Fade performance prediction of automotive friction materials by means of artificial neural networks

Temperature sensitivity of friction materials has always been a critical aspect while ensuring their smooth and reliable functioning, and that sensitivity need to be constantly optimized. The performance of friction materials at elevated temperatures is defined by their fading performance. In this paper, possibilities for predicting the fading performance of the friction materials, regarding their formulation and manufacturing conditions, have been investigated by means of artificial neural networks. The neural modelling of the friction materials behaviour at elevated temperatures has been based on the two different training data sets regarding the number, type, and distribution of the stored data. The first training data set is consisted by 360 data related to cold, fading, and recovery performance. These data have been used for developing of the neural model for predicting not only the fading performance but also cold and recovery performance. The second training data set, consisted by 120 data, has been used for developing the neural model that is going to be only used for predicting the fading performance of the friction materials. In this paper, 18 neural networks have been trained by the 5 training algorithms. These networks have been tested by the testing data set formed using the parameters of formulating, manufacturing, and testing of the two friction materials which input parameters were completely unknown for the networks.