Application of artificial neural network to predict Vickers microhardness of AA6061 friction stir welded sheets

Abstract

The application of friction stir welding (FSW) is growing owing to the omission of difficulties in traditional welding processes. In the current investigation, artificial neural network (ANN) technique was employed to predict the microhardness of AA6061 friction stir welded plates. Specimens were welded employing triangular and tapered cylindrical pins. The effects of thread and conical shoulder of each pin profile on the microhardness of welded zone were studied using tow ANNs through the different distances from weld centerline. It is observed that using conical shoulder tools enhances the quality of welded area. Besides, in both pin profiles threaded pins and conical shoulders increase yield strength and ultimate tensile strength. Mean absolute percentage error (MAPE) for train and test data sets did not exceed 5.4% and 7.48%, respectively. Considering the accurate results and acceptable errors in the models’ responses, the ANN method can be used to economize material and time.

Keywords

friction stir welding / artificial neural network / aluminum 6061 alloy / Vickers microhardness

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