• Journal of the Computational Structural Engineering Institute of Korea
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• v.18 no.3
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• pp.219-226
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• 2005

In this study, the problem formulation and solution technique using genetic algorithms for design optimization of laminate composite cylindrical beam section are presented. The hollow cylindrical beams we usually used in the wheel chair. If the weight of wheel chair is reduced, it will lead to huge improvement in passenger's mobility and comfort. In this context, the replacement of steel by high performance and light weight composite material along with optimal design will be a good contribution in the process of weight reduction of a wheel chair. An artificial genetics approach for the design optimization of hollow cylindrical composite beam is presented. On applying the genetic algorithm, the optimal dimensions of hollow cylindrical composite beams which have equivalent rigidities to those of corresponding hollow cylindrical steel beams are obtained. Also structural analysis is conducted on the entire wheel chair structure incorporating Tsai-Wu failure criteria. The maximum Tsai-Wu failure criteria index is $0.192\times10^{-3}$ which is moth less than value of 1.00 indicating no failure is observed under excessive loading condition. It is found that the substitution of steel by composite material could reduce the weight of wheel chair up to 45%.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.127-133
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• 2003

In this study, a problem formulation and solution for design optimization of laminate composite cylindrical beam section is presented. The objective of this research is to determine the optimal dimension of the laminated composite cylindrical beam sections which has the equivalent flexural rigidities to those of the steel cylindrical beam sections. The analytical model is based on the laminate theory and accounts for the material coupling for arbitrary laminate stacking sequence configuration. The outer diameter and thickness of the beam are design variables. The solutions described are found using a global search algorithm, Genetic Algorithms (GA).