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Optimization of cutting conditions using an evolutive online procedure

  • Antonio Del Prete
  • Rodolfo FranchiEmail author
  • Stefania Cacace
  • Quirico Semeraro
Article
  • 62 Downloads

Abstract

This paper proposes an online evolutive procedure to optimize the Material Removal Rate in a turning process considering a stochastic constraint. The usual industrial approach in finishing operations is to change the tool insert at the end of each machining feature to avoid defective parts. Consequently, all parts are produced at highly conservative conditions (low levels of feed and speed), and therefore, at low productivity. In this work, a framework to estimate the stochastic constraint of tool wear during the production of a batch is proposed. A simulation campaign was carried out to evaluate the performances of the proposed procedure. The results showed that it was possible to improve the Material Removal Rate during the production of the batch and keeping the probability of defective parts under a desired level.

Keywords

Tool wear Stochastic constraint Machining Optimization 

Notes

Acknowledgements

The authors sincerely thank the reviewers for their very helpful comments on earlier drafts of this manuscript, for their time and for their encouragement.

References

  1. Angün, E., Kleijnen, J., den Hertog, D., & Gürkan, G. (2009). Response surface methodology with stochastic constraints for expensive simulation. Journal of the Operational Research Society, 60(6), 735–746.CrossRefGoogle Scholar
  2. Box, G. E. P., & Wilson, K. B. (1992). “On the experimental attainment of optimum conditions”. Breakthroughs in statistics (pp. 270–310). New York: Springer.Google Scholar
  3. Costa, A., Celano, G., & Fichera, S. (2011). Optimization of multi-pass turning economies through a hybrid particle swarm optimization technique. International Journal of Advanced Manufacturing Technology, 53, 421–433.CrossRefGoogle Scholar
  4. D’Addona, D. M., Ullah, A. S., & Matarazzo, D. (2017). Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing. Journal of Intelligent Manufacturing, 28(6), 1285–1301.CrossRefGoogle Scholar
  5. Davim, J. P. (Ed.). (2008). Machining: Fundamentals and recent advances. London: Springer.Google Scholar
  6. Del Castillo, E. (2007). Process optimization: A statistical approach (Vol. 105). New York: Springer.Google Scholar
  7. Devillez, A., Schneider, F., Dominiak, S., Dudzinski, D., & Larrouquere, D. (2007). Cutting forces and wear in dry machining of Inconel 718 with coated carbide tools. Wear, 262, 931–942.CrossRefGoogle Scholar
  8. Draper, N., & Smith, H. (2005). Applied regression analysis (3rd ed.). New York: Wiley.Google Scholar
  9. Ganesan, H., Mohankumar, G., Ganesan, K., & Ramesh Kumar, K. (2011). Optimization of machining parameters in turning process using genetic algorithm and particle swarm optimization with experimental verification. International Journal of Engineering Science and Technology (IJEST), 3(2), 1091–1102.Google Scholar
  10. Kalpakjian, S., & Schmidt, S. R. (2001). Manufacturing engineering and technology (4th ed.). Upper Saddle River: Prentice Hall International.Google Scholar
  11. Klocke, F., Zeis, M., Klink, A., & Veselovac, D. (2012). Technological and economical comparison of roughing strategies via milling, EDM and ECM for titanium- and nickel-based blisks. Procedia CIRP, 2, 98–101.CrossRefGoogle Scholar
  12. Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response surface methodology: Process and product optimization using designed experiments (3rd ed.). New York: Wiley.Google Scholar
  13. Rao, S. S. (2009). Engineering optimization theory and practice. New York: Wiley.CrossRefGoogle Scholar
  14. Schorník, V., Zetek, M., & Daňa, M. (2015). The influence of working environment and cutting conditions on milling nickel–based super alloys with carbide tools. Procedia Engineering, 100, 1262–1269.CrossRefGoogle Scholar
  15. Taylor, F. W. (1907). On the art of cutting metals. New York: American Society of Mechanical Engineers.Google Scholar
  16. Venkata Rao, R. (2016). Teaching learning based optimization algorithm and its engineering applications. New York: Springer.Google Scholar
  17. Venkata Rao, R., & Pawar, P. J. (2010). Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms. Applied Soft Computing, 10, 445–456.CrossRefGoogle Scholar
  18. Wang, G., & Cui, Y. (2013). On line tool wear monitoring based on auto associative neural network. Journal of Intelligent Manufacturing, 24(6), 1085–1094.CrossRefGoogle Scholar
  19. Wang, G., Guo, Z., & Qian, L. (2014). Online incremental learning for tool condition classification using modified fuzzy ARTMAP network. Journal of Intelligent Manufacturing, 25(6), 1403–1411.CrossRefGoogle Scholar
  20. Yildiz, A. R. (2013). A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Applied Soft Computing, 13(3), 1561–1566.CrossRefGoogle Scholar
  21. Zainal, N., Zain, A. M., Radzi, N. H. M., & Othman, M. R. (2016). Glowworm swarm optimization (GSO) for optimization of machining parameters. Journal of Intelligent Manufacturing, 27(4), 797–804.CrossRefGoogle Scholar
  22. Zhang, J., Liang, S. Y., Yao, J., Chen, J. M., & Huang, J. L. (2006). Evolutionary optimization of machining processes. Journal of Intelligent Manufacturing, 17(2), 203–215.CrossRefGoogle Scholar
  23. Zhu, D., Zhang, X., & Ding, H. (2013). Tool wear characteristics in machining of nickel-based superalloys. International Journal of Machine Tools & Manufacture, 64, 60–77.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria dell’InnovazioneUniversità del SalentoLecceItaly
  2. 2.Dipartimento di MeccanicaPolitecnico di MilanoMilanItaly

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