Tribological Behaviour Analysis of Elastomer-Plastomer Couples in Dry Friction Conditions

Abstract

For couples working in dry friction conditions, the friction force is an important parameter, influencing both energy losses and wear of the components. In the elastomer-plastomer couples' case, the minimization of the friction force is a must, otherwise, the parts show a quick degradation. As previous studies have shown, there is the possibility to lower the friction force by choosing an optimal set of materials and functioning conditions, leading to a transferred material layer appearance. This layer stands as a lubricant, offering a prolonged life for the parts in contact. Taking into account that the relationship between materials and working conditions cannot be framed into a mathematical equation, artificial neural networks (ANN) are a valid alternative. The present work proposes a neural network model for friction force value evolution, in the case of pneumatic cylinders with rods made by plastomer and sealed with elastomeric gaskets. The model allows not only the identification of the most influencing parameters on the friction process both also the prediction and the optimization of friction force value.