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The brake friction materials in an automotive brake system play important role in the overall braking performance of a vehicle. A previous study by the same authors was focused on wear testing for a 1040 steel disc interacting with Powder metallurgy (PM) copper-based brake lining material with and without MoS₂ additive at constant applied load and sliding velocity. In this paper, a non-Linear Autoregressive model (ARX) Model structure with sigmoid network having one hidden layer and nonlinear ANFIS (Adaptive Neuro-Fuzzy Inference System) model structure was used to find the best possible wear prediction results and both approaches have been applied to simulate wear behavior of the brake lining material. Preliminary results showed that ARX provides closer results to the experiments than the ANFIS model. As a result, nonlinear ARX modeling can be used as an effective tool in the prediction of brake lining material properties instead of time-consuming experimental processes.

Prediction of brake disc materials wear versus their formulation with brake operating conditions can play a critical role in the development of future brake disc materials. In this paper identification of the dry sliding wear behavior of magnesium (Mg) matrix (MMCs) reinforced with 0-3-6 wt % B4C particulates (B4Cp) was investigated. Wear tests were performed on a pin-on-disk configuration against SAE 1040 steel counter body under constant load and sliding speed. The wear resistance of composites was evaluated as a function of B4C particulates reinforcement. Identified models were based on experimental results. The wear load was considered as the input parameter, whereas the wear rate and friction of coefficient as the output parameter. A first order continuous-time linear model structure was chosen for the modeling. Simulations using the identified models were compared with experimental results and it was found that the modeling of wear process was satisfactory.