Lam Suvarna Raju,Venu Borigorla,



FSW,Process Parameters, Mechanical Properties,Genetic Algorithm,TLBO,


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 thisAluminium 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.


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