Method for Determining Extrusion Parameters and Die Plate Designing Using an Algorithm

The extrusion of rubber components plays a critical role in tire manufacturing. Modifications in rubber formulations necessitate recalibration of extrusion parameters to ensure dimensional accuracy of the final extrudate. Traditionally, this optimization requires significant time and expert intervention. This work presents an algorithmic framework that combines statistical analysis and machine learning to refine critical extrusion parameters, including draw-off and screw speeds, thereby enabling more efficient process optimization and die design. Using Minitab and Python-based regression algorithms, a predictive model was developed and validated against a benchmark die. The results demonstrate that the proposed approach offers reliable accuracy, enabling enhanced efficiency and product quality. Furthermore, this method exhibits the flexibility to be adapted for different rubber profiles and extrusion configurations.