PREDICTION OF THE FRICTION COEFFICIENT BASED ON THE HYSTERESIS VALUE OF SHOE SOLE RUBBER

This paper present research focused on the prediction of the friction coefficient of shoe sole rubber by utilizing its measured hysteresis values, along with other influencing factors such as hardness, tile surface roughness, sliding speed, and surface conditions. Previous authors research determined that rubber hysteresis is an important property of rubber (among other mechanical and physical properties) to consider when performing tribological research of contact between rubber soles and a hard substrate (tiles, laminate, vinyl, concrete). Data required for design and training of a neural network were gathered by friction coefficient testing conducted on a specially designed test apparatus. Additionally, rubber hysteresis data were obtained using a uniaxial tensile testing machine. Given the role of rubber hysteresis in determining its properties, this study identifies it as a parameter that influences the friction coefficient and aids friction coefficient prediction through artificial neural networks (ANN). The research results showed a high correlation between the friction coefficient values predicted by ANN and actual experimental results, confirming that designed ANN can be used to predict the values of friction coefficient when the rubber hysteresis value is known.