GENERALIZATION OF MODELS OF GRINDING ALUMINUM OXIDE AND SILICON CARBIDE BY METHODS OF MACHINE LEARNING
A kinetic model of grinding aluminum oxide and silicon carbide based on experimental data has been developed. The resulting model iteratively calculates each step in time of the grinding process, which reduces efficiency. Therefore, a module including a kinetic model was used to generate a training set for machine learning algorithms. Based on the obtained sample, a fast model was trained predicting the time of particle grinding from the initial to the required final size.