Real-time tracking method for motion spatter in high-power laser welding of stainless steel plate based on a lightweight deep learning model

Abstract

The high-power laser welding of stainless steel is prone to spatter defects. The dynamic behavior of motion spatter is closely related to the welding state, but the relevant features are difficult to obtain. This paper extensively describes a motion spatter tracking method based on a lightweight deep learning model and a multi-target tracking algorithm. Firstly, an in-situ observation platform with a high-speed camera-based high spatiotemporal resolution for motion spatters is established. This platform can record the generation and motion process of different spatters during welding. To reduce the interferences and highlight the spatter characteristics, a linear point operation-based image low-brightness metal vapor plume removal method is also proposed. Then, a deep learning-based lightweight spatter detection model is established, and an automatic spatter label generation method is proposed to quickly obtain a sufficient amount of highly discriminative training data to train the established model comprehensively. Compared with the basic model, the validation results show that the lightweight spatter detection model runs 5.11 times faster, with a spatter detection accuracy of 96.71%. Finally, a motion spatter tracking method based on the DeepSORT algorithm is proposed to distinguish different spatters and associate the same spatters. The spatter size, velocity, quantity, and other features are also able to be accurately extracted via this method, achieving spatter defect monitoring. In summary, this study describes a novel and robust method for motion spatter real-time tracking during laser welding and this method can provide reliable data support for accurate and fast process condition monitoring as well as weld quality assessment.