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<p>Metal matrix composites (MMCs) offer a promising strategy to enhance the mechanical and functional properties of metals by tailoring reinforcement phases and microstructures. This study investigates the correlation between microstructural features and mechanical properties of Ta-reinforced Fe–Mn-Si MMCs fabricated by laser powder bed fusion (LPBF) for biomedical applications. Sixteen heat treatment conditions were applied to tailor microstructures, which were quantified using electron backscatter diffraction and used as input for an XGBoost-based machine learning model. The model demonstrated high predictive accuracy for yield strength (YS), elongation, and strain-hardening capability (UTS/YS). Feature importance analysis identified geometrically necessary dislocation density, grain size, and the length of high-angle boundaries as key determinants. The results reveal that precipitation hardening, recrystallization, and transformation-induced plasticity significantly influence mechanical behavior. The optimal condition (1100&#xa0;°C, 3&#xa0;h) yielded a MMC with a YS of 216&#xa0;MPa, elongation of 12.6%, and a strain-hardening ratio of 2.96, satisfying the criteria of mechanical behaviour for temporary implant use. This work presents a data-driven strategy for designing LPBF-fabricated MMCs with enhanced biomechanical performance.</p>

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Correlations between Microstructure and Mechanical Properties for Laser Powder Bed Fusion Fabricated Ta/Fe–Mn-Si Metal Matrix Composites Revealed by Machine Learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Zi Li,
  • Zhuohan Cao,
  • Zuhao Zhang,
  • Michael Ferry,
  • Jamie J. Kruzic,
  • Xiaopeng Li

摘要

Metal matrix composites (MMCs) offer a promising strategy to enhance the mechanical and functional properties of metals by tailoring reinforcement phases and microstructures. This study investigates the correlation between microstructural features and mechanical properties of Ta-reinforced Fe–Mn-Si MMCs fabricated by laser powder bed fusion (LPBF) for biomedical applications. Sixteen heat treatment conditions were applied to tailor microstructures, which were quantified using electron backscatter diffraction and used as input for an XGBoost-based machine learning model. The model demonstrated high predictive accuracy for yield strength (YS), elongation, and strain-hardening capability (UTS/YS). Feature importance analysis identified geometrically necessary dislocation density, grain size, and the length of high-angle boundaries as key determinants. The results reveal that precipitation hardening, recrystallization, and transformation-induced plasticity significantly influence mechanical behavior. The optimal condition (1100 °C, 3 h) yielded a MMC with a YS of 216 MPa, elongation of 12.6%, and a strain-hardening ratio of 2.96, satisfying the criteria of mechanical behaviour for temporary implant use. This work presents a data-driven strategy for designing LPBF-fabricated MMCs with enhanced biomechanical performance.