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.

Understanding the intricate relationship between composition, processing conditions, and material properties is essential for optimizing Cu-based alloys. Machine learning offers a powerful tool for decoding these complex interactions, enabling more efficient alloy design. This work introduces a comprehensive machine learning framework aimed at accurately predicting key properties such as hardness and electrical conductivity of low-alloyed Cu-based alloys. By integrating various input parameters, including chemical composition and thermo-mechanical processing parameters, the study develops and validates multiple machine learning models, including Multi-Layer Perceptron with Production-Aware Deep Architecture (MLP-PADA), Deep Feedforward Network with Multi-Regularization Framework (DFF-MRF), Feedforward Network with Self-Adaptive Optimization (FFN-SAO), and Feedforward Network with Materials Mapping (FFN-TMM). On a held-out test set, DFF-MRF achieved the best generalization (R²_test = 0.9066; RMSE_test = 5.3644), followed by MLP-PADA (R²_test = 0.8953; RMSE_test = 5.7080) and FFN-TMM (R²_test = 0.8914; RMSE_test = 5.8126), with FFN-SAO slightly lower (R²_test = 0.8709). Additionally, a computational performance analysis was conducted to evaluate inference time, memory usage, energy consumption, and batch scalability across all models. Feature importance analysis was conducted, revealing that aging temperature, Cr, and aging duration were the most influential factors for hardness. In contrast, aging duration, aging temperature, solution treatment temperature, and Cu played key roles in electrical conductivity. The results demonstrate the effectiveness of these advanced machine learning models in predicting critical material properties, offering insightful advancements for materials science research. This study introduces the first controlled, statistically validated, multi-model benchmark that integrates composition and thermo-mechanical processing with deployment-grade profiling for property prediction of low-alloyed Cu alloys.