Abstract
Due to their light weights and high load carrying capacities, composite structures are widely used in various industrial applications especially in aerospace industry. Strength to weight ratio is known to be as one of the most critical design parameters in these structures. In this paper, geometrical parameters of composite lattice structures are optimized to obtain the desired strength to weight ratio using finite element method, neural networks and ABC algorithm. At first, the finite element model is validated by experimental results and neural network is employed as the fitness function. The ABC algorithm is also applied to achieve the optimized strength to weight ratio. The results obtained from PSO algorithm on the basis of neural network have shown reasonable agreement with those of the finite element simulation. Increasing the thickness of the outer shell causes the structural strength-to-weight ratio to rise by 50 percent. The next effective parameter is reduction of rib angle which provides an increase of 30 percent in strength-to-weight ratio. Although Stiffeners (ribs) have a major role in load carrying, increasing the rib thickness causes the structural weight to rise. Thus compared with the two previous parameters, they do not have a significant effect on the strength of structures.
Published Version
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