Physical and tribological properties of a new polycarbonate-organoclay nanocomposite

Determination of a homogeneity factor for composite materials by a microstructural image analysis method

The physical properties of particle-reinforced composite materials are highly affected by the distribution of particles within a matrix material. In this study, a microstructural image analysis method with a new distribution index for quantifying the degree of distribution in composite materials was developed. The free-path spacing between particles was measured to calculate the distribution (D) index based on the coefficient of variation. The proposed method was applied to six digitally created reference patterns as representative binary composite microstructures and three actual ceramic-matrix composites, respectively. It is found that the D index increased from 0.00 to 0.67 depending on the degree of distribution or homogeneity level based on the reference patterns. The homogeneity levels for the binary composites are then classified from a perfect (maximum) to very low level (minimum) based on increasing D index values, where a high D index presents a poorer distribution. The results obtained for reference patterns and metal silicide-refractory oxide composite microstructures indicate that the proposed method is a useful tool to quantify the degree of distribution with high accuracy, and can be efficiently used for different types of composite microstructures.

Optimization of Injection Molding Process for SGF and PTFE Reinforced PC Composites Using Response Surface Methodology and Simulated Annealing Approach

This study is analyzed variations of ultimate strength, friction coefficient and wear mass loss that depend on the injection molding techniques during the blending of short glass fiber (SGF) and polytetrafluoroethylene (PTFE) reinforced polycarbonate (PC) composites. A hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrating simulated annealing algorithm (SAA) are proposed to determine an optimal parameter setting of the injection molding process. The specimens are prepared under different injection molding processing conditions based on a Taguchi orthogonal array table. The results of eighteen experimental runs were utilized to train the BPNN predicting ultimate strength, friction coefficient and wear mass loss. Simultaneously, the RSM and SAA approaches were individually applied to search for an optimal setting. In addition, the analysis of variance (ANOVA) was implemented to identify significant factors for the injection molding process parameters and the result of BPNN integrating SAA was also compared with RSM approach. The results of optimal parameters of injection molding process for the ultimate strength of x-direction and y-direction based on BPNN/SAA approach were increased 3.12%, and 6.18%, respectively.