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Optimization of cutting conditions using an evolutive online procedure

Author

Listed:
  • Antonio Del Prete

    (Università del Salento)

  • Rodolfo Franchi

    (Università del Salento)

  • Stefania Cacace

    (Politecnico di Milano)

  • Quirico Semeraro

    (Politecnico di Milano)

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.

Suggested Citation

  • Antonio Del Prete & Rodolfo Franchi & Stefania Cacace & Quirico Semeraro, 2020. "Optimization of cutting conditions using an evolutive online procedure," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 481-499, February.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-01460-x
    DOI: 10.1007/s10845-018-01460-x
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    References listed on IDEAS

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    1. Doriana M. D’Addona & A. M. M. Sharif Ullah & D. Matarazzo, 2017. "Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1285-1301, August.
    2. E Angün & J Kleijnen & D den Hertog & G Gürkan, 2009. "Response surface methodology with stochastic constraints for expensive simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 735-746, June.
    3. Nurezayana Zainal & Azlan Mohd Zain & Nor Haizan Mohamed Radzi & Muhamad Razib Othman, 2016. "Glowworm swarm optimization (GSO) for optimization of machining parameters," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 797-804, August.
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