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Journals / Journal of Scientific Reports-A / 2015 / Sayı: 034

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

EKSPONANSİYEL AĞIRLIKLI PARÇACIK SÜRÜ ALGORİTMASI İLE TORNALAMA İŞLEMLERİNDE KESME KOŞULLARININ OPTİMİZASYONU

Yasin CANTAŞ
Burhanettin DURMUŞ
Pages
111–123
Publication Date
2015-06-15
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

And the results show that e2 – PSO has decreased cost.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, Exponential Inertia Particle Swarm Optimization (e2 - PSO) method has been used for optimization of cutting conditions in turning operations.
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Key Findings & Data:The optimum cost has been performed using processing parameters such as cutting speed, feed, depth of metal to be removed and number of passes.
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Conclusion & Impact:And the results show that e2 – PSO has decreased cost.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Optimizasyon · parçacık sürü optimizasyonu · tornalama

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.

Özet

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.

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Cite This Article

CANTAŞ, Y., DURMUŞ, B. (2015). CUTTING CONDITIONS OPTIMIZATION OF TURNING OPERATIONS WITH EXPONENTIAL INERTIA IN PARTICLE SWARM OPTIMIZATION. Journal of Scientific Reports-A, Sayı: 034, 111–123. https://ajindex.com/articles/777663

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References (2)

  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
  2. 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, “Coevoluti