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Optimization of multi-pass turning parameters through an improved flower pollination algorithm

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Abstract

The multi-pass turning process is one of the most widely used machining methods in modern manufacturing industry, and selecting proper values for the machining parameters used in this operation such as cutting speed, feed rate, and depth of cut is a very important and difficult task. In this paper, an improved flower pollination algorithm is proposed for solving this problem. With keeping the global search operator and the local search operator of the basic flower pollination algorithm, the proposed algorithm utilizes a new population initialization method which is based on the good point set theory, and utilizes Deb’s heuristic rules to deal with the existing constraints. The proposed algorithm inherits the simplicity of the basic flower pollination algorithm. A famous model, which takes the unit production cost as the minimizing objective and involves some practical constraints, is used to examine the efficiency of the proposed algorithm. In addition, the obtained results are compared with some previously published results to examine the superiority of the proposed algorithm. The experimental and comparative results suggest that the proposed algorithm has outstanding performance and practical value.

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References

  1. Chauhan P, Pant M, Deep K (2015) Parameter optimization of multi-pass turning using chaotic PSO. Int J Mach Learn Cybern 6(2):319–337

    Article  Google Scholar 

  2. Belloufi A, Assas M, Rezgui I (2014) Intelligent selection of machining parameters in multipass turnings using firefly algorithm. Model Simul Eng 2014:1–6

    Article  Google Scholar 

  3. Aryanfar A, Solimanpur M (2012) Optimization of multi-pass turning operations using genetic algorithms. In: Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management. Istanbul, Turkey

  4. Yildiz AR (2013) Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Inf Sci 220(1):399–407

    Article  MathSciNet  Google Scholar 

  5. Costa A, Celano G, Fichera S (2010) Optimization of multi-pass turning economies through a hybrid particle swarm optimization technique. Int J Adv Manuf Technol 53(5):421–433

    Google Scholar 

  6. Srinivas J, Giri R, Yang SH (2009) Optimization of multi-pass turning using particle swarm intelligence. Int J Adv Manuf Technol 40(1):56–66

    Article  Google Scholar 

  7. Mellal MA, Williams EJ (2016) Total production time minimization of a multi-pass milling process via cuckoo optimization algorithm. Int J Adv Manuf Technol 1–8

  8. Nataraj M, Balasubramanian K (2016) Parametric optimization of CNC turning process for hybrid metal matrix composite. Int J Adv Manuf Technol 1–10

  9. Yildiz AR (2013) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64(1-4):55–61

    Article  Google Scholar 

  10. Zou P, Kim MI, Liu F (2016) Modeling and optimization of grinding parameters for custom-oriented twist drill with a Biglide parallel machine. Int J Adv Manuf Technol 1–9

  11. Yang X-S (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation, vol 7445. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp 240–249. doi:10.1007/978-3-642-32894-7_27

  12. Yang X-S, Karamanoglu M, He X (2013) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237. doi:10.1080/0305215X.2013.832237

    Article  MathSciNet  Google Scholar 

  13. Yang XS, Karamanoglu M, He X (2013) Multi-objective flower algorithm for optimization. Procedia Comput Sci 18(1):861–868

    Article  Google Scholar 

  14. Alam DF, Yousri DA, Eteiba MB (2015) Flower pollination algorithm based solar PV parameter estimation. Energy Convers Manag 101(1):410–422

    Article  Google Scholar 

  15. Chiroma H, Shuib NLM, Muaz SA, Abubakar AI, Ila LB, Maitama JZ (2015) A review of the applications of bio-inspired flower pollination algorithm. Procedia Comput Sci 62:435–441

    Article  Google Scholar 

  16. Dubey H, Pandit M, Panigrahi BK (2015) A biologically inspired modified flower pollination algorithm for solving economic dispatch problems in modern power systems. Cogn Comput 7(5):594–608

    Article  Google Scholar 

  17. Dubey HM, Pandit M, Panigrahi BK (2015) Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch. Renew Energy 83:188–202

    Article  Google Scholar 

  18. Hegazy O, Soliman OS, Salam MA (2015) Comparative study between FPA, BA, MCS, ABC, and PSO algorithms in training and optimizing of LS-SVM for stock market prediction. IJACR 5(18):35–45

    Google Scholar 

  19. Łukasik S, Kowalski P (2015) Study of flower pollination algorithm for continuous optimization. In: Angelov P, Atanassov KT, Doukovska L et al. (eds) Intelligent Systems’2014, vol 322. Advances in Intelligent Systems and Computing. Springer International Publishing, pp 451–459

  20. Prathiba R, Moses MB, Sakthivel S (2014) Flower pollination algorithm applied for different economic load dispatch problems. IJET 6(2):1009–1016

    Google Scholar 

  21. Sakib N, Kabir MWU, Subbir M, Alam S (2014) A comparative study of flower pollination algorithm and bat algorithm on continuous optimization problems. IJAIS 7(9):13–19

    Article  Google Scholar 

  22. Abdel-Baset M, Hezam IM (2015) An improved flower pollination algorithm for ratios optimization problems. Appl Math Inf Sci Lett 3(2):83–91

    Google Scholar 

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

    Article  Google Scholar 

  24. M-C C, D-M T (1996) A simulated annealing approach for optimization of multi-pass turning operations. Int J Prod Res 34(10):2803–2825

    Article  MATH  Google Scholar 

  25. Chen M-C, Su C-T (1998) Optimization of machining conditions for turning cylindrical stocks into continuous finished profiles. Int J Prod Res 36(8):2115–2130

    Article  MATH  Google Scholar 

  26. Onwubolu GC, Kumalo T (2001) Optimization of multipass turning operations with genetic algorithms. Int J Prod Res 39(16):3727–3745

    Article  MATH  Google Scholar 

  27. Chen M-C, Chen K-Y (2003) Optimization of multipass turning operations with genetic algorithms: a note. Int J Prod Res 41(14):3385–3388

    Article  Google Scholar 

  28. Vijayakumar K, Prabhaharan G, Asokan P, Saravanan R (2003) Optimization of multi-pass turning operations using ant colony system. Int J Mach Tools Manuf 43(15):1633–1639

    Article  Google Scholar 

  29. Wang YC (2007) A note on ‘optimization of multi-pass turning operations using ant colony system’. Int J Mach Tools Manuf 47(12):2057–2059

    Article  Google Scholar 

  30. Zheng LY, Ponnambalam SG (2010) A hybrid GA-AIS heuristic for optimization of multipass turning operations. In: Liu H, Ding H, Xiong Z, Zhu X (eds) Intelligent Robotics and Applications: Third International Conference, ICIRA 2010, Shanghai, China, November 10–12, 2010. Proceedings, Part II. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 599–611

  31. Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210(22):81–88

    Article  Google Scholar 

  32. Yildiz AR (2013) Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl Soft Comput 13(3):1433–1439

    Article  Google Scholar 

  33. Yildiz AR (2012) Optimization of multi-pass turning operations using hybrid teaching learning-based approach. Int J Adv Manuf Technol 66(9):1319–1326

    Google Scholar 

  34. Rao RV, Kalyankar VD (2013) Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm. Sci Iran 20(3):967–974

    Google Scholar 

  35. Belloufi A (2012) Optimization of cutting conditions in multi-pass turning using hybrid genetic algorithm-sequential quadratic programming. J Appl Mech Eng 1(1):3–7

    Article  Google Scholar 

  36. Mellal MA, Williams EJ (2014) Cuckoo optimization algorithm for unit production cost in multi-pass turning operations. Int J Adv Manuf Technol 76(1-4):647–656

    Article  Google Scholar 

  37. Liu H, Cai Z, Wang Y (2007) A new constrained optimization evolutionary algorithm by using good point set. In: Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, pp 1247–1254

  38. Liu Y, Li S (2010) Hybrid good point set evolutionary strategy for constrained optimization. In: Huang D-S, McGinnity M, Heutte L, Zhang X-P (eds) Advanced intelligent computing theories and applications: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18–21, 2010. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 30–39

  39. Wang F, Zhang Y, Ma J (2008) Modeling and analysis of a self-learning worm based on good point set scanning. Wirel Commun Mob Comput 9(4):573–586

    Article  Google Scholar 

  40. Zhang C, Lin Q, Gao L, Li X (2015) Backtracking search algorithm with three constraint handling methods for constrained optimization problems. Expert Syst Appl 42(21):7831–7845

    Article  Google Scholar 

  41. Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2–4):311–338

    Article  MATH  Google Scholar 

  42. Altuntas S, Dereli T, Yilmaz MK (2015) Evaluation of excavator technologies: application of data fusion based multimoora methods. J Civ Eng Manag 21(8):977–997

    Article  Google Scholar 

  43. Karande P, Chakraborty S (2012) Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Mater Des 37:317–324

    Article  Google Scholar 

  44. Gong W, Cai Z (2009) An improved multiobjective differential evolution based on Pareto-adaptive [epsilon]-dominance and orthogonal design. Eur J Oper Res 198(2):576–601

    Article  MathSciNet  MATH  Google Scholar 

  45. Rai SC, Misra BB, Nayak AK, Mall R (2009) A multi-objective pareto-optimal genetic algorithm for QoS multicasting. In: Advance Computing Conference, 2009. IACC 2009. IEEE International, pp 1303–1307

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Authors and Affiliations

  1. School of Mechanical Engineering, Shandong University, Jinan, 250061, China

    Shuhui Xu, Yong Wang & Fengyue Huang

  2. Key Laboratory of High-efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University, Jinan, 250061, China

    Shuhui Xu, Yong Wang & Fengyue Huang

Authors
  1. Shuhui Xu
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  2. Yong Wang
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  3. Fengyue Huang
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Correspondence to Yong Wang.

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Xu, S., Wang, Y. & Huang, F. Optimization of multi-pass turning parameters through an improved flower pollination algorithm. Int J Adv Manuf Technol 89, 503–514 (2017). https://doi.org/10.1007/s00170-016-9112-4

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  • DOI: https://doi.org/10.1007/s00170-016-9112-4

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