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<p>In this work, the stir casting route has been used to fabricate biocompatible hybrid metal matrix composites containing AZ31 magnesium alloy as matrix and aluminium oxide nanoparticles (2 wt.% Al<sub>2</sub>O<sub>3</sub>) and silicon nitride microparticles (2, 4, or 6 wt.% Si<sub>3</sub>N<sub>4</sub>) as reinforcement. The tribological properties, such as wear rate and coefficient of friction (COF), were evaluated for the fabricated hybrid composites. The study considered process parameters such as wt.% of Si<sub>3</sub>N<sub>4</sub>, load (N), sliding speed (m/s), and sliding distance (m) for performing the wear test. The central composite design-based RSM technique has been used to plan the experimental design for performing the wear test. The study used ANOVA and surface plots to investigate wear rate and COF values for selected input variables. Multi-Objective Optimization by Ratio Analysis was used to find the optimal combinations for minimum wear rate and COF. A novel feedforward neural network model is selected to predict wear rate and COF. Under dry sliding wear conditions, the wear rate and COF value decrease with the addition of Si<sub>3</sub>N<sub>4</sub> reinforcement up to 4 wt.% content. The FESEM study shows the different wear mechanisms, such as abrasive wear and plastic deformation with delamination, micro-ploughing, and micro-cutting.</p>

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Optimization and prediction of the tribological parameters of biocompatible AZ31/Al2O3/Si3N4 metal matrix composites using CCD-RSM, MOORA and FNN models

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • C. Veera Ajay,
  • K. Manisekar,
  • A. Andrews,
  • K. Thoufiq Mohammed

摘要

In this work, the stir casting route has been used to fabricate biocompatible hybrid metal matrix composites containing AZ31 magnesium alloy as matrix and aluminium oxide nanoparticles (2 wt.% Al2O3) and silicon nitride microparticles (2, 4, or 6 wt.% Si3N4) as reinforcement. The tribological properties, such as wear rate and coefficient of friction (COF), were evaluated for the fabricated hybrid composites. The study considered process parameters such as wt.% of Si3N4, load (N), sliding speed (m/s), and sliding distance (m) for performing the wear test. The central composite design-based RSM technique has been used to plan the experimental design for performing the wear test. The study used ANOVA and surface plots to investigate wear rate and COF values for selected input variables. Multi-Objective Optimization by Ratio Analysis was used to find the optimal combinations for minimum wear rate and COF. A novel feedforward neural network model is selected to predict wear rate and COF. Under dry sliding wear conditions, the wear rate and COF value decrease with the addition of Si3N4 reinforcement up to 4 wt.% content. The FESEM study shows the different wear mechanisms, such as abrasive wear and plastic deformation with delamination, micro-ploughing, and micro-cutting.