Optimization of Cutting Parameters for Sustainable Machining of Titanium Ti-5553 Alloy using Genetic Algorithm

Çok yüksek takım aşınması, yüksek kesme kuvvetleri, yüksek sıcaklık ve işlenmiş parçaların düşük yüzey kalitesi nedeniyle, Titanyum Ti-5553 alaşımları işlenmesi zor malzemelerden biri olarak kabul edilmiştir. Malzemenin işleme performansını artırmak ve bu arada üretim maliyetini düşürmek için proses parametrelerinin optimize edilmesi en önemli araştırmaların başında gelir. Bu çalışma, bu yeni nesil Titanyum Ti-5553 alaşımı için sürdürülebilir bir işleme süreci önermektedir. Takım aşınmasını, enerji tüketimini ve yüzey pürüzlülüğünü azaltmak ve bu sırada talaş kaldırma oranını artırmak için kesme derinliği, kesme hızı ve ilerleme hızı gibi proses parametreleri optimize edilirken kesme hızı, ilerleme oranı ve kesme derinliği dikkate alınmıştır. Proses parametrelerinin optimize edilmesi için genetik algoritma kullanılmıştır. Genetik algoritma kullanarak yapılan optimizasyon sonucu elde edilen değerler, bu alaşımın işleme performansını büyük ölçüde iyileştirmek ve işleme sürecinin enerji tüketimini, üretim maliyetini düşürmekle birlikte yeni nesil titanyum alaşımının talaşlı imalat sürecindeki talaş kaldırma oranını artırarak daha sürdürülebilir hale getirmek için çok etkili bir yaklaşım olduğunu göstermiştir.

Anahtar Kelimeler:

Ti-5333 Alaşımı, Optimizasyon, Genetik Algoritma, İşleme Performansı

Optimization of Cutting Parameters for Sustainable Machining of Titanium Ti-5553 Alloy using Genetic Algorithm

Titanium Ti-5553 alloys have been considered as difficult-to-machine materials due to the extremely high tool wear, high cutting forces, high temperature, and poor surface quality of machined parts. Process parameters needs to be optimized in order to improve machining performance and in the meantime reducing manufacturing cost. This study proposes sustainable machining process for this new generation Titanium Ti-5553 alloy. Process parameters including depth of cut, cutting speed, and feed rate were taken into account to optimize parameters for reducing tool wear, energy consumption, and surface roughness, and in the meantime increase material removal rate. Genetic algorithm was utilized for optimizing the process parameters. Obtained results illustrated that optimization using genetic algorithm is a very effective approach to substantially improve machining performance of this alloy and make machining process of this alloy more sustainable by reducing energy consumption, manufacturing cost and increasing material removal rate in machining process of new generation titanium alloy.

Keywords:

Ti-5553 Alloy, Optimization, Genetic Algorithm, Machining Performance,

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Ozkutuk, M. and Y. Kaynak, The effect of material parameters on chip formation in orthogonal cutting simulation of Ti-5553 Alloy. Procedia CIRP, 2017. 58: p. 305-310.
Hua, K., et al., Characterization of hot deformation microstructure of a near beta titanium alloy Ti-5553. Journal of Alloys and Compounds, 2014. 615: p. 531-537.
Jayal, A., et al., Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels. CIRP Journal of Manufacturing Science and Technology, 2010. 2(3): p. 144-152.
Camposeco-Negrete, C., J.d.D.C. Nájera, and J.C. Miranda-Valenzuela, Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design. The International Journal of Advanced Manufacturing Technology, 2016. 83(5-8): p. 1341-1347.
Camposeco-Negrete, C., Optimization of cutting parameters using Response Surface Method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum. Journal of cleaner production, 2015. 91: p. 109-117.
Bhushan, R.K., Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. Journal of Cleaner Production, 2013. 39: p. 242-254.
Asokan, P., R. Saravanan, and K. Vijayakumar, Machining parameters optimisation for turning cylindrical stock into a continuous finished profile using genetic algorithm (GA) and simulated annealing (SA). The International Journal of Advanced Manufacturing Technology, 2003. 21(1): p. 1-9.
Mukherjee, I. and P.K. Ray, A review of optimization techniques in metal cutting processes. Computers & Industrial Engineering, 2006. 50(1-2): p. 15-34.
Coit, D.W. and A.E. Smith, Reliability optimization of series-parallel systems using a genetic algorithm. IEEE Transactions on reliability, 1996. 45(2): p. 254-260.
Venkatesan, D., K. Kannan, and R. Saravanan, A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Computing and Applications, 2009. 18(2): p. 135-140.
Khan, Z., B. Prasad, and T. Singh, Machining condition optimization by genetic algorithms and simulated annealing. Computers & Operations Research, 1997. 24(7): p. 647-657.
Yildiz, A.R., Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. The International Journal of Advanced Manufacturing Technology, 2013. 64(1-4): p. 55-61.
Jawahir, I. and X. Wang, Development of hybrid predictive models and optimization techniques for machining operations. Journal of Materials Processing Technology, 2007. 185(1-3): p. 46-59.
Deb, K., M. Mohan, and S. Mishra, Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evolutionary computation, 2005. 13(4): p. 501-525.
Kuriakose, S. and M. Shunmugam, Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm. Journal of materials processing technology, 2005. 170(1-2): p. 133-141.
Deb, K. and D.K. Saxena, On finding pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. Kangal report, 2005. 2005011.
Van Veldhuizen, D.A. and G.B. Lamont. Evolutionary computation and convergence to a pareto front. in Late breaking papers at the genetic programming 1998 conference. 1998.
Marler, R.T. and J.S. Arora, Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization, 2004. 26(6): p. 369-395.
Konak, A., D.W. Coit, and A.E. Smith, Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 2006. 91(9): p. 992-1007.
Goel, T., et al., Response surface approximation of Pareto optimal front in multi-objective optimization. Computer methods in applied mechanics and engineering, 2007. 196(4-6): p. 879-893.
Zwickl, D.J., Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion. 2006.
Poon, P.W. and J.N. Carter, Genetic algorithm crossover operators for ordering applications. Computers & Operations Research, 1995. 22(1): p. 135-147.
Razali, N.M. and J. Geraghty. Genetic algorithm performance with different selection strategies in solving TSP. in Proceedings of the world congress on engineering. 2011. International Association of Engineers Hong Kong.
Safe, M., et al. On stopping criteria for genetic algorithms. in Brazilian Symposium on Artificial Intelligence. 2004. Springer.
Tascioglu, E., A. Gharibi, and Y. Kaynak, High speed machining of near-beta titanium Ti-5553 alloy under various cooling and lubrication conditions. The International Journal of Advanced Manufacturing Technology, 2019. 102(9-12): p. 4257-4271.