An Application of Machine Learning Methods to Cutting Tool Path Clustering and RUL Estimation in Machining
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Laboratorio de Sistemas Inteligentes, EPIME, Universidad Nacional Tecnológica de Lima Sur (UNTELS), Peru
Aerospace Sciences & Health Research Laboratory (INCAS-Lab), Universidad Nacional Tecnológica de Lima Sur (UNTELS), Peru
Submission date: 2023-06-13
Final revision date: 2023-08-14
Acceptance date: 2023-08-21
Online publication date: 2023-08-25
Publication date: 2023-12-14
Corresponding author
Alberto M. Coronado   

Laboratorio de Sistemas Inteligentes, EPIME, Universidad Nacional Tecnológica de Lima Sur (UNTELS), Av. Bolivar S/N, sector 3 grupo 1, mz. A, sublote, 15834, Lima, Peru
Journal of Machine Engineering 2023;23(4):5-17
Machine learning has been widely used in manufacturing, leading to significant advances in diverse problems, including the prediction of wear and remaining useful life (RUL) of machine tools. However, the data used in many cases correspond to simple and stable processes that differ from practical applications. In this work, a novel dataset consisting of eight cutting tools with complex tool paths is used. The time series of the tool paths, corresponding to the three-dimensional position of the cutting tool, are grouped according to their shape. Three unsupervised clustering techniques are applied, resulting in the identification of DBA-k-means as the most appropriate technique for this case. The clustering process helps to identify training and testing data with similar tool paths, which is then applied to build a simple two-feature prediction model with the same level of precision for RUL prediction as a more complex four-feature prediction model. This work demonstrates that by properly selecting the methodology and number of clusters, tool paths can be effectively classified, which can later be used in prediction problems in more complex settings.
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