Prediction Modeling of Friction Behavior of Aluminum Matrix Composites Using Neural Network and Genetic Algorithms under High Velocity and Dry Sliding Condition
铝基复合材料高速干摩擦行为的遗传神经网络预测模型

Ever increasing application of discontinuous reinforced aluminum(DRA) composites in braking materials arises from their specific properties.However,it is difficult to describe exactly friction behavior of such composites for reasonable selecting.In the present study,using genetic algorithms and radius basis function neural network(GARBF),prediction modeling of friction behavior was established based on a measured database for DRA composites under high velocity and dry sliding condition.Friction tests with pin-ondisc arrangement had carried out at five sliding velocities(40,55,70,85,and 100 m/s) and four different nominal pressures(0.1333,0.4667,0.60,and 0.7333 MPa).Modeling results confirm the feasibility of GARBF network and its good correlation with experiment results.Using GARBF modeling data to predict analysis,results show that friction coefficients of composites increased with increasing stored heat capability.It is proposed that a well-trained GARBF modeling is expected to be very helpful for selecting composite materials under different working conditions,for prediction dynamic tribological properties.