| Summary: | Facing the issue of cost and efficiency in experiments and tests in determining the properties of the welded structure is a challenge in friction stir spot welding (FSSW) optimization. Employing the machine learning technique of artificial neural network (ANNs) to develop a prediction model with fewer experiments and tests is a gentle solution to forecast the properties of the spot weld structures. In this study, the extended full factorial design with respect to thetool speed, plunge depth, anddwell time are applied to the FSSW specimens of aluminium A5052-H122 of 2mm thick through 27 experiments and evaluated via tensile shearing load testing. The multilayer neural network of feed-forward and back-propagation (FFBP) algorithm was engaged to learn and train the neural network iteratively with a set of weights and bias of 27 variations of inputs to fit the predicted tensile shear loads of the spot weld structures. Based on the resulted of regression plot, it is shown that the correlation coefficient (R) is perfect for training with the value of 0.999 and for testing the correlation coefficient (R) is reached to 0.958. However, the correlation coefficient is relatively good for validation with R equal to 0.921. For all data sets, the correlation coefficient is good with R of 0.833. It can be seen that the ANNs prediction model is relatively good since the correlation coefficient relatively close to 1. © 2020 Published under licence by IOP Publishing Ltd.
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