Optimization of fiber-orientation distribution in fiber-reinforced composite injection molding by Taguchi, back propagation neural network, and genetic algorithm–particle swarm optimization

Abstract

Fiber orientation induced by injection molding of short-fiber-reinforced composites causes anisotropy in material properties and produces warping. Fiber-orientation distribution is very important to research for mold design and quality to produce sound molded parts. In this study, three kinds of methods are used to solve the optimization problem. Fiber-orientation distribution is described by fiber-orientation tensor variation. The objective function is a minimum problem of the fiber-orientation tensor variation. Parameters such as fiber content, fiber aspect ratio, melting temperature, injection pressure, holding pressure, and filling time are considered as design variables. Based on orthogonal experiment design, Moldflow software is used in the fiber-reinforced acrylonitrile butadiene styrene composite injection molding. The effects of process parameters for the plastic part are studied using the signal-to-noise ratio. The most important design parameter influencing fiber-orientation tensor variation is determined by finite element analysis results based on the analysis of variance. The optimization model is established on the basis of the back propagation neural network. The Taguchi, the particle swarm optimization, and genetic algorithm–particle swarm optimization hybrid algorithm are used to find the minimum fiber-orientation tensor variation value. Results show that the quality index of the fiber-orientation tensor variation in the part is improved.