Skip to main content

Advertisement

SpringerLink
Log in

Efficient genetic algorithm for multi-objective robust optimization of machining parameters with taking into account uncertainties

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The respect of the machined piece quality and productivity is closely related to the mastery of uncertain factors. Indeed, the efficient solutions obtained from the machining parameter optimization based on classical methods are assigned of uncertain deviations which affect the cutting process. In the present paper, we propose multi- and mono-objective optimization approach of parameter turning with taking into account both production constraints related to piece quality, to machine power, or to tool life, than uncertainty factors related to the tool wear and to piece geometry defaults. To this end, we developed and implemented an efficient genetic algorithm, based on an evaluation mechanism of “objective” functions, which integrate the Monte Carlo simulations to calculate the robustness of objective function and different constraints. Our approach has been validated by two applications implemented with Matlab™ for the minimization of cost and machining time, which has allowed obtaining simultaneously efficient and robust results and offering the possibility to choose beforehand a compromise between efficiency and robustness of solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Amitay G, Malkin S, Koren Y (1981) Adaptive control optimization of grinding. J Eng Ind Trans ASME 103:103–108

    Article  Google Scholar 

  2. Chibane H, Serra R, Leroy R (2011) Mise en œuvre d'une optimisation multi-objectif en tournage d'un acier 100C6: compromis entre qualité de surface et productivité. 20ème Congrès Français de Mécanique. 25044 Besançon, France

  3. Saffar RJ, Razfar MR, Salimi AH et al (2009) Optimization of machining parameters to minimize tool deflection in the end milling operation using Genetic Algorithm. World Appl Sci J 6:64–69

    Google Scholar 

  4. Sahoo P (2011) Optimization of turning parameters for surface roughness using RSM and GA. Adv Product Eng Manag 6(3):197–208

    Google Scholar 

  5. Aggarwal A, Singh H (2005) Optimization of machining techniques—a retrospective and literature review. Sadhana 30(6):699–711

    Article  MATH  Google Scholar 

  6. Shin YC, Joo YS (1992) Optimization of machining conditions with practical constraints. Int J Prod Res 30(12):2907–2919

    Article  Google Scholar 

  7. Bharathi Raja S, Baskar N (2012) Application of particle swarm optimization technique for achieving desired milled surface roughness in minimum machining time. Expert Syst Appl 39:5982–5989

    Article  Google Scholar 

  8. Venkata Rao R, Kalyankar VD (2013) Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm. Scientia Iranica 20(3):967–974

    Google Scholar 

  9. Chibane H, Morandeau A, Serra R et al (2013) Optimal milling conditions for carbon/epoxy composite material using damage and vibration analysis. Int J Adv Manuf Technol 68(5–8):1111–1121

    Article  Google Scholar 

  10. Belaidi I, Mohammedi K, Brachemi B (2011) Mise en œuvre de l'algorithme génétique de type NSGAII pour l'optimisation multi-objectif d'une opération de fraisage en bout

  11. Yildiz AR (2013) Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Inf Sci 220:399–407

    Article  MathSciNet  Google Scholar 

  12. D’Addona DM, Teti R (2013) Genetic algorithm-based optimization of cutting parameters in turning processes. Procedia CIRP 7:323–328

    Article  Google Scholar 

  13. Cus F, Balic J (2003) Optimization of cutting process by GA approach. Robot Comput Integr Manuf 19:113–121

    Article  Google Scholar 

  14. Zuperl U, Cus F (2003) Optimization of cutting conditions during cutting by using neural networks. Robot Comput Integr Manuf 19:189–199

    Article  Google Scholar 

  15. Alexander HS (1992) Precision machine design. SME, Dearborn

    Google Scholar 

  16. Nawara L, Kowalski M, Sladek J (1989) The influence of kinematic errors on the profile shapes by means of CMM. CIRP Ann-Manuf Tech 38:511–516

    Article  Google Scholar 

  17. Tran H-D, Su, J-C, Claudet A- A (2008) Quantification of Uncertainty in Machining Operations for On-Machine Acceptance

  18. Deshayes L, Welsch L, Donmez A et al (2005) Robust optimization for smart machining systems: an enabler for agile manufacturing. Proceedings of ASME. 5--11

  19. Vigouroux JL, Deshayes L, Foufou S, et al. (2007) An approach for optimization of machining parameters under uncertainties using intervals and evolutionary algorithms. In: International Conference On Smart Machining Systems

  20. Moore R-E (1966) Interval analysis. Englewood Cliffs, Prentice-Hall

    MATH  Google Scholar 

  21. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press, Oxford, p 183

    Google Scholar 

  22. Gurevsky E (2011) Conception de lignes de fabrication sous incertitudes: analyse de sensibilité et approche robuste. Thèse de doctorat. Ecole Nationale Supérieure des Mines de Saint-Etienne

  23. Bai G, Zhang C, Wang B (2000) Optimization of machining datum selection and machining tolerance allocation with genetic algorithms. Int J Prod Res 38:1407–1424

    Article  MATH  Google Scholar 

  24. Kurdi M-H, Schmitz T-L, Haftka R-T et al (2009) Milling optimisation of removal rate and accuracy with uncertainty: Part 1: parameter selection. Int J Mater Prod Technol 35:3–25

    Article  Google Scholar 

  25. Aykut S, Kentli A, Gülmez S et al (2012) Robust multiobjective optimization of cutting parameters in face milling. Acta Polytechnica Hungarica 9:85–100

    Google Scholar 

  26. Sun G, LI G, Gong Z et al (2010) Multiobjective robust optimization method for drawbead design in sheet metal forming. Mater Des 31:1917–1929

    Article  Google Scholar 

  27. Jeang A (2011) Robust cutting parameters optimization for production time via computer experiment. Appl Math Model 35:1354–1362

    Article  MATH  Google Scholar 

  28. Kim SI, Landers RG, Ulsoy AG (2003) Robust machining force control with process compensation. J Manuf Sci Eng 125(3):423–430

    Article  Google Scholar 

  29. Ivester RW, Danai K (1996) Intelligent control of machining under modeling uncertainty. CIRP Manuf Syst 25:73–79

    Google Scholar 

  30. Huang Y-A, Zhang X, Xiong Y (2012) Finite element analysis of machining thin-wall parts: error prediction and stability analysis

  31. Paiva AP, Campos PH, Ferreira JR et al (2012) A multivariate robust parameter design approach for optimization of AISI 52100 hardened steel turning with wiper mixed ceramic tool. Int J Refract Met Hard Mater 30:152–163

    Article  Google Scholar 

  32. Malakooti B, Wang J, Tandler EC (1990) A sensor-based accelerated approach for multi attribute machinability and tool life evaluation. Int J Prod Res 28:2373–2392

    Article  Google Scholar 

  33. Davim J-P (ed) (2011) Machining of hard materials. Springer

  34. Apley D-W, Liu J, Chen W (2006) Understanding the effects of model uncertainty in robust design with computer experiments. J Mech Des 128:945

    Article  Google Scholar 

  35. Lee K-H, Park G-J (2011) Robust optimization considering tolerances of design variables. Comput Struct 79:77–86

    Article  Google Scholar 

  36. ISO/FDIS 14649 (2001) Industrial automation systems and integration physical device control—data model for computerized Numerical Controllers. ISO

  37. Schlenoff C, Gruninger M, Tissot F, Valois J, Lubell J, Lee JD (2000) The Process Specification Language (PSL) Overview and Version 1.0 Specification, NISTIR 6459

  38. Chipperfield AJ, Fleming PJ (1995) The MATLAB genetic algorithm toolbox. In : Applied Control Techniques Using MATLAB, IEE Colloquium on. IET p 10/1--10/4

Download references

Author information

Authors and Affiliations

  1. UMBB, Equipe de recherche en mécanique et ingénierie des systèmes et procédés, Laboratoire d’Energétique, Mécanique et Ingénieries, Université M’hamed Bougara de Boumerdes, Boumerdes, 35000, Algerie

    M. A. Sahali & I. Belaidi

  2. INSA Centre Val de Loire, Université François Rabelais, Laboratoire de Mécanique et Rhéologie - CEROC, 3 rue de la chocolaterie CS 23410, 41034, Blois Cedex, France

    R. Serra

Authors
  1. M. A. Sahali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. Belaidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Serra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. A. Sahali.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahali, M.A., Belaidi, I. & Serra, R. Efficient genetic algorithm for multi-objective robust optimization of machining parameters with taking into account uncertainties. Int J Adv Manuf Technol 77, 677–688 (2015). https://doi.org/10.1007/s00170-014-6441-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6441-z

Keywords

Search

Navigation