Closed-Loop Control of Robotic Arc Welding System with Full-penetration Monitoring

The real-time detection of the state of the gap and weld penetration control are two fundamental issues in robotic arc welding. However, traditional robotic arc welding lacks external information feedback and the function of real-time adjusting. The objective of this research is to adopt new sensing techniques and artificial intelligence to ensure the stability of the welding process through controlling penetration depth and weld pool geometry. A novel arc welding robot system including function modules (visual modules, data acquisition modules) and corresponding software system was developed. Thus, the autonomy and intelligence of the arc welding robot system is realized. Aimed at solving welding penetration depth, a neural network (NN) model is developed to calculate the full penetration state, which is specified by the back-side bead width (Wb), from the top-side vision sensing technique. And then, a versatile algorithm developed to provide robust real-time processing of images for use with a vision-based computer control system is discussed. To this end, the peak current self adaptive regulating controller with weld gap compensation was designed in the robotic arc welding control system. Using this closed-loop control experiments have been conducted to verify the effectiveness of the proposed control system for the robotic arc welding process. The results show that the standard error of the Wb is 0.124 regardless of the variations in the state of the gap.