Shrinkage Optimization in Talc- and Glass-Fiber-Reinforced Polypropylene Composites

The shrinkage of reinforced polymer composites in injection molding varies, depending on the properties of the reinforcing agent. Therefore, the study of optimal reinforcement conditions, to minimize shrinkage when talc and glass fibers (GF) (which are commonly used as reinforcements) are incorporated into polypropylene (PP), is required. In this study, we investigated the effect of reinforcement factors, such as reinforcement type, reinforcement content, and reinforcement particle size, on the shrinkage, and optimized these factors to minimize the shrinkage of the PP composites. We measured the shrinkage of injection-molded samples, and, based on the measured values, the optimal conditions were obtained through analysis of variance (ANOVA), the Taguchi method, and regression analysis. It was found that reinforcement type had the largest influence on shrinkage among the three factors, followed by reinforcement content. In contrast, the reinforcement size was not significant, compared to the other two factors. If the reinforcement size was set as an uncontrollable factor, the optimum condition for minimizing directional shrinkage was the incorporation of 20 wt % GF and that for differential shrinkage was the incorporation of 20 wt % talc. In addition, a shrinkage prediction method was proposed, in which two reinforcing agents were incorporated into PP, for the optimization of various dependent variables. The results of this study are expected to provide answers about which reinforcement agent should be selected and incorporated to minimize the shrinkage of PP composites.

Optimization of Short Glass Fiber and PTFE Reinforced PC Composites Using D-optimal Mixture Design

This paper deals with the application of D-optimal mixture design (DMD) integrating response surface methodology (RSM) to discuss the variation of tribological behaviors and impact energy depending on injection molding techniques during production of short glass fiber (SGF) and polytetrafluoroethylene (PTFE) reinforced polycarbonate (PC) composites. Planning of experiments was based on a D-optimal mixture design (DMD). By applying RSM analysis, a mathematical predictive model of the friction coefficient, wear mass loss and impact energy was developed in terms of the mixture ratio of PC, SGF, and PTFE. In addition, analysis of variance (ANOVA) was also applied to identify the significant effect of mixture ratio of SGF and PTFE reinforced PC composites for the friction coefficient, wear mass loss and impact energy.