Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters |
| |
Authors: | K N Gowtham M Vasudevan V Maduraimuthu T Jayakumar |
| |
Affiliation: | (1) Department of Metallurgical Engineering, PSG College of Technology, Coimbatore, 641 004, Tamil Nadu, India;(2) Advanced Welding Processes and Modeling, Materials Technology Division, Indira Gandhi Centre for Atomic Research, Kalpakkam, 603 102, Tamil Nadu, India;(3) Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam, 603 102, Tamil Nadu, India; |
| |
Abstract: | Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally,
tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during
autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed
in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld
bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical
properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ
width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is
used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with
weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine
the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|