Automatic detection and prediction of discontinuities in laser beam butt welding utilizing deep learning

Laser beam butt welding of thin sheets of high-alloy steel can be really challenging due to the formation of joint gaps, affecting weld seam quality. Industrial approaches rely on massive clamping systems to limit joint gap formation. However, those systems have to be adapted for each individually component geometry, making them very cost-intensive and leading to a limited flexibility. In contrast, jigless welding can be a high flexible alternative to substitute conventionally used clamping systems. Based on the collaboration of different actuators, motions systems or robots, the approach allows an almost free workpiece positioning. As a result, jigless welding gives the possibility for influencing the formation of the joint gap by realizing an active position control. However, the realization of an active position control requires an early and reliable error prediction to counteract the formation of joint gaps during laser beam welding. This paper proposes different approaches to predict the formation of joint gaps and gap induced weld discontinuities in terms of lack of fusion based on optical and tactile sensor data. Our approach achieves 97.4 % accuracy for video-based weld discontinuity detection and a mean absolute error of 0.02 mm to predict the formation of joint gaps based on tactile length measurements by using inductive probes.