TY - GEN
T1 - WeldMon
T2 - 14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023
AU - Tian, Beitong
AU - Eslaminia, Ahmadreza
AU - Lu, Kuan Chieh
AU - Wang, Yaohui
AU - Shao, Chenhui
AU - Nahrstedt, Klara
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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 dataset shift 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.
AB - 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 dataset shift 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.
KW - Data Acquisition System
KW - Dataset Shift
KW - Edge Computing
KW - Industrial Internet of Things
KW - Tool Condition Monitoring
KW - Ultrasonic Welding
UR - http://www.scopus.com/inward/record.url?scp=85179760744&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179760744&partnerID=8YFLogxK
U2 - 10.1109/UEMCON59035.2023.10315983
DO - 10.1109/UEMCON59035.2023.10315983
M3 - Conference contribution
AN - SCOPUS:85179760744
T3 - 2023 IEEE 14th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023
SP - 310
EP - 319
BT - 2023 IEEE 14th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023
A2 - Chakrabarti, Satyajit
A2 - Paul, Rajashree
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 October 2023 through 14 October 2023
ER -