Neural network for prediction of composite mechanical properties based on niche genetic algorithm

This paper proposes to apply the artificial neural network theory and the genetic algorithm to solve the difficulties of predicting composite mechanical properties. Niche technique based on crowding mechanism is used in genetic algorithm, and punishing function is adopted to adjust individual fitness, so as to promote global search capability. Taking the wheat straw-reinforced composite for instance, four influence factors (mold temperature, mold pressure, fibre content and time) are selected to test its tensile strength and toughness. The simulation results show the founded network model has preferable learning and generalization capabilities, which performs effectively in predicting mechanical properties. Besides, the model is used to optimize process parameters of injection molding and find the range of best parameters.