Hybrid neural network‐based prediction model for tribological properties of polyamide6‐based friction materials

The hybrid neural network employed to predict the tribological properties of solid lubricants reinforced nano‐TiO 2 /polyamide6 composites was established based on the back propagation and radial basis function networks, and optimized by adaptive genetic algorithm. With such three factors as material composition, testing loads, and velocities, orthogonal tests were designed and the data obtained was used for training the neural network. The correlation index between the predicted and the experimental values for friction coefficient and ware rate were 0.992 and 0.998, respectively, and 3D plots for the predicted friction coefficient and wear rate as a function of material compositions and testing conditions were established. It shows that the results are in good agreement with the data measured. It is demonstrated that the well‐optimized neural network had remarkable capability for modeling concern. POLYM. COMPOS., 38:1705–1711, 2017.