Abstract
Dynamic industrial data growth necessitates the development of several new concepts of these data analysis that will allow to select not only the right data, but also to apply appropriate methods in order to extract knowledge from them. For this purpose, the possible use of decision trees as a decision support tool for a machining process data analysis was discussed in this article. With the use of the generated decision rules, we identify parameters that affect the state of a blade (blunt, sharp). In consequence that makes it possible to predict its future state at specific values of the identified parameters. Decision trees enable the analyses of the importance of each variable for the dependent variable. This makes it possible to analyse how each individual parameter and the relationships between them affect the condition of a cutter blade. The results of variables importance for a decision tree analysis can be used to determine the most important input variables, while rejecting those which do not affect the condition of a cutter blade. The study offers some promising results. It is confirmed by the achieved prediction model quality indicators.
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Antosz, K., Mazurkiewicz, D., Kozłowski, E., Sęp, J., Żabiński, T. (2022). Machining Process Time Series Data Analysis with a Decision Support Tool. In: Machado, J., Soares, F., Trojanowska, J., Ottaviano, E. (eds) Innovations in Mechanical Engineering. icieng 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-79165-0_2
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