Prediction of Tensile Strength of Friction Stir Welded A356 Cast Aluminium Alloy Using Response Surface Methodology and Artificial Neural Network

A356 is a high strength Aluminium-Silicon cast alloy used in food, chemical, marine, electrical and automotive industries. Fusion welding of these cast alloys will lead to many problems such as porosity, micro-fissuring, hot cracking etc. However, Friction Stir Welding (FSW) can be used to weld these cast alloys without the above-mentioned defects. The FSW process parameters such as tool rotational speed, welding speed, axial force etc., play a major role in deciding the weld quality. The experiments were conducted based on three factors, three-level, and central composite face centered design with full replications technique and models were developed to predict the tensile strength of A356 alloy using, response surface methodology and artificial neural network (ANN). The results obtained through RSM were compared with ANN. It is found that the error rate predicted by the artificial network is smaller than predicted by the response surface methodology.

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