Process parameters critically affect the flow, heat transfer, and mixing behaviors of rubber melts during single-screw extrusion. However, direct observation of the internal flow field is difficult, and high-fidelity numerical simulations are computationally expensive, limiting rapid analysis under multiple operating conditions. To address these challenges, a three-dimensional non-isothermal CFD model validated against experimental outlet temperature data was developed and coupled with an IPSO-BP surrogate model to establish a rapid prediction framework. The effects of screw speed, inlet temperature, and barrel temperature on flow, heat transfer, and local mixing characteristics were systematically investigated. The results indicate that screw speed predominantly affects melt flow and mixing behavior by enhancing drag flow, shear action, and internal heat transfer. Inlet temperature exerts the greatest influence on the mean outlet temperature, whereas barrel temperature primarily regulates the thermal state of the melt through wall heat transfer. Compared with the BP, GA-BP, PSO-BP, GPR, and XGBoost surrogate models, the IPSO-BP model demonstrated superior overall predictive performance, particularly achieving higher prediction accuracy and stability in predicting complex temperature responses. The proposed CFD–IPSO-BP framework provides an efficient tool for rapid prediction and optimization of EPDM single-screw extrusion processes.

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.