One camera-based laser keyhole welding monitoring system using deep learning

Abstract

The laser-beam absorptance changes dynamically during laser keyhole welding due to unstable keyhole movements, and monitoring the absorptance can provide a deep understanding of the process. Recently, Kim et al. [1,2] developed a deep-learning-based method to monitor the absorptance by detecting the top and bottom keyhole apertures and estimating the absorptance from the reconstructed keyhole shape based on the detected apertures. However, this method was limited in that it required simultaneously observing the top and bottom keyhole apertures using two cameras. In this study, we proposed a novel deep-learning-based method to monitor the laser-beam absorptance in a keyhole using only one camera during laser keyhole welding of Al 5052-H32 alloy. In this method, both the top and bottom keyhole apertures were simultaneously detected from the images coaxially obtained from the top side. Although part of the bottom apertures may be sometimes obscured when viewed from above, this study demonstrated that the predicted absorptance was accurate enough and sufficient for monitoring laser welding processes of aluminum alloys. Using the developed method, changes in welding mode and generation of welding defects were successfully detected.