Performance prediction of Co-injection self-reinforced composites parts based on temperature field

Abstract

In this paper, polypropylene (PP) was used as raw material to prepare rectangular parts. The temperature change data of the reinforcement with different molding parameters during the filling process were collected by using the injection molding temperature visualization experimental analysis platform. The electronic universal mechanical testing machine (EMUTM) was used for mechanical testing, and the micro-morphology of co-injection self-reinforced composites(CI-SRCs) parts and conventional parts with different temperature fields was observed and analyzed by Polarizing microscope (PLM) and Wide angle X-ray diffraction (WAXD), and the dimensionless equations among four variables (including molding parameters, area ratio of critical temperature field, area ratio of skin layer and mechanical properties) were established. From the results, it was found that the tensile properties of CI-SRCs parts with different molding parameters are superior to that of conventional parts, with a maximum increase of 18.64%. The overall performance of CI-SRCs parts is positively correlated with the performance of the reinforcement, and the performance of reinforcement is mainly determined by the area ratio of skin layer. The difference in the micromorphology characteristics of the parts depends on the change in the temperature field. Therefore, through microscope observation and simulation software analysis, it was obtained that the theoretical critical temperature field forming the orientation skin of the parts was 154.88 °C, and the temperature visualization platform was used to correct the critical temperature field obtained by simulation, and the real critical temperature field was about 170 °C. In the randomized trials, the simulated and actual area ratio of skin layer were in good agreement, with a maximum deviation of 8.9%, which proved that it was reliable to estimate the skin layer area ratio based on theoretical critical temperature field through the change of molding parameters, and then to predict the performance change of the parts.