OPTIMIZATION IN RECYCLED VULCANIZED RUBBER - BLOOMING REDUCTION USING RESPONSE SURFACE METHODOLOGY: BLOOMING REDUCTION USING RESPONSE SURFACE METHODOLOGY

Abstract

This study presents a novel image processing-based vision system for the objective quantification of additive blooming on the surface of rubber blends with containing grind tire rubber (GTR). The method utilizes RGB color channel decomposition to compare pre- and post-blooming states, employing the Euclidean distance between channels to provide a quantitative metric of blooming intensity over time. A central composite design (2⁴ with 8 axial and 12 center points, including replicates) was employed to investigate the effect of formulation parameters on both blooming and key mechanical properties: tensile strength, elongation, and Young’s modulus. Statistical analysis revealed that blooming is significantly influenced by the additive concentration [CCA1], the vulcanizing agent [S], and their interaction, with the regression model exhibiting a good fit (R² = 81.86%, adj. R² = 76.15%). While elongation was primarily governed by the accelerator [CCD2] (adj. R² = 91.52%), both tensile strength and Young’s modulus were significantly affected by [CCD2], [S], and key interactions (adj. R² = 72.93% and 72.35%, respectively). The results demonstrate that optimizing the formulation, particularly by avoiding redundant additive loading already present in GTR, can effectively mitigate blooming without compromising mechanical performance. This work provides two key contributions: a robust, objective methodology for blooming quantification and data-driven insights for optimizing sustainable rubber formulations, thereby supporting the development of Industry 4.0-oriented quality control strategie