Abstract
In order to optimize multi-pass milling process, selection of optimal values for the parameters of the process is of great importance. The mathematical model for optimization of multi-pass milling process is a multi-constrained nonlinear programing formulation which is hard to be solved. Therefore, a novel robust meta-heuristic algorithm named Robust Grey Wolf Optimizer (RGWO) is proposed. In order to develop a RGWO, a robust design methodology named Taguchi method is utilized to tune the parameters of the algorithm. Therefore, in contradiction to previous researches, there is no need to design costly experiments to obtain the optimal values of the parameters of the GWO. In addition, an efficient constraint handling approach is implemented to handle complex constraints of the problem. A real-world problem is adopted to show the effectiveness and efficiency of the proposed RGWO in optimizing the milling process within different strategies. The results indicated that the RGWO outperforms the other solution methods in the literature as well as two novel meta-heuristic algorithms by obtaining better and feasible solutions for all cutting strategies.
Similar content being viewed by others
References
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513. doi:10.1007/s00521-015-1870-7
Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077–1097. doi:10.1007/s00521-014-1597-x
Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl Based Syst 75:1–18. doi:10.1016/j.knosys.2014.07.025
Mirjalili S, Mirjalili SM, Yang XS (2014) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681. doi:10.1007/s00521-013-1525-5
Mirjalili S, Wang GG, Coelho LDS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435. doi:10.1007/s00521-014-1629-6
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Soft 69:46–61. doi:10.1016/j.advengsoft.2013.12.007
Jayakumar N, Subramanian S, Ganesan S, Elanchezhian EB (2016) Grey wolf optimization for combined heat and power dispatch with cogeneration systems. Int J Elect Power Energy Sys 74:252–264. doi:10.1016/j.ijepes.2015.07.031
Song X, Tang L, Zhao S, Zhang X, Li L, Huang J, Cai W (2015) Grey Wolf Optimizer for parameter estimation in surface waves. Soil Dyn Earthq Eng 75:147–157. doi:10.1016/j.soildyn.2015.04.004
Pradhan M, Roy PK, Pal T (2016) Grey wolf optimization applied to economic load dispatch problems. Int J Elect Power Energy Sys 83:325–334. doi:10.1016/j.ijepes.2016.04.034
Medjahed SA, Saadi TA, Benyettou A, Ouali M (2016) Gray Wolf Optimizer for hyperspectral band selection. Appl Soft Comput 40:178–186. doi:10.1016/j.asoc.2015.09.045
Roy R (1990) A primer on the Taguchi method. Society of Manufacturing Engineers, New York
Taguchi G, Chowdhury S, Wu Y (2005) Taguchi’s quality engineering handbook. Wiley, Hoboken
Najafi AA, Niaki STA, Shahsavar M (2009) A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations. Comput Oper Res 36(11):2994–3001. doi:10.1016/j.cor.2009.01.016
Sadeghi J, Sadeghi A, Saidi-Mehrabad M (2011) A parameter-tuned genetic algorithm for vendor managed inventory model for a case single-vendor single-retailer with multi-product and multi-constraint. J Optim Ind Eng 4(9):57–67
Sadeghi J, Sadeghi S, Niaki STA (2014) A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: an NSGA-II with tuned parameters. Comput Oper Res 41:53–64. doi:10.1016/j.cor.2013.07.024
Sonmez AI, Baykasoglu A, Dereli T, Filiz IH (1999) Dynamic optimization of multi-pass milling operations via geometric programming. Int J Mach Tools Manuf 39:297–320. doi:10.1016/S0890-6955(98)00027-3
Wang ZG, Rahman M, Wong YS, Sun J (2005) Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing. Int J Mach Tools Manuf 45:1726–1734. doi:10.1016/j.ijmachtools.2005.03.009
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
Baskar N, Asokan P, Saravanan R, Prabhaharan G (2006) Selection of optimal machining parameters for multi-tool milling operations using a memetic algorithm. J Mater Process Technol 174:239–249. doi:10.1016/j.jmatprotec.2005.09.032
Yıldız AR (2009) A novel hybrid immune algorithm for global optimization in design and manufacturing. Robot Comput Integr Manuf 25:261–270. doi:10.1016/j.rcim.2007.08.002
Gao L, Huang J, Li X (2012) An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process. Appl Soft Comput 12:3490–3499. doi:10.1016/j.asoc.2012.06.007
Rao RV, Pawar PJ (2010) Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms. Appl Soft Comput 10:445–456. doi:10.1016/j.asoc.2009.08.007
Pawar PJ, Rao RV (2013) Parameter optimization of machining processes using teaching–learning-based optimization algorithm. Int J Adv Manuf Technol 67:995–1006. doi:10.1007/s00170-012-4524-2
Yang WA, Guo Y, Liao WH (2011) Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm. Int J Adv Manuf Technol 54:45–57. doi:10.1007/s00170-010-2927-5
Mellal MA, Williams EJ (2016) Total production time minimization of a multi-pass milling process via cuckoo optimization algorithm. Int J Adv Manuf Technol 1:1–8. doi:10.1007/s00170-016-8498-3
Mellal MA, Williams EJ (2014) Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic. J Intell Manuf. doi:10.1007/s10845-014-0925-4
Mellal MA, Williams EJ (2015) Cuckoo optimization algorithm for unit production cost in multi-pass turning operations. Int J Adv Manuf Technol 76(1–4):647–656. doi:10.1007/s00170-014-6309-2
Pal SK, Chakraborty D (2005) Surface roughness prediction in turning using artificial neural network. Neural Comput Appl 14(4):319–324. doi:10.1007/s00521-005-0468-x
Venkatesan D, Kannan K, Saravanan R (2009) A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput Appl 18(2):135–140. doi:10.1007/s00521-007-0166-y
Prabhu S, Uma M, Vinayagam BK (2015) Surface roughness prediction using Taguchi-fuzzy logic-neural network analysis for CNT nanofluids based grinding process. Neural Comput Appl 26(1):41–55. doi:10.1007/s00521-014-1696-8
Arriandiaga A, Portillo E, Sánchez JA, Cabanes I, Pombo I (2016) A new approach for dynamic modelling of energy consumption in the grinding process using recurrent neural networks. Neural Comput Appl 27(6):1577–1592. doi:10.1007/s00521-015-1957-1
Arriandiaga A, Portillo E, Sánchez JA, Cabanes I, Zubizarreta A (2016) Recurrent ANN-based modelling of the dynamic evolution of the surface roughness in grinding. Neural Comput Appl 1–15. doi:10.1007/s00521-016-2568-1
Gen M (1997) Genetic algorithm and engineering design. Wiley, New York
Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166. doi:10.1016/j.compstruc.2012.07.010
Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015) Water cycle algorithm for solving multi-objective optimization problems. Soft Comput 19(9):2587–2603. doi:10.1007/s00500-014-1424-4
Sadollah A, Eskandar H, Kim JH (2015) Water cycle algorithm for solving constrained multi-objective optimization problems. Appl Soft Comput 27:279–298. doi:10.1016/j.asoc.2014.10.042
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. pp 1942–1948. doi:10.1109/ICNN.1995.488968
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence. pp 69–73. doi:10.1109/ICEC.1998.699146
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. doi:10.1016/j.ins.2009.03.004
Khalilpourazari S, Pasandideh SHR (2016) Multi-item EOQ model with nonlinear unit holding cost and partial backordering: moth-flame optimization algorithm. J Ind Prod Eng 1:1–10. doi:10.1080/21681015.2016.1192068
Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287. doi:10.1016/S0045-7825(01)00323-1
Khalilpourazari S, Pasandide SHR, Niaki STA (2016) Optimization of multi-product economic production quantity model with partial backordering and physical constraints: SQP, SFS, SA, and WCA. Appl Soft Comput. doi:10.1016/j.asoc.2016.08.054
Homaifar A, Lai SHY, Qi X (1994) Constrained optimization via genetic algorithms. Simulation 62:242–254. doi:10.1177/003754979406200405
Khalilpourazari S, Khalilpourazary S (2016) A lexicographic weighted Tchebycheff approach for multi-constrained multi-objective optimization of the surface grinding process. Eng Optim. doi:10.1080/0305215X.2016.1214437
Khalilpourazari S, Pasandideh SHR (2016) Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate. In: 12th international conference on industrial engineering. pp 36–40. doi:10.1109/INDUSENG.2016.7519346
Khalilpourazari S, Mohammadi M (2016) Optimization of closed-loop Supply chain network design: a water cycle algorithm approach. In: 12th international conference on industrial engineering. pp 41–45. doi:10.1109/INDUSENG.2016.7519347
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073. doi:10.1007/s00521-015-1920-1
Yang Y, Li X, Gao L (2013) Parameters optimization of a multipass milling process based on imperialist competitive algorithm. In: IEEE 17th international conference on computer supported cooperative work in design (CSCWD), pp 406–410. doi:10.1109/CSCWD.2013.6580997
Huang J, Gao L, Li X (2015) An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Appl Soft Comput 36:349–356. doi:10.1016/j.asoc.2015.07.031
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Khalilpourazari, S., Khalilpourazary, S. Optimization of production time in the multi-pass milling process via a Robust Grey Wolf Optimizer. Neural Comput & Applic 29, 1321–1336 (2018). https://doi.org/10.1007/s00521-016-2644-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-016-2644-6