Prediction of ultimate tensile strength of friction stir welding joint using deep learning-based-multilayer perceptron and long short term memory networks

Abstract

Friction stir welding (FSW) is a solid-state joining technique where the joint strength is mainly influenced by three process parameters, namely, spindle speed (N), welding speed (V), and plunge force (Fz). The modelling of complex relationships between the process parameters and joint strength requires many experiments, which is a challenging, time-consuming, and non-economical affair. To tackle this problem, computational mathematical models such as deep learning (DL) can be employed to predict the joint strength reliably. In this paper, DL techniques, namely, deep multilayer perceptron (DMLP) and long short-term memory (LSTM) networks have been proposed for such a purpose. The DL networks were first trained with the FSW experimental data and then, the pre-trained models were used for predicting the weld strength. It was found that the DMLP and LSTM models provided lower prediction errors, which are RMSE of 3.30 and 7.63, respectively, and can be effectively utilized for determining weld quality. The proposed DL-based techniques were further compared with the traditional models – the shallow artificial neural network (SANN) model having an RMSE of 27.11 and the ANFIS model having an RMSE of 5.31. DMLP was found to be superior in determining the weld strength most accurately.

Keywords:

• Aluminium alloy
• deep learning
• deep multilayer perceptron
• friction stir welding
• long-short-term memory networks

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from Elsevier. Restrictions apply to the availability of these data, which were used under license for this study. Data are available at https://www.sciencedirect.com/science/article/abs/pii/S0264127515308650 with the permission of Elsevier.