Hybrid Intelligent Modeling for Optimizing Welding Process Parameters for Reduced Activation Ferritic-Martensitic (RAFM) Steel

Abstract

<p class="MsoNormal"> <span lang="EN-US">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)</span><span lang="EN-US"> </span><span lang="EN-US">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.</span><span lang="EN-US"><o:p></o:p></span> </p

Dynamic Modeling of Weld Bead Geometry Features in Thick Plate GMAW Based on Machine Vision and Learning

“…In some studies, the prediction or optimization of WBGFs is the main objective using regression models between WBGFs and welding process parameters. In this case, offline modeling methods are presented via the metallurgical microscope [ 14 ], the caliper [ 15 ], the optical microscope [ 16 , 17 ], and the machinist’s microscope [ 18 ] in lap-joint GMAW. These studies are dependent on the visual ability of the evaluator, where the measurement process is conducted with human intervention [ 19 ].…”