Online Monitoring and Model-Free Adaptive Control of Weld Penetration in VPPAW Based on Extreme Learning Machine

Abstract

Monitoring and controlling of weld joint penetration are essential issues in variable polarity plasma arc welding (VPPAW). In this paper, we develop a flexible visual sensor system to measure the backside keyhole characteristic parameters such as keyhole length, width, and area. Further, data analysis from dynamic welding experiments reveals a nonlinear correlation of the keyhole features and the backside beam width. To provide accurate feedback information, an extreme learning machine method is applied to predict the weld width with sufficient accuracy and less computing time. Then a novel model-free adaptive control (MFAC) is designed to control the weld penetration evaluated by the weld width. Closed-loop experimental results confirm that the MFAC system can simultaneously adjust the welding current and plasma gas flow rate to control the VPPAW process for obtaining a full-penetrated weld under various initial welding conditions and disturbances.