CUTTING CONDITIONS OPTIMIZATION OF TURNING OPERATIONS WITH EXPONENTIAL INERTIA IN PARTICLE SWARM OPTIMIZATION

Year 2015, Issue: 034, 111 - 123, 15.06.2015

Yasin Cantaş , Burhanettin Durmuş

Abstract

In this study, Exponential Inertia
Particle Swarm Optimization (e₂ -
PSO) method has been used for optimization of cutting
conditions in turning operations. The optimum cost has been performed using processing
parameters such as cutting speed, feed, depth of metal to be removed and number
of passes. Tool life, cutting force, power, temperature, surface roughness,
cutting speed, feed and cutting depth are considered in constraints. The
obtained results have been compared to other methods in the literature. And the
results show that e₂ – PSO has decreased cost.

Keywords

Optimization, particle swarm optimization, turning, optimum cutting conditions

EKSPONANSİYEL AĞIRLIKLI PARÇACIK SÜRÜ ALGORİTMASI İLE TORNALAMA İŞLEMLERİNDE KESME KOŞULLARININ OPTİMİZASYONU

Abstract

Bu çalışmada,Eksponansiyel Ağırlıklı Parçacık Sürü
Optimizasyonu (e₂ - PSO) algoritması tornalama işlemlerinde kesme
koşullarının optimizasyonu için kullanılmıştır. İşleme parametrelerinden kesme
hızı, ilerleme hızı, kaldırılacak talaş derinliği, paso sayısı kullanılarak minumum
maliyet hesabı gerçekleştirilmiştir. Kısıtlamalarda takım ömrü, kesme kuvveti,
güç, sıcaklık, yüzey pürüzlülüğü, kesme hızı, ilerleme ve kesme derinliği
dikkate alınmıştır. Elde edilen sonuçlar literatürdeki diğer sonuçlar ile
karşılaştırılmış ve e₂ – PSO tabanlı hesaplama ile maliyetin düştüğü
görülmüştür.

Keywords

Optimizasyon, parçacık sürü optimizasyonu, tornalama, optimum kesme koşulları

References

• [1] Y.C. Shin , Y.S. Joo, “Optimization of machining conditions with practical constraints”, International Journal of Production Research,30(12),2907-2919(1992).
• [2]. M.C. Chen, D.M. Tsai, “A simulated annealing approach for optimization of multi-pass turning operations”, International Journal of Production Research,34(10),2803-2825(1996).
• [3] K Vijayakumar, G Prabhaharan, P Asokan, R Saravanan, “Optimization of multi-pass turning operations using ant colonysystem”, International Journal of Machine Tools &Manufacture,43,1633-1639(2003)
• [4] M.C. Chen, ” Optimizing machining economics models of turning operations using the scatter search approach”, Int J Prod Res,42(13),2611-2625(2004)
• [5] A.R. Yıldız, ”A novel hybrid immune algorithm for global optimization in design and manufacturing”, Robotics and Computer-Integrated Manufacturing,25(2),261-270(2009).
• [6] J. Srinivas, R. Giri, SH. Yang, ”Optimization of multi-pass turning using particle swarm intelligence”, Int J Adv Manuf Technol,40(1-2),56-66(2009).
• [7] S. Xie, Y. Guo, “Intelligent Selection of Machining Parameters in Multi-pass Turnings Using a GA-based Approach”, Journal of Computational Information Systems, 7(5), 1714-1721(2011).
• [8] J. Kennedy, R. Eberhart, ”Particle swarm optimization”, Proc. IEEE International Conference on Neural Networks, Piscataway, NJ, 1942-1948(1995).
• [9] X. Hu, Y. Shi, R. Eberhart, “Recent advances in particle swarm”, Evolutionary Computation, Portland, 90-97(2004).
• [10] K.E. Parsopoulos, M.N. Vrahatis, “Particle swarm optimization method for constrained optimization problems” In: Kvasnicˇka, V. et al. (Eds), Proceedings of the second Euro-International Symposium on Computational Intelligence, Kosˇice, Slovakia, pp. 214–220(2002).
• [11] T. Ray, K.M. Liew, “A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimisation problems”, In: Kim, J.H. et al. (Eds.), Proceedings of the 2001 Congress on Evolutionary Computation, IEEE Service Center, Piscataway, NJ, pp. 75–80(2001).
• [12] Q. Shen, W.M. Shi, W. Kong, B.X. Ye, “A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification”, Talanta, 71(4), 1679–1683(2007)
• [13] M. Clerc, J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multi dimensional complex space”, IEEE Transactions on Evolutionary Computation 6 (1), 58–73(2002).
• [14] R.A. Krohlingand, L. Dos Santos Coelho, “Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems”, IEEE Trans. Syst., Man, Cybern. B, Cybern.,vol. 36, no. 6, pp. 1407–1416, Dec. 2006.
• [15] N. Frankenand, A.P. Engelbrecht, “Particle swarm optimization approaches to coevolve strategies for the iterated prisoner’s dilemma,” IEEE Trans. Evol. Comput.,vol. 9, no. 6, pp. 562–579, Dec. 2005.
• [16] G. Ciuprina, D. Ioan, I. Munteanu, “Use of intelligent-particle swarm optimization in electromagnetics” IEEE Trans. Magn., vol. 38, no. 2, pp. 1037–1040, Mar. 2002.
• [17] Y. Shi, R. Eberhart, ”A modified particle swarm optimizer”, Proceedings of the IEEE International Conference on Evolutionary Computation, Piscataway, 69-73(1998)
• [18] G. Chen, X. Huang, J. Jia, Z. Min, “Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization”, Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, 3672-3675(2006)
• [19] M.C. Chen, K.Y. Chen, ”Optimization of multi pass turning operations with genetic algorithms: a note”, Int J Prod Res, 41, 3385-3388(2003).
• [20] R.S. Sankar, P. Asokan, R. Saravanan, S. Kumanan, G. Prabhaharan, “Selection of machining parameters for constrained machining problem using evolutionary computation”, Int J Adv Manuf Technol, 32, 892-901(2007).
• [21] A.R. Yildiz, "Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach." Information Sciences 220,399-407(2013).
• [22] A.R. Yildiz, "Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations." Applied Soft Computing 13(3),1433-1439(2013).
• [23] M. Hrelja, S. Klancnik, T. Irgolic, M. Paulic, Z. Jurkovic, J. Balic, M. Brezocnik, “Particle swarm optimization approach for modelling a turning process”, Advances in Production Engineering & Management, 9(1),21-30 (2014).