Ultrasonic metal welding (UMW) is a solid-state joining technology with widespread industrial applications. However, the weld quality in UMW is highly sensitive to process disturbances such as tool degradation and surface contamination. To address this challenge, this paper presents an integrated learning, monitoring, and control (LMC) system to improve process robustness and weld quality in UMW. The proposed system integrates in-situ sensing, online process monitoring, and within-cycle process adjustment to automatically compensate for process disturbances. Extensive experiments involving 700 welds with varied acting time, pressure adjustments, and contamination levels, are carried out to thoroughly evaluate the effectiveness of the LMC system. It is shown that the proposed method significantly and consistently outperforms the existing controller. Specifically, the weld success rate is increased from 0% to 92% under 20% surface contamination, and from 6% to 72% under 10% surface contamination. Furthermore, a response surface model is developed to quantify the causal relationships between control inputs (i.e., acting time and pressure increase amount) and the resulting weld success rate, which enables efficient optimization of control parameters. Overall, the proposed LMC approach improves the UMW process robustness and weld quality, demonstrating strong potential for industrial-scale implementation. To the best of our knowledge, this study represents one of the first integrated LMC systems developed for UMW.
Lu et al. (Thu,) studied this question.