These active systems should find value from deeper analyses of the dry friction properties of the interfacial materials and the consequent inference on the dynamic behaviour of braking systems and their transmission arising from many factors such as applied load, sliding velocity, interface temperature and other material properties.

This paper introduces the experimental outcomes concerning the frictional performance of typical material for brake and clutch facings. These results have been carried out through a tribometer setup using a pin-on-disk sliding contact along a series of short time experiments for simulating operating conditions at different levels of loading, slip speed and its time-derivative, the sliding acceleration. Infrared sensors and thermocouples have been used for interface temperature measurement.

Moreover, in this research the use of an artificial neural network was necessary to overcome the limits of the experimental setup speed tracking; interesting applications of the neural network have been already presented in the tribological area [3,4].

The main conclusions could be stated as follows: after a first phase at low speeds, the friction coefficient, for both the materials, is essentially constant at a level which depends on the sliding acceleration and contact pressure. In particular, increasing both the acceleration and pressure so the level of the coefficient of friction increases; the dependence of friction on the sliding acceleration, shown in this work, can be a good starting point for improving the performance of active control systems for brakes and clutches.

The analysis also shows that artificial neural networks can be used for modelling of complex nonlinear brake-clutch performance influenced by sliding speed, sliding acceleration and contact pressure and can be easily extended to the analysis of material composition.

1

S.-H. You, J.-S. Jo, S. Yoo, J.-O. Hahn, K.I. Lee, "Vehicle lateral stability management using gain scheduled robust control", J. Mechanical Science and Technology, 20(11), 1898-1913, 2006. doi:10.1007/BF03027583

2

M. Kulkarni, T. Shim, Y. Zhang, "Shift dynamics and control of dual-clutch transmissions", Mech. Mach. Theory, 42, 168-182, 2007. doi:10.1016/j.mechmachtheory.2006.03.002

3

A. Aleksendrick, D.C. Barton, "Neural network prediction of disc brake performance", Tribology Int., 42, 1074-1080, 2009. doi:10.1016/j.triboint.2009.03.005

4

V. D'Agostino, A. Senatore, S. Ciortan, "Neural networks based behaviour prediction for dry clutch materials", DIPRE 2009 Conf., Galati, RO, 2009.

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