Efficient Prediction and Enhancement in Friction Wear Performance of Synthetic Brake Pads Using Machine Learning

To tackle traditional synthetic brake pads’ friction instability and performance degradation at high speeds, as well as the costly and time-consuming empirical formula optimization, a multi-stage synergistic optimization (MSSO) framework driven by two-stage machine learning is proposed in this study. The novelty lies in integrating Pearson correlation filtering with Gaussian noise for data enhancement, employing a hybrid sparrow search algorithm-gray neural network model for dataset expansion, and utilizing a red-billed blue magpie optimization-backpropagation neural network for high-precision multi-target prediction. Experimental verification shows that brake pads manufactured using the optimized formulations exhibit improved average friction coefficient and wear rate, with reduced error compared to traditional methods. The friction characterization results of composite brake pads show the features of optimized composite brake pads at the surface microscopic level. This provides an efficient solution for developing lightweight brake materials for high-speed trains.