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The neural computation ability to model complex non-linear relationships directly from experimental data, without any prior assumptions about nature of the input/output relationships, has been used in this paper. An artificial neural network technique was used to develop a neural model for predicting the friction materials behavior under prescribed testing conditions. By means of neural modeling of the friction materials behavior, the relationship between 26 input parameters and one output parameter (brake factor C) has been established. The input parameters are defined by the friction material formulation (18 parameters), manufacturing conditions (5 parameters), and testing conditions (3 parameters). Prediction abilities of the neural model have been evaluated by comparison the real cold performance obtained during friction material testing on the single end full-scale inertia dynamometer and predicted ones.

The demands imposed on a braking system of passenger cars, under wide range of operating conditions, are high and manifold. Improvement of automotive braking systems' performance, under different operating conditions, is complicated by the fact that braking process has stochastic nature. The automotive brake's performance primarily affected the braking system's performance because their performance results from the complex interrelated phenomena occurring in the contact of the friction pair. These complex braking phenomena are mostly affected by the physicochemical properties of friction materials ingredients, its manufacturing conditions, and brake's operation regimes. Analytical models of brakes performance are difficult, even impossible to obtain due to complex and highly nonlinear phenomena involved during braking. That is why, in this paper all relevant influences on the disc brake operation of a passenger car have been integrated by means of artificial neural networks.

Prediction of the brake friction material performance versus changes of its composition, manufacturing, and operation conditions is considered as an important step in the friction materials development. Due to complex synergy effects of these influencing factors on the friction coefficient stability, an analytical model of the brake friction materials performance is difficult to obtain. That is why in this paper artificial neural networks have been used for modelling and predicting the effects of these influencing factors on the brake friction materials speed sensitivity. A two hidden-layer neural network model, trained by the Bayesian Regulation algorithm, has been developed with inherent abilities to generalize the complex influences on the speed sensitivity performance of the brake friction materials.

Wear of brake friction materials were found to be a complex combination of abrasion, adhesion, fatigue, delamination, and thermal decomposition. Stochastic nature of wear of brake friction materials is result of these wear mechanisms and their transition from one combination to another. The dominant wear mechanism of brake friction materials is influenced by braking regimes and friction material characteristics. Regarding friction material characteristics, the most important influences are related to its formulation and manufacturing conditions. Prediction of friction materials wear versus their manufacturing conditions can be considered as an important issue for further friction materials development.

Monitoring, modeling, prediction, and control of the braking process is a difficult task due to a complex interaction between the brake contact surfaces (disc pads and brake disc). It is caused by different influences of braking regimes and brake operation conditions on its performance. Faster and better control of the braking process is extremely important in order to provide harmonization of the generated braking torque with the tire-road adhesion conditions. It has significant influence on the stopping distance. The control of the braking process should be based on monitoring of the previous and current values of parameters that have influence on the brake performance. Primarily, it is related to the disc brake actuation pressure, the vehicle speed, and the brake interface temperature. The functional relationship between braking regimes and braking torque has to be established and continuously adapted according to the change of mentioned influencing factors.