RESUMEN
The in-line control of curing during the molding process significantly improves product quality and ensures the reliability of packaging materials with the required thermo-mechanical and adhesion properties. The choice of the morphological and thermo-mechanical properties of the molded material, and the accuracy of their determination through carefully selected thermo-analytical methods, play a crucial role in the qualitative prediction of trends in packaging product properties as process parameters are varied. This work aimed to verify the quality of the models and their validation using a highly filled molding resin with an identical chemical composition but 10 wt% difference in silica particles (SPs). Morphological and mechanical material properties were determined by dielectric analysis (DEA), differential scanning calorimetry (DSC), warpage analysis and dynamic mechanical analysis (DMA). The effects of temperature and injection speed on the morphological properties were analyzed through the design of experiments (DoE) and illustrated by response surface plots. A comprehensive approach to monitor the evolution of ionic viscosity (IV), residual enthalpy (dHrest), glass transition temperature (Tg), and storage modulus (E) as a function of the transfer-mold process parameters and post-mold-cure (PMC) conditions of the material was established. The reliability of Tg estimation was tested using two methods: warpage analysis and DMA. The noticeable deterioration in the quality of the analytical signal for highly filled materials at high cure rates is discussed. Controlling the temperature by increasing the injection speed leads to the formation of a polymer network with a lower Tg and an increased storage modulus, indicating a lower density and a more heterogeneous structure due to the high heating rate and shear heating effect.
RESUMEN
Monitoring of molding processes is one of the most challenging future tasks in polymer processing. In this work, the in situ monitoring of the curing behavior of highly filled EMCs (silica filler content ranging from 73 to 83 wt%) and the effect of filler load on curing kinetics are investigated. Kinetic modelling using the Friedman approach was applied using real-time process data obtained from in situ DEA measurements, and these online kinetic models were compared with curing analysis data obtained from offline DSC measurements. For an autocatalytic fast-reacting material to be processed above the glass transition temperature Tg and for an autocatalytic slow-reacting material to be processed below Tg, time-temperature-transformation (TTT) diagrams were generated to investigate the reaction behavior regarding Tg progression. Incorporating a material containing a lower silica filler content of 10 wt% enabled analysis of the effects of filler content on sensor sensitivity and curing kinetics. Lower silica particle content (and a larger fraction of organic resin, respectively) favored reaction kinetics, resulting in a faster reaction towards Tg1. Kinetic analysis using DEA and DSC facilitated the development of highly accurate prediction models using the Friedman model-free approach. Lower silica particle content resulted in enhanced sensitivity of the analytical method, leading, in turn, to more precise prediction models for the degree of cure.
