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<p>Developing advanced lightweight composites is essential for different industries. The integration of rigid ceramic reinforcements in the Al matrix enhances the properties. This study uses ultrasonic probe assisted stir casting route to investigate the effect of niobium carbide particles with 0, 2.5, and 5 weight percentages into an Al 8009 alloy matrix. The microstructural analysis confirmed uniform dispersion of reinforcements, enhancing hardness from 78.27 to 124.9 HV and tensile strength from 97 to 144&#xa0;MPa. The central composite design method was employed in RSM to design the wear experiments by considering reinforcement weight percentage, load, sliding velocity, and sliding distance as input variables, and the outcomes were specific wear rate and coefficient of friction. The optimized conditions found through the desirability approach at 4.39 reinforcement weight percentage, a load of 20N, a sliding velocity of 1.45&#xa0;m/s, and a 520.58&#xa0;m sliding distance, with a minimum SWR of 8.105 × 10^<sup>−14</sup> m<sup>3</sup>/N m and COF of 0.267. The artificial neural network model achieved an overall regression coefficient for a SWR of 0.98531 and a COF of 0.96112. The experimental data aligned closely with the predicted values obtained from RSM and ANN models, exhibiting a strong correlation.</p>

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Al 8009 alloy/niobium carbide composites: development, characterisation, and wear studies

  • 期刊论文

  • 发表日期：

    2025年4月12日

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  • Gollapinni Gowthami,
  • Jeevan Vemula

摘要

Developing advanced lightweight composites is essential for different industries. The integration of rigid ceramic reinforcements in the Al matrix enhances the properties. This study uses ultrasonic probe assisted stir casting route to investigate the effect of niobium carbide particles with 0, 2.5, and 5 weight percentages into an Al 8009 alloy matrix. The microstructural analysis confirmed uniform dispersion of reinforcements, enhancing hardness from 78.27 to 124.9 HV and tensile strength from 97 to 144 MPa. The central composite design method was employed in RSM to design the wear experiments by considering reinforcement weight percentage, load, sliding velocity, and sliding distance as input variables, and the outcomes were specific wear rate and coefficient of friction. The optimized conditions found through the desirability approach at 4.39 reinforcement weight percentage, a load of 20N, a sliding velocity of 1.45 m/s, and a 520.58 m sliding distance, with a minimum SWR of 8.105 × 10^−14 m3/N m and COF of 0.267. The artificial neural network model achieved an overall regression coefficient for a SWR of 0.98531 and a COF of 0.96112. The experimental data aligned closely with the predicted values obtained from RSM and ANN models, exhibiting a strong correlation.