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<p>Hybrid aluminium matrix composites often suffer from excessive wear and material loss due to improper selection of reinforcement content and operating parameters, and conventional optimization techniques inadequately capture the trade-off between multiple tribological responses. To address this issue, the present study employs the AA8079 aluminium alloy as the matrix material reinforced with aluminium nitride (AlN) and fly ash (FA) as hybrid particulates to enhance wear resistance while promoting material sustainability. The composites were fabricated using a stir casting technique to ensure uniform reinforcement dispersion, and tribological behavior was evaluated using a Taber-type abrasion tester under varying reinforcement content (3–12 wt%), applied load (250–1000&#xa0;g), and sliding speed (200–800&#xa0;rpm). A Taguchi L16 orthogonal array was adopted for experimental design, and multi-response optimization was carried out using Grey Relational Analysis integrated with Pareto–Edgeworth–Grierson (PEG) vector-based decision analysis. The results revealed that sliding speed was the most influential parameter, followed by reinforcement content and applied load, with the optimal parameter combination (A₂B₃C₄) yielding a minimum wear index of 0.016 and a reduced volume loss of 17.11&#xa0;mm³. PEG analysis identified Experiment 4 as the best condition with the highest PEG score of 2.916, confirming convergence toward the Pareto-efficient region and superior overall tribological performance. The optimized AA8079/6 wt% AlN/FA hybrid composites are therefore suitable for wear-critical applications, such as automotive brake components, aerospace structural parts, bearing surfaces, and lightweight industrial machinery, that require enhanced wear resistance and reduced material loss. The prime novelty of the present study lies in the integration of the Taguchi design of experiments with Pareto–Edgeworth–Grierson (PEG) vector analysis for the optimization of hybrid composites prepared by the novel liquid metallurgy route.</p>

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Optimization of tribological properties of AA8079 matrix composites reinforced with fly ash and AlN using Taguchi and grey relational analysis

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • P Sagayaraj,
  • D Ganesh Kumar,
  • K Parimala,
  • Soundarya Kasi,
  • S. Krishnakumar,
  • R. Senthil

摘要

Hybrid aluminium matrix composites often suffer from excessive wear and material loss due to improper selection of reinforcement content and operating parameters, and conventional optimization techniques inadequately capture the trade-off between multiple tribological responses. To address this issue, the present study employs the AA8079 aluminium alloy as the matrix material reinforced with aluminium nitride (AlN) and fly ash (FA) as hybrid particulates to enhance wear resistance while promoting material sustainability. The composites were fabricated using a stir casting technique to ensure uniform reinforcement dispersion, and tribological behavior was evaluated using a Taber-type abrasion tester under varying reinforcement content (3–12 wt%), applied load (250–1000 g), and sliding speed (200–800 rpm). A Taguchi L16 orthogonal array was adopted for experimental design, and multi-response optimization was carried out using Grey Relational Analysis integrated with Pareto–Edgeworth–Grierson (PEG) vector-based decision analysis. The results revealed that sliding speed was the most influential parameter, followed by reinforcement content and applied load, with the optimal parameter combination (A₂B₃C₄) yielding a minimum wear index of 0.016 and a reduced volume loss of 17.11 mm³. PEG analysis identified Experiment 4 as the best condition with the highest PEG score of 2.916, confirming convergence toward the Pareto-efficient region and superior overall tribological performance. The optimized AA8079/6 wt% AlN/FA hybrid composites are therefore suitable for wear-critical applications, such as automotive brake components, aerospace structural parts, bearing surfaces, and lightweight industrial machinery, that require enhanced wear resistance and reduced material loss. The prime novelty of the present study lies in the integration of the Taguchi design of experiments with Pareto–Edgeworth–Grierson (PEG) vector analysis for the optimization of hybrid composites prepared by the novel liquid metallurgy route.