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Heat source parameters have a greater influence on the accuracy of numerical modelling for predicting residual stress and temperature field. Experimental measurements of stress and temperature during arc welding are cumbersome due to dynamic transfer of heat happening in a very short span of time. So, for modelling such a high temperature process, determining the heat source model parameters are critical. In this article, a novel method for figuring out the double ellipsoid heat distribution model’s heat source parameters is demonstrated. Here, finite element analysis (FEA) is done to predict the weld bead dimensions, thermal and structural cycles of tungsten inert gas (TIG) welding of AISI S304 stainless steel plates. 25 different sets of heat source parameters are generated for 100 and 120 A input power separately. Using this generated values, weld bead dimensions are determined from the simulation. The optimization is done with the Taguchi technique taking root mean square error (RMSE) value of heat source parameters and measured weld bead dimensions as response parameters. The model is validated using experimental data and the effects of each parameter on weld pool formation during TIG welding are also studied. Optimum values of heat source parameters for stainless steel AISI 304 at 100 A welding current are 2.3283, 2.3687 and 2.667, respectively, and that for 120 A weld current are 2.5909, 2.613 and 3.4949, respectively. The prediction of temperature and welding residual stress (WRS) distribution using optimizing heat source model parameters shows closer approximation with experimental results. The demonstrated model is very much reliable and simple to predict the heat source parameters for TIG welding with partial penetration with a very lesser number of operations and minimum error.

Estimation of heat source model parameters for partial penetration of TIG welding using numerical optimization method