Wear Characterization and Coefficient of Friction Prediction Using a Convolutional Neural Network Model for Chromium-Coated SnSb11Cu6 Alloy

Enhancing the durability and tribological performance of babbitt alloys is critical for high-stress applications in automotive, marine, and industrial machinery. The present study explores the electrodeposition of chromium coatings on SnSb11Cu6 alloys to improve their microstructural, mechanical, and tribological properties. The coatings were applied through an electrolytic process and systematically characterized using scanning electron microscopy and energy-dispersive X-ray spectroscopy to evaluate their morphology, composition, and wear performance. The chromium coating exhibited a uniform thickness of 20.2 µm and significantly improved the surface hardness to 715.2 HV, far surpassing the matrix and intermetallic phases of the uncoated alloy. Tribological testing under dry sliding conditions demonstrated a 44% reduction in the coefficient of friction (COF) and a 54% decrease in mass wear for the coated alloy, highlighting the protective role of the chromium layer against abrasive and adhesive wear. To further analyze the frictional behavior, a deep learning model based on a one-dimensional convolutional neural network was employed to predict COF trends over time, achieving excellent accuracy with R2 values of 0.9971 for validation and 0.9968 for testing. Feature importance analysis identified coating hardness as the most critical factor influencing COF and wear resistance, followed by matrix hardness near the coating. These findings underscore the effectiveness of chromium coatings in mitigating wear damage and improving the operational lifespan of SnSb11Cu6 alloys in high-stress applications. This study not only advances the understanding of chromium coatings for babbitt materials but also demonstrates the potential of machine learning in optimizing tribological performance.