Data driven modeling of human welder intelligence: A neuro-fuzzy approach

Abstract

Modeling of skilled human welder's response to 3D weld pool surface can help develop next generation intelligent robotic welding systems and train welders faster. In this paper, neuro-fuzzy based data driven modeling of human welder intelligence is conducted. An innovative vision system is utilized to real-time measure the specular 3D weld pool surface under strong arc interference. 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 is proposed to correlate skilled human welder response to the fluctuating 3D weld pool surface. It is found that the proposed neuro-fuzzy model can model the human welder intelligence with good accuracy. Comparison of the novice and skilled human welder also reveals detailed adjustments made by the skilled human welder and help train the novice welder. A foundation is thus established to explore the mechanism and transformation of human welder's intelligence into robotic welding system.