Root hump defect monitoring of high power fiber laser thick plate welding based on the character of keyhole and molten pool

The root hump defect is easy to appear in the process of high power laser welding. Through the observation of welding experiment, it is found that there is obvious correlation between root hump defect and the character of keyhole and molten pool. Therefore, this paper proposes a method to monitor the root hump defect by identifying the keyhole and molten pool features in the welding process. In this method, image sensing technology and machine vision method are used to analyze and extract the keyhole and weld pool information in real time. The BP neural network algorithm is used to classify the welding states. It is found that adding the feature of weld pool length as input will greatly improve the recognition accuracy of the model.