Sustainable Manufacturing and Foundry Practices
1Department of Mechanical Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, India
2Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
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Reinforcement content and sliding duration strongly influence the wear behaviour of aluminium (Al)-based hybrid composites, and evaluating these parameters experimentally remains time-consuming and resource-challenging. This study presents an extensive machine-learning framework to forecast the wear depth of Al-graphene nanoplatelet (GnP)-hexagonal boron nitride (hBN) hybrid nanocomposites with a data set of 3,016 experimentally obtained measurements, with wt% reinforcement and sliding time as inputs. Five supervised regression models (Linear Regression, Support Vector Regression, Multi-Layer Perceptron, Random Forest and Extreme Gradient Boosting) were trained and evaluated with the performance metrics of R2, root mean square error and mean squared error. The minimal prediction error with the highest accuracy (R2 ≈ 1.000) was obtained with the Random Forest model, exceeding both linear and kernel-based approaches capable of describing the non-linear wear responses shown by Al hybrid composites. SHAP analysis indicated that sliding time is the dominant factor governing wear depth, while reinforcement wt% contributes secondary but meaningful effects. The projected machine learning-driven method delivers a fast, dependable and characterisation-free method for expecting wear behaviour and delivers interpretable insights into parameter influence, offering a valuable tool for the design and optimisation of Al hybrid nanocomposites for advanced engineering applications.
Aluminium hybrid composites, GnP–hBN reinforcement, machine learning regression, wear depth modelling, explainable AI (SHAP)
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