Weld penetration identification for VPPAW based on keyhole features and extreme learning machine

Abstract

Variable polarity plasma arc welding, as an advanced manufacturing technology, has been successfully used in industrial production due to high energy density. The need for the control of the weld penetration remains of a long term interest in VPPAW process. In this study, a simple-flexible vision system was established to acquire a series of keyhole images, and the geometrical appearance of keyhole including the keyhole width and area are extracted based on part-based tree model. Then the acquired keyhole features are used to predict the weld penetration by using a novel extreme learning machine model. The research shows that ELM model can predict the penetration state of variable polarity plasma arc welding credibly and achieve real time monitoring for welding quality.