Mechanical properties estimation of multi-layer friction stir plug welded aluminium plates using time-series neural network models

Abstract

Amulti-layer friction stir plug welding can be used to fix the thick aluminium plates. Optical microscopy and tensile tests are utilized to study the microstructural and mechanical characteristics of the welded aluminium plates. However, finding the relation between the indexes of the process and the mechanical properties would be challenging. The present work aims to devise a time-series machine learning model including a recurrent neural network (RNN) and nonlinear autoregressive network with the external state (NARX) to estimate the mechanical properties of the repaired aluminium plate using the force-extension plot. The ultimate tensile strength, yield strength, impact energy and elongation of the repaired aluminium plate can be calculated based on a force-extension plot trained and extracted using the developed networks. In addition, the Bayesian technique is employed to recalculate the optimal hyperparameters of RNN and NARX, targeting the lowest root mean square error (RMSE) between the target and the estimated force during the testing. The investigated methods (RNN and NARX) with the addition of classical estimation methods, including decision tree and support vector regression, are modelled in MATLAB, and the outcomes prove the proposed NARX model efficiency in terms of lower RMSE in comparison with support vector regression, decision tree and RNN.