Reduced-activated ferritic-martensitic steels are being considered for use in fusion energy reactor and subsequent fusion power reactor applications. Typically, those reduced activated steels can loose their radioactivity in approximately 100 years, compared to thousands of years for the non-reduced-activated steels. The commonly used welding process for fabricating this steel are electron-beam welding, and tungsten inert gas (TIG) welding. Therefore, Activated-flux tungsten inert gas (A-TIG) welding, a variant of TIG welding has been developed in-house to increase the depth of penetration in single pass welding. In structural materials produced by A-TIG welding process, weld bead width, depth of penetration and heat affected zone (HAZ) width play an important role in determining in mechanical properties and also the performance of the weld joints during service. To obtain the desired weld bead geometry, HAZ width and make a good weld joint, it becomes important to set up the welding process parameters. The current work attempts to develop independent models correlating the welding process parameters like current, voltage and torch speed with weld bead shape will bead shape parameters like depth of penetration, bead width, HAZ width using ANFIS. These models will be used to evaluate the objective function in the genetic algorithm. Then genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

This study aims to reduce the number of weld passes and total heat input to 316LN stainless steel during hot-wire tungsten inert gas (HW-TIG) welding process by optimizing process parameters. Therefore, the response surface methodology (RSM) of design of experiments (DOE) approach has been adopted to optimize HW-TIG welding parameters and study the interaction between the parameters and responses. In order to minimize the total number of experimental runs, a design matrix was generated with 30 sets of process parameters. The bead-on plate welding experiments were carried out based on the above generated process parameters. Using the RSM, a quadratic model was developed based on a central composite design to establish the regression equations between input variables (welding current, wire feed current, welding speed, and wire feed rate) and responses (bead width, depth of penetration, and weld cross sectional area). The input process parameters and their responses were correlated with the regression model. Further, parameter values were optimized using the desirability approach and the optimized solutions were generated. Using the optimized process parameters, 316LN stainless steel weld joints were fabricated. Tensile testing and metallography samples were extracted from the fabricated weld joints. The results reveal that the optimization of process parameters by RSM based approach reduced the number of weld passes and improved the weld quality with lower heat input to 316LN stainless steel. In addition, the fabricated weld joint of 316LN stainless steel using the optimized process parameters exhibited better microstructure and strength properties.