Applying the neural network method to the design of the processing parameters in liquid composite molding

In the resin transfer molding (RTM) process, there are several important processing parameters, such as injection pressure, resin injection temperature, mold pre-heated temperature, mold heating rate, and cure temperature, which have major effect on the quality of a RTM product. In general, these parameters are determined based on engineer's experience. In order to establish an efficient way for selecting the process parameters, optimization methods based on the computer aided process simulation could be used. The optimization of manufacturing parameters on RTM was reported by several researchers. Yet there exists a problem that impedes a wide application of this approach. The RTM manufacturing process simulation usually takes a long computation time and the entire optimization is slow. In this study, the RTM process simulation program is replaced by the artificial neural network. A neural network is used to learn the correlation between input and output data of the RTM simulation. The neural network is trained to create a rapid RTM process model based on several simulation results. Genetic algorithm is then applied to this neural network model to search for the optimum solution for a RTM process. With this method, the optimization of a RTM process can be performed more efficiently.