Evolving application of machine learning in the synthesis of CHA/ZrO2 nanocomposite for the microhardness prediction

Abstract

• An efficient approach of machine learning in the field of materials science. • Prediction of CHA/ZrO 2 nanocomposites microhardness using of LS-SVM and GWOA. • High-precision models (R 2 = 0.9986) were presented using the machine learning. • Variables that affect the microhardness of CHA/ZrO 2 nanocomposites are identified. Nanocomposites containing ZrO 2 and HA have been considered in various fields due to their unique mechanical properties. The principal purpose of this paper is to select the models with the maximum accuracy for the prediction of microhardness of CHA/ZrO 2 nanocomposite. For this purpose, three models, including gene expression programming (GEP), gray wolf optimization algorithm (GWOA), and least squares support vector machine (LS-SVM), were implemented to predict and optimize the microhardness of the CHA/ZrO 2 nanocomposite. Finally, the results showed that the data obtained from the LS-SVM model were closer to the preliminary data than the others. According to the results, the LS-SVM could predict the microhardness by R 2 = 0.9986, MSE = 0.0086, MAPE = 4.3, MSE = 0.018, and RRSE = 0.0143. Therefore, it seems that the LS-SVM algorithm and preliminary data are completely utilitarian to predict the microhardness of CHA/ZrO 2 nanocomposites, accurately.