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Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model

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Abstract

In a high speed milling operation the cutting tool acts as a backbone of machining process, which requires timely replacement to avoid loss of costly workpiece or machine downtime. To this aim, prognostics is applied for predicting tool wear and estimating its life span to replace the cutting tool before failure. However, the life span of cutting tools varies between minutes or hours, therefore time is critical for tool condition monitoring. Moreover, complex nature of manufacturing process requires models that can accurately predict tool degradation and provide confidence for decisions. In this context, a data-driven connectionist approach is proposed for tool condition monitoring application. In brief, an ensemble of Summation Wavelet-Extreme Learning Machine models is proposed with incremental learning scheme. The proposed approach is validated on cutting force measurements data from Computer Numerical Control machine. Results clearly show the significance of our proposition.

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Notes

  1. Note: classical definition of reliability “the ability of an item to perform a required function under given conditions for a given time interval” (NF EN 13306 2010) is not retained here. Actually, the acception used in this paper is according to application of machine learning approaches in PHM, that do not consider reliability as dependability measure (Bosnić and Kononenko 2009).

References

  • An, D., Kim, N. H., & Choi, J. H. (2015). Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering & System Safety, 133, 223–236.

    Article  Google Scholar 

  • Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 26(2), 213–223.

    Article  Google Scholar 

  • Bhat, A. U., Merchant, S., & Bhagwat, S. S. (2008). Prediction of melting point of organic compounds using extreme learning machines. Industrial and Engineering Chemistry Research, 47(3), 920–925.

    Article  Google Scholar 

  • Bosnić, Z., & Kononenko, I. (2009). An overview of advances in reliability estimation of individual predictions in machine learning. Intelligent Data Analysis, 13(2), 385–401.

    Article  Google Scholar 

  • Camci, F., & Chinnam, R. B. (2010). Health-state estimation and prognostics in machining processes. IEEE Transactions on Automation Science and Engineering, 7(3), 581–597.

    Article  Google Scholar 

  • Cojbasic, Z., Petkovic, D., Shamshirband, S., Tong, C. W., Ch, S., Jankovic, P., et al. (2015). Surfaceroughnessprediction by extreme learning machine constructed withabrasivewater jet. Precision Engineering. doi:10.1016/j.precisioneng.2015.06.013.

    Article  Google Scholar 

  • Das, S., Hall, R., Herzog, S., Harrison, G., & Bodkin, M. (2011). Essential steps in prognostic health management. In IEEE Conference on prognostics and health management. Denver, CO, USA.

  • Ding, F., & He, Z. (2011). Cutting tool wear monitoring for reliability analysis using proportional hazards model. The International Journal of Advanced Manufacturing Technology, 57(5–8), 565–574.

    Article  Google Scholar 

  • Echo state network. http://reservoir-computing.org/software.

  • NF EN 13306. (2010). Terminologie de la maintenance.

  • Feng, G., Huang, G. B., Lin, Q., & Gay, R. (2009). Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks, 20(8), 1352–1357.

    Article  Google Scholar 

  • Gao, R., Wang, L., Teti, R., Dornfeld, D., Kumara, S., Mori, M., et al. (2015). Cloud-enabled prognosis for manufacturing. CIRP Annals-Manufacturing Technology. doi:10.1016/j.cirp.2015.05.011.

    Article  Google Scholar 

  • Ghasempoor, A., Moore, T., & Jeswiet, J. (1998). On-line wear estimation using neural networks. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 212(2), 105–112.

    Article  Google Scholar 

  • Grzenda, M., & Bustillo, A. (2013). The evolutionary development of roughness prediction models. Applied Soft Computing, 13(5), 2913–2922.

    Article  Google Scholar 

  • Haddadi, E., Shabghard, M. R., & Ettefagh, M. M. (2008). Effect of different tool edge conditions on wear detection by vibration spectrum analysis in turning operation. Journal of Applied Sciences, 8(21), 3879–3886.

    Article  Google Scholar 

  • Hu, C., Youn, B. D., Wang, P., & Yoon, J. T. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering & System Safety, 103, 120–135.

    Article  Google Scholar 

  • Huang, G. B., & Chen, L. (2007). Convex incremental extreme learning machine. Neurocomputing, 70(16), 3056–3062.

    Article  Google Scholar 

  • Huang, G. B., & Chen, L. (2008). Enhanced random search based incremental extreme learning machine. Neurocomputing, 71(16), 3460–3468.

    Article  Google Scholar 

  • Huang, G. B., Chen, L., & Siew, C. K. (2006). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17(4), 879–892.

    Article  Google Scholar 

  • Huang, G. B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: A survey. International Journal of Machine Learning and Cybernetics, 2(2), 107–122.

    Article  Google Scholar 

  • Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. In International Joint conference on neural networks. Budapest, Hungary.

  • Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489–501.

    Article  Google Scholar 

  • Jaeger, H. (2001). The echo state approach to analyzing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report, 148, 34.

    Google Scholar 

  • Jaeger, H. (2002). Tutorial on training recurrent neural networks, covering BPPT, RTRL. GMD-Forschungszentrum Informationstechnik: EKF and the echo state network approach.

  • Jalab, H. A., & Ibrahim, R. W. (2011). New activation functions for complex-valued neural network. International Journal of the Physical Sciences, 6(7), 1766–1772.

    Google Scholar 

  • Javed, K. (2014). A robust & reliable data-driven prognostics approach based on extreme learning machine and fuzzy clustering. Ph.D. thesis, Université de Franche-Comté.

  • Javed, K., Gouriveau, R., & Zerhouni, N. (2014). SW-ELM: A summation wavelet extreme learning machine algorithm with a priori parameter initialization. Neurocomputing, 123, 299–307.

    Article  Google Scholar 

  • Javed, K., Gouriveau, R., Zerhouni, N., & Nectoux, P. (2015). Enabling health monitoring approach based on vibration data for accurate prognostics. IEEE Transactions on Industrial Electronics, 62(1), 647–656.

    Article  Google Scholar 

  • Javed, K., Gouriveau, R., Zerhouni, N., Zemouri, R., & Li, X. (2012). Robust, reliable and applicable tool wear monitoring and prognostic: approach based on an improved-extreme learning machine. In IEEE conference on prognostics and health management. Denver, CO, USA.

  • Khosravi, A., Nahavandi, S., Creighton, D., & Atiya, A. (2011). Comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions on Neural Networks, 22(9), 1341–1356.

    Article  Google Scholar 

  • Li, X., Lim B. S., Zhou J. H., Huang, S., Phua S. J., & Shaw, K. C. (2009). Fuzzy neural network modeling for tool wear estimation in drymilling operation. In Annual conference of the prognostics and health management society. San Diego, CA, USA.

  • Liao, L. (2010). An adaptive modeling for robust prognostics on a reconfigurable platform. Ph.D. thesis, University of Cincinnati.

  • Massol, O., Li, X., Gouriveau, R., Zhou, J. H., & Gan, O. P. (2010). An exTS based neuro-fuzzy algorithm for prognostics and toolcondition monitoring. In 11th international conference on control automation robotics & vision ICARCV’10. Singapore, pp. 1329–1334.

  • Mathworks: Curve fitting toolbox. (2010). http://mathworks.com/help/toolbox/curvefit/smooth.html

  • Nguyen, D., & Widrow, B. (1990). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In International joint conference on neural networks IJCNN. San Diego, CA, USA.

  • Oussar, Y., & Dreyfus, G. (2000). Initialization by selection for wavelet network training. Neurocomputing, 34(1–4), 131–143.

    Article  Google Scholar 

  • Pal, S., Heyns, P. S., Freyer, B. H., Theron, N. J., & Pal, S. K. (2011). 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.

    Article  Google Scholar 

  • Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: A review. International Journal Advance Manufacturing Technology, 50, 297–313.

    Article  Google Scholar 

  • Petkovi, D., Danesh, A. S., Dadkhah, M., Misaghian, N., Shamshirband, S., & Pavlovi, N. D. (2016). Adaptive control algorithm of flexible robotic gripper by extreme learning machine. Robotics and Computer-Integrated Manufacturing, 37, 170–178. doi:10.1016/j.rcim.2015.09.006.

    Article  Google Scholar 

  • Rajesh, R., & Prakash, J. S. (2011). Extreme learning machines—A review and state-of-the-art. International Journal of Wisdom Based Computing, 1, 35–49.

    Google Scholar 

  • Rao, C. R., & Mitra, S. K. (1971). Generalized inverse of matrices and its applications. New York: John Wiley and Sons.

    Google Scholar 

  • Ren, L., Lv, W., & Jiang, S. (2015). Machine prognostics based on sparse representation model. Journal of Intelligent Manufacturing pp. 1–9. doi:10.1007/s10845-015-1107-8.

    Article  Google Scholar 

  • Rizal, M., Ghani, J. A., Nuawi, M. Z., & Haron, C. H. C. (2013). Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system. Applied Soft Computing, 13(4), 1960–1968.

    Article  Google Scholar 

  • Saikumar, S., & Shunmugam, M. (2012). Development of a feed rate adaption control system for high-speed rough and finish end-milling of hardened en24 steel. International Journal Advance Manufacturing Technology, 59(9–12), 869–884.

    Article  Google Scholar 

  • Shamshirband, S., Mohammadi, K., Chen, H. L., Samy, G. N., Petkovi, D., & Ma, C. (2015). Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran. Journal of Atmospheric and Solar-Terrestrial Physics, 134, 109–117. doi:10.1016/j.jastp.2015.09.014.

    Article  Google Scholar 

  • Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Journal of Mechanical Systems and Signal Processing, 26(5), 1803–1836.

    Article  Google Scholar 

  • Singh, R., & Balasundaram, S. (2007). Application of extreme learning machine method for time series analysis. International Journal of Intelligent Technology, 2(4), 256–262.

    Google Scholar 

  • Wang, G., & Cui, Y. (2013). On line tool wear monitoring based on auto associative neural network. Journal of Intelligent Manufacturing, 24(6), 1085–1094.

    Article  Google Scholar 

  • Wu, Y., Hong, G., & Wong, W. (2015). Prognosis of the probability of failure in tool condition monitoring application—A time series based approach. The International Journal of Advanced Manufacturing Technology, 76(1–4), 513–521.

    Article  Google Scholar 

  • Zemouri, R., Gouriveau, R., & Zerhouni, N. (2010). Improving the prediction accuracy of recurrent neural network by a pid controller. International Journal of Systems Applications, Engineering & Development, 4(2), 19–34.

    Google Scholar 

  • Zhai, L. Y., Er, M. J., Li, X., Gan, O. P., Phua, S. J., Huang, S., Zhou, J. H., Linn, S., & Torabi, A. J. (2010). Intelligent monitoring of surfaceintegrity and cutter degradation in high-speed milling processes. In Annual conference of the prognostics and health management society. Portland, Oregon, USA.

  • Zhao, G., Shen, Z., Miao, C., & Man, Z. (2009). On improving the conditioning of extreme learning machine: a linear case. In 7th International conference on information, communications and signal processing. ICICS 09. Piscataway, NJ, USA.

  • Zhou, J., Li, X., Gan, O. P., Han, S., & Ng, W. K. (2006). Genetic algorithms for feature subset selection in equipment fault diagnostics. Engineering Asset Management, 10, 1104–1113.

    Article  Google Scholar 

  • Zhou, J. H., Pang, C. K., Lewis, F., & Zhong, Z. W. (2009). Intelligent diagnosis and prognosis of tool wear using dominant feature identification. IEEE Transactions on Industrial Informatics, 5(4), 454–464.

    Article  Google Scholar 

  • Zhou, J. H., Pang, C. K., Zhong, Z. W., & Lewis, F. L. (2011). Tool wear monitoring using acoustic emissions by dominant-feature identification. IEEE Transactions on Instrumentation and Measurement, 60(2), 547–559.

    Article  Google Scholar 

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Acknowledgments

This work was carried out within the Laboratory of Excellence ACTION funded by the French Government through the program “Investments for the future” managed by the National Agency for Research (ANR-11-LABX-01-01).

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Authors and Affiliations

  1. FEMTO-ST Institute (AS2M Department), UMR CNRS 6174, UBFC/ UFC/ ENSMM / UTBM, 24 Rue Alain Savary, 25000, Besançon, France

    Kamran Javed, Rafael Gouriveau & Noureddine Zerhouni

  2. Singapore Institute of Manufacturing Technology, 71 Nayang Drive, 638075, Singapore, Singapore

    Xiang Li

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  1. Kamran Javed
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  2. Rafael Gouriveau
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  4. Noureddine Zerhouni
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Correspondence to Kamran Javed.

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Javed, K., Gouriveau, R., Li, X. et al. Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model. J Intell Manuf 29, 1873–1890 (2018). https://doi.org/10.1007/s10845-016-1221-2

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  • DOI: https://doi.org/10.1007/s10845-016-1221-2

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