WeldMon: A Cost-effective Ultrasonic Welding Machine Condition Monitoring System

TL;DR

WeldMon is an affordable, real-time ultrasonic welding machine condition monitoring system that uses advanced data analysis to improve accuracy and reliability, reducing costs and downtime in lithium battery manufacturing.

Contribution

The paper introduces WeldMon, a novel cost-effective monitoring system with a unique data analysis pipeline and augmentation techniques, outperforming existing methods in accuracy and speed.

Findings

Achieved 95.8% classification accuracy, surpassing the previous 92.5%.

Enhanced accuracy by 8.3% through data augmentation addressing concept drift.

Processed data within 385 milliseconds per welding cycle.

Abstract

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.