Friction materials development by using DOE/RSM and artificial neural network

A group of non-asbestos organic based friction materials containing 16 ingredients were investigated in this work using the techniques of design of experiment (2k DOE), response surface methodology (RSM), and artificial neural network (ANN). The ingredients effects on three friction characteristics including 1st fading rate, 2nd fading rate, and speed sensitivity were studied by 2k DOE. Five ingredients of phenolic resin, synthetic graphite, potassium titanate, mineral fiber, and calcium silicate were found to be statistically significant for these responses and should be studied further. In the meantime, an artificial neural network with Elman recurrent configuration was trained and tested using the data generated from dynamometer tests in 2k DOE experiments. Concerning the confounding of two-ingredient interaction effects and main effects, response surface methodology was employed to optimize the friction material formulation. The well trained and tested Elman artificial neural network was then used to predict the friction characteristics of the trials generated by RSM. Based on the ANN prediction and RSM analysis, an optimization of material formulation was obtained and validated by experiments.