Abstract
This article reports an unsupervised approach for estimation of the tool condition in precision machining processes. Three campaigns of run-to-failure experiments were conducted on different machines of varying capabilities to develop a generalized solution that is independent of machine intricacies and settings. The proposed approach uses real-time sensory information, such as vibration and spindle power, to infer the tool condition. The proposed approach utilizes distance metrics, Mahalanobis and Euclidean, determined from the sensory information as health indicators of tool wear, which are shown to be strongly correlated with the tool condition. The health indicators have a high correlation coefficient of 0.94 with tool wear measurements, across machines. Unsupervised approaches, such as Jenks Natural Breaks and K-means clustering, use these health indicators to estimate the tool condition. The developed unsupervised approach is also benchmarked using the IEEE PHM 2010 data. A Goodness of Variance Fit of 0.95 and 0.96 is achieved in classifying tool wear across the machining tests conducted in the three campaigns and IEEE PHM 2010 data, respectively. We highlight the explainability of the methods which improve the ease of deployment and engender trust.
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Abdulhameed, O., Al-Ahmari, A., Ameen, W., & Mian, S. H. (2019). Additive manufacturing: Challenges, trends, and applications. Advances in Mechanical Engineering, 11(2), 168781401882288. https://doi.org/10.1177/1687814018822880
Aggogeri, F., Pellegrini, N., & Tagliani, F. L. (2021). Recent advances on machine learning applications in machining processes. Applied Sciences, 11(18), 8764. https://doi.org/10.3390/app11188764
Alonso, F. J., & Salgado, D. R. (2005). Application of singular spectrum analysis to tool wear detection using sound signals. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 219(9), 703–710. https://doi.org/10.1243/095440505x32634
Awasthi, U., Wang, Z., Mannan, N., Pattipati, K. R., & Bollas, G. M. (2022). Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining. Journal of Manufacturing Processes, 81, 127–140. https://doi.org/10.1016/j.jmapro.2022.06.027
Axinte, D., & Gindy, N. (2004). Assessment of the effectiveness of a spindle power signal for tool condition monitoring in machining processes. International Journal of Production Research, 42(13), 2679–2691. https://doi.org/10.1080/00207540410001671642
Brili, N., Ficko, M., & Klančnik, S. (2021). Tool condition monitoring of the cutting capability of a turning tool based on thermography. Sensors, 21(19), 6687. https://doi.org/10.3390/s21196687
Cao, K., Han, J., Xu, L., Shi, T., Liao, G., & Liu, Z. (2022). Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning. Frontiers of Mechanical Engineering, 17(1), 5. https://doi.org/10.1007/s11465-021-0661-3
Chen, J. C., & Chen, J. C. (2004). An artificial-neural-networks-based in-process tool wear prediction system in milling operations. The International Journal of Advanced Manufacturing Technology, 25(5–6), 427–434. https://doi.org/10.1007/s00170-003-1848-y
Cho, S., Asfour, S., Onar, A., & Kaundinya, N. (2005). Tool breakage detection using support vector machine learning in a milling process. International Journal of Machine Tools and Manufacture, 45(3), 241–249. https://doi.org/10.1016/j.ijmachtools.2004.08.016
Dahe, S. V., Manikandan, G. S., Jegadeeshwaran, R., Sakthivel, G., & Lakshmipathi, J. (2021). Tool condition monitoring using random forest and FURIA through statistical learning. Materials Today: Proceedings, 46, 1161–1166. https://doi.org/10.1016/j.matpr.2021.02.059
Dai, W., Liang, K., Huang, T., & Lu, Z. (2021). Tool condition monitoring in the milling process based on multisource pattern recognition model. The International Journal of Advanced Manufacturing Technology, 119(3–4), 2099–2114. https://doi.org/10.1007/s00170-021-08012-3
Delio, T., Tlusty, J., & Smith, S. (1992). Use of audio signals for chatter detection and control. Journal of Engineering for Industry, 114(2), 146–157. https://doi.org/10.1115/1.2899767
Dimla, D. E., Lister, P. M., & Leighton, N. J. (1997). Neural network solutions to the tool condition monitoring problem in metal cutting—A critical review of methods. International Journal of Machine Tools and Manufacture, 37(9), 1219–1241. https://doi.org/10.1016/s0890-6955(97)00020-5
Dornfeld, D. (1992). Application of acoustic emission techniques in manufacturing. NDT & E International, 25(6), 259–269. https://doi.org/10.1016/0963-8695(92)90636-u
Ertunc, H. M., & Oysu, C. (2004). Drill wear monitoring using cutting force signals. Mechatronics, 14(5), 533–548. https://doi.org/10.1016/j.mechatronics.2003.10.005
Ghorbani, H. (2019). Mahalanobis distance and its application for detecting multivariate outliers. Facta Universitatis, Series: Mathematics and Informatics. https://doi.org/10.22190/fumi1903583g
Gouarir, A., Martínez-Arellano, G., Terrazas, G., Benardos, P., & Ratchev, S. (2018). In-process tool wear prediction system based on machine learning techniques and force analysis. Procedia CIRP, 77, 501–504. https://doi.org/10.1016/j.procir.2018.08.253
Guo, J., Li, A., & Zhang, R. (2020). Tool condition monitoring in milling process using multifractal detrended fluctuation analysis and support vector machine. The International Journal of Advanced Manufacturing Technology, 110(5–6), 1445–1456. https://doi.org/10.1007/s00170-020-05931-5
Han, S., Mannan, N., Stein, D. C., Pattipati, K. R., & Bollas, G. M. (2021). Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems. Journal of Manufacturing Systems, 61, 45–53. https://doi.org/10.1016/j.jmsy.2021.08.004
Han, S., Yang, Q., Pattipati, K. R., & Bollas, G. M. (2022). Sensor selection and tool wear prediction with data-driven models for precision machining. Journal of Advanced Manufacturing and Processing, 4(4), e10143. https://doi.org/10.1002/amp2.10143
Jain, A. K., & Lad, B. K. (2017). A novel integrated tool condition monitoring system. Journal of Intelligent Manufacturing, 30(3), 1423–1436. https://doi.org/10.1007/s10845-017-1334-2
Jamshidi, M., Chatelain, J.-F., Rimpault, X., & Balazinski, M. (2022). Tool condition monitoring using machine tool spindle electric current and multiscale analysis while milling steel alloy. Journal of Manufacturing and Materials Processing, 6(5), 115. https://doi.org/10.3390/jmmp6050115
Jenks, G. F. (1977). Optimal Data Classification for Choropleth Maps. Occasional paper. https://books.google.co.in/books?id=HvAENQAACAAJ
Khajavi, M. N., Nasernia, E., & Rostaghi, M. (2016). Milling tool wear diagnosis by feed motor current signal using an artificial neural network. Journal of Mechanical Science and Technology, 30(11), 4869–4875. https://doi.org/10.1007/s12206-016-1005-9
Kious, M., Ouahabi, A., Boudraa, M., Serra, R., & Cheknane, A. (2010). Detection process approach of tool wear in high speed milling. Measurement, 43(10), 1439–1446. https://doi.org/10.1016/j.measurement.2010.08.014
Kuntoğlu, M., & Sağlam, H. (2021). Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Measurement, 173, 108582. https://doi.org/10.1016/j.measurement.2020.108582
Lauro, C. H., Brandão, L. C., Baldo, D., Reis, R. A., & Davim, J. P. (2014). Monitoring and processing signal applied in machining processes—A review. Measurement, 58, 73–86. https://doi.org/10.1016/j.measurement.2014.08.035
Li, G., Wang, Y., He, J., Hao, Q., Yang, H., & Wei, J. (2020). Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM. The International Journal of Advanced Manufacturing Technology, 110(1–2), 511–522. https://doi.org/10.1007/s00170-020-05890-x
Li, Y., Wang, X., He, Y., Wang, Y., Wang, Y., & Wang, S. (2022). Deep spatial-temporal feature extraction and lightweight feature fusion for tool condition monitoring. IEEE Transactions on Industrial Electronics, 69(7), 7349–7359. https://doi.org/10.1109/tie.2021.3102443
Li, H. Z., Zeng, H., & Chen, X. Q. (2006). An experimental study of tool wear and cutting force variation in the end milling of inconel 718 with coated carbide inserts. Journal of Materials Processing Technology, 180(1–3), 296–304. https://doi.org/10.1016/j.jmatprotec.2006.07.009
Liu, Y., Liu, Z., Wang, X., & Huang, T. (2020). Experimental study on tool wear in ultrasonic vibration-assisted milling of c/SiC composites. The International Journal of Advanced Manufacturing Technology, 107(1–2), 425–436. https://doi.org/10.1007/s00170-020-05060-z
Maesschalck, R. D., Jouan-Rimbaud, D., & Massart, D. L. (2000). The mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50(1), 1–18. https://doi.org/10.1016/s0169-7439(99)00047-7
Martínez-Arellano, G., Terrazas, G., & Ratchev, S. (2019). Tool wear classification using time series imaging and deep learning. The International Journal of Advanced Manufacturing Technology, 104(9–12), 3647–3662. https://doi.org/10.1007/s00170-019-04090-6
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
Mohanraj, T., Yerchuru, J., Krishnan, H., Aravind, R. S. N., & Yameni, R. (2021). Development of tool condition monitoring system in end milling process using wavelet features and hoelder’s exponent with machine learning algorithms. Measurement, 173, 108671. https://doi.org/10.1016/j.measurement.2020.108671
Murphy, K. P. (2022). Probabilistic machine learning: An introduction. MIT Press.
Özel, T., & Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45(4–5), 467–479. https://doi.org/10.1016/j.ijmachtools.2004.09.007
Pagani, L., Parenti, P., Cataldo, S., Scott, P. J., & Annoni, M. (2020). Indirect cutting tool wear classification using deep learning and chip colour analysis. The International Journal of Advanced Manufacturing Technology, 111(3–4), 1099–1114. https://doi.org/10.1007/s00170-020-06055-6
Pal, S., Heyns, P. S., Freyer, B. H., Theron, N. J., & Pal, S. K. (2009). Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. Journal of Intelligent Manufacturing, 22(4), 491–504. https://doi.org/10.1007/s10845-009-0310-x
Pal, S. K., Mishra, D., Pal, A., Dutta, S., Chakravarty, D., & Pal, S. (2021a). Artificial intelligence and machine learning in manufacturing (pp. 337–412). Springer. https://doi.org/10.1007/978-3-030-81815-9_6
Pal, S. K., Mishra, D., Pal, A., Dutta, S., Chakravarty, D., & Pal, S. (2021b). Signal processing for digital twin (pp. 117–187). Springer. https://doi.org/10.1007/978-3-030-81815-9_3
Patra, K., Pal, S. K., & Bhattacharyya, K. (2007). Artificial neural network based prediction of drill flank wear from motor current signals. Applied Soft Computing, 7(3), 929–935. https://doi.org/10.1016/j.asoc.2006.06.001
Paul, P. S., & Varadarajan, A. (2012). A multi-sensor fusion model based on artificial neural network to predict tool wear during hard turning. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 226(5), 853–860. https://doi.org/10.1177/0954405411432381
Pimenov, D. Y., Bustillo, A., Wojciechowski, S., Sharma, V. S., Gupta, M. K., & Kuntoğlu, M. (2022a). Artificial intelligence systems for tool condition monitoring in machining: Analysis and critical review. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-01923-2
Pimenov, D. Y., Gupta, M. K., da Silva, L. R. R., Kiran, M., Khanna, N., & Krolczyk, G. M. (2022b). Application of measurement systems in tool condition monitoring of milling: A review of measurement science approach. Measurement, 199, 111503. https://doi.org/10.1016/j.measurement.2022.111503
Prasad, B. S., & Babu, M. P. (2017). Correlation between vibration amplitude and tool wear in turning: Numerical and experimental analysis. Engineering Science and Technology, an International Journal, 20(1), 197–211. https://doi.org/10.1016/j.jestch.2016.06.011
Prognostics, T. (2022). H.M.S..-: 2010 PHM society conference data challenge. Retrieved December 28, 2022, fromhttps://phmsociety.org/phm_competition/2010-phm-society-conference-data-challenge/
Ranjan, J., Patra, K., Szalay, T., Mia, M., Gupta, M. K., Song, Q., Krolczyk, G., Chudy, R., Pashnyov, V. A., & Pimenov, D. Y. (2020). Artificial intelligence-based hole quality prediction in micro-drilling using multiple sensors. Sensors, 20(3), 885. https://doi.org/10.3390/s20030885
Ravindra, H. V., Srinivasa, Y. G., & Krishnamurthy, R. (1997). Acoustic emission for tool condition monitoring in metal cutting. Wear, 212(1), 78–84. https://doi.org/10.1016/s0043-1648(97)00137-3
Sadat, A. B., & Raman, S. (1987). Detection of tool flank wear using acoustic signature analysis. Wear, 115(3), 265–272. https://doi.org/10.1016/0043-1648(87)90216-x
Scheffer, C., Kratz, H., Heyns, P. S., & Klocke, F. (2003). Development of a tool wear-monitoring system for hard turning. International Journal of Machine Tools and Manufacture, 43(10), 973–985. https://doi.org/10.1016/s0890-6955(03)00110-x
Schueller, A., & Saldaña, C. (2022). Indirect tool condition monitoring using ensemble machine learning techniques. Journal of Manufacturing Science and Engineering, 145(1), 011006. https://doi.org/10.1115/1.4055822
Shi, R., & Huang, H.: Current study and innovative ideas of online monitoring technology of tool wear. In 2022 The 3rd international conference on artificial intelligence in electronics engineering. AIEE 2022 (pp. 104–108). Association for Computing Machinery (2022). https://doi.org/10.1145/3512826.3512845
Sick, B. (2001). Tool wear monitoring in turning: A neural network application. Measurement and Control, 34(7), 207–222. https://doi.org/10.1177/002029400103400704
Silva, R. G., Reuben, R. L., Baker, K. J., & Wilcox, S. J. (1998). Tool wear monitoring of turning operations by neural network and expert system classification of a feature set generated from multiple sensors. Mechanical Systems and Signal Processing, 12(2), 319–332. https://doi.org/10.1006/mssp.1997.0123
Swearingen, C. J. (2014). In F. M. Hammond, J. F. Malec, T. G. Nick, & R. M. Buschbacher (Eds.), Missing data and imputation (pp. 105–107) Springer. https://doi.org/10.1891/9781617050992.0026
Tarng, Y. S. (1993). Monitoring of tool fracture in milling. The International Journal of Advanced Manufacturing Technology, 8(1), 2–8. https://doi.org/10.1007/bf01756630
Traini, E., Bruno, G., & Lombardi, F. (2020). Tool condition monitoring framework for predictive maintenance: A case study on milling process. International Journal of Production Research, 59(23), 7179–7193. https://doi.org/10.1080/00207543.2020.1836419
Venkatesh, S. N., Balaji, P. A., Elangovan, M., Annamalai, K., Indira, V., Sugumaran, V., & Mahamuni, V. S. (2022). Transfer learning-based condition monitoring of single point cutting tool. Computational Intelligence and Neuroscience, 2022, 1–14. https://doi.org/10.1155/2022/3205960
Wanigarathne, P. C., Kardekar, A. D., Dillon, O. W., Poulachon, G., & Jawahir, I. S. (2005). Progressive tool-wear in machining with coated grooved tools and its correlation with cutting temperature. Wear, 259(7–12), 1215–1224. https://doi.org/10.1016/j.wear.2005.01.046
Wu, J. (2012). Advances in K-means clustering. https://doi.org/10.1007/978-3-642-29807-3
Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A comparative study on machine learning algorithms for smart manufacturing: Tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, 139(7), 071018. https://doi.org/10.1115/1.4036350
Xiaoli, L., Yingxue, Y., & Zhejun, Y. (1997). On-line tool condition monitoring system with wavelet fuzzy neural network. Journal of Intelligent Manufacturing, 8(4), 271–276. https://doi.org/10.1023/a:1018585527465
Yang, Q., Pattipati, K. R., Awasthi, U., & Bollas, G. M. (2022). Hybrid data-driven and model-informed online tool wear detection in milling machines. Journal of Manufacturing Systems, 63, 329–343. https://doi.org/10.1016/j.jmsy.2022.04.001
Yeo, S. H., Khoo, L. P., & Neo, S. S. (2000). Tool condition monitoring using reflectance of chip surface and neural network. Journal of Intelligent Manufacturing, 11(6), 507–514. https://doi.org/10.1023/a:1026583821221
Yuan, C., & Yang, H. (2019). Research on k-value selection method of k-means clustering algorithm. J, 2(2), 226–235. https://doi.org/10.3390/j2020016
Yuan, J., Liu, L., Yang, Z., Bo, J., & Zhang, Y. (2021). Tool wear condition monitoring by combining spindle motor current signal analysis and machined surface image processing. The International Journal of Advanced Manufacturing Technology, 116(7–8), 2697–2709. https://doi.org/10.1007/s00170-021-07366-y
Zhou, C., Guo, K., & Sun, J. (2021). Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing. Mechanical Systems and Signal Processing, 157, 107738. https://doi.org/10.1016/j.ymssp.2021.107738
Zhou, Y., Orban, P., & Nikumb, S. (1995). Sensors for intelligent machining-a research and application survey. In 1995 IEEE international conference on systems, man and cybernetics. Intelligent systems for the 21st century. IEEE. https://doi.org/10.1109/icsmc.1995.537900
Zhou, Y., Zhi, G., Chen, W., Qian, Q., He, D., Sun, B., & Sun, W. (2022). A new tool wear condition monitoring method based on deep learning under small samples. Measurement, 189, 110622. https://doi.org/10.1016/j.measurement.2021.110622
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We gratefully acknowledge the Air Force Research Laboratory, Materials and Manufacturing Directorate (AFRL/RXMS) for support via Contract No. FA8650-20-C-5206.
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Mishra, D., Awasthi, U., Pattipati, K.R. et al. Tool wear classification in precision machining using distance metrics and unsupervised machine learning. J Intell Manuf (2023). https://doi.org/10.1007/s10845-023-02239-5
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DOI: https://doi.org/10.1007/s10845-023-02239-5