Continuous improvement of HSM process by data mining

  • Victor Godreau
  • Mathieu Ritou
  • Etienne Chové
  • Benoit Furet
  • Didier Dumur


The efficient use of digital manufacturing data is a key leverage point of the factories of the future. Automatic analysis tools are required to provide smart and comprehensible information from large process databases collected on shopfloor machines-tools. In this paper, an original and dedicated approach is proposed for the data mining of HSM (High Speed Machining) flexible productions. It relies on an unsupervised learning (by statistical modelling of machining vibrations) for the classification of machining critical events and their aggregation. Moreover, a contextual clustering is suggested for a better data selection, and a visualization of machining KPI for decision aiding. It results in new leverages for decision making and process improvement; through automatic detection of the main faulty programs, tools or machine conditions. This analysis has been performed over two spindle lifespans (18 months) of industrial HSM production in aeronautics and results are presented, which assess the proposed approach.


Monitoring Machining Data mining 



The financial support of the French government on FUI QuaUsi and ANR SmartEmma (ANR-16-CE10-0005) is acknowledged. The authors also thank the contributions of the industrial partners.


  1. Abellan-Nebot, J. V., & Romero Subirón, F. (2010). A review of machining monitoring systems based on artificial intelligence process models. International Journal of Advanced Manufacturing Technology, 47(1), 1–21.Google Scholar
  2. Ben Chabane, S., Stoica Maniu, C., Camacho, E., Alamo, T., & Dumur, D. (2016). Fault detection using set-membership estimation based on multiple model systems. In European control conference, Aalborg, Denmark (pp. 1105–1110).Google Scholar
  3. Brecher, C., Quintana, G., & Rudolf, T. (2011). Use of NC kernel data for surface roughness monitoring in milling operations. International Journal of Advanced Manufacturing Technology, 53, 953–962.CrossRefGoogle Scholar
  4. Chen, J. C. (2000). An effective fuzzy-nets training scheme for monitoring tool breakage. Journal of Intelligent Manufacturing, 11(1), 85–101.CrossRefGoogle Scholar
  5. Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501–521.CrossRefGoogle Scholar
  6. de Castelbajac, C., Ritou, M., Laporte, S., & Furet, B. (2014). Monitoring of distributed defects on HSM spindle bearings. Applied Acoustics, 77, 159–168.CrossRefGoogle Scholar
  7. Denkena, B., Dittrich, M. A., & Uhlich, F. (2016). Self-optimizing cutting process using learning process models. Procedia Technology, 26, 221–226.CrossRefGoogle Scholar
  8. Eleftheriadis, R. J., & Myklebust, O. (2015). Benchmark and best practice of IFaCOM industrial demonstrators. In 2015, 9th CIRP conference on intelligent computation in manufacturing engineering, procedia CIRP (Vol. 33, pp. 311–314).Google Scholar
  9. Gao, R., Wang, L., Teti, R., Dornfeld, D., Kumara, S., & Mori, M. (2015). Cloud enabled prognosis for manufacturing. Annals of the CIRP, 64(2), 749–772.CrossRefGoogle Scholar
  10. IEC 62541-1. (2016). OPC unified architecture—Part 1: Overview and concepts. International Electrotechnical Commission.Google Scholar
  11. Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2, 59–64.CrossRefGoogle Scholar
  12. Karandikar, J. M., Abbas, A. E., & Schmitz, T. L. (2014). Tool life prediction using Bayesian updating. Part 1: Milling tool life model using a discrete grid method. Precision Engineering, 38(1), 18–27.CrossRefGoogle Scholar
  13. Lamraoui, M., Thomas, M., & El Badaoui, M. (2014). Cyclostationarity approach for monitoring chatter and tool wear in high speed milling. Mechanical Systems and Signal Processing, 44(1–2), 177–198.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letter, 3, 18–23.CrossRefGoogle Scholar
  16. Quintana, G., Garcia-Romeu, M. L., & Ciurana, J. (2011). Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. Journal of Intelligent Manufacturing, 22(4), 607–617.CrossRefGoogle Scholar
  17. Ritou, M., Rabréau, C., Loch, S. Le, & Furet, B. (2018). Influence of spindle condition on the dynamic behaviour. Annals of the CIRP, 67(1), 413–416.Google Scholar
  18. Ritou, M., Garnier, S., Furet, B., & Hascoët, J. Y. (2014). Angular approach combined to mechanical model for tool breakage detection by eddy current sensors. Mechanical Systems and Signal Processing, 44(1), 211–220.CrossRefGoogle Scholar
  19. Tangjitsitcharoen, S., Saksri, T., & Ratanakuakangwan, S. (2015). Advance in chatter detection in ball end milling process by utilizing wavelet transform. Journal of Intelligent Manufacturing, 26(3), 485–499.CrossRefGoogle Scholar
  20. Teti, R., Jemielniak, K., O’Donnell, G., & Dornfeld, D. (2010). Advanced monitoring of machining operations. Annals of the CIRP, 59(2), 717–739.CrossRefGoogle Scholar
  21. Vijayaraghavan, A., Huet, L., Dornfeld, D., Sobel, W., & Blomquist, B., & Conley, M. , (2009). Process planning and verification with MTConnect. Transactions of NAMRI/SMI, 37, 443–450.Google Scholar
  22. Wang, G. F., Yang, Y. W., Zhang, Y. C., & Xie, Q. L. (2014). Vibration sensor based tool condition monitoring using support vector machine and locality preserving projection. Sensors and Actuators, A: Physical, 209, 24–32.CrossRefGoogle Scholar
  23. Whittaker, J. (2009). Graphical models in applied multivariate statistics. New York: Wiley.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Victor Godreau
    • 1
    • 2
  • Mathieu Ritou
    • 1
  • Etienne Chové
    • 2
  • Benoit Furet
    • 1
  • Didier Dumur
    • 3
  1. 1.LS2N (Laboratory of Digital Sciences of Nantes, UMR CNRS 6004)University of NantesNantesFrance
  2. 2.Europe TechnologiesCarquefouFrance
  3. 3.L2S (Laboratory of Signals and Systems, UMR CNRS 8506)Centrale SupélecGif sur YvetteFrance

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