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AA4032-TiC-h-BN-related composites: a machine learning model-based experimental study with performance prediction

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Abstract

This study attempts to investigate the geometrical tolerance of AA4032-based metal matrix composites (MMC) machined using EDM. Two variants of AA4032-based composites are created with titanium carbide (TiC) and boron nitride (BN) using stir casting method. Before machining through EDM, the composites are examined for mechanical properties. Eighty-one experiments are designed and conducted to explore the geometrical tolerance recorded during EDM under the considered operating conditions. From the experiments, it is inferred that adding TiC particles to AA4032 base composites increases the tensile and hardness. At the same time, geometric tolerance decreases with the addition of TiC wt% and increases with the BN wt% towards the base composites. As a secondary objective, this study designed and developed two neural network-based machine learning models to predict the geometric tolerance recorded by the real-time EDM. For this, current, pulse on time, and gap voltage are considered as input. Circularity, cylindricity, perpendicularity and overcut are considered as the outputs. Both the proposed models recorded an overall prediction accuracy of around 99%. To ensure the results predicted by NN models, validation experiments are conducted and compared with the model-predicted results. The results of validation experiments are in line with the results predicted by the model.

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Authors and Affiliations

  1. Department of Mechanical Engineering, K. Ramakrishnan College of Technology, Tiruchirappalli, Tamil Nadu, 621 112, India

    T. S. Senthilkumar & V. Vignesh Kumar

  2. Department of Mechanical Engineering, Sethu Institute of Technology, Pullor, Tamil Nadu, 626 115, India

    R. Muralikannan

  3. Department of Mechanical Engineering, SRM Institute of Science and Technology, Tiruchirappalli Campus, Tiruchirappalli, Tamil Nadu, 621 105, India

    M. Sridharan & S. Senthil Kumar

  4. Department of Aeronautical Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu, 641 105, India

    S. Rathinavel

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Senthilkumar, T.S., Muralikannan, R., Sridharan, M. et al. AA4032-TiC-h-BN-related composites: a machine learning model-based experimental study with performance prediction. J Braz. Soc. Mech. Sci. Eng. 46, 30 (2024). https://doi.org/10.1007/s40430-023-04615-x

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