Genetic Algorithms Combined with the Finite Elements Method as an Efficient Methodology for the Design of Tapered Roller Bearings
This research presents an efficient hybrid approach based on soft computing techniques and the finite element method for the design of mechanical systems. The use of non-linear finite element models to design mechanical systems provides solutions that are consistent with experimental results; but this use is often limited in practice by a high computational cost. In order to reduce this cost, we propose a linear finite element model that replaces the non-linear elements of the mechanical system with beam and plate elements of equivalent stiffness that are adjusted by means of genetic algorithms. Thus, the adjusted linear model behaves in the same way as the non-linear model, but with a much lower computational cost, which would allow to redesign any mechanical system in a more efficient and faster way. A case study demonstrates the validity of this methodology as applied to the design of a tapered roller bearing.
KeywordsGenetic Algorithms Finite Element Method Mechanical Systems Tapered Roller Bearing
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- 1.Badiola, V., Pintor, J.M., Gainza, G.: Axle housing and unitize bearing pack set modal characterisation. In: FISITA, World Automotive Congress (2004)Google Scholar
- 2.Banerjee, T., Das, S., Roychoudhury, J., Abraham, A.: Implementation of a new hybrid methodology for fault signal classification using short -time fourier transform and support vector machines. In: Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010). Advances in Intelligent and Soft Computing, vol. 73, pp. 219–225 (2010)Google Scholar
- 3.Bäck, T., Fogel, D., Michalewicz, Z. (eds.): Evolutionary Computation 1: Basic Algorithms and Operators, Evolutionary Computation 2: Advanced Algorithms and Operators. Institute of Physics Publishing, Bristol (2000)Google Scholar
- 4.Corchado, E., Herrero, A.: Neural visualization of network traffic data for intrusion detection. Applied Soft Computing (2010), doi:10.1016/j.asoc.2010.07.002Google Scholar
- 6.Pratihar, D.K.: Soft computing. Alpha Science International (2008)Google Scholar
- 7.Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.: A soft computing method for detecting lifetime building thermal insulation failures. Integrated Computer-Aided Engineering 17(2), 103–115 (2010)Google Scholar
- 8.Shigley, J.E., Mischke, C.R., Budynas, R.G.: Mechanical engineering design. McGraw-Hill, New York (2003)Google Scholar