Iterative Local ANFIS-Based Human Welder Intelligence Modeling and Control in Pipe GTAW Process: A Data-Driven Approach

Combining human welder (with intelligence and versatility) and automated welding systems (with precision and consistency) can lead to intelligent welding systems. This paper aims to present a data-driven approach to model human welder intelligence and use the resultant model to control automated gas tungsten arc welding process. To this end, an innovative machine-human cooperative virtualized welding platform is teleoperated to conduct training experiments. The welding current is randomly changed to generate fluctuating weld pool surface and the human welder tries to adjust his arm movement (welding speed) based on his observation on the real-time weld pool feedback/image superimposed with an auxiliary visual signal which instructs the welder to increase/reduce the speed. Linear model is first identified from the experimental data to correlate welder's adjustment on the welding speed to the 3-D weld pool surface and a global adaptive neuro-fuzzy inference system (ANFIS) model is then proposed to improve the model accuracy. To better distill the detailed behavior of the human welder, K -means clustering is performed on the input space such that a local ANFIS model is identified. To further improve the accuracy, an iterative procedure has been performed. Compared to the linear, global and local ANFIS model, the iterative local ANFIS model provides better modeling performance and reveals more detailed intelligence human welders possess. To demonstrate the effectiveness of the proposed model as an effective intelligent controller, automated control experiments have been conducted. Experimental results verified that the controller is robust under different welding currents and welding speed disturbance.