Enhancing SMT Quality and Efficiency With Self-Adaptive Collaborative Optimization
提出自适应协同优化框架，结合贝叶斯优化和粒子群优化，根据自动光学检测实时数据动态调整工艺参数，以减少缺陷并提升SMT装配效率。
In the field of smart surface mount technology (SMT) production, integrating machines through a cyber-physical system (CPS) architecture holds significant potential for improving assembly quality and efficiency. However, fully unifying inspection and production systems to effectively address assembly-related quality issues remains a challenge. This study seeks to close these gaps by introducing collaborative optimization methods to ensure seamless operations. The research is driven by the need for precise control of key assembly parameters, such as placement height, x-offset, y-offset, rotation angle deviations, and blowing durations, all of which are major contributors to defects. To address these challenges, we propose a self-adaptive collaborative optimization (SACO) framework that prioritizes enhancements based on their impact on both quality and efficiency. The SACO framework combines customized Bayesian optimization and particle swarm optimization techniques, allowing for dynamic adjustments to process parameters, guided by real-time data from automatic optical inspection (AOI) systems. The primary goal of this study is to reduce defects and improve efficiency in the SMT assembly process through these targeted improvements. Experimental results validate the effectiveness of the proposed methods, demonstrating significant advancements in placement accuracy and overall assembly efficiency. Our findings confirm that the SACO framework provides a robust solution to persistent challenges in SMT production, addressing critical gaps in quality control and process optimization.