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<p>In this study, the ultrasonic cavitation and streaming effect were simulated using ANSYS Fluent software to understand the flow dynamics and dispersion mechanism of self-lubricating two-dimensional structures, hexagonal boron nitride (hBN) and graphene (Gr) nanoparticle reinforcements in the aluminum alloy AA7075 matrix. A novel hybrid ultrasonication (HUT) casting method was developed, which combined both rotating radial flow and ultrasonication effect simultaneously for uniform dispersion of hBN and Gr (0, 0.25, 0.5, 0.75, 1 wt%) in AA7075 matrix. The optical microscope images and SEM micrographs confirmed the grain refinement by the pinning effect and homogeneous declustered hBN and Gr dispersion, which is attributed to the enhanced mechanical properties of AA7075-0.75wt.% hBN/Gr nanocomposite. The dry sliding wear behaviour of AA7075/hBN/Gr nanocomposites tested under normal load of 30&#xa0;N, sliding distance of 628&#xa0;m and EN31 counter disk material, showed formation of lubricating tribo-layer of hBN and Gr nanoparticles over the AA7075 surface during wear, which has enhanced the wear resistance of the nanocomposite when compared to monolithic alloy. Four machine learning (ML) models for regression were used to predict the wear rate and coefficient of friction of AA7075/hBN/Gr nanocomposites. According to the ML study, it was concluded that the changes in weight% of hBN and Gr, homogeneous dispersion, and hardness have appeared to be important factors for enhanced tribological characteristics of AA7075/hBN/Gr nanocomposites.</p> Graphical abstract <p></p>

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Simulation and prediction of tribological behavior in AA7075/hBN/Graphene aluminium nanocomposites using hybrid ultrasonication and machine learning techniques

  • 期刊论文

  • 发表日期：

    2025年8月27日

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  • S. C. Amith,
  • Poovazhagan Lakshmanan,
  • Arun David,
  • Thirugnanasambandam Arunkumar

摘要

In this study, the ultrasonic cavitation and streaming effect were simulated using ANSYS Fluent software to understand the flow dynamics and dispersion mechanism of self-lubricating two-dimensional structures, hexagonal boron nitride (hBN) and graphene (Gr) nanoparticle reinforcements in the aluminum alloy AA7075 matrix. A novel hybrid ultrasonication (HUT) casting method was developed, which combined both rotating radial flow and ultrasonication effect simultaneously for uniform dispersion of hBN and Gr (0, 0.25, 0.5, 0.75, 1 wt%) in AA7075 matrix. The optical microscope images and SEM micrographs confirmed the grain refinement by the pinning effect and homogeneous declustered hBN and Gr dispersion, which is attributed to the enhanced mechanical properties of AA7075-0.75wt.% hBN/Gr nanocomposite. The dry sliding wear behaviour of AA7075/hBN/Gr nanocomposites tested under normal load of 30 N, sliding distance of 628 m and EN31 counter disk material, showed formation of lubricating tribo-layer of hBN and Gr nanoparticles over the AA7075 surface during wear, which has enhanced the wear resistance of the nanocomposite when compared to monolithic alloy. Four machine learning (ML) models for regression were used to predict the wear rate and coefficient of friction of AA7075/hBN/Gr nanocomposites. According to the ML study, it was concluded that the changes in weight% of hBN and Gr, homogeneous dispersion, and hardness have appeared to be important factors for enhanced tribological characteristics of AA7075/hBN/Gr nanocomposites.

Graphical abstract