Welder rating system based learning of human welder intelligence in GTAW

Current industrial welding robots are mostly articulated arms with pre-programmed sets of movement, which lack the intelligence skilled human welders possess. In this paper human welder's response against 3D weld pool surface is learned and transferred to the welding robots to perform automated welding tasks. To this end, an innovative teleoperated virtualized welding platform is utilized to conduct dynamic training experiments by a human welder whose arm movements together with the 3D weld pool characteristic parameters are recorded. The data is off-line rated by the welder and a welder rating system is consequently trained, using an Adaptive Neuro-Fuzzy Inference System (ANFIS), to automate the rating. Data from the training experiments are then automatically classified such that top rated data pairs are selected to model and extract 'good response' minimizing the effect from 'bad operation' made during the training. A supervised ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder's adjustment on the welding speed. The obtained welder response model is then transferred to the welding robot to perform automated welding task as an intelligent controller. Experiment results verified that the proposed model is able to control the process under different welding current as well as under speed disturbance. A foundation is thus established to selectively learn 'good response' to rapidly extract human intelligence to transfer into welding robots.