Using a d-optimal mixture design to study the thermal properties of short glass fiber- and polytetrafluoroethylene-reinforced polycarbonate composites

Abstract

This study analyzed variations of thermal properties that depend on the injection molding techniques during the blending of short glass fiber (SGF)- and polytetrafluoroethylene (PTFE)-reinforced polycarbonate (PC) composites. The proposed planning of blending experiments is using a d-optimal mixture design (DMD). The thermal conductivity and thermal expansion coefficient were selected for discussion. Nine experimental runs, based on a DMD method, utilized to train the back-propagation neural network (BPNN), and then the simulated annealing algorithm (SAA) approach is applied to search for an optimal mixture ratio setting. In addition, the result of BPNN integrating SAA was also compared with response surface methodology (RSM) approach. The results of confirmation experiment show that DMD, RSM, and BPNN integrating SAA method are effective tools for the optimization of reinforced process. Furthermore, analysis of variance and response surface graphs were applied to identify the effect of mixture ratio of SGF- and PTFE-reinforced PC composites for the thermal conductivity and thermal expansion coefficient.