Abstract
Automated welding robots are preferred in many applications since they outperform human welders who suffer from various physical limitations. Unfortunately, current industrial welding robots are basically articulated arms with a pre-programmed set of movement, lacking the intelligence skilled human welders possess. This paper serves as the first study to learn human welder intelligence in pipe Gas Tungsten Arc Welding (GTAW) utilizing a recently developed virtualized welding system. In particular, a 6-DOF UR-5 industrial robot arm is equipped with sensors to observe the welding process. Human welder operates a virtualized welding torch, whose motion is tracked/recorded. A correlation between the welding current and welding speed controlled by human welder is proposed for GTAW pipe welding with specified welding conditions. Satisfactory welds with consistent penetration are generated in welding experiments under different welding currents. It is also concluded that for top part of the pipe welding, instead of manipulating a full set of welding parameters, adjusting welding speed is sufficient to generate satisfactory welds. A foundation is thus established to transfer human intelligence to the welding robot.
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