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<p>This paper investigates the mechanical and microstructural behaviour of Al6061-tungsten carbide (WC) metal matrix composites (MMCs) fabricated via powder metallurgy. Seven composite samples were prepared with varying WC content (0–12&#xa0;wt%) using compression molding and sintering. Mechanical characterization, including Brinell hardness and compressive strength testing, showed significant improvement in both properties with increasing WC content, reaching up to 33% and 56% enhancement, respectively, at 12&#xa0;wt% WC. Optical and SEM analyses confirmed a relatively uniform dispersion of WC particles in the matrix. Statistical validation using one-way ANOVA revealed a significant influence of WC content on hardness, supported by post-hoc Tukey’s HSD test. Additionally, machine learning models linear, polynomial, and random forest regression, were developed to predict hardness, with polynomial regression (degree 3) yielding the highest accuracy (R<sup>2</sup> = 0.9372). A 3D surface model further highlighted the coupled effect of WC content and indentation diameter on hardness. The results affirm the suitability of WC reinforcement in enhancing Al6061 properties for load-bearing and wear-resistant applications, with 4–10&#xa0;wt% identified as an optimal range.</p>

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Effect of Tungsten Carbide Composites on Al6061 Using Powder Metallurgy Technique: Experimental and Machine Learning Approach

  • 期刊论文

  • 发表日期：

    2025年12月5日

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  • Y. P. Ravitej,
  • Rajeev Gupta,
  • S. B. Karthik,
  • Krantikumar Kshaurad,
  • Rayappa S. Mahale,
  • B. V. N. Ramakumar,
  • Balachandra Halemani,
  • N Abhijith

摘要

This paper investigates the mechanical and microstructural behaviour of Al6061-tungsten carbide (WC) metal matrix composites (MMCs) fabricated via powder metallurgy. Seven composite samples were prepared with varying WC content (0–12 wt%) using compression molding and sintering. Mechanical characterization, including Brinell hardness and compressive strength testing, showed significant improvement in both properties with increasing WC content, reaching up to 33% and 56% enhancement, respectively, at 12 wt% WC. Optical and SEM analyses confirmed a relatively uniform dispersion of WC particles in the matrix. Statistical validation using one-way ANOVA revealed a significant influence of WC content on hardness, supported by post-hoc Tukey’s HSD test. Additionally, machine learning models linear, polynomial, and random forest regression, were developed to predict hardness, with polynomial regression (degree 3) yielding the highest accuracy (R2 = 0.9372). A 3D surface model further highlighted the coupled effect of WC content and indentation diameter on hardness. The results affirm the suitability of WC reinforcement in enhancing Al6061 properties for load-bearing and wear-resistant applications, with 4–10 wt% identified as an optimal range.