WeldMon: A Cost-effective Ultrasonic Welding Machine Condition Monitoring System
This work addresses the need for affordable and reliable tool condition monitoring in the lithium battery industry, representing an incremental improvement over existing methods.
The paper tackled the problem of monitoring ultrasonic welding machine conditions for quality control in the lithium battery industry by developing WeldMon, a cost-effective system that achieved 95.8% cross-validation accuracy in condition classification, outperforming the state-of-the-art method at 92.5%, and improved accuracy by 8.3% through data augmentation.
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.