Abstract
Skilled welders can estimate and control the weld penetration based on weld pool observation. This implies that an advanced control system could be developed to control the penetration by emulating the decision making process of the human welder. In this paper a nonlinear dynamic model is established to correlate the process inputs (welding current and traveling speed) and weld penetration in Gas Tungsten Arc Welding (GTAW). An innovative 3D vision sensing system capable of measuring the weld pool characteristic parameters in real-time is utilized. Dynamic experiments are conducted under various welding conditions. Dynamic linear model is first constructed and the results are analyzed. The linear model is then improved by incorporating a nonlinear operating point modeled by Adaptive Neuro Fuzzy Inference System (ANFIS). It is found that the penetration state can be better modeled by the proposed ANFIS model.
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