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Parameter optimization of multi-pass turning using chaotic PSO

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Abstract

Determination of an optimal set of machining parameters is needed to produce an ordered product of considerable quality and minimal manufacturing cost. The nonlinear and highly constrained nature of machining models restricts the application of classical gradient based techniques for handling such problems. The present study focuses on obtaining the optimal machining conditions during multi-pass turning operations. Methodology used is, a chaotic PSO namely Totally Disturbed Particle Swarm Optimization (TDPSO), an enhanced variant of PSO is employed for obtaining the optimal machining conditions during multi-pass turning operations subject to various constraints. In TDPSO, the phenomenon of chaos is embedded at different stages of PSO in order to make the search process more efficient. Results obtained by TDPSO are compared with results available in literature and it is observed that TDPSO is quite efficient for dealing with such problems.

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References

  1. Agapiou JS (1992) An optimization of multi-stage machining system, part I: mathematical solution part 2: the algorithm and application. J Eng Ind 114:524–538

    Article  Google Scholar 

  2. Agapiou JS (1992) The optimization of machining operation based on a combined criterion, part I: the use of combined objectives in single-pass operations. J Eng Ind 114:500–507

    Article  Google Scholar 

  3. Amiolemhen E, Ibhadode AOA (2004) Application of genetic algorithm determination of the optimal machining parameters in the conversion of a cylindrical bar stock into a continuous finished profile. Int J Mech Tools Manuf 44(12–13):1403–1412

    Article  Google Scholar 

  4. Bouzid W (2005) Cutting parameter optimization to minimize production time in high speed turning. J Mater Process Technol 161(3):388–395

    Article  Google Scholar 

  5. Cakir MC, Gurarda A (1998) Optimization and graphical representation of machining conditions in multi-pass turning operations. Comput Integr Manuf Syst 11(3):157–170

    Article  Google Scholar 

  6. Chandrasekaran M, Muralidhar M, Krishna CM, Dixit US (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46(5–8):445–464

    Article  Google Scholar 

  7. Chauhan P, Deep K, Pant M (2011) ‘Optimizing CNC turning process using real coded genetic algorithm and differential evolution’, transaction on evolutionary algorithm and continuous optimization : ISSN: 2229-871. Glob J Technol Optim 2:157–165

    Google Scholar 

  8. Chen MC, Chen KY (2003) Optimization of multipass turning operations with genetic algorithms: a note. Int J Prod Res 41(14):3385–3388

    Article  Google Scholar 

  9. Chen MC, Tsai DM (1996) A simulated annealing approach for optimization of multi-pass turning operations. Int J Prod Res 34(10):2803–2825

    Article  MATH  MathSciNet  Google Scholar 

  10. Chien WT, Tsai CS (2003) The investigation on the prediction of tool wear and determination of optimum cutting conditions in machining 17-4PH stainless steel. J Mater Process Technol 140(1–3):340–345

    Article  Google Scholar 

  11. Chua MS, Loh HT, Wong YS, Rahman M (1991) Optimization of cutting conditions for multi-pass turning operations using sequential quadratic programming. J Mater Process Technol 28(1–2):253–262

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Deep K, Chauhan P, Pant M (2012) Totally disturbed chaotic particle swarm optimization. In: Proc. of IEEE conference on evolutionary computing (CEC-2012), June 10–15, 2012, pp 1–8

  14. Deep K, Chauhan P, Pant M (2011) Optimizing machining parameters using a novel real coded GA. Int J Appl Math Mech 7(3):53–69

    Google Scholar 

  15. Gopala krishnan B, Al-Khayyal F (1991) Machine parameter selection for turning with constraints: an analytical approach based on geometric programming. Int J Prod Res 29(9):1897–1908

    Article  Google Scholar 

  16. Gupta R, Batra JL, Lal GK (1995) Determination of optimal subdivision of depth of cut in multi-pass turning with constraints. Int J Prod Res 33(9):2555–2565

    Article  MATH  Google Scholar 

  17. Hui YV, Leung LC, Linn R (2001) Optimal machining conditions with cost of quality and tool maintenance for turning. Int J Prod Res 39(4):647–665

    Article  MATH  Google Scholar 

  18. Kim SS, Kim IH, Mani V, Kim HJ (2008) Real-coded genetic algorithm for machining condition optimization. Int J Adv Manuf Technol 38:884–895

    Article  Google Scholar 

  19. Liang M, Mgwatu M, Zuo M (2001) Integration of cutting parameter selection and tool adjustment decision for multi-pass turning. Int J Adv Manuf Technol 17:861–869

    Article  Google Scholar 

  20. Lin C-M, Li M-C, Ting A-B, Lin M-H (2011) A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm. Int J Mach Learn Cybern 2(4):225–234

    Article  Google Scholar 

  21. Molinari A, Nouari M (2002) Modeling of tool wear by diffusion in metal cutting. Wear 252(1–2):135–149

    Article  Google Scholar 

  22. Natarajan U, Saravanan R, Periasamy VM (2006) Application of particle swarm optimization in artificial neural network for prediction of tool life. Int J Adv Manuf Technol 28:1084–1088

    Article  Google Scholar 

  23. Nian CY, Yang WH, Tarng YS (1999) Optimization of turning operations with multiple performance characteristics. J Mater Process Technol 95(1–3):90–96

    Article  Google Scholar 

  24. Ojha DK, Dixit US, Davim JP (2009) A soft computing based optimization of multi-pass turning processes. Int J Mater Prod Technol 35:145–166

    Article  Google Scholar 

  25. 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 

  26. Prasad AVS, Rao RK, Rao VKS (1997) Optimal selection of process parameter for turning operation in CAPP system. Int J Prod Res 35(6):1495–1522

    Article  MATH  Google Scholar 

  27. Rajemi MF, Mativenga PT, Aramcharoen A (2010) Sustainable machining: selection of optimum turning conditions based on minimum energy considerations. J Clean Prod 18(10–11):1059–1065

    Article  Google Scholar 

  28. RaoVR, Kalyankar VD (2013) Multi-pass turning process parameter optimization using teaching-learning based optimization algorithm, Scientia Iranica, available online 10 Jan, 2013

  29. Rana S, Jasola S, Kumar R (2013) A boundary restricted adaptive particle swarm optimization for data clustering. Int J Mach Learn Cybern 4(4):391–400

    Article  Google Scholar 

  30. Reddy SVB, Shunmugam MS, Narendran TT (1998) Optimal sub-division of the depth of cut to achieve minimum production cost in multi-pass turning using a genetic algorithm. J Mater Process Technol 79(1–3):101–108

    Article  Google Scholar 

  31. Saravanan R, Ashokan P, Sachithanandam M (2001) Comparative analysis of conventional and non-conventional optimization technique for CNC-turning process. Int J Adv Manuf Technol 17:471–476

    Article  Google Scholar 

  32. Sardinas RQ, Reis P, Davim JP (2006) Multi-objective optimization of cutting parameters for drilling laminate composite materials by using genetic algorithms. Compos Sci Technol 66(15):3083–3088

    Article  Google Scholar 

  33. Satish kumar S, Asokan P, Kumanan S (2006) Optimization of depth of cut in multi-pass turning using nontraditional optimization techniques. Int J Adv Manuf Technol 29(3):230–238

    Article  Google Scholar 

  34. Sharma VS, Dhiman S, Sehgal R, Sharma SK (2008) Estimation of cutting forces and surface roughness for hard turning using neural networks. Int J Adv Manuf Technol 19(4):473–483

    Google Scholar 

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

    Article  Google Scholar 

  36. Singh Y, Chauhan P (2012) Analyzing constrained machining conditions in multi-pass turning by differential evolution. Adv Mech Eng Appl 2(3):201–206 (special issue: NICA)

    Google Scholar 

  37. Soodamani R, Liu ZQ (2000) GA-based learning for a model-based object recognition system. Int J Approx Reason 23(2):85–109

    Article  MATH  Google Scholar 

  38. Srinivas J, Giri R, Yang SH (2009) Optimization of multi-pass turning using particle swarm intelligence. Int J Adv Manuf Technol 40(1–2):56–66

    Article  Google Scholar 

  39. 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 

  40. Wang DX, Zuo MJ, Qi KZ, Liang M (1996) Online tool adjustment with adaptive tool wear function identification. Int J Prod Res 34(9):2499–2515

    Article  MATH  Google Scholar 

  41. Wang X, Da ZJ, Balaji AK, Jawahir IS (2002) Performance-based optimal selection of cutting conditions and cutting tools in multi-pass turning operations using genetic algorithms. Int J Prod Res 40(9):2053–2065

    Article  MATH  Google Scholar 

  42. Wang X, He Y, Dong L, Zhao H (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252

    Article  MATH  Google Scholar 

  43. Wang YC (2007) A note on optimization of multi-pass turning operations using ant colony system. Int J Mach Tools Manuf 47(12–13):2057–2059

    Article  Google Scholar 

  44. Wang Y-C, Chiu Y-C, Hung Y-P (2011) Optimization of multi-task turning operations under minimal tool waste consideration. Robotics Comput Integr Manuf 27(4):674–680

    Article  Google Scholar 

  45. Yildiz AR (2009) A novel hybrid immune algorithm for global optimization in design and manufacturing. Robotics Comput Integr Manuf 25(2):261–270

    Article  Google Scholar 

  46. Yildiz AR (2009) An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. J Mater Process Technol 50(4):224–228

    Google Scholar 

  47. Yildiz AR (2009) Hybrid immune-simulated annealing algorithm for optimal design and manufacturing. Int J Mater Prod Technol 34(3):217–226

    Article  Google Scholar 

  48. Yildiz AR (2009) A novel particle swarm optimization approach for product design and manufacturing. Int J Adv Manuf Technol 40(5–6):617–628

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  50. 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 

  51. Yildiz AR (2013) Optimization of multi-pass turning operations using hybrid teaching learning-based approach. Int J Adv Manuf Technol. doi:10.1007/s00170-012-4410-y

    Google Scholar 

  52. 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 

  53. Yildiz AR, Ozturk F (2006) Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation. J Eng Manuf 220(12):2041–2053

    Article  Google Scholar 

  54. Yusup N, Zain AM, Hashim SZM (2012) Evolutionary techniques in optimizing machining parameters: review and recent applications (2007–2011). Expert Syst Appl 39(10):9909–9927

    Article  Google Scholar 

  55. Yusup N, Zain AM, Hashim SZM (2012) Overview of PSO for Optimizing Process Parameters of Machining. Procedia Eng 29:914–923

    Article  Google Scholar 

  56. Zheng LY, Ponnambalam SG (2010) Optimization of multipass turning operations using particle swarm optimization. In: Proc. of 7th international symposium on mechatronics and its applications (ISMA), 1–6

  57. Zuperl U, Cus F (2003) Optimization of cutting conditions during cutting by using neural networks. Robotics Comput Integr Manuf 19(1–2):189–199

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India

    Pinkey Chauhan & Kusum Deep

  2. Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur, 247001, Uttarakhand, India

    Millie Pant

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  1. Pinkey Chauhan
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  2. Millie Pant
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  3. Kusum Deep
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Correspondence to Pinkey Chauhan.

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Chauhan, P., Pant, M. & Deep, K. Parameter optimization of multi-pass turning using chaotic PSO. Int. J. Mach. Learn. & Cyber. 6, 319–337 (2015). https://doi.org/10.1007/s13042-013-0221-1

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