The main challenge presented by the design of laminated composite material is the laminate layup, involving a set of fiber orientations, composite material systems, and stacking sequences. In nature, it is a combinatorial optimization problem with constraints that can be solved by the genetic algorithm. The traditional approach to solve a constrained problem is reformulating the objective function. In the present study, a new variant of the genetic algorithm is proposed for the design of composite material by using a mix of selection strategies, instead of modifying the objective function. To check the feasibility of a laminate subject to in-plane loading, the effect of the fiber orientation angles and material components on the first ply failure is studied. The algorithm has been validated by successfully optimizing the design of cross-ply laminate under different inplane loading cases. The results obtained by this algorithm are better than works in related literature.
In this paper, an alternative methodology to obtain the strength ratio for the laminated composite material is presented. Traditionally, classical lamination theory and related failure criteria are used to calculate the numerical value of strength ratio of laminated composite material under in-plane and out-ofplane loading from a knowledge of the material properties and its layup. In this study, to calculate the strength ratio, an alternative approach is proposed by using an artificial neural network, in which the genetic algorithm is proposed to optimize the search process at four different levels: the architecture, parameters, connections of the neural network, and active functions. The results of the present method are compared to those obtained via classical lamination theory and failure criteria. The results show that an artificial neural network is a feasible method to calculate the strength ratio concerning in-plane loading instead of classical lamination and associated failure theory.
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