Optimization in recycled vulcanized rubber - blooming reduction using response surface methodology

This study introduces a novel image processing–based vision system for the objective quantification of additive blooming on the surface of rubber blends containing ground tire rubber (GTR). The method applies RGB color channel decomposition to compare pre- and post-blooming states, using Euclidean distance between channels to provide a quantitative metric of blooming intensity over time. Alongside surface analysis, mechanical characterization (tensile strength, elongation at break, and Young’s modulus) was performed to correlate blooming intensity with mechanical performance. A central composite design (24 with 8 axial and 12 center points, including replicates) was used to evaluate the effects of formulation parameters on both blooming and key mechanical properties. Statistical analysis revealed that blooming is mainly influenced by the additive concentration (CCA1), the vulcanizing agent (S), and their interaction, with a good model fit (R² = 81.86%, R² adj. = 76.15%). Elongation was primarily governed by the accelerator (CCD2) (R² adj. = 91.52%), while tensile strength and Young’s modulus were significantly affected by (CCD2), (S), and their interactions (R² adj. = 72.93%, R² adj. = 72.35%, respectively). The optimized formulation, which avoids redundant additive loading already present in GTR, effectively mitigates blooming without compromising mechanical performance. This approach demonstrates strong potential for sustainable rubber recycling with improved mechanical reliability and reduced surface migration. Overall, this work provides two main contributions: (i) a robust, objective methodology for blooming quantification and (ii) data-driven insights for optimizing sustainable rubber formulations, supporting the development of Industry 4.0 oriented quality control strategies.