Deep Learning-Enhanced Wear Analysis of Fe-Cr Composite Coatings for Automotive Brake Applications under Euro 7 Requirements

The increasing transition to electric vehicles and new Euro 7 standards has intensified the focus on brake-derived particulate emissions. Current braking systems exhibit significant susceptibility to corrosion with an accelerated wear rate. This susceptibility highlights the need for materials that can maintain stable friction coefficients under various operating conditions. Therefore, this study investigated the application of Fe-Cr-based composite coatings on gray cast iron substrates for brake-disc applications, while also developing an automated wear pattern recognition methodology. High-velocity oxygen fuel-sprayed Fe-Cr-based composite coatings were evaluated via ball-on-disk tests against Si₃N₄ and WC counterfaces under standardized conditions (load: 20 N, sliding distance: 1000 m, speed: 100 RPM). Microstructural analysis revealed that the composite coatings achieved significantly enhanced mechanical properties (hardness: 6.52–7.51 GPa) compared to the gray cast iron substrate (1.98 GPa). Tribological testing demonstrated superior wear resistance, with Composite B exhibiting a specific wear rate of 7.37 × 10⁻⁶ m³ N⁻¹ m⁻¹, representing a three-fold improvement over that of conventional materials. Energy-dispersive X-ray spectroscopy analysis identified two distinct tribochemical interactions: SiO₂ and WO₂ formation with the Si₃N₄ and WC counterfaces, respectively. A deep ensemble convolutional neural network, trained on 500 scanning electron microscopy images, achieved a superior classification performance (training: 0.947, validation: 1.000, test: 0.960 accuracy) in automated wear pattern recognition. This integrated approach, which combines materials science with machine learning, provides an effective methodology for both material development and automated wear analysis in tribological applications.