Human-on-the-Loop Control in Surface Mount Technology via Deep Reinforcement Learning

Considering the importance of solder paste printing in the production process of surface mounted technology (SMT), as well as the decisive impact of key process parameters on the solder paste printing effect. Traditional methods, whether manual or machine tuning, suffer from significant production capacity losses due to long downtime, and machines cannot adaptively adjust parameters based on human expert knowledge, thereby affecting the qualification rate of solder paste printing and the efficiency of SMT production lines. This paper proposes a human–machine integration optimization method for key printing process parameters. By establishing a printing quality prediction model and a key process parameter strategy model, a closed-loop control system has been formed to achieve machine autonomous parameter tuning with expert knowledge. And this paper has completed the establishment of the strategy model based on deep reinforcement learning methods, enabling the SMT production line to predict and adjust key process parameters in real time based on SPI data. In addition, the optimization method described in this paper retains the final decision-making authority of human operators to ensure emergency correction of prediction bias and decision failure history in the system. The final experimental results of this paper indicate that the proposed optimization method performs well in terms of qualification rate, correction effect, SPI data prediction, etc. These demonstrate the effectiveness and value of the proposed human-on-the-loop optimization method in SMT production lines.