PREDICTING THE MICROHARDNESS OF ALUMINA-BASED CERAMICS USING MACHINE LEARNING METHODS
To mitigate the substantial labor, time, and material costs associated with laboratory testing, this study proposes predicting the microhardness of Al2O3-based ceramics using machine learning methods. A database was compiled from a comprehensive analysis of literature to predict the properties of alumina ceramics. The input variables include chemical composition, density, sintering temperature, and dwell time for alumina ceramics doped with ZrO2, ZrO2−Y2O3, CeO2, MgO, CaO, and SrO.