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 quality quickly, reliably, and non-destructively. State-of-the-art quality assessment methods such as destructive tensile testing and binary quality classification cannot meet such requirements. This paper develops a novel end-to-end online quality prediction method for UMW based on sensor fusion and deep learning. The proposed method offers important advantages compared to state-of-the-art approaches, including automatic feature generation and good robustness to UMW tool conditions. The effectiveness of the developed method is demonstrated using real-world data generated from an UMW process with four different tool conditions, showing that it is readily applicable to industrial-scale UMW processes to enable accurate online quality prediction.
Read the full paper here.