Abstract
Tool wear is one of the consequences of a machining process. Excessive tool wear can lead to poor surface finish, and result in a defective product. It can also lead to premature tool failure, and may result in process downtime and damaged components. With this in mind, it has long been desired to monitor tool wear/tool condition. Kernel principal component analysis (KPCA) is proposed as an effective and efficient method for monitoring the tool condition in a machining process. The KPCA-based method may be used to identify faults (abnormalities) in a process through the fusion of multi-sensor signals. The method employs a control chart monitoring approach that uses Hotelling’s T2-statistic and Q-statistic to identify the faults in conjunction with control limits, which are computed by kernel density estimation (KDE). KDE is a non-parametric technique to approximate a probability density function. Four performance metrics, abnormality detection rate, false detection rate, detection delay, and prediction accuracy, are employed to test the reliability of the monitoring system and are used to compare the KPCA-based method with PCA-based method. Application of the proposed monitoring system to experimental data shows that the KPCA based method can effectively monitor the tool wear.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics,2(4), 433–459. https://doi.org/10.1002/wics.101.
Abellan-Nebot, J. V., & Subirón, R. F. (2010). A review of machining monitoring systems based on artificial intelligence process models. International Journal of Advanced Manufacturing Technology,47, 237–257. https://doi.org/10.1007/s00170-009-2191-8.
Agogino, A., & Goebel, K. (2007). Milling data set. NASA Ames Prognostics Data Repository. https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#milling.
Amami, R., Ben Ayed, D., & Ellouze, N. (2013). Practical selection of SVM supervised parameters with different feature representations for vowel recognition. International Journal of Digital Content Technology and Its Applications,7(9), 418–424. https://doi.org/10.4156/jdcta.vol7.issue9.50.
Chen, S.-L., & Jen, Y. W. (2000). Data fusion neural network for tool condition monitoring in CNC milling machining. International Journal of Machine Tools and Manufacture,40(3), 381–400. https://doi.org/10.1016/S0890-6955(99)00066-8.
Chen, Q., Wynne, R. J., Goulding, P., & Sandoz, D. (2000). The application of principal component analysis and kernel density estimation to enhance process monitoring. Control Engineering Practice,8, 531–543.
Cho, S., Binsaeid, S., & Asfour, S. (2010). Design of multisensor fusion-based tool condition monitoring system in end milling. International Journal of Advanced Manufacturing Technology,46(5–8), 681–694. https://doi.org/10.1007/s00170-009-2110-z.
Choi, S. W., Lee, C., Lee, J., Hyun, J., & Lee, I. (2005). Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laboratory Systems,75, 55–67. https://doi.org/10.1016/j.chemolab.2004.05.001.
Dimla, D. E. (2002). The correlation of vibration signal features to cutting tool wear in a metal turning operation. International Journal of Advanced Manufacturing Technology,19(10), 705–713.
Dutta, S., Pal, S. K., & Sen, R. (2018). Tool condition monitoring in turning by applying machine vision. ASME Journal of Manufacturing Science and Engineering,138(5), 051008. https://doi.org/10.1115/1.4031770.
ISO 3685. (1977). Tool-life testing with single-point turning tools. Geneva: ISO.
Jiang, Q., & Yan, X. (2013). Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring. Chemometrics and Intelligent Laboratory Systems,127, 121–131. https://doi.org/10.1016/j.chemolab.2013.06.013.
Ketelaere, B., Hubert, M., & Schmitt, E. (2015). Overview of PCA-based statistical process-monitoring methods for time-dependent, high-dimensional data. Journal of Quality Technology,47(4), 318–335. https://doi.org/10.1080/00224065.2015.11918137.
Kothuru, A., Nooka, S. P., & Liu, R. (2018). Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling. International Journal of Advanced Manufacturing Technology,95(9–12), 3797–3808. https://doi.org/10.1007/s00170-017-1460-1.
Kurada, S., & Bradley, C. (1997). A review of machine vision sensors for tool condition monitoring. Computers in Industry,34(1), 55–72. https://doi.org/10.1016/s0166-3615(96)00075-9.
Lee, W. J., Mendis, G. P., & Sutherland, J. W. (2019). Development of an intelligent tool condition monitoring system to identify manufacturing tradeoffs and optimal machining conditions. Procedia Manufacturing,33, 256–263. https://doi.org/10.1016/j.promfg.2019.04.031.
Lee, J. M., Yoo, C. K., Choi, S. W., Vanrolleghem, P. A., & Lee, I. B. (2004). Nonlinear process monitoring using wavelet kernel principal component analysis. Chemical Engineering Science,59, 223–234. https://doi.org/10.1109/ICSAI.2012.6223652.
Lever, J., Krzywinski, M., & Altman, N. (2017). Points of significance: Principal component analysis. Nature Methods,14(7), 641–642. https://doi.org/10.1038/nmeth.4346.
Li, X. (2001). Detection of tool flute breakage in end milling using feed-motor current signatures. IEEE/ASME Transactions on Mechatronics,6, 491–498. https://doi.org/10.1109/3516.974863.
Li, X., & Yuan, Z. (1998). Tool wear monitoring with wavelet packet transform fuzzy clustering method. Wear,219(2), 145–154.
Mesina, O. S., & Langari, R. (2000). A neuro-fuzzy system for tool. ASME Journal of Manufacturing Science and Engineering,123(2), 312–318. https://doi.org/10.1115/1.1363599.
Qehaja, N., & Kyçyku, A. (2017). Tool life modeling based on cutting parameters and work material hardness in turning process. In Scientific proceedings of XIV international congress, IV (pp. 278–281).
Samuel, R. T., & Cao, Y. (2014). Fault detection in a multivariate process based on kernel PCA and kernel density estimation. In 20th international conference on automation and computing (Vol. 4, pp. 165–174). https://doi.org/10.1109/iconac.2014.6935477.
Scholkopf, B., Smola, A., & Muller, K. R. (1997). Kernel principal component analysis. International Conference on Artificial Neural Networks,1327, 583–588. https://doi.org/10.1162/089976698300017467.
Schölkopf, B., Smola, A., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation,10(5), 1299–1319. https://doi.org/10.1162/089976698300017467.
Segreto, T., Simeone, A., & Teti, R. (2013). Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion. Procedia CIRP,12, 85–90. https://doi.org/10.1016/j.procir.2013.09.016.
Silverman, B. (1986). Density estimation for statistics and data analysis. New York: Chapman and Hall.
Twardowski, P., Legutko, S., Krolczyk, G. M., & Hloch, S. (2015). Investigation of wear and tool life of coated carbide and cubic boron nitride cutting tools in high speed milling. Advances in Mechanical Engineering,7(6), 1–9. https://doi.org/10.1177/1687814015590216.
Vanhatalo, E., Kulahci, M., & Bergquist, B. (2017). On the structure of dynamic principal component analysis used in statistical process monitoring. Chemometrics and Intelligent Laboratory Systems,167(February), 1–11. https://doi.org/10.1016/j.chemolab.2017.05.016.
Wang, Q. (2014). Kernel principal component analysis and its applications in face recognition and active shape models. In CoRR. arXiv:1207.3538.
Wang, G., Zhang, Y., Liu, C., Xie, Q., & Xu, Y. (2019). A new tool wear monitoring method based on multi-scale PCA. Journal of Intelligent Manufacturing,30(1), 113–122. https://doi.org/10.1007/s10845-016-1235-9.
Wu, D., Jennings, C., Terpenny, J., Kumara, S., & Gao, R. X. (2018). Cloud-based parallel machine learning for tool wear prediction. Journal of Manufacturing Science and Engineering,140(4), 041005. https://doi.org/10.1115/1.4038002.
Yu, J. (2012). Machine tool condition monitoring based on an adaptive gaussian mixture model. Journal of Manufacturing Science and Engineering,134(3), 1–13. https://doi.org/10.1115/1.4006093.
Zhang, X., Gao, H., & Xu, M. (2016). On-line tool condition monitoring based on PCA and integrated neural networks for cold blast machining operation. In International conference on applied mechanics and materials engineering. https://doi.org/10.12783/dtmse/ammme2016/6891.
Acknowledgements
The authors would like to acknowledge the NASA repository for publishing and hosting the milling data used in the paper. This work is supported by the Wabash Heartland Innovation Network (WHIN), the Indiana Next Generation Manufacturing Competitiveness Center (IN-MaC), and the U.S. National Science Foundation (Grant No. 1512217). Any opinions, findings, conclusions, and/or recommendations expressed are those of the authors and do not necessarily reflect the views of the funding agencies.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lee, W.J., Mendis, G.P., Triebe, M.J. et al. Monitoring of a machining process using kernel principal component analysis and kernel density estimation. J Intell Manuf 31, 1175–1189 (2020). https://doi.org/10.1007/s10845-019-01504-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-019-01504-w