Modeling of optimal probe measurement time on a machine tool using machine learning methods

• Autorzy: Józwik Jerzy , Zawada-Michałowska Magdalena , Kulisz Monika , Tomiło Paweł , Barszcz Marcin , Pieśko Paweł , Leleń Michał , Cybul Kamil

Identyfikatory

DOI

10.35784/acs-2024-15

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

optimal measurement time; CNC machine tool; machine learning method

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 2
• Strony: 43--59
• Opis fizyczny: Bibliogr. 25 poz., fig., tab.

Twórcy

autor

Józwik Jerzy

• Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals, Poland

• https://orcid.org/0000-0002-8845-0764

autor

Zawada-Michałowska Magdalena

• Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals, Poland

• https://orcid.org/0000-0003-3330-6340

autor

Kulisz Monika

• Lublin University of Technology, Management Faculty, Department of Enterprise Organisation, Poland

• https://orcid.org/0000-0002-8111-2316

autor

Tomiło Paweł

• Lublin University of Technology, Management Faculty, Department of Quantitative Methods, Poland

• https://orcid.org/0000-0003-4461-3194

autor

Barszcz Marcin

• Lublin University of Technology, Electrical Engineering and Computer Science Faculty, Department of Computer Science, Poland

• https://orcid.org/0000-0002-9061-4414

autor

Pieśko Paweł

• Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals, Poland

• https://orcid.org/0000-0002-4152-5159

autor

Leleń Michał

• Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals, Poland

• https://orcid.org/0000-0002-6398-4014

autor

Cybul Kamil

• Lublin University of Technology, Doctoral School at the Lublin University of Technology, Poland

• https://orcid.org/k.cybul%40pollub.pl

Bibliografia

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