Predicting and Controlling the Quality of Injection Molding Properties for Fiber-Reinforced Composites

Fiber-reinforced composites are widely used in injection molding processes because of their high strength and high elastic modulus. However, the addition of reinforcing agents such as glass fibers has a significant impact on their injection molding quality. The difference in shrinkage and hardness between the plastic and the reinforcement will bring about warpage and deformation in the injection molding of the product. At the same time, the glass fibers will be oriented in the flow direction during the injection molding process. This will enhance the mechanical properties in the flow direction and increase the shrinkage in the vertical direction, reducing the molding quality of the product. In this study, a test program was developed based on the Box-Behnken test design in the Design-Expert software, using a plastic part as an example. Moldflow software was used for simulation, and data analysis of the experimental data was carried out to investigate the significance of the influence of each injection molding process parameter on the molding quality. In addition to this, a mathematical model between the injection molding process parameters and the quality objectives was established by optimizing the model parameters of the back-propagation (BP) neural network through the ant colony optimization (ACO) algorithm. The established mathematical model is then globally optimized using a multi-objective function optimization based on the non-dominated rank-based sorting genetic algorithm (NSGA-II) to obtain the optimal combination of process parameters. The research in this article provides a theoretical basis for further combining intelligent algorithms to improve injection molding quality.