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<p>The study focuseson the effect of reinforcement percentage of Waste glass powder, Titanium Diboride (TiB<sub>2</sub>), and copper-nano oxide (CuO) on the wear rate, coefficient of friction, and hardness of Al2024-based composite materials. Weight percentages of TiB<sub>2</sub>and waste glass powder vary; however, CuO is used in a fixed percentage. The experimental results depicted significant improvements in wear resistance and hardness with the inclusion of TiB<sub>2</sub>, waste glass powder, and CuO. Further, the effect of process parameters viz. including TiB<sub>2</sub> (wt%), waste glass powder (wt%), load (N), (m/s), sliding distance (m), and sliding velocityon the wear rate is optimized with the help of Taguchi L16 orthogonal array. A regression model was developed to predict the wear ratein terms of process parameters. The developed model is adequate in the 95% confidence interval. The findings reveal that the maximum hardness is found to be 176.4 HV, while the minimum coefficient of friction observed was 0.262. The confirmation test reveals that at an optimum level of process parameters wear rate is found to be 1.56 × 10<sup>−3</sup> (g/m).</p>

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Experimental investigation of reinforcementTiB2, waste glass powder, and CuO on the wear rate, coefficient of friction, and hardness of Al2024 matrix composites

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • Rohit Sahu,
  • Krovvidi Srinivas,
  • Atul Kumar Agrawal

摘要

The study focuseson the effect of reinforcement percentage of Waste glass powder, Titanium Diboride (TiB2), and copper-nano oxide (CuO) on the wear rate, coefficient of friction, and hardness of Al2024-based composite materials. Weight percentages of TiB2and waste glass powder vary; however, CuO is used in a fixed percentage. The experimental results depicted significant improvements in wear resistance and hardness with the inclusion of TiB2, waste glass powder, and CuO. Further, the effect of process parameters viz. including TiB2 (wt%), waste glass powder (wt%), load (N), (m/s), sliding distance (m), and sliding velocityon the wear rate is optimized with the help of Taguchi L16 orthogonal array. A regression model was developed to predict the wear ratein terms of process parameters. The developed model is adequate in the 95% confidence interval. The findings reveal that the maximum hardness is found to be 176.4 HV, while the minimum coefficient of friction observed was 0.262. The confirmation test reveals that at an optimum level of process parameters wear rate is found to be 1.56 × 10−3 (g/m).