Multi Objective Optimization Of Fsw Process Parameters Using Genetic Algorithm And Tlbo Algorithm

AA2014 has been extensively used in manufacture of light weight fabricated
components similar to commercial automobile components, which requires high strength with minimal weight and along with decent corrosion effect. The traditional welding of this Aluminium alloyed materials generally encounter solidification
problems like hot cracking. Friction Stir Welding (FSW) is an ecofriendly joining process where in the actual melting of material and recasting will not happen. Many of the researchers carried out sufficient experiments for optimizing process
parameters and to establish empirical relationships in order to predict better mechanical properties. In the present investigation, a comparative study of FSW between experimentation and optimization of process parameters such as tool rotation speed and weld speed, to attain maximum mechanical properties using Genetic Algorithm (GA) and Teaching Learning Based Optimization (TLBO) algorithm.
From the results it shows that the TLBO gives the better combinations of process parameters which give superior mechanical properties compared to experimental results as well as other optimization techniques.