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<p>Designing alloys for additive manufacturing opens opportunities for next-generation biomedical orthopaedic implants. However, most biomedical alloys currently in use are legacy compositions that cannot fully harness the potential of additive manufacturing. Here, we present a machine-learning-driven computational framework for discovering additive-manufacturing-specific β-titanium alloys with low modulus, optimised printability, mechanical and corrosion properties, and biocompatibility. Using this framework, an additive-manufacturing-specific Ti-Nb-Ta-Zr-Sn alloy was designed and validated via laser powder bed fusion. The alloy shows good printability and reduced sensitivity to keyhole pore formation compared with commercial Ti-6Al-4V. Under optimised process parameters, the as-built alloy combines a low Young’s modulus (~42.7 GPa) with high ductility (~30.9%), attributed to its metastable β-phase microstructure, cubic &lt;001&gt; texture, and reduced dislocation density. In this work, we show that this machine-learning-driven approach can facilitate the efficient design of biomedical β-titanium alloys, offering promising opportunities for advancing additive manufacturing in medical and related applications.</p>

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Machine learning driven discovery of low modulus biomedical titanium alloys for additive manufacturing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

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  • Jinlong Su,
  • Fulin Jiang,
  • Jin Wu,
  • Yuehua Li,
  • Ri Han,
  • Lu Wang,
  • Raymond Chung Wen Wong,
  • Jian Wang,
  • Swee Leong Sing

摘要

Designing alloys for additive manufacturing opens opportunities for next-generation biomedical orthopaedic implants. However, most biomedical alloys currently in use are legacy compositions that cannot fully harness the potential of additive manufacturing. Here, we present a machine-learning-driven computational framework for discovering additive-manufacturing-specific β-titanium alloys with low modulus, optimised printability, mechanical and corrosion properties, and biocompatibility. Using this framework, an additive-manufacturing-specific Ti-Nb-Ta-Zr-Sn alloy was designed and validated via laser powder bed fusion. The alloy shows good printability and reduced sensitivity to keyhole pore formation compared with commercial Ti-6Al-4V. Under optimised process parameters, the as-built alloy combines a low Young’s modulus (~42.7 GPa) with high ductility (~30.9%), attributed to its metastable β-phase microstructure, cubic <001> texture, and reduced dislocation density. In this work, we show that this machine-learning-driven approach can facilitate the efficient design of biomedical β-titanium alloys, offering promising opportunities for advancing additive manufacturing in medical and related applications.