Optimization of composite stiffened panels under mechanical and hygrothermal loads using neural networks and genetic algorithms


The present work develops an optimization procedure for a geometric design of a composite material stiffened panel with conventional stacking sequence using static analysis and hygrothermal effects. The procedure is based on a global approach strategy, composed by two steps: first, the response of the panel is obtained by a neural network system using the results of finite element analyses and, in a second step, a multi-objective optimization problem is solved using a genetic algorithm. The neural network implemented in the first step uses a sub-problem approach which allows to consider different temperature ranges. The compression load and relative humidity of the air are assumed to be constants throughout the considered temperature range. The mass, the hygrothermal expansion and the stresses between the skin and the stiffeners are defined as the optimality criteria. The presented optimization procedure is shown to yield the optimal structure design without compromising the computational efficiency.