Machine Learning in Prediction of Vickers Hardness for Fe-Cu-HA Composite

Abstract

The paper studies the effectiveness of machine learning techniques in predicting the microhardness of composite materials manufactured from a mixture of iron-copper (Fe-Cu) nanopowders and hydroxyapatite (HA) particles. It is shown that the different proportion of Fe-Cu and HA powders and the polymer fraction in the composite significantly affect its hardness. A surrogate model based on the artificial neural network (ANN) and the analytical model, is proposed to quantitatively evaluate the microhardness, depending on the powder/polymer ratio. The ANN models show the probability distribution of indentation values after microhardness testing, which is the input data for the analytical model to compute the hardness. This approach can reduce the time and cost of the material research and minimizes the reliance on expensive materials and experimental equipment.