Viscosity and composition prediction of B2O3-SiO2-based glass lubricants using machine learning

Key Points

• Viscosity and composition of B2O3-SiO2-based glass lubricants are predicted using machine learning techniques, revealing substantial correlations.
• Key evidence shows significant accuracy in predictions, enhancing the performance of lubricants in industrial applications.
• The approach employs predictive modeling applied to material compositions and viscosity measures to inform further research.
• This highlights the potential for improving lubricant formulations, but further validation in real-world conditions is needed.