Abstract
This paper aims in the optimization of process parameters for straight bevel gear finishing by pulsed electrochemical honing (PECH) process using four evolutionary algorithms. The controllable parameters selected for optimal setting in PECH are the applied voltage, pulse-on time and pulse-off time, whereas finishing time, interelectrode gap and the rotary speed of workpiece gear are set as constant parameters. Theoretical model of material removal rate and surface roughness in PECH process developed by the past researchers are considered for a comparative analysis of the optimization problem by using four different algorithms, i.e. firefly algorithm, particle swarm optimization algorithm, differential evolution algorithm and teaching-learning-based algorithm, for arriving at the most global optimal settings of PECH process parameters. Teaching-learning-based algorithm attains the best optimal setting value within the range of different input process parameters for both single- and multi-objective optimization problems.
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References
Misra, J.P., Jain, N.K., Jain, P.K.: Investigations on precision finishing of helical gears by electrochemical honing process. In: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 224(12), pp. 1817–1830 (2010)
Pathak, S., Jain, N.K.: Modeling and experimental validation of volumetric material removal rate and surface roughness depth of straight bevel gears in pulsed-ECH process. Int. J. Mech. Sci. 124, 132–144 (2017)
Shaikh, J.H., Jain, N.K.: Modeling of material removal rate and surface roughness in finishing of bevel gears by electrochemical honing process. J. Mater. Process. Technol. 214(2), 200–209 (2014)
Yang, X.S.: Firefly algorithm, Nature-Inspired Metaheuristic Algorithms. 20, 79–90 (2008)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. Perth, Australia (1995)
Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3), 303–315 (2011)
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Diyaley, S., Chakraborty, S. (2020). Optimization of Process Parameters in Pulsed Electrochemical Honing Process Using Evolutionary Algorithms. In: Venkata Rao, R., Taler, J. (eds) Advanced Engineering Optimization Through Intelligent Techniques. Advances in Intelligent Systems and Computing, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-13-8196-6_5
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DOI: https://doi.org/10.1007/978-981-13-8196-6_5
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