Study and Optimization of Friction Stir Welding (FSW) Process by using the Genetic Algorithms (GA)

Friction stir processing (FSP) has recently become an effective micro structural modifications technique. Reported results showed that for different alloys, Friction stir processing (FSP) produces very fine equated and homogeneous grain structure. This study focused on the optimization of friction stir welding (FSW) process for an optimal parametric combination to yield favorable tensile strength and elongation using the Genetic algorithms (GA). The objective functions have been selected in relation to parameters of FSW parameters; rotating speed, welding speed and tool shoulder diameter. The experiments were planned using Genetic algorithms L8 orthogonal array. Optimization was applied using Genetic algorithms and optimization approach is used to solve the problem. Genetic Algorithms (GAs) that are related to the stochastic optimization techniques which have been used with a very success in solving problems. The genetic engineering models were originally developed by Genetic Algorithms (GAs) and evolution in natural systems. The "survival-of the-fittest" and "genetic propagation of characteristics" are the principles of biological evolutions and uses for finding the solution of an optimization problem enforces by Genetic Algorithms (Gas)The significance of the factors on overall quality characteristics of the welding process has also been evaluated quantitatively by the Genetic algorithms method. Optimization results have been verified through confirmation experiments. GA coding is develop from the MATLAB, similarly to other method, can be used to improve optimization of the manufacturing process