Weld penetration in situ prediction from keyhole dynamic behavior under time-varying VPPAW pools via the OS-ELM model

Abstract

In situ monitoring and accurate detecting of welding quality have been one of the common challenges of automatic welding process. This paper contributes an intelligent decision-making framework for the weld penetration prediction from the keyhole dynamic behavior under time-varying VPPAW pools. Initially, a series of dynamic experiments under different welding conditions were conducted to acquire the backside images of keyhole and corresponding backside bead width. Then, the geometry appearance of keyhole was described by the supervised descent method (SDM)–based image processing algorithm. Subsequently, the internal correlation between the keyhole characteristics and the backside width was further derived to help understand the nonlinear and time-varying VPPAW process. Finally, a novel dynamic model based on an online sequential extreme learning machine (OS-ELM) was designed to predict the weld penetration as measured by the backside bead width in real time. Extensive experiment results further verify and validate that the proposed dynamic OS-ELM model is significantly better than other state-of-the-art algorithms in terms of predicting accuracy, efficiency, and robustness.