ABSTRACT
Over the last few decades, numerous models and simulations have been introduced in an attempt to provide tribologists with an analysis and characterization of tribo-systems. This chapter, in this context, reports the implementation of diverse methods in predicting tribological behavior. The excellence of empirical equations and mathematical models depends on how well the theoretical bases agree with the results of repeatable experiments. More recently, finite element analysis (FE) has provided an effective method to simulate and predict tribological performance of tribo-systems. Dimensional analysis can be used to characterize a phenomenon in terms of the relationships among dimensionless variables. In particular, dimensional analysis of wear based on sliding parameters can be developed. Besides, Artificial Neural Networks (ANN) have the capability to simulate complex, nonlinear, multi-dimensional relationships, without any prior assumptions. Recently, this approach become a promising method for predicting tribological performance of tribo-systems. The potential of artificial neural network techniques to predict friction and wear behavior is highlighted.
