Automatic gap tracking during high power laser welding based on particle filtering method and BP neural network

Abstract

A numerical simulation model was established to investigate characteristics of keyhole and molten pool during the laser butt welding. The sharp point in front of the keyhole and molten pool revealed the position of the gap, and its deviation in transversal direction between the centroid of the keyhole demonstrated the real-time deviation between the laser beam and real gap. A visual system was designed to capture real-time infrared images of keyhole and molten pool, and the real-time deviation data between the laser beam and gap was extracted from these images. The state and measurement equations of real gap position prediction were developed based on the welding system. The particle filtering (PF) method was employed to improve the accuracy of the prediction of the gap position. Considering the non-linear and unknown distribution of system error and measurement error, a Back Propagation (BP) neural network was developed to compensate for these errors. The effectiveness of the established PF method combined with BP network was validated by experimental results, and higher prediction accuracy of gap position tracking was achieved.