Tornalama İşlemlerinde Minimum Maliyet Optimizasyonu

Year 2016, Volume: 4 Issue: 2, 424 - 430, 11.03.2016

Yasin Cantaş , Sezgin Kaçar , Burhanettin Durmuş

Abstract

Bu çalışmada, kaotik ağırlıklı PSO (Parçacık Sürü Optimizasyonu) algoritması, tornalama işlemlerinde minimum maliyeti sağlayacak parametreleri belirlemede kullanılmıştır. Problemin amacı, Shin ve Joo tarafından tanımlanan çok geçişli tornalama işlemlerine ait matematiksel formülün minimum maliyet değerini belirlemektir. Kaotik ağırlıklı PSO, literatürdeki sonuçlara göre daha düşük maliyet elde etmiştir. 

Keywords

Tornalama, minimum maliyet, parçacık sürü optimizasyonu, optimum kesme parametreleri

Minimum Cost Optimization in Turning Operations

Abstract

In this study, weighted chaotic PSO (Particle Swarm Optimization) algorithm is used to determine the parameters to provide a minimum cost in turning. The objective of the problem defined by Shin and Joo is to determine the minimum cost of the multi-pass turning mathematical formula. Chaotic weighted PSO has achieved a lower cost compared to results in the literature.

Keywords

Turning, minimum cost, particle swarm optimization, optimum cutting parameters

References

• Y. Cantaş, Parçacık Sürü Optimizasyonu ile Tornalama İşlemlerinde Kesme Koşullarının Belirlenmesi, Yüksek Lisans Tezi, Dumlupınar Üniversitesi, Kütahya-Türkiye, (2014).
• Y. Şahin, İmal Usulleri, Gazi Yayınevi, Ankara, (2003).
• M.C. Chen Int J Prod Res 42(13) (2004) 2611.
• K. Vijayakumar, G. Prabhaharan, P. Asokan, R. Saravanan International Journal of Machine Tools &Manufacture 43 (2003) 1633.
• M.C. Chen, D.M. Tsai International Journal of Production Research 34(10) (1996) 2803.
• S. Xie, Y. Guo Journal of Computational Information Systems 7(5) (2011) 1714.
• J. Kennedy, R. Eberhart, Particle swarm optimization, Proc. IEEE International Conference on Neural Networks, Piscataway, NJ, (1995), 1942.
• Y. Shi, R. Eberhart, A modified particle swarm optimizer, Proceedings of the IEEE International Conference on Evolutionary Computation, Piscataway, (1998), 69.
• M. Clerc, The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization, Proceedings of the Congress on Evolutionary Computation (CEC99), (1999), 1951.
• Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway, NJ: IEEE Service Center, (1999), 1945.
• Y. Shi, R. Eberhart, Fuzzy Adaptive Particle Swarm Optimization, Proc. Congress on Evolutionary Computation, Seoul, Korea. Piscataway, NJ: IEEE Service Center, (2001), 101.
• Y. Feng, G. F. Teng, A. X. Wang, Y. M. Yao, Chaotic inertia weight in particle swarm optimization. In Innovative Computing, Information and Control, 2007. ICICIC'07. Second International Conference on IEEE, (2007), 475.
• Y.C. Shin, Y.S. Joo International Journal of Production Research 30(12) (1992) 2907.
• M. Dorigo, V. Maniezzo A. Colorni Techreport, Politecnico di Milano (1999) 91.
• D. Karaboğa, An idea based honey bee swarm for numerical optimization, Technical Report- TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, (2005).
• F. Zhao, Z. Ren, D. Yu, Y. Yang, Application of An Improved Particle Swarm Optimization Algorithm for Neural Network Training, In: Proceedings of the IEEE International Conference on Neural Networks and Brain, Beijing, China, (2005), 1693.
• X. Hu, Y. Shi, R. Eberhart, Recent advances in particle swarm, Evolutionary Computation, Portland, (2004) 90.
• Y. C. Shin, Y. S. Joo The International Journal of Production Research 30(12) (1992) 2907.
• M.C. Chen, D.M. Tsai International Journal of Production Research 34(10) (1996) 2803.
• M.C. Chen, K.Y. Chen International Journal of Production Research 41 (2003) 3385.
• R.S. Sankar, P. Asokan, R. Saravanan, S. Kumanan, G. Prabhaharan International Journal of Advanced Manufacturing Technology 32 (2007) 892.
• J. Srinivas, R. Giri, S. H. Yang International Journal of Advanced Manufacturing Technology 40(1-2) (2009) 56.