RESUMEN
This study proposes a novel process that integrates the molding and patterning of solid-state polymers with the force generated from the volume expansion of the microcellular-foaming process (MCP) and the softening of solid-state polymers due to gas adsorption. The batch-foaming process, which is one of the MCPs, is a useful process that can cause thermal, acoustic, and electrical characteristic changes in polymer materials. However, its development is limited due to low productivity. A pattern was imprinted on the surface using a polymer gas mixture with a 3D-printed polymer mold. The process was controlled with changing weight gain by controlling saturation time. A scanning electron microscope (SEM) and confocal laser scanning microscopy were used to obtain the results. The maximum depth could be formed in the same manner as the mold geometry (sample depth: 208.7 µm; mold depth: 200 µm). Furthermore, the same pattern could be imprinted as a layer thickness of 3D printing (sample pattern gap and mold layer gap: 0.4 mm), and surface roughness was increased according to increase in the foaming ratio. This process can be used as a novel method to expand the limited applications of the batch-foaming process considering that MCPs can impart various high-value-added characteristics to polymers.
RESUMEN
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.