Modeling the Mechanical Properties of Heat-Treated Mg-Zn-RE-Zr-Ca-Sr Alloys with the Artificial Neural Network and the Regression Model

Abstract

In this study, an artificial neural network approach and a regression model are adopted to predict the mechanical properties of heat-treated Mg-Zn-RE-Zr-Ca-Sr magnesium alloys. The dataset for artificial neural network (ANN) modeling is generated by investigating the microhardness of heat-treated Mg-Zn-RE-Zr-Ca-Sr alloys using Vickers hardness tests. A back-propagation (BP) neural network is established using experimental data that enable the prediction of mechanical properties as a function of the composition and heat treatment process. The input variables for the BP network model are Ca and Sr contents, ageing temperature and ageing time. The output variable corresponds to the microhardness. The optimal BP network model is acquired by optimizing the number of the hidden layer nodes. The results indicate that a reliable correlation coefficient is above 0.95 for architecture (4-8-1), which has a high level of accuracy for prediction. In addition, a second-order polynomial regression model is developed based on the least squares method. The results of determination coefficients and Fisher’s criterion indicate that the regression model is capable of modeling mechanical properties as a function of composition and the ageing process. Furthermore, supplemental experiments are conducted to check the accuracy of the BP model and the regression model, suggesting that the model predictions are well in accordance with experimental results. Therefore, both the BP network and regression models have high accuracy in modeling and predicting mechanical properties of heat-treated Mg-Zn-RE-Zr-Ca-Sr alloys.