Application of artificial neural networks for the optimal design of sheet molding compound (SMC) compression molding

Abstract A sequential design optimization scheme based on artificial neural networks (ANN) is proposed. It is a combination of an ANN model and a nonlinear programming algorithm. The proposed scheme is implemented with network training, optimization, and sheet molding compound (SMC) process simulation in a closed loop. A “cyclic coordinate search” technique is employed to initiate the optimization process, to collect training data for the neural network model, and to perform a preliminary design sensitivity analysis. Emphasis is placed on the development of an integrated, automatic optimization‐simulation design tool that does not rely on the designer's experience and interpretation. Testing results based on the design of heating channels in an SMC compression molding tool show that the optimal design can be achieved with fewer data points than other methods, such as factorial design. The efficiency of the ANN method would be greater as the number of design variables grows.