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Estimation of the heat flux parameters during a static Gas Tungsten Arc Welding experiment

Rouquette, S (University of Montpellier 2)
Friday 16 December 2011, 10:30-11:00

Seminar Room 1, Newton Institute


Gas Tungsten Arc (GTA) welding process is mainly used for assembly metallic structures which require high level safety (so excellent joint quality). This welding process is based on electrical arc created between a tungsten electrode and the base metal (work-pieces to assemble). An inert gaseous flow (argon or/and helium) shields the tungsten electrode and the molten metal against the oxidation. The energy required for melting the base metal is brought from the heat generated by the electrical arc.GTAW process involves a combination of physical phenomena: heat transfer, fluid flow, self-induced electromagnetic force. Mechanisms involved in the weld pool formation and geometry are surface tension, impigning arc pressure, buoyancy force and Lorentz force. . It is well known that for welding intensities inferior to 200A, GTAW phenomena are well described with a heat transfer - fluid flow modelling and the Marangoni force on the weld pool. The knowledge of the heat flux, at the a rc plasma work-piece interface, is one of the key parameter for establishing a predictive multiphysics GTAW simulation.

In this work, we investigated the estimation of the heat source by an inverse technique with a heat transfer and fluid model modelling for the GTAW process. The heat source is described with a Gaussian function involving two parameters: process efficiency and Gaussian radius. These two parameters are not known accurately and they require to be estimated. So an inverse technique regularized with the Levenberg-Marquardt Algorithm (LMA) is employed for the estimation of these two parameters. All the stages of the LMA are described. A sensitivity analysis has been done in order to determine if the thermal data and thermocouple locations are relevant for making the estimation of the two parameters simultaneously. The linear dependence between the two estimated parameters is studied. Then the sensitivity matrix is build and the IHFP is solved. The robustness of the stated IHFP is investigated through few numerical cases. Lastly, the IHFP is solved with experimental thermal data an d the results are discussed.


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