<u> Studies On Modeling Of Bead Geometric Parameters In Welding Processes Using Design Of Experiments, Artificial Neural Network And Genetic Algorithm.:< /u> The properties of welded joints are affected by a large number of welding parameters. The physical geometry of the molten pool which determines the weld bead profile is a major factor in determining the structural adequacy of a fusion weld. The correlation between welding process variables and bead geometric parameters can provide important information for the quality definition of welds. Weld modeling is thus important for advancing knowledge on the mechanics of welding processes and for determining how best they can be controlled and utilized. In the published literature, fairly exhaustive experimental data are available on Shielded Metal Arc Welding (SMAW), Gas-shielded Metal Arc Welding (GMAW) and Gas Tungsten Arc Welding (GTAW) processes. For control of weld properties, use of mathematical tools are considered indispensable in the present age. To a limited extent references are available on mathematical modeling based on fractional factorial design, multiple regression analysis and artificial neural network applied to some welding processes.