Design optimization of laminated composite structures using artificial neural network and genetic algorithm

In this paper, an efficient method for performing minimum weight optimization of composite laminates using artificial neural network (ANN) based surrogate models is proposed. By predicting the buckling loads of the laminates using ANN the use of time-consuming buckling evaluations during the iterative optimization process are avoided. Using for the first time lamination parameters, laminate thickness and other dimensional parameters as inputs for these ANN models significantly reduces the number of models required and therefore computational cost of considering laminates with many different numbers of layers and total thickness. Besides, as the stacking sequences are represented by lamination parameters, the number of inputs of the ANN models is also signifi-cantly reduced, avoiding the curse of dimensionality. Finite element analysis (FEA) is employed together with the Latin hypercube sampling (LHS) method to generate the database for the training and testing of the ANN models. The trained ANN models are then employed within a genetic algorithm (GA) to optimize the stacking sequences and structural dimensions to minimize the weight of the composite laminates. The advantages of using ANN in predicting buckling load is proved by comparison with other machine learning methods, and the effectiveness and efficiency of the proposed optimization method is demonstrated through the optimization of flat, blade -stiffened and hat-stiffened laminates.

Automated summary

This paper proposes an efficient method for minimum weight optimization of composite laminates using artificial neural network (ANN) based surrogate models. By predicting the buckling loads of the laminates using ANN, the need for time-consuming buckling evaluations in the optimization process is eliminated. The use of lamination parameters and other dimensional inputs significantly reduces the number of required models and computational cost.