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Modeling of human welder behavior in gas tungsten arc welding of stainless steel tubes

Henry Granjon prize 2013 winner category D: human related subjects

  • RESEARCH PAPER
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Abstract

While automated welding tends to be the mainstream in modern joining, manual welding still plays an irreplaceable role. Human welder’s experience and skills are critical for many applications where decisions need to be made based on interactions with the process. Understanding the mechanism of human welder intelligence may establish the foundation to develop new methods to better train welders and develop intelligent welding robots that combine intelligence with high physical strength and motion accuracy. As the first effort to study the welders’ behavior mechanism, this paper focuses on answering two fundamental questions: (1) What information that the welders acquire through their observation of the weld pool? 2) how human welders intelligently respond, i.e., adjust welding parameter(s), to the sensory information they obtain? In particular, the principle of welders’ behavior is analyzed from modeling’s point of view. To answer the first question, the weld pool surface is characterized to find the information which is able to indicate the weld quality/penetration. It is found that the length, width, and convexity of weld pool surface provide an optimal estimation of the weld penetration. They can be considered the characteristic parameters/information welders acquire. For the second question, the mechanism of a welder’s behavior/adjustment on welding current as a response to the characteristic parameters is mathematically formulated. The dynamic models of the welder’s behavior are obtained and analyzed. To demonstrate one of the applications of the obtained model, i.e., transferring the welder’s wisdom into automated welding, the dynamic model is implemented as a controller into the automated gas tungsten arc welding (GTAW). Experiments have been designed and conducted with different welding conditions (arc length, i.e., the length of arc by a conversion of the measured arc voltage and joint gap) and initial currents. It has been found that the model is capable of adjusting the current intelligently such that a consistent full penetration is achieved despite the variations in those conditions/parameters.

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Acknowledgments

This work is funded by the National Science Foundation under grant CMMI-0927707 entitled “Machine-Human Cooperative Control of Welding Process” and CMMI- 1208420 entitled “NRI-Small: Virtualized Welding: A New Paradigm for Intelligent Welding Robots in Unstructured Environment”. We wish to thank Mr. Yi Lu and Mr. Yukang Liu for their assistance on experiments and graphics, and Mr. Lee Kvidahl for his technical guidance on manual pipe welding.

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Authors and Affiliations

  1. Institute for Sustainable Manufacturing, and Electrical Engineering Department, University of Kentucky, Lexington, KY, 40506, USA

    WeiJie Zhang

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  1. WeiJie Zhang
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Correspondence to WeiJie Zhang.

Additional information

Doc. IIW-2452, recommended for publication by Commission VIII “Health, Safety and Environment.”

This research work was supervised by Dr. YuMing Zhang at the University of Kentucky, USA

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Zhang, W. Modeling of human welder behavior in gas tungsten arc welding of stainless steel tubes. Weld World 58, 601–617 (2014). https://doi.org/10.1007/s40194-014-0137-8

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  • DOI: https://doi.org/10.1007/s40194-014-0137-8

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