Abstract
In this paper, we present a Hybrid Particle Swarm Optimization (HPSO) algorithm that combines the Particle Swarm Optimization (PSO) algorithm with the Genetic Algorithm (GA) method. The algorithm is used for investigating the plastic deformation behavior of titanium alloy (Ti-6Al-4V) in a multi-hole extrusion process. The simulation used rigid-plastic finite element (FE) DEFORMTM-3D software to obtain the minimum mandrel bias angle and exit tube bending angle. Results of the simulation indicate that these two angles were significantly less than 0.3 degrees, suggesting that metaheuristic algorithms based on HPSO and FE analysis could be used for general multi-hole extrusion processes.
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References
Li, L.X., Rao, K.P., Lou, Y., Peng, D.S.: A Study on Hot Extrusion of Ti–6Al–4V using Simulations and Experiments. International Journal of Mechanical Sciences 44, 2415–2425 (2002)
Peng, Z., Sheppard, T.: Simulation of Multi-Hole Die Extrusion. Materials Science and Engineering A 367, 329–342 (2004)
Chen, F.K., Chuang, W.C., Shan, T.: Finite Element Analysis of Multi-Hole Extrusion of Aluminum-Alloy Tubes. Journal of Materials Processing Technology 201, 150–155 (2008)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE, Perth (1995)
Bergh, F.V., Engelbrecht, A.P.: A Study of Particle Swarm Optimization Particle Trajectories. Information Sciences 176, 937–971 (2006)
Kennedy, J., Eberhart, R.C.: The Particle Swarm: Social Adaptation in Informal-Processing Systems. In: New Ideas in Optimization, pp. 379–387. McGraw-Hill, Maidenhead (1999)
Eberhart, R.C., Shi, Y.H.: Particle Swarm Optimization: Developments, Applications and Resources. In: IEEE Proceedings of the Congress on Evolutionary Computation, pp. 81–86. IEEE, Seoul (2001)
Gaing, Z.L.: A Particle Swarm Optimization Approach for Optimumdesign of PID Controller in AVR System. IEEE Transactions on Energy Conversion 19, 384–391 (2004)
Andrews, P.S.: An Investigation Into Mutation Operators for Particle Swarm Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, pp. 1044–1051. IEEE, Canade (2006)
Higasshi, N., Iba, H.: Particle swarm optimization with Gaussian mutation. In: Proceedings Swarm Intelligence Symposium, pp. 72–79. IEEE, Washington, D.C., USA (2003)
Stacey, A., Jancic, M., Grundy, I.: Particle swarm optimization with mutation. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1425–1430. IEEE, Washington, D.C., USA (2003)
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Chen, WJ., Chen, DC., Su, WC., Nian, FL. (2012). Optimization Design of Parameters with Hybrid Particle Swarm Optimization Algorithm in Multi-hole Extrusion Process. In: Jin, D., Lin, S. (eds) Advances in Electronic Engineering, Communication and Management Vol.2. Lecture Notes in Electrical Engineering, vol 140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27296-7_44
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DOI: https://doi.org/10.1007/978-3-642-27296-7_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27295-0
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