Artificial Neural Network-Based Methodology for Tribological Evaluation and Optimization of PEEK Composites

Polymer-based composites are increasingly employed in tribological applications due to their lightweight nature, chemical inertness, and ease of modification compared with conventional metallic materials. However, optimizing their performance through empirical and analytical approaches remains time-consuming and resource-intensive. In this study, three artificial neural networks (ANNs) were systematically integrated into a unified tribological workflow for polyetheretherketone (PEEK)-based composites, encompassing automated data processing, quantitative analysis of worn surface morphology, and prediction of friction and wear behavior across multi-dimensional compositional spaces. The Long Short-Term Memory (LSTM) network reduced data-processing time by more than 95% compared with manual analysis, while the U-Net segmentation model achieved a third-body patch area estimation error of only 7.2% and accelerated image analysis by a factor of approximately 50000. The Feedforward Backpropagation Neural Network (FBNN) captured the complex nonlinear relationships between filler composition, test conditions, and tribological performance, enabling the prediction of the tribological behavior of more than 23000 potential composite formulations. Based on these predictions, a cost–performance evaluation framework was established to enable the efficient design of new tribomaterials. These results demonstrate the potential of ANN-based methods to accelerate the research and design of high-performance tribological composites and to support their targeted optimization for industrial applications.