Recently, our paper titled “Explainable few-shot learning for online anomaly detection in ultrasonic metal welding with varying configurations” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yuquan Meng, Kuan-Chieh Lu, Zhiqiao Dong, Shichen Li, and Prof. Chenhui Shao. Modern manufacturing is featured by rapid reconfiguration and agile adaptation that necessitate varying process configurations. In ultrasonic metal welding, a process […]
Yuquan Meng has successfully defended his doctoral dissertation titled “Physics-Informed Machine Learning for Smart Decision-Making in Ultrasonic Metal Welding.” The defense was conducted before an esteemed Examination Committee consisting of Prof. Chenhui Shao, Prof. Placid Ferreira, Prof. Srinivasa Salapaka, and Prof. Pingfeng Wang. In his research, Yuquan Meng has made significant contributions to the field […]
Our paper titled “End-to-end online quality prediction for ultrasonic metal welding using sensor fusion and deep learning” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yulun Wu, Yuquan Meng and Prof. Chenhui Shao. In industrial-scale production applications of ultrasonic metal welding (UMW), there is a strong need for predicting joint […]