Study of water penetration length and processing parameters optimization in water-assisted injection molding

Abstract

Water penetration length is one of the most important indexes of the water-assisted injection molding parts, the maximization of which is a particularly significant optimization objective. The effects of processing parameters, such as the short shot size, melt temperature, water pressure, and delay time, on water penetration length were exploited by using single factor experiment method and computational fluid dynamics analysis. In addition, the maximization of water penetration length on dimensional transition and curved-section parts by integrating the Taguchi orthogonal array design, radial basis function neural network, and particle swarm optimization was investigated. The research results showed that the two primary parameters affecting the water penetration length were the short shot size and water pressure, and that the effects of the melt temperature and delay time were little. Furthermore, the maximum water penetration length after optimization was slightly bigger than that of the confirmation experiment, which indicated that the optimization methodology was reliable and effective.