This article deals with the multi objective optimization of square hybrid tubes (metal-composite) under axial impact load. Maximum crushing load and absorbed energy are objective functions and fiber orientation angles of the composite layers are chosen as design parameters while the maximum crush load is limited. Back-propagation artificial neural networks (ANNs) are utilized to construct the mapping between the variables and the objectives. Non-dominated sorting Genetic algorithm–II (NSGAII) is applied to obtain the optimal solutions and the finite element commercial software LS-DYNA is used to generate the training and test sets for the ANNs. Optimum results are presented as a Pareto frontier.
Multi-Objective Optimization of Axial Crush Performance of Square Metal–Composite Hybrid Tubes
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Kalhor, R, Akbarshahi, H, & Case, SW. "Multi-Objective Optimization of Axial Crush Performance of Square Metal–Composite Hybrid Tubes." Proceedings of the ASME 2013 International Mechanical Engineering Congress and Exposition. Volume 9: Mechanics of Solids, Structures and Fluids. San Diego, California, USA. November 15–21, 2013. V009T10A035. ASME. https://doi.org/10.1115/IMECE2013-63071
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