Neural network response surface optimization design for composite stiffened structures

To avoid drawbacks of conventional structural optimization approaches, a neural network (NN) response surface optimization method is proposed for the design of composite stiffened structures. Such NN-based structural analysis response surfaces can reflect the global mapping relationship between design inputs and structural response outputs. By using the orthotropic experiment method to select the appropriate structural finite element analysis samples, neural network response surfaces can be trained with reasonable accuracies. The constructed response surfaces can be either used as objective function or constraints or both. Together with other conventional constraints, an revised optimization design model can be formed which can be solved by using genetic algorithm (GA). Taking a hat-stiffened composite panel of blended wing-body aircraft as example, the structural weight response surface is developed as objective function, and strength and buckling factor response surfaces as constraints. All these neural networks are trained by finite element samples computed through PATRAN/ NASTRAN software. The optimization results illustrate that it can significantly reduce the cycles of finite element model analysis and achieve highly accurate response approximation results. Eventually, the approach can greatly save the computation time and raise the efficiency of optimization process.