An application of machine learning methods to cutting tool path clustering and rul estimation in machining

• Autorzy: Zegarra Fabio C. , Vargas-Machuca Juan , Roman-Gonzalez Avid , Coronado Alberto M.

Identyfikatory

DOI

10.36897/jme/171432

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

feature extraction; k-means clustering; time series; unsupervised learning

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2023
• Tom: Vol. 23, No. 4
• Strony: 5--17
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Zegarra Fabio C.

• Laboratorio de Sistemas Inteligentes, EPIME, Universidad Nacional Tecnológica de Lima Sur (UNTELS), Peru

• https://orcid.org/0000-0002-7113-5100

autor

Vargas-Machuca Juan

• Laboratorio de Sistemas Inteligentes, EPIME, Universidad Nacional Tecnológica de Lima Sur (UNTELS), Peru

• https://orcid.org/0000-0003-1336-077X

autor

Roman-Gonzalez Avid

• Aerospace Sciences & Health Research Laboratory (INCAS-Lab), Universidad Nacional Tecnológica de Lima Sur (UNTELS), Peru

• https://orcid.org/0000-0002-3338-0041

autor

Coronado Alberto M.

• Laboratorio de Sistemas Inteligentes, EPIME, Universidad Nacional Tecnológica de Lima Sur (UNTELS), Peru

• https://orcid.org/0000-0002-2529-8478

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).