Search Results

Aluminum metal matrix composites reinforced with particulates have attracted much attention in the automotive industry, due to their improved wear resistance in comparison to aluminum alloys, in recent years. The wear behavior is the critical factor influencing the product life and performance in engineering components. Carbon nanotubes (CNT) are one of the most promising candidates of reinforcements used to improve mechanical strength such as wear in metal matrix composites (MMCs). However, in industrial applications, wear tests are relatively expensive and prolonged. As a result, for several years, research has been increasingly concentrated on development of wear prediction models. In this study, prediction of wear behavior of aluminum (Al) matrix (MMCs) reinforced with different amounts (0, 0.5, 1 and 2 wt%) of CNTs was investigated. A nonlinear autoregressive exogenous (NARX) model structure was chosen for the modeling.

The brake friction materials in an automotive brake system play an important role in the overall braking performance of a vehicle. Metal Matrix Composites (MMCs) have been widely investigated and applied due to their advantages of improved strength, stiffness and increased wear resistance over the monolithic alloys in automobile industries. In this paper, Al/B4CP and Mg/B4CP composites were compared to find a suitable candidate material for automotive disk brake application, in terms of wear behavior results of the materials. In addition, the experimental data was also used to model this behavior by identification. The measured tangential force was considered as the input parameter, whereas the weight loss as the output parameter. Preliminary results of this work showed that B4CP addition improved wear resistance of both aluminum and magnesium matrix composites. Additionally, the study pointed out that identified models provide a reliable and cost effective tool for wear prediction.

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.