  Ultrasonic welding machines play a critical role in the lithium battery
industry, facilitating the bonding of batteries with conductors. Ensuring
high-quality welding is vital, making tool condition monitoring systems
essential for early-stage quality control. However, existing monitoring methods
face challenges in cost, downtime, and adaptability. In this paper, we present
WeldMon, an affordable ultrasonic welding machine condition monitoring system
that utilizes a custom data acquisition system and a data analysis pipeline
designed for real-time analysis. Our classification algorithm combines
auto-generated features and hand-crafted features, achieving superior
cross-validation accuracy (95.8% on average over all testing tasks) compared to
the state-of-the-art method (92.5%) in condition classification tasks. Our data
augmentation approach alleviates the concept drift problem, enhancing tool
condition classification accuracy by 8.3%. All algorithms run locally,
requiring only 385 milliseconds to process data for each welding cycle. We
deploy WeldMon and a commercial system on an actual ultrasonic welding machine,
performing a comprehensive comparison. Our findings highlight the potential for
developing cost-effective, high-performance, and reliable tool condition
monitoring systems.
