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

Treść / Zawartość
Identyfikatory
Warianty tytułu
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.
Rocznik
Strony
5--17
Opis fizyczny
Bibliogr. 23 poz., rys.
Twórcy
  • Laboratorio de Sistemas Inteligentes, EPIME, Universidad Nacional Tecnológica de Lima Sur (UNTELS), Peru
  • 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
  • Laboratorio de Sistemas Inteligentes, EPIME, Universidad Nacional Tecnológica de Lima Sur (UNTELS), Peru
Bibliografia
  • [1] RAI R., TIWARI M.K., IVANOV D., DOLGUI A., 2021, Machine Learning in Manufacturing and Industry 4.0 Applications, Int. J. Prod. Res., 59, 4773–4778.
  • [2] MAYNARD A.D., 2015, Navigating the Fourth Industrial Revolution, Nat. Nanotechnol., 10/12, 1005–1006.
  • [3] TETI R., MOURTZIS D., ADDONA D.M.D., CAGGIANO A., 2022, Process Monitoring of Machining, CIRP Annals, 71/2, 529–552.
  • [4] SAENZ de UGARTE B., ARTIBA A., PELLERIN R., 2009, Manufacturing Execution System – a Literature Review, Prod. Plan. Control, 20, 525–539.
  • [5] BENFRIHA K., et al., 2021, Development of an Advanced MES for the Simulation and Optimization of Industry 4.0 Process, Int. J. Simul. Multi. Design Optim., 12, 23.
  • [6] VYSKOCIL J., DOUDA P., NOVAK P., WALLY B., 2023, A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered on-the-Fly Replanning Capabilities, Sustain. Sci. Pract. Policy, 15/7, 6251.
  • [7] TETI R., JEMIELNIAK K., O’DONNELL G., DORNFELD D., 2010, Advanced Monitoring of Machining Operations, CIRP Annals, 59/2, 717–739.
  • [8] KUNTOGLU M., et al., 2020, A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends, Sensors, 21/1, https://doi.org/10.3390/s21010108.
  • [9] RAPTIS T.P., PASSARELLA A., CONTI M., 2019, Data Management in Industry 4.0: State of the art and Open Challenges, IEEE Access, 7, 97052–97093.
  • [10] AGGOGERI F., PELLEGRINI N., TAGLIANI F.L., 2021, Recent Advances on Machine Learning Applications in Machining Processes, Applied Sciences, https://doi.org/10.3390/app11188764.
  • [11] MOHAMED A., HASSAN M., M’SAOUBI R., ATTIA H., 2022, Tool Condition Monitoring for High-Performance Machining Systems–A Review, Sensors, 22/6. 2206, https://doi.org/10.3390/s22062206.
  • [12] GITTLER T., SCHOLZE S., RUPENYAN A., WEGENER K., 2020, Machine Tool Component Health Identification with Unsupervised Learning, Journal of Manufacturing and Materials Processing, 4/3, 86, https://doi.org/10.3390/jmmp4030086.
  • [13] LIU C., ZHANG L., NIU J., YAO R., WU C., 2020, Intelligent Prognostics of Machining Tools Based on Adaptive Variational Mode Decomposition and Deep Learning Method with Attention Mechanism, Neurocomputing, 417, 239–254.
  • [14] ZEGARRA F.C., VARGAS-MACHUCA J., CORONADO A.M., 2021, Tool Wear and Remaining Useful Life (RUL) Prediction Based on Reduced Feature Set and Bayesian Hyperparameter Optimization, Prod. Eng., 16/4, 465–480.
  • [15] DHILLON I.S., GUAN Y., KULIS B., 2004, Kernel K-Means, Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD, https://doi.org/10.1145/1014052.1014118.
  • [16] SAKOE H., CHIBA S., 1978, Dynamic Programming Algorithm Optimization for Spoken Word Recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing, 26/1. 43–49, https://doi.org/10.1109/tassp.1978.1163055.
  • [17] PETITJEAN F., KETTERLIN A., GANÇARSKI P., 2011, A Global Averaging Method for Dynamic Time Warping, with Applications to Clustering, Pattern Recognition, 44/3. 678–693, https://doi.org/10.1016/j.patcog.2010.09.013.
  • [18] GOODFELLOW I., BENGIO Y., COURVILLE A., 2016, Deep Learning, MIT Press.
  • [19] REN W., WEN G., ZHANG Z., MAZUMDER J., 2021, Quality Monitoring in Additive Manufacturing Using Emission Spectroscopy and Unsupervised Deep Learning, Materials and Manufacturing Processes, 1–8, https://doi.org/10.1080/10426914.2021.1906891.
  • [20] VAPNIK V.N., 2000, The Nature of Statistical Learning Theory, Springer-Verlag, New York.
  • [21] VAPNIK V.N., 1999, An Overview of Statistical Learning Theory, IEEE Trans. Neural Netw., 10/5, 988–999.
  • [22] TAVENARD R., et al., 2020, Tslearn, A Machine Learning Toolkit for Time Series Data, J. Mach. Learn. Res., 21, 118, 1–6.
  • [23] CASUSOL A.J., ZEGARRA F.C., VARGAS-MACHUCA J., CORONADO A.M., 2021, Optimal Window Size for the Extraction of Features for Tool Wear Estimation, IEEE, https://doi.org/10.1109/intercon52678.2021.9532759.
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).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-83e3ed4c-96ed-4877-8f74-3042c9ae96b6
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