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 - This research was funded by the NSF (award number 2126246). The opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the view of the NSF.
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 -