In this paper, Subpopulation Firefly Algorithm is proposed for optimization of machining parameters in multi-pass turning and multi-pass face milling operations. Basic Firefly Algorithm is modified with the aim to avoid space of local minimum and to meet the operation constraints in each iteration step. For that purpose, the following modifications are made: one firefly population is divided into two, a crossover operator is introduced and the searching for new design variables is continued until constraint functions are fulfilled. For turning operation, optimization is carried out for one objective: minimization of production cost. For face milling operation, multi-objective optimization is used for minimizing production cost and machining time, and maximizing profit rate at the same time. In both cases of multi-pass machining operations, optimization process implies meeting all operation constraints. For multi-pass turning operation, the best results from literature are confirmed with good convergence and low value of standard deviation. For multi-pass milling operation, better results are achieved compared with existing results from literature. The proposed algorithm showed capability of achieving global optimum for complex optimization problems.
MellalMAWilliamsEJ. Cuckoo optimization algorithm for unit production cost in multi-pass turning operations. Int J Adv Manuf Technol2015; 76: 647–656.
2.
ZadshakoyanMPourmostaghimiV. Genetic equation for the prediction of tool-chip contact length in orthogonal cutting. Eng Appl Artif Intell2013; 26: 1725–1730.
3.
RaoRDKalyankarVD. Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm. Eng Appl Artif Intell2013; 26: 524–531.
4.
Venkata RaoRKalyankarVD. Multi-pass turning process parameter optimization using teaching-learning-based optimization algorithm. Scientia Iranica2013; 20: 967–974.
5.
ShinYCJooYS. Optimization of machining conditions with practical constraints. Int J Prod Res1992; 30: 2907–2919.
6.
ChenMCTsaiDM. A simulated annealing approach for optimization of multi-pass turning operations. Int J Prod Res1996; 34: 2803–2825.
7.
OnwuboluGCKumaloT. Optimization of multi-pass turning operations with genetic algorithms. Int J Prod Res2001; 39: 3727–3745.
8.
SrinivasJGiriRYangSH. Optimization of multi-pass turning using particle swarm intelligence. Int J Adv Manuf Technol2009; 40: 56–66.
9.
YildizAR. Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Inf Sci2013; 220: 399–407.
10.
CostaACelanoGFicheraS. Optimization of multi-pass turning economies through a hybrid particle swarm optimization technique. Int J Adv Manuf Technol2011; 53: 421–433.
11.
YildizAR. A comparative study of population-based optimization algorithms for turning operations. Inform Sci2012; 210: 81–88.
12.
VijayakumarKPrabhaharanGAsokanPet al.Optimization of multi-pass turning operations using ant colony system. Int J Mach Tool Manuf2003; 43: 1633–1639.
13.
WangY. A note on ‘optimization of multi-pass turning operations using ant colony system’. Int J Mach Tool Manuf2007; 47: 2057–2059.
14.
YildizAR. An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. J Mater Process Technol2009; 209: 2773–2780.
15.
YildizAR. Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. App Soft Comput2013; 13: 1433–1439.
16.
MajhiBPSahuS. Optimization of machining parameters using bio-geography based algorithm technique. Int J Res Eng Technol2015; 3: 9–18.
17.
Aryanfar A and Solimanpur M. Optimization of multi-pass turning operations using genetic algorithms. In: Proceedings of the 2012 international conference on industrial engineering and operations management, Istanbul, Turkey, 3–6 July 2012.
18.
SaravananRSiva SankarRAsokanPet al.Optimization of cutting conditions during continuous finished profile machining using non-traditional techniques. Int J Adv Manuf Technol2005; 26: 30–40.
19.
YildizAR. Optimization of multi-pass turning operations using hybrid teaching learning-based approach. Int J Adv Manuf Technol2013; 66: 1319–1326.
20.
YangWGuoYLiaoW. Multi-objective optimization of multi-pass face milling using particle swarm intelligence. Int J Adv Manuf Technol2011; 56: 429–443.
21.
YangWGuoYLiaoW. Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm. Int J Adv Manuf Technol2011; 54: 45–57.
22.
GilbertWW. Economics of machining, machining theory and practice, Cleveland, OH: American Society of Metals, 1950.
23.
CrookallJRVenkataramaniN. Computer optimization of multi-pass turning. Int J Prod Res1971; 9: 247–259.
24.
IwataKIwatsuboTFujiiSet al.A probabilistic approach to the determination of the optimum cutting conditions. J Eng Ind1972; 94: 1099–1107.
25.
ErmerDSKromodihardjoS. Optimization of multipass turning with constraints. J Eng Industry Transact ASME1981; 103: 462–468.
26.
Alberti N. Optimization of multi-pass turning. In: Proceedings of the 14th North American manufacturing research conference, University of Minnesota, Minneapolis, Minnesota, USA, 28-30 May 1986.
27.
GopalakrishnanBAl-KhayyalF. Machine parameter selection for turning with constraints: an analytical approach based on geometric programming. Int J Prod Res1991; 29: 1897–1908.
28.
ChenMCSuST. Optimization of machining conditions for turning cylindrical stocks into continuous finished profiles. Int J Prod Res1998; 36: 2115–2130.
29.
GuptaRBatraJLLalGK. Determination of optimal subdivision of depth of cut in multi-pass turning with constraints. Int J Prod Res1995; 33: 2555–2565.
30.
ChenMCChenKY. Optimization of multi-pass turning operations with genetic algorithms: a note. Int J Prod Res2003; 41: 3385–3388.
31.
Aryanfar A and Solimanpur M. Optimization of multipass turning operations using genetic algorithms. In: Proceedings of the 2012 international conference on industrial engineering and operations management, Istanbul, Turkey, 3 – 6 July 2012.
32.
Zheng LY and Ponnambalam SG. A hybrid GA-AIS heuristic for optimization of multi-pass turning operations. In: Goebel R, Siekmann J and Wahlster W (Eds), Intelligent robotics and applications. Germany: Springer-Verlag Berlin Heidelberg, 2010, pp.599–611.
33.
YildizAR. A novel particle swarm optimization approach for product design and manufacturing. Int J Adv Manuf Technol2009; 40: 617–628.
34.
YildizAR. A novel hybrid immune algorithm for global optimization in design and manufacturing. Robot Cim-Int Manuf2009; 25: 261–270.
35.
YildizAR. Hybrid immune-simulated annealing algorithm for optimal design and manufacturing. Int J Mater Prod Technol2009; 34: 217–226.
36.
YildizAROzturkF. Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation. Proc IMechE, Part B: J Engineering Manufacture2006; 220: 2041–2053.
37.
Belloufi A, Assas M and Rezgui I. Intelligent selection of machining parameters in multi-pass turnings using firefly algorithm. Hindawi Publishing Corporation, Modelling and Simulation in Engineering 2014: 592627: 6, DOI: 10.1155/2014/592627.
38.
BelloufiAAssasMRezguiI. Optimization of cutting conditions in multi-pass turning using hybrid genetic algorithmsequential quadratic programming. J Appl Mech Eng2012; 1: 3–7.
39.
IwataKMurotsuYIwotsuboTet al.A probabilistic approach to the determination of the optimum cutting conditions. Trans ASME J Eng Ind1972; 94: 1099–1107.
40.
GopalakrishnanBFaizAK. Machining parameter selection for turning with constraints: an analytical approach based on geometric programming. Int J Prod Res1991; 29: 1897–1908.
41.
SonmezAIBaykasogluADereliTet al.Dynamic optimization of multipass milling operations via geometric programming. Int J Mach Tools Manuf1999; 39: 297–320.
42.
JhaNK. A discrete data base multiple objective optimization of milling operation through geometric programming. Trans ASME J Eng Ind1990; 112: 368–374.
43.
Wang J. Constrained optimization of rough peripheral and end milling operations. PhD Thesis, University of Melbourne, Australia, 1993.
44.
PetropoulosPG. Optimal selection of machining rate variable by geometric programming. Int J Prod Res1973; 11: 305–314.
45.
ShunmugamMSBhaskara ReddySVNarendranTT. Selection of optimal conditions in multi-pass face-milling using a genetic algorithm. Int J Mach Tools Manuf2000; 40: 401–414.
46.
An LB and Chen MY. On optimization of machining parameters. In: Proceedings of the 4th international conference on control and automation, Montreal, Canada, 10-12 June 2003, pp.839–843.
47.
WangZGWongYSRahmanM. Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing. Int J Adv Manuf Technol2004; 24: 727–732.
48.
Saha SK. Genetic algorithm based optimization and post optimality analysis of multi-pass face milling. 2009; Journals/CoRR/abs/0902.0763, http://arxiv.org/abs/0902.0763
49.
Conceição AntónioCACastroCFDavimJP. Optimisation of multi-pass cutting parameters in face-milling based on genetic search. Int J Adv Manuf Technol2009; 44: 1106–1115.
50.
Yang XS. Firefly algorithms for multimodal optimization, In: Watanabe O, Zeugmann T. (Eds), Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications, Sapporo, Japan, October 26 – 28, SAGA 2009, Springer-Verlag Berlin, Heidelberg, pp. 169–178.
51.
Yang XS. Firefly algorithm, L'evy flights and global optimization, In: Bramer M, Ellis R and Petridis M. (Eds), Research and Development in Intelligent Systems, 2010, XXVI, Springer London, pp. 209–218.
52.
BarthelemyPBertolottiJWiersmaDS. A Lévy-flight for light. Nature2008; 453: 495–498.
53.
BrownCLiebovitchLSGlendonR. Lévy-flights in Dobe Ju/’hoansi foraging patterns. Hum Ecol2007; 35: 129–138.
