Optimization of welding parameters for better tensile strength through Coupled Genetic Algorithm and Firefly Algorithm for AA7010-SiC-Al2O3 hybrid composite
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Friction stir welding (FSW) is a green manufacturing process that does not liberate smoke, fume and odour unlike the conventionalarc welding. This research article aims at finding the ultimate tensile strength of the Aluminium matrix compositeswelded by FSW with the process parameters such as tool rotation speed, weld traverse speed and axial force. The searchoptimization is carried out in two phases using MATLAB environment. Firstly, the regression equation of the experimentsis utilized to find the better design points by Genetic Algorithm (GA) through pool generation, cross-over and mutation. Secondly, the top design points obtained in GA are stored in a new pool, from which the global best optimal design is selectedby Firefly Algorithm (FA). Since, every algorithm has different features and highlights, the coupled GA-FA algorithm isutilized to obtain the optimal point that gives the best ultimate tensile strength of the welded composite. The results demonstratethat the optimal points are distributed in several points of design space that needs to be searched out by the effectiveoptimization strategy. The convergence rate, speed of the optimization and coverage of the design points are also improved. The algorithm shows good agreement with the confirmation tests also with errors less than 5%.
Abstract
Friction stir welding (FSW) is a green manufacturing process that does not liberate smoke, fume and odour unlike the conventionalarc welding. This research article aims at finding the ultimate tensile strength of the Aluminium matrix compositeswelded by FSW with the process parameters such as tool rotation speed, weld traverse speed and axial force. The searchoptimization is carried out in two phases using MATLAB environment. Firstly, the regression equation of the experimentsis utilized to find the better design points by Genetic Algorithm (GA) through pool generation, cross-over and mutation. Secondly, the top design points obtained in GA are stored in a new pool, from which the global best optimal design is selectedby Firefly Algorithm (FA). Since, every algorithm has different features and highlights, the coupled GA-FA algorithm isutilized to obtain the optimal point that gives the best ultimate tensile strength of the welded composite. The results demonstratethat the optimal points are distributed in several points of design space that needs to be searched out by the effectiveoptimization strategy. The convergence rate, speed of the optimization and coverage of the design points are also improved. The algorithm shows good agreement with the confirmation tests also with errors less than 5%.
참고문헌 (20)
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