Modeling of Human Welder Response Against 3D Weld Pool Surface Using Machine-Human Cooperative Virtualized Welding Platform

This paper aims to present the modeling of human welder intelligence in pipe Gas Tungsten Arc Welding (GTAW) process. 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 and global Adaptive Neuro-Fuzzy Inference System (ANFIS) model are identified from the experimental data to correlate welder's adjustment on the welding speed to the 3D weld pool surface. 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.