Neuro-fuzzy based human intelligence modeling and robust control in Gas Tungsten Arc Welding process

Abstract

Human welder's experiences and skills are critical for producing quality welds in Gas Tungsten Arc Welding (GTAW) process. Modeling of the human welder's response to 3D weld pool surface can help develop next generation intelligent welding machines and train welders faster. In this paper, a neuro-fuzzy based human intelligence model is constructed and implemented as an intelligent controller in automated GTAW process to maintain a consistent desired full penetration. An innovative vision system is utilized to real-time measure the specular 3D weld pool surface under strong arc interference in GTAW process. Experiments are designed to produce random changes in the welding speed and voltage resulting in fluctuations in the weld pool surface. Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to correlate the human welder's response to the 3D weld pool surface. Control experiments are designed to start welding using different initial current and have various disturbances including variations of arc length and welding speed. It is found that the proposed human intelligence model can adjust the current to robustly control the process to a desired penetration state despite different initial conditions and various disturbances. A foundation is thus established to explore the mechanism and transformation of human welder's intelligence into robotic welding system.