Effects of Pin Arrangement on Rubber Melt Mixing in a Pin-Barrel Cold-Feed Extruder: Finite Element Analysis and MEA-BP-Based Flow-Field Parameter Prediction

Pin arrangement significantly affects rubber-melt mixing and extrusion in pin-barrel cold-feed extruders. However, internal flow details are difficult to observe experimentally, and efficient prediction of flow-field parameters remains unavailable. This study used a finite-element model preliminarily validated against measured temperatures, together with particle tracing, to compare configurations with 0, 2, 4, and 6 pins per group. A dataset of 140 pin arrangements was generated by Latin hypercube sampling and numerical simulation. A mind evolutionary algorithm-optimized back-propagation neural network (MEA-BP) was then developed to predict melt volume-averaged temperature and average shear rate. Pins increased melt velocity and shear heating and improved cross-sectional temperature uniformity. Among the four uniform configurations, the 4-pin-per-group configuration showed the fastest reduction in segregation scale with a moderate residence time, achieving a favorable balance between mixing adequacy and processing efficiency. Particle tracing indicated repeated fluid splitting and recombination, whereas further increases in the number of pins yielded limited benefits. Under identical data partitions, network settings, and evaluation conditions, MEA-BP achieved R2 values of 0.957 and 0.872 for temperature and shear-rate prediction, respectively, outperforming GA-BP, PSO-BP, and conventional BP.