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A modified cuckoo search algorithm to optimize Wire-EDM process while machining Inconel-690

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Abstract

Cuckoo search (CS) algorithm was found to be efficient in yielding the global optimal value, and this algorithm was found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) techniques. However, the accuracy of CS heavily depends upon the initial solution and its location from the target value and, therefore, it may involve many generations. Furthermore, the evolutionary operators are applied in each generation. This could lead to delay in convergence. To improve the performance of cuckoo search further, an attempt has been made in the present work to propose a modified cuckoo search involving two-stage initialization. Benchmark functions have been used to test the performance of the proposed method. Furthermore, the proposed method has been applied to wire electrical discharge machining (WEDM) process. Inconel-690, a nickel-based superalloy, has extensive applications in aerospace and nuclear power sectors. Although WEDM is one of the advanced machining processes used to machine such hard-to-cut materials, machining data for this material is not available in the literature. The proposed algorithm was found to be accurate and fast as compared to the GA, PSO, and existing cuckoo search. The machining data generated in this work will also be useful to the industry.

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Abbreviations

ANOVA:

Analysis of variance

BPNN:

Back-propagated neural network

CS:

Cuckoo search

GA:

Genetic algorithm

GP:

Genetic programming

MRR:

Material removal rate

NSGA:

Non-dominated sorting genetic algorithm

PSO:

Particle swarm optimization

RSM:

Response surface methodology

SR, Ra :

Surface roughness

WEDM:

Wire electrical discharge machine

α :

Constant generated randomly in between −1 and 1

D :

Diameter of the hole

d :

Diameter of the boss

Fit p :

Fitness value of an individual particle

I p :

Peak current

Ncv:

Number of control variables

Nhn:

Number of host nests

p, f :

Particle or host nest numbers

p a :

Probability for an egg to be identified by host bird

Ps :

Population size

Pv :

Population vector

s pq :

Step size of qth variable for pth particle

S v :

Servo voltage

T :

Machining time

T off :

Pulse off time

T on :

Pulse on time

W t :

Thickness of work piece

x ij :

Value of jth variable in ith particle

x max j :

Upper bound for jth variable

x min j :

Lower bound for jth variable

x pq (t):

Value of qth variable in pth host nest at current generation, t

x pq (t + 1):

Value of qth variable in pth host nest at next generation

λ:

Constant generated randomly in between 1 and 3

References

  1. Kuriakose S, Shunmugam MS (2004) Characteristics of wire-electro discharge machined Ti6Al4 V surface. Mater Lett 58:2231–2237

    Article  Google Scholar 

  2. Tosun N (2003) The effect of the cutting parameters on performance of WEDM. KSME Int J 17(6):816–824

    Article  Google Scholar 

  3. Aravind SRN , Sowmya S, Yuvaraj KP (2012) Optimization of metal removal rate and surface roughness on Wire Edm using Taguchi method. International Conference on Advances in Engineering, Science and Management. IEEE 155–159

  4. Spedding TA, Wang ZQ (1997) Parametric optimization and surface characterization of wire electrical discharge machining process. Precis Eng 20(1):5–15

    Article  Google Scholar 

  5. Saha P, Singha A, Pal SK (2008) Soft computing modelsbased prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite. Int J Adv Manuf Technol 39(1):74–84

    Article  Google Scholar 

  6. Sarkar S, Mitra S, Bhattacharyya B (2006) Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model. Int J Adv Manuf Technol 27(5–6):501–508

    Article  Google Scholar 

  7. Manna A, Bhattacharyya B (2006) Taguchi and Gausselimination method: a dual response approach for parametric optimization of CNC wire cut EDM of PRAlSiCMMC. Int J Adv Manuf Technol 28:67–75

    Article  Google Scholar 

  8. Sadeghi M, Razavi H, Esmaeilzadeh A, Kolahan F (2011) Optimization of cutting conditions in WEDM process using regression modelling and Tabu-search algorithm. Proc Inst Mech Eng Part B J Eng Manuf 225(10):1825–1834

    Article  Google Scholar 

  9. Mahapatra SS, Patnaik A (2007) Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method. Int J Adv Manuf Technol 34:911–925

    Article  Google Scholar 

  10. Montgomery DC (2005) Design and analysis of experiments, 6th edn. John Wiley, US

    MATH  Google Scholar 

  11. Sharma N, Khanna R, Gupta RD, Sharma R (2013) Modeling and multiresponse optimization on WEDM for HSLA by RSM. Int J Adv Manuf Technol 67:2269–2281

    Article  Google Scholar 

  12. Shandilya P, Jain PK, Jain NK (2012) Parametric optimization during wire electrical discharge machining using response surface methodology. Procedia Eng 38:2371–2377

    Article  Google Scholar 

  13. Garg SK, Manna A, Jain A (2016) Experimental investigation of spark gap and material removal rate of Al/ZrO2 (P)-MMC machined with wire EDM. J Braz Soc Mech Sci Eng 38(2):481–491

    Article  Google Scholar 

  14. Narendranath S, Manjaiah M, Basavarajappa S, Gaitonde VN (2013) Experimental investigation on performance characteristics in wire electro discharge machining of Ti50 Ni42.4 Cu7.6 shape memory alloy. Proc Inst Mech Eng Part B J Eng Manuf 227(8):1180–1187

    Article  Google Scholar 

  15. Zhang G, Zhang Z, Guo J, Ming W, Li M, Huang Y (2013) Modeling and optimization of medium-speed WEDM process parameters for machiningSKD11. Mater Manuf Processes 28(10):1124–1132

    Article  Google Scholar 

  16. Chiang KT, Chang FP (2006) Optimization of the WEDM process of particle-reinforced material with multiple performance characteristics using grey relational analysis. J Mater Process Technol 189(1):96–101

    Article  Google Scholar 

  17. Ramakrishnan R, Karunamoorthy L (2006) Multi response optimization of wire EDM operations using robust design of experiments. Int J Adv Manuf Technol 29(1–2):105–112

    Article  Google Scholar 

  18. Ghodsiyeh D, Golshan A, Izman S (2014) Multi-objective process optimization of wire electrical discharge machining based on response surface methodology. J Braz Soc Mech Sci Eng 36(2):301–313

    Article  Google Scholar 

  19. Kondayya D, Krishna AG (2011) An integrated evolutionary approach for modelling and optimization of wire electrical discharge machining. Proc Inst Mech Eng Part B J Eng Manuf 225(4):549–567

    Google Scholar 

  20. Chalisgaonkar R, Kumar J (2015) Investigation of the machining parameters and integrity of the work and wire surfaces after finish cut WEDM of commercially pure titanium. J Braz Soc Mech Sci Eng 38(3):1–29

  21. Rao RV, Pawar PJ (2010) Process parameters modeling and optimization of wire electric discharge machining. Adv Product Eng Manag 5(3):139–150

    Google Scholar 

  22. Rao RV, Pawar PJ (2009) Modelling and optimization of process parameters of wire electrical discharge machining. Proc Inst Mech Eng Part B J Eng Manuf 223(11):1431–1440

    Article  Google Scholar 

  23. Yang XS, Deb S (2009) Cuckoo search via Levy flights. Proc World Congress Nat Biol Inspired Comp NaBIC 210–214. doi:10.1109/NABIC.2009.5393690

  24. Yang XS, Deb S (2010) Engineering optimisation by Cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  25. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo searchalgorithm: a metaheuristic approach to solve structural optimization problems. Eng Comp 29(1):17–35

    Article  Google Scholar 

  26. Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508–5518

    Article  Google Scholar 

  27. Valian E, Tavakoli S, Mohanna S, Haghi A (2013) Improved cuckoo search for reliability optimization problems. Comput Ind Eng 64(1):459–468

    Article  Google Scholar 

  28. El Ela AA, Abido MA, Spea SR (2010) Optimal power flow using differential evolution algorithm. Electr Power Syst Res 89(7):878–885

    Article  Google Scholar 

  29. Chandrasekaran K, Simon SP (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evolut Comput 5:1–16

    Article  Google Scholar 

  30. Deb K (2013) Optimization for engineering design: algorithms and examples, Second Ed. PHI Learning Private Limited, New Delhi, India

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Acknowledgments

Authors are thankful to the DST-SERB of India for the financial assistance to carry out this research work through project No. SR/FTP/ETA-10/2012.

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Authors and Affiliations

  1. Mechanical Engineering Department, National Institute of Technology, Warangal, 506004, India

    M. Sreenivasa Rao & N. Venkaiah

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  1. M. Sreenivasa Rao
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  2. N. Venkaiah
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Correspondence to N. Venkaiah.

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Technical Editor: Márcio Bacci da Silva.

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Sreenivasa Rao, M., Venkaiah, N. A modified cuckoo search algorithm to optimize Wire-EDM process while machining Inconel-690. J Braz. Soc. Mech. Sci. Eng. 39, 1647–1661 (2017). https://doi.org/10.1007/s40430-016-0568-9

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  • DOI: https://doi.org/10.1007/s40430-016-0568-9

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