Human welder's experiences and skills are critical for producing quality welds in manual GTAW process. Learning human welder's behavior can help develop next generation intelligent welding machines and train welders faster. In this tutorial paper, various aspects of mechanizing the welder's intelligence are surveyed, including sensing of the weld pool, modeling of the welder's adjustments and this model-based control approach. Specifically, different sensing methods of the weld pool are reviewed and a novel 3D vision-based sensing system developed at University of Kentucky is introduced. Characterization of the weld pool is performed and human intelligent model is constructed, including an extensive survey on modeling human dynamics and neuro-fuzzy techniques. Closed-loop control experiment results are presented to illustrate the robustness of the model-based intelligent controller despite welding speed disturbance. A foundation is thus established to explore the mechanism and transformation of human welder's intelligence into robotic welding system. Finally future research directions in this field are presented.

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Published in Journal of Manufacturing Processes, v. 16, issue 1, p. 123-136.

Per the publisher Elsevier: "NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Manufacturing Processes. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Manufacturing Processes, v. 16, issue 1, (January 2014). DOI: http://dx.doi.org/10.1016/j.jmapro.2013.09.004"

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