Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework

• Autorzy: Patange Abhishek , Soman Rohan , Pardeshi Sujit , Kuntoglu Mustafa , Ostachowicz Wieslaw

Identyfikatory

DOI

10.17531/ein/178274

• Języki publikacji: EN

Abstrakty

EN

Tool condition affects the tolerances and the energy consumption and hence needs to be monitored. Artificial intelligence (AI) based data-driven techniques for tool condition determination are proposed. Unfortunately, the data-driven techniques are data-hungry. This paper proposes a methodology for classification based on unsupervised learning using limited unlabeled training data. The work presents a multi-class classification problem for the tool condition monitoring. The principal component analysis (PCA) is employed for dimensionality reduction and the principal components (PCs) are used as input for classification using k-means clustering. New collected data is then projected on the PC space, and classified using the clusters from the training. The methodology has been appliedforclassification of tool faults in 6 classes in a vertical milling center. The use of limited input parameters from the user makes the method ideal for monitoring a large number of machines with minimal human intervention. Furthermore, due to the small amount of data needed for the training, the method has the potential to be transferable.

Słowa kluczowe

EN

tool condition monitoring; TCM; milling cutter; PCA; k-means clustering

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 1
• Strony: art. no. 178274
• Opis fizyczny: Bibliogr. 39 poz., rys., tab., wykr.

Twórcy

autor

Patange Abhishek

• COEP Technological University, India

• https://orcid.org/0000-0002-9130-694X

autor

Soman Rohan

• Institute of Fluid Flow Machinery, Polish Academy of Sciences,, Poland

• https://orcid.org/0000-0002-5499-2565

autor

Pardeshi Sujit

• COEP Technological University, India

• https://orcid.org/0000-0002-3580-8830

autor

Kuntoglu Mustafa

• Selcuk University, Turkey

• https://orcid.org/0000-0002-7291-9468

autor

Ostachowicz Wieslaw

• Institute of Fluid Flow Machinery, Polish Academy of Sciences,, Poland

• https://orcid.org/0000-0002-8061-8614

Bibliografia

• 1. Ashfahani, A., Pratama, M.. Unsupervised continual learning in streaming environments. IEEE Transactions on Neural Networks and Learning Systems 2022;.https://doi.org/10.1109/TNNLS.2022.3163362
• 2. Brito, L.C., da Silva, M.B., Duarte, M.A.V.. Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data. Journal of Intelligent Manufacturing 2021;32:127–140.https://doi.org/10.1007/s10845-020-01564-3
• 3. Cheng, C., Li, J., Liu, Y., Nie, M., Wang, W.. Deep convolutional neural network based in-process tool condition monitoring in abrasive belt grinding. Computers in Industry 2019;106:1–13.https://doi.org/10.1016/j.compind.2018.12.002
• 4. Diaz-Rozo, J., Bielza, C., Larranaga, P.. Machine tool condition monitoring with gaussian mixture models based dynamic probabilistic clustering. Engineering Applications of Artificial Intelligence, https://doi.org/10.1016/j.engappai.2019.103434
• 5. Elangovan, M., Ramachandran, K., Sugumaran, V.. Studies on bayesclassifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features. Expert Systems with Applications 2010;37(3):2059–2065.https://doi.org/10.1016/j.eswa.2009.06.103
• 6. Elsheikh, A.H.. Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis. Engineering Applications of Artificial Intelligence 2023;121:105961.
• 7. Gittler, T., Glasder, M., O¨ ztu¨rk, E., Lu¨thi, M., Weiss, L., Wegener, K.. International conference on advanced and competitive manufacturing technologies milling tool wear prediction using unsupervised machine learning. The International Journal of Advanced Manufacturing Technology 2021;117(7-8):2213–2226.https://doi.org/10.1007/s00170-021-07281-2
• 8. Grandini, M., Bagli, E., Visani, G.. Metrics for multi-class classification: an overview. arXiv preprint arXiv:200805756 2020;.
• 9. Iliyas Ahmad, M., Yusof, Y., Daud, M.E., Latiff, K., Abdul Kadir, A.Z., Saif, Y.. Machine moni-toring system: a decade in review. The International Journal of Advanced Manufacturing Technology 2020;108:3645–3659.https://doi.org/10.1007/s00170-020-05620-3
• 10. Khade, H., Patange, A., Pardeshi, S., Jegadeeshwaran, R.. Design of bagged tree ensemble for carbide coated inserts fault diagnosis. Materials Today: Proceedings 2021;46:1283–1289.https://doi.org/10.1016/j.matpr.2021.02.128
• 11. Khairnar, A., Patange, A., Pardeshi, S., Jegadeeshwaran, R.. Supervision of carbide tool condition by training of vibration-based statistical model using boosted trees ensemble. International Journal of Performability Engineering 2021;17(2). https://doi.org/10.23940/ijpe.21.02.p7.229240
• 12. Kumar, D.P., Muralidharan, V., Ravikumar, S.. Histogram as features for fault detection of multi point cutting tool–a data driven approach. Applied Acoustics,https://doi.org/10.1016/j.apacoust.2021.108456
• 13. Kunto˘glu, M., Sa˘glam, H.. Investigation of progressive tool wear for determining of optimized machining parameters in turning. Measurement 2019;140:427–436.https://doi.org/10.1016/j.measurement.2019.04.022
• 14. Kunto˘glu, M., Sa˘glam, H.. Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Measurement https://doi.org/10.1016/j.measurement.2020.108582
• 15. Lee, C.H., Jwo, J.S., Hsieh, H.Y., Lin, C.S.. An intelligent system for grinding wheel condition monitoring based on machining sound and deep learning. https://doi.org/10.1109/ACCESS.2020.2982800.
• 16. Li, Z., Wang, G., He, G.. Milling tool wear state recognition based on partitioning around medoids (pam) clustering. The International Journal of Advanced Manufacturing Technology 2017;88:1203–1213.https://doi.org/10.1007/s00170-016-8848-1
• 17. Liu, B., Liu, C., Zhou, Y., Wang, D., Dun, Y.. An unsupervised chatter detection method based on ae and merging gmm and k-means. Mechanical Systems and Signal Processing.https://doi.org/10.1016/j.ymssp.2022.109861.
• 18. Madhusudana, C., Kumar, H., Narendranath, S.. Condition monitoring of face milling tool using k-star algorithm and histogram features of vibration signal. Engineering science and technology, an international journal 2016;19(3):1543–1551.https://doi.org/10.1016/j.jestch.2016.05.009
• 19. Mazzoleni, M., Sarda, K., Acernese, A., Russo, L., Manfredi, L., Glielmo, L., Del Vecchio, C.. A fuzzy logic-based approach for fault diagnosis and condition monitoring of industry 4.0 manufacturing processes. Engineering Applications of Artificial Intelligence 2022;115:105317.
• 20. Mohanraj, T., Shankar, S., Rajasekar, R., Sakthivel, N., Pramanik, A.. Tool condition monitoring techniques in milling process—a review. Journal of Materials Research and Technology 2020;9(1):1032–1042.https://doi.org/10.1016/j.jmrt.2019.10.031
• 21. Mohanraj, T., Yerchuru, J., Krishnan, H., Aravind, R.N., Yameni, R.. Development of tool condition monitoring system in end milling process using wavelet features and hoelder’s exponent with machine learning algorithms. Measurement 2021;173:108671.
• 22. Nie, Z., Guo, E., Li, J., Hao, H., Ma, H., Jiang, H.. Bridge condition monitoring using fixed moving principal component analysis. Structural Control and Health Monitoring 2020;27(6):e2535.https://doi.org/10.1002/stc.2535
• 23. Painuli, S., Elangovan, M., Sugumaran, V.. Tool condition monitoring using k-star algorithm. Expert Systems with Applications 2014;41(6):2638–2643.https://doi.org/10.1016/j.eswa.2013.11.005
• 24. Patange, A.D., Jegadeeshwaran, R.. A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (vmc). Measurement 2021;173:108649.
• 25. Sabbagh, R., Ameri, F.. A framework based on k-means clustering and topic modeling for analyz-ing unstructured manufacturing capability data. Journal of Computing and Information Science in Engineering 2020;20(1).https://doi.org/10.1115/1.4044506
• 26. Seber, G.A.. Multivariate observations. John Wiley & Sons, 2009.
• 27. Shi, C., Luo, B., Li, H., Li, B., Mao, X., Peng, F.. Anomaly detection via unsupervised learning for tool breakage monitoring. International Journal of Machine Learning and Computing 2016;6(5):256.https://doi.org/10.18178/ijmlc.2016.6.5.607
• 28. Shi, C., Panoutsos, G., Luo, B., Liu, H., Li, B., Lin, X.. Using multiple-feature-spaces-based deep learning for tool condition monitoring in ultraprecision manufacturing. IEEE Transactions on industrial electronics 2018;66(5):3794–3803.https://doi.org/10.1109/TIE.2018.2856193
• 29. Shlens, J.. A tutorial on principal component analysis. arXiv preprint arXiv:14041100 2014;.
• 30. Sun, H., Zhang, J., Mo, R., Zhang, X.. In-process tool condition forecasting based on a deep learning method. Robotics and Computer-Integrated Manufacturing 2020;64:101924.
• 31. Sun, I.C., Cheng, R.C., Chen, K.S.. Evaluation of transducer signature selections on machine learning performance in cutting tool wear prognosis. The International Journal of Advanced Manufacturing Technology 2022;119(9-10):6451–6468.https://doi.org/10.1007/s00170-021-08526-w
• 32. Torabi, A.J., Er, M.J., Li, X., Lim, B.S., Peen, G.O.. Application of clustering methods for online tool condition monitoringand fault diagnosis in high-speed milling processes. IEEE Systems Journal 2015;10(2):721–732.https://doi.org/10.1109/JSYST.2015.2425793
• 33. Tran, M.Q., Doan, H.P., Vu, V.Q., Vu, L.T.. Machine learning and iot-based approach for tool condition monitoring: A review and future prospects. Measurement 2022;:112351.
• 34. Wang, G., Feng, X.. Tool wear state recognition based on linear chain conditional random field model. Engineering Applications of Artificial Intelligence 2013;26(4):1421–1427.https://doi.org/10.1016/j.engappai.2012.10.015
• 35. Wong, S.Y., Chuah, J.H., Yap, H.J.. Technical data-driven tool condition monitoring challenges for cnc milling: a review. The International Journal of Advanced Manufacturing Technology 2020;107:4837–4857.https://doi.org/10.1007/s00170-020-05303-z
• 36. Xia, Y., Wang, W., Song, Z., Xie, Z., Chen, X., Li, H.. Fault diagnosis of flexible production line machining center based on pca and abc-lvq. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2021;235(4):594–604.https://doi.org/10.1177/0954405420970513
• 37. Yu, J.. Machine tool condition monitoring based on an adaptive gaussian mixture model. Journal of manufacturing science and engineering 2012;134(3).https://doi.org/10.1115/1.4006093
• 38. Zhou, Y., Xue, W.. Review of tool condition monitoring methods in milling processes. The Interna-tional Journal of Advanced Manufacturing Technology 2018;96:2509–2523.https://doi.org/10.1007/s00170-018-1768-5
• 39. Zhu, K.. Big data oriented smart tool condition monitoring system. In: Smart Machining Systems: Modelling, Monitoring and Informatics. Springer; 2021. p. 361–381.https://doi.org/10.1007/978-3-030-87878-8_11