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

Nanocomposites containing ZrO2 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/ZrO2 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/ZrO2 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 R2 = 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/ZrO2 nanocomposites, accurately. © 2022 Elsevier B.V.