Mechano-Tribological Property Forecasts in AL-BN Metal-Ceramic Composites Driven by Artificial Intelligence

The work uses artificial intelligence and machine learning to anticipate and optimize the mechano-tribological characteristics of Al-Boron Nitride, a metal-ceramic composite. Conventional experimental approaches are sometimes laborious and time-consuming. AI predictive applications may assist minimizes prolonged trial exercises and provides alternate approaches for estimating the mechano-tribological characteristics of these composites. Thus, regression analysis, artificial neural networks, and deep learning may be used to assess the effect of these variables on composite wear rate, friction coefficient, and hardness. It also analyzes micro-structural pictures with CNNs to detect crucial tribological traits. The proposed system improves prediction accuracy and cost effectiveness, establishing AI-driven material design as a viable choice for industrial applications