Friction stir welding (FSW) is a relatively new welding process that may have significant advantages compared to the fusion processes as follows: joining of conventionally non-fusion weldable alloys, reduced distortion and improved mechanical properties of weldable alloys joints due to the pure solid-state joining of metals. This work presents a systematic approach to develop the mathematical model by three methods such as artificial neural networks using software, Response surface methodology (RSM) and regression Analysis for predicting the ultimate tensile strength, percentage of elongation and hardness of 6061 aluminum alloy which is widely used in automotive, aircraft and defense Industries by incorporating (FSW) friction stir welding process parameter such as tool rotational speed, welding speed and material thickness. The results obtained through regression analysis and response surface methodology were compared with those through artificial neural networks
Keywords: Aluminum pipe, Artificial Neural Network, Friction stir welding, Regression Analysis, Response surface methodology
