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Optimisation of multi-pass cutting parameters in face-milling based on genetic search

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abstract

The problem of face-milling optimisation is revisited in this work, and a numerical model to optimise machining parameters in multi-pass processes is presented. Considering the technological constraints of the machining process, the optimisation algorithm sought to minimise manufacturing costs. The design variables were the depth of cut, the cutting speed and the feed in each pass. Bounds on depth of cut, cutting speed, feed, surface finishing, cutting force, cutting power and tool life were considered as technological constraints. With the machining process operating in a multi-pass way, the depth of cut for each pass depends upon the total volume of material to be removed. In this paper, a new approach based on substituting the depth of cut with a sequence of depths of cut is presented. After generation of feasible values for sequences of depths of cut, the optimal solution was found by searching over the entire domain of design variables using a genetic algorithm based on an elitist strategy. The performance of the developed model is compared with other published models in the literature, and the multi-pass strategy is discussed.

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References

  1. Armarego EJA, Smith AJR, Wang J (1993) Constrained optimization strategies and CAM software for single-pass peripheral milling. Int J Prod Res 31:2139–2160. doi:10.1080/00207549308956849

    Article  Google Scholar 

  2. Tolouei-Rad M, Bidhendi IM (1997) On the optimization of machining parameters for milling operations. Int J Mach Tools Manuf 37(1):1–16. doi:10.1016/S0890-6955(96)00044-2

    Article  Google Scholar 

  3. Sonmez AI, Baykasoglu A, Turkay D, Filiz IH (1999) Dynamic optimization of multi-pass milling operations via geometric programming. Int J Mach Tools Manuf 39(2):297–320. doi:10.1016/S0890-6955(98)00027-3

    Article  Google Scholar 

  4. Shunmugam MS, Bhaskara Reddy SV, Narendran TT (2000) Selection of optimal conditions in multi-pass face-milling using a genetic algorithm. Int J Mach Tools Manuf 40:401–414. doi:10.1016/S0890-6955(99)00063-2

    Article  Google Scholar 

  5. Tandon V, El-Mounayri H, Kishawy H (2002) NC end milling optimization using evolutionary computation. Int J Mach Tools Manuf 42:595–605. doi:10.1016/S0890-6955(01)00151-1

    Article  Google Scholar 

  6. Wang ZG, Wong YS, Rahman M (2004) Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing. Int J Adv Manuf Technol 24:727–732. doi:10.1007/s00170-003-1789-5

    Article  Google Scholar 

  7. Wang ZG, Wong YS, Rahman M, Sun J (2006) Multi-objective optimization of high-speed milling with parallel genetic simulated annealing. Int J Adv Manuf Technol 31:209–218. doi:10.1007/s00170-005-0191-x

    Article  Google Scholar 

  8. Palanisamy P, Rajendran I, Shanmugasundaram S (2007) Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. Int J Adv Manuf Technol 32:644–655. doi:10.1007/s00170-005-0384-3

    Article  Google Scholar 

  9. Onwubolu GC (2006) Performance-based optimization of multi-pass face milling operations using Tribes. Int J Mach Tools Manuf 46:717–727. doi:10.1016/j.ijmachtools.2005.07.041

    Article  Google Scholar 

  10. Childs THC, Maekawa K, Obikawa T, Yamane Y (2000) Metal Machining, Theory and Applications. Arnold, London

    Google Scholar 

  11. Nefedov N, Osipov K (1987) Typical examples and problems in metal cutting and tool design. MIR, Moscow

    Google Scholar 

  12. Boothroyd G (1985) Fundamentals of metal machining and machine tools. McGraw-Hill, New Delhi

    Google Scholar 

  13. Conceição António CA (2002) A multilevel genetic algorithm for optimization of geometrically non-linear stiffened composite structures. Struct Multidisciplinary Optim 24(5):372–386. doi:10.1007/s00158-002-0249-4

    Article  Google Scholar 

  14. Spears WM, De Jong SK (1991) On the virtues of parametrized uniform vrossover. In: Belew RK, Booker LB (eds) Proceedings of fourth international conference on genetic algorithms. Morgan Kaufmann, San Francisco, pp 230–236

    Google Scholar 

  15. Castro CF, António CAC, Sousa LC (2004) Optimisation of shape and process parameters in metal forging using genetic algorithms. J Mater Process Technol 146:356–364. doi:10.1016/j.jmatprotec.2003.11.027

    Article  Google Scholar 

  16. Paulo Davim J, Conceição António CA (2001) Optimisation of cutting conditions in machining of aluminium matrix composites using a numerical and experimental model. J Mater Process Technol 112(1):78–82. doi:10.1016/S0924-0136(01)00551-9

    Article  Google Scholar 

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António, C.C., Castro, C.F. & Davim, J.P. Optimisation of multi-pass cutting parameters in face-milling based on genetic search. Int J Adv Manuf Technol 44, 1106–1115 (2009). https://doi.org/10.1007/s00170-009-1933-y

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  • DOI: https://doi.org/10.1007/s00170-009-1933-y

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