Novel beam shaping and computer vision methods for laser beam welding

Abstract: Laser beam welding has been widely applied in different industrial sectors due to its unique advantages. However, there are still challenges, such as beam positioning in T-joint welding, and gap bridging in butt joint welding,especially in the case of varying gap width along a joint. It is expected that enabling more advanced control to a welding system, and obtaining more in-depth process knowledge could help to solve these issues. The aim of this work is to address such welding issues by a laser beam shaping technology using a novel deformable mirror together with computer vision methods and also to increase knowledge about the benefits and limitations with this approach.Beam shaping in this work was realized by a novel deformable mirror system integrated into an industrial processing optics. Together with a wave front sensor, a controlled adaptive beam shaping system was formed with a response time of 10 ms. The processes were monitored by a coaxial camera with selected filters and passive or active illumination. Conduction mode autogenous bead-on-plate welding and butt joint welding experiments have been used to understand the effect of beam shaping on the melt pool geometry. Circular Gaussian, and elliptical Gaussian shapes elongated transverse to and along the welding direction were studied. In-process melt pool images and cross section micrographs of the weld seams/beads were analyzed. The results showed that the melt pool geometry can be significantly modified by beam shaping using the deformable mirror. T-joint welding with different beam offset deviations relative to the center of the joint line was conducted to study the potential of using machine learning to track the process state. The results showed that machine learning can reach sufficient detection and estimation performance, which could also be used for on-line control. In addition, in-process and multidimensional data were accurately acquired using computer vision methods. These data reveal weaknesses of current thermo-fluid simulation model, which in turn can help to better understand and control laser beam welding. The obtained results in this work shows a huge potential in using the proposed methods to solve relevant challenges in laser beam welding.