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<p>This study investigates the optimization of friction stir processing parameters to prepare the surface composites of AA2024-T351 alloy with MWCNTs. The process parameters, including 1000&#xa0;rpm-1400&#xa0;rpm spindle speed, 40-80&#xa0;mm/min feed rate, and volume fraction of MWCNTs from 2% to 6%, were analyzed by the RSM technique by Box–Behnken Design (BBD). The estimated mechanical characteristics produced with optimal values of ultimate tensile strength of 585.7&#xa0;MPa, a wear rate of 24.15 mm<sup>3</sup>/Nm, hardness of 172.36 HV and surface roughness of 0.151&#xa0;μm were identified under specific experimental conditions of 1400&#xa0;rpm, 60&#xa0;mm/min feed rate, and 4% MWCNT content. Microstructural evaluation at these optimized process parameters showed the uniform distribution of the reinforcement MWCNTs in the aluminum matrix, with a refined grain size of ~ 3.81&#xa0;µm. The analytical model displayed high consistency, supported by individual response desirability indices exceeding 0.90, indicating that each response closely achieved the optimization target. The combining effect of all responses exhibited a composite desirability of 0.95 and <i>R</i><sup>2</sup> values greater than 0.95 across all responses, which confirmed the accuracy of the model’s prediction. Overall, the optimization considerably improved both the mechanical behavior and the characterization of the fabricated surface composites.</p>

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Integrated Optimization and Multiscale Characterization of AA2024-T351/MWCNTs Surface Composites via Friction Stir Processing

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • J. Murugesan,
  • V. S. Senthil Kumar

摘要

This study investigates the optimization of friction stir processing parameters to prepare the surface composites of AA2024-T351 alloy with MWCNTs. The process parameters, including 1000 rpm-1400 rpm spindle speed, 40-80 mm/min feed rate, and volume fraction of MWCNTs from 2% to 6%, were analyzed by the RSM technique by Box–Behnken Design (BBD). The estimated mechanical characteristics produced with optimal values of ultimate tensile strength of 585.7 MPa, a wear rate of 24.15 mm3/Nm, hardness of 172.36 HV and surface roughness of 0.151 μm were identified under specific experimental conditions of 1400 rpm, 60 mm/min feed rate, and 4% MWCNT content. Microstructural evaluation at these optimized process parameters showed the uniform distribution of the reinforcement MWCNTs in the aluminum matrix, with a refined grain size of ~ 3.81 µm. The analytical model displayed high consistency, supported by individual response desirability indices exceeding 0.90, indicating that each response closely achieved the optimization target. The combining effect of all responses exhibited a composite desirability of 0.95 and R2 values greater than 0.95 across all responses, which confirmed the accuracy of the model’s prediction. Overall, the optimization considerably improved both the mechanical behavior and the characterization of the fabricated surface composites.