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 better tool life, material removal rate and surface roughness together with energy consumption for the first time in literature. 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.

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[1] M. Ozkutuk and Y. Kaynak, "The effect of material parameters on chip formation in orthogonal cutting simulation of Ti-5553 Alloy," Procedia CIRP, vol. 58, pp. 305-310, 2017.

[2] K. Hua, X. Xue, H. Kou, J. Fan, B. Tang, and J. Li, "Characterization of hot deformation microstructure of a near beta titanium alloy Ti-5553," Journal of Alloys and Compounds, vol. 615, pp. 531-537, 2014.

[3] A. Jayal, F. Badurdeen, O. Dillon Jr, and I. Jawahir, "Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels," CIRP Journal of Manufacturing Science and Technology, vol. 2, no. 3, pp. 144-152, 2010.

[4] C. Camposeco-Negrete, 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, vol. 83, no. 5-8, pp. 1341- 1347, 2016.

[5] C. Camposeco-Negrete, "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, vol. 91, pp. 109-117, 2015.

[6] R. K. Bhushan, "Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites," Journal of Cleaner Production, vol. 39, pp. 242-254, 2013.

[7] P. Asokan, 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, vol. 21, no. 1, pp. 1-9, 2003.

[8] D. Venkatesan, K. Kannan, and R. Saravanan, "A genetic algorithm-based artificial neural network model for the optimization of machining processes," Neural Computing and Applications, vol. 18, no. 2, pp. 135-140, 2009.

[9] M. Cong, T. Han, and Q. Zhao, "Fuzzy multiobjective optimization of sliding rack based on six sigma and goal driven," in 2010 International Conference on Mechanic Automation and Control Engineering, 2010: IEEE, pp. 556-559.

[10] I. Mukherjee and P. K. Ray, "A review of optimization techniques in metal cutting processes," Computers & Industrial Engineering, vol. 50, no. 1-2, pp. 15-34, 2006.

[11] D. W. Coit and A. E. Smith, "Reliability optimization of series-parallel systems using a genetic algorithm," IEEE Transactions on reliability, vol. 45, no. 2, pp. 254-260, 1996.

[12] Z. Khan, B. Prasad, and T. Singh, "Machining condition optimization by genetic algorithms and simulated annealing," Computers & Operations Research, vol. 24, no. 7, pp. 647-657, 1997.

[13] A. R. Yildiz, "Cuckoo search algorithm for the selection of optimal machining parameters in milling operations," The International Journal of Advanced Manufacturing Technology, vol. 64, no. 1-4, pp. 55-61, 2013.

[14] I. Jawahir and X. Wang, "Development of hybrid predictive models and optimization techniques for machining operations," Journal of Materials Processing Technology, vol. 185, no. 1-3, pp. 46-59, 2007.

[15] K. Deb, M. Mohan, and S. Mishra, "Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions," Evolutionary computation, vol. 13, no. 4, pp. 501-525, 2005.

[16] S. Kuriakose and M. Shunmugam, "Multiobjective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm," Journal of materials processing technology, vol. 170, no. 1- 2, pp. 133-141, 2005.

[17] K. Deb and D. K. Saxena, "On finding paretooptimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems," Kangal report, vol. 2005011, 2005.

[18] D. A. Van Veldhuizen and G. B. Lamont, "Evolutionary computation and convergence to a pareto front," in Late breaking papers at the genetic programming 1998 conference, 1998, pp. 221-228.

[19] R. T. Marler and J. S. Arora, "Survey of multiobjective optimization methods for engineering," Structural and multidisciplinary optimization, vol. 26, no. 6, pp. 369- 395, 2004.

[20] A. Konak, D. W. Coit, and A. E. Smith, "Multiobjective optimization using genetic algorithms: A tutorial," Reliability Engineering & System Safety, vol. 91, no. 9, pp. 992-1007, 2006.

[21] T. Goel, R. Vaidyanathan, R. T. Haftka, W. Shyy, N. V. Queipo, and K. Tucker, "Response surface approximation of Pareto optimal front in multi-objective optimization," Computer methods in applied mechanics and engineering, vol. 196, no. 4-6, pp. 879-893, 2007.

[22] D. J. Zwickl, "Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion," 2006.

[23] P. W. Poon and J. N. Carter, "Genetic algorithm crossover operators for ordering applications," Computers & Operations Research, vol. 22, no. 1, pp. 135-147, 1995.

[24] N. M. Razali and J. Geraghty, "Genetic algorithm performance with different selection strategies in solving TSP," in Proceedings of the world congress on engineering, 2011, vol. 2: International Association of Engineers Hong Kong, pp. 1134-1139.

[25] M. Safe, J. Carballido, I. Ponzoni, and N. Brignole, "On stopping criteria for genetic algorithms," in Brazilian Symposium on Artificial Intelligence, 2004: Springer, pp. 405-413.

[26] E. Tascioglu, 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, vol. 102, no. 9-12, pp. 4257-4271, 2019.

[27] E. Ezugwu, J. Bonney, D. Fadare, and W. Sales, "Machining of nickel-base, Inconel 718, alloy with ceramic tools under finishing conditions with various coolant supply pressures," Journal of materials processing technology, vol. 162, pp. 609-614, 2005.

[28] A. Altin, M. Nalbant, and A. Taskesen, "The effects of cutting speed on tool wear and tool life when machining Inconel 718 with ceramic tools," Materials & design, vol. 28, no. 9, pp. 2518-2522, 2007.