Real–time prediction of penetration depths of laser surface melting based on coaxial visual monitoring

Abstract

Laser surface melting (LSM) is an important laser processing technique that can be used in laser conduction welding, laser remelting, and defect elimination. Penetration depths are essential to determine LSM qualities. To enhance the ability to predict penetration depths, a real-time prediction method based on coaxial visual characteristics and the convection state of a molten pool is reported. First, the coaxial visual characteristics of the molten pool are analyzed, and its image is divided into convection, transition, and boundary regions. Based on these region characteristics, the convection state can be indicated. Then, the relationships among process parameters, coaxial visual characteristics, and penetration depths are presented. Finally, we attempt to predict the penetration depths based on these coaxial visual characteristics. The results show that the convection region characteristics are more sensitive to the changes in process parameters. The oscillation amplitude of the convection region can be considered monitored objects to indicate the convection state. The convection region can predict the penetration depths better than the entire molten pool. Furthermore, this work will be beneficial to understanding the experimental phenomenon and enhance the monitoring ability of other laser processing techniques, such as laser alloying, laser cladding, and laser-based additive manufacturing.