MODELING THE OPTIMAL MEASUREMENT TIME WITH A PROBE ON THE MACHINE TOOL USING MACHINE LEARNING METHODS
Jerzy JÓZWIK
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals (Poland)
https://orcid.org/0000-0002-8845-0764
Magdalena ZAWADA-MICHAŁOWSKA
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals (Poland)
https://orcid.org/0000-0003-3330-6340
Monika KULISZ
Lublin University of Technology, Management Faculty, Department of Enterprise Organisation (Poland)
https://orcid.org/0000-0002-8111-2316
Paweł TOMIŁO
Lublin University of Technology, Management Faculty, Department of Quantitative Methods (Poland)
https://orcid.org/0000-0003-4461-3194
Marcin BARSZCZ
Lublin University of Technology, Electrical Engineering and Computer Science Faculty, Department of Computer Science (Poland)
https://orcid.org/0000-0002-9061-4414
Paweł PIEŚKO
p.piesko@pollub.plLublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals (Poland)
https://orcid.org/0000-0002-4152-5159
Michał LELEŃ
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals (Poland)
https://orcid.org/0000-0002-6398-4014
Kamil CYBUL
Lublin University of Technology, Doctoral School (Poland)
https://orcid.org/0009-0008-4321-3045
Abstract
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 as 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.
Keywords:
optimal measurement time, CNC machine tool, machine learning methodsReferences
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Authors
Jerzy JÓZWIKLublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals Poland
https://orcid.org/0000-0002-8845-0764
Authors
Magdalena ZAWADA-MICHAŁOWSKALublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals Poland
https://orcid.org/0000-0003-3330-6340
Authors
Monika KULISZLublin University of Technology, Management Faculty, Department of Enterprise Organisation Poland
https://orcid.org/0000-0002-8111-2316
Authors
Paweł TOMIŁOLublin University of Technology, Management Faculty, Department of Quantitative Methods Poland
https://orcid.org/0000-0003-4461-3194
Authors
Marcin BARSZCZLublin University of Technology, Electrical Engineering and Computer Science Faculty, Department of Computer Science Poland
https://orcid.org/0000-0002-9061-4414
Authors
Paweł PIEŚKOp.piesko@pollub.pl
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals Poland
https://orcid.org/0000-0002-4152-5159
Authors
Michał LELEŃLublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering Fundamentals Poland
https://orcid.org/0000-0002-6398-4014
Authors
Kamil CYBULLublin University of Technology, Doctoral School Poland
https://orcid.org/0009-0008-4321-3045
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