Author Identifier (ORCID)
Abstract
Additive manufacturing of biomedical high-entropy alloys (BioHEAs) demands a combination of low elastic modulus, high strength, and damage tolerance, yet composition discovery remains largely empirical. Here, we establish a machine-learning framework that couples virtual screening with physical prototyping to link composition, deformation mechanism, and properties. Ensemble models for strength, elongation, and modulus were applied to screen Ti–Zr–Nb–Ta–Mo-centered quinary-to-septenary spaces (∼15 million compositions), revealing discrete performance islands anchored by a Ti–Zr backbone. A Zr-rich BCC alloy (Zr₃₉.₃Ti₁₉.₅Nb₁₇.₉Ta₁₆.₈Mo₆.₅) was identified and validated. In the as-cast state, it delivers ∼1.0 GPa yield strength, 22.3% elongation, and an 88 GPa elastic modulus; ductility originates from dislocation-mediated kink-band plasticity. Preliminary laser powder bed fusion (LPBF) trials confirm printability (97% relative density, single BCC, 821 MPa ultimate tensile strength, 87.9 GPa modulus) but exhibit limited elongation (2.5%) due to residual porosity and stresses, indicating that defect suppression is needed to approach cast-state ductility. This digital-to-physical workflow offers a transferable strategy for prioritizing BioHEA compositions before costly AM optimization.
Keywords
biomedical high-entropy alloys, kink-band plasticity, laser powder bed fusion, machine learning
Document Type
Journal Article
Date of Publication
1-1-2026
Article Number
e2689841
ISSN
17452759
Volume
21
Issue
1
Publication Title
Virtual and Physical Prototyping
Publisher
Taylor & Francis
School
Centre for Advanced Materials and Manufacturing / School of Engineering
Funding Information
This work was supported by National Additive Manufacturing Innovation Cluster (NAMIC) Singapore; National Key Research and Development Program of China: [Grant Number 2024YFE0109000]; National Natural Science Foundation of China: [Grant Number 52274387, 52311530772]; National Major Scientific Instruments and Equipment Development Project of National Natural Science Foundation of China: [Grant Number 52227807]; Medical-Engineering Cross Foundation of Shanghai Jiao Tong University: [Grant Number YG2024LC04]; National Natural Science Fund for Excellent Young Scholars: [Grant Number 52222410].
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Jiang, D., Zhang, L., Wang, K., Wang, W., Zhu, C., Fu, Y., Wang, K., Zhai, W., Yen, C., Lu, W., Zhang, D., & Wang, L. (2026). Machine-learning-guided design of a biomedical high-entropy alloy for additive manufacturing: Cast-state benchmark and preliminary LPBF feasibility assessment. Virtual and Physical Prototyping, 21(1), Article e2689841. https://doi.org/10.1080/17452759.2026.2689841