Optimization and Simulation of Extrusion Parameters in Polymer Compounding: A Comparative Study Using BBD and 3LFFD

Many research studies have looked at process characteristics to improve color choices and create more simulation-accurate models. This research evaluated the processing factors speed (Sp), temperature (T), and feed rate (FRate) and employed two response surface techniques, the three-level full-factorial design (3LFFD) and Box-Behnken design (BBD), to optimize uniform processing settings. An experimental approach was employed to optimize process parameters while holding all other variables constant. The Design Expert software enabled the creation of statistical and numerical optimization models, as well as simulated regression models, to find the optimal tristimulus color values with minimal color variance (<b><i>dE*</i></b>). The three examined parameters significantly affected the color parameters <b><i>dL*</i></b>, <b><i>da*</i></b>, and <b><i>db*</i></b>, and specific mechanical energy (SME) based on the analysis of variance (ANOVA). In addition, SME was calculated for the experimental trials. A decrease in SME was found as the FRate increased. The collected data were analyzed to determine pigment dispersion using scanning electron microscopy (SEM) as well as micro-CT (MCT) scanner images. Regarding the BBD, the processing conditions revealed a minimum deviation of 0.26 but a maximum design desirability appeal of 87%. The three-level full-factorial design (3LFFD) revealed a maximum desirability of 77% and a minimum acceptable color variation (<b><i>dE*</i></b>) of 0.25. Therefore, BBD had a marginally superior performance. These results demonstrate that the processing parameters have a significant impact on the output quality, including reducing variation, improving color consistency, minimizing waste, and promoting sustainable production. This study found that both sets of process parameters were statistically significant after comparing the two designs. However, BBD is the preferred design for the selection needed and offers better outcomes in future experiments.