Research on the Optimization Method of SMT Process Parameters Based on Improved PSO Algorithm

Surface mount technology (SMT) is pivotal in electronic manufacturing, with 60%–70% of quality issues linked to solder paste printing (SPP). This article presents a real-time optimization strategy using an improved particle swarm optimization (PSO) algorithm. Enhancements in the particle velocity update and a multipopulation parallel computing PSO algorithm with ring migration topology improve global search capabilities and optimization speed. A SPP quality prediction model, based on extreme gradient boosting (XGBoost), defines the optimization objective. The improved PSO algorithm optimizes five key process parameters, employing a data-driven approach to systematically determine weights. In defect cases, predefined printing parameters undergo adjustments based on defect categories, integrating expert experience and theoretical calculations. The efficacy of the enhanced PSO algorithm is rigorously validated through multiple experiments. Additionally, experiments using RNN and radial basis function (RBF) neural network models offer insights into its effectiveness. This comprehensive approach not only addresses real-time quality concerns but also contributes to advancing the understanding and application of advanced optimization algorithms in the context of SMT.