Solder Paste Printing Quality Prediction Model Based on PSO Optimization

Statistics show that about 70% of the surface mount quality problems are in the solder paste printing process, so it is particularly important to optimize the solder paste printing process parameters. The traditional parameter adjustment method still relies on the engineer’s own experience to manually adjust, which is inefficient. In recent years, artificial intelligence technology has achieved excellent performance in various cities, providing a new idea for the optimization of key process parameters of SMT production lines. Therefore, this paper proposes a method for optimizing process parameters of solder paste printing based on machine learning. It mainly includes two parts, one is the construction of the solder paste prediction model, and the other is the optimization algorithm based on the model prediction parameters. We choose the SVR model as the regressor of parameter prediction, and use the value predicted by the model as the evaluation standard, and use the PSO algorithm to optimize the search in the search space of process parameters. Finally, after experimental tests, the process parameters optimized by this method can well meet the actual production requirements and effectively improve the production efficiency.