Estimation of keyhole geometry and prediction of welding defects during laser welding based on a vision system and a radial basis function neural network

Abstract

In keyhole welding, welding quality is closely related to the stability of the keyhole, which is primarily determined by keyhole geometry during the welding process. Three essential attributes to describe the simplified three-dimensional keyhole shape include keyhole size, penetration depth, and keyhole inclination angle. However, when using traditional measurement techniques, it is very challenging to take in-process measurements of penetration depth and inclination angle, even if the keyhole size can be detected by using a visual monitoring system. To realize the on-line estimation of keyhole dynamics and welding defects, a data-based radial basis function neural network state observer is adopted for estimating penetration depth and inclination angle in the transient state when welding parameters change suddenly. First, a static neural network is trained in advance to establish a correlation between the welding parameters and unobservable keyhole geometry. The dynamic state observer is trained based on the transient welding conditions predicted by a numerical model and then used to estimate the time-varying keyhole geometry. Meanwhile, a coaxial monitoring system is used to observe the keyhole shape from the top side in real time, which not only provides input to the neural network but also indicates the potential welding porosities. The predicted results are validated by experimental data obtained by welding of stainless steel 304 and magnesium alloy AZ31B.