Lattice-inspired NiTi-based metamaterials with widely tunable mechanical-superelastic synergy

Inspired by austenite and martensite crystal lattices in the NiTi microstructure with versatile performances, the bionic microlattice metamaterials with strut diameter from 0.4∼0.8 mm were constructed and prepared by the laser powder bed fusion for expanding the tailored mechanical-superelastic rang...

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Bibliographic Details
Main Authors: Zhi Zhang, Jianbao Gao, Shuaishuai Wei, Bo Song, Yonggang Yao, Xu Zheng, Yuanjie Zhang, Lei Zhang, Qiaojiao Li, Jiafeng Wu, Yusheng Shi
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Virtual and Physical Prototyping
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Online Access:https://www.tandfonline.com/doi/10.1080/17452759.2024.2444572
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Summary:Inspired by austenite and martensite crystal lattices in the NiTi microstructure with versatile performances, the bionic microlattice metamaterials with strut diameter from 0.4∼0.8 mm were constructed and prepared by the laser powder bed fusion for expanding the tailored mechanical-superelastic range, and the machine learning was utilized for mapping the relationship of various parameters. The mechanical-superelastic were highly related to orientation, the martensite-inspired microlattice metamaterial in the x-axis loading direction (M-x) possessed higher mechanical properties than that of the martensite-inspired microlattice metamaterials in the z-axis loading direction (M-z). For mechanical properties, the M-x possessed the highest Young's modulus (E=1001.5∼3720.4 MPa) and simultaneously the widest tailored range (87.32%), while the austenite-inspired microlattices metamaterial (A) exhibited a fully tailored ability for yield strength (σ). For superelastic, the austenite- and martensite-inspired microlattice metamaterial had superior superelasticity (98.10%∼99.36% recoverability) with wide volume tuning space, the M-x possessed the highest recoverability with a narrow tailored range. The relation between different parameters with mechanical and superelastic properties was established through machine learning, and multiple performance optimizations were carried out with vascular stents as typical application objectives. This research provides novel ideas for designing NiTi components, contributing to the future developments of different applications.
ISSN:1745-2759
1745-2767