUL
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This paper explores the application of various machine learning techniques to model the optimal measurement time required after machining with a probe on CNC machine tools. Specifically, the research employs four different machine learning models: Elastic Net, Neural Networks, Decision Trees, and Support Vector Machines, each chosen for their unique strengths in addressing different aspects of predictive modeling in an industrial context. The study examines the input parameters such as material type, post-processing wall thickness, cutting depth, and rotational speed over measurement time. This approach ensures that the models account for the variables that significantly affect CNC machine operations. Regression value, mean square error, root mean square error, mean absolute percentage error, and mean absolute error were used to evaluate the quality of the obtained models. As a result of the analyses, the best modeling results were obtained using neural networks. Their ability to accurately predict measurement times can significantly increase operational efficiency by optimizing schedules and reducing downtime in machining processes.

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  • APA 7th style
Józwik, J., Zawada-Michałowska, M., Kulisz, M., Tomiło, P., Barszcz, M., Pieśko, P., Leleń, M., & Cybul, K. (2024). Modeling the optimal measurement time with a probe on the machine tool using machine learning methods. Applied Computer Science, 20(2), 43–59. https://doi.org/10.35784/acs-2024-15
  • Chicago style
Józwik, Jerzy, Magdalena Zawada-Michałowska, Monika Kulisz, Paweł Tomiło, Marcin Barszcz, Paweł Pieśko, Michał Leleń, and Kamil Cybul. „Modeling the Optimal Measurement Time with a Probe on the Machine Tool Using Machine Learning Methods”. Applied Computer Science 20, no. 2 (2024): 43–59.
  • IEEE style
J. Józwik et al., „Modeling the optimal measurement time with a probe on the machine tool using machine learning methods”, Applied Computer Science, vol. 20, no. 2, pp. 43–59, 2024, doi: 10.35784/acs-2024-15.
  • Vancouver style
Józwik J, Zawada-Michałowska M, Kulisz M, Tomiło P, Barszcz M, Pieśko P, et al. Modeling the optimal measurement time with a probe on the machine tool using machine learning methods. Applied Computer Science. 2024; 20(2):43–59.

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