Tire-Pavement Interaction Noise (TPIN) is the dominant noise source for passenger vehicles at speeds above 30 mph. Due to the multiple noise generation mechanisms involved, modelling TPIN becomes a complex task. A large amount of experimental data was collected (42 tires, and 26 different pavement surfaces). This data was used to develop two Artificial Neural Networks (ANN). Both were configured to predict only positive acoustic sound pressure values. The first ANN uses the tire tread pattern geometry as input to predict tread pattern related noise (TPN). The second ANN uses tread rubber hardness, tire size and vehicle speed to predict the noise component not related to the tread pattern (NTPN). TPN is predicted at a fixed vehicle speed (60 mph) and it is then scaled to other speeds using the tire size and a speed scaling law. On the other hand, NTPN is predicted first for a fixed tire size (215/60R16) and a reference pavement surface. This is also modified for other user-defined tire sizes and pavement surfaces. Finally, both ANNs were integrated into one TPIN prediction tool using MATLAB. Validation was performed using experimental data. The overall A-weighted sound pressure level (OASPL) error between measured and predicted total tire noise was 1.1 dBA.
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