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第 46 卷 肖李军,等: 数据驱动点阵超材料多目标优化设计 第 5 期
10.1016/j.matdes.2018.11.035.
[5] YIN H F, ZHANG W Z, ZHU L C, et al. Review on lattice structures for energy absorption properties [J]. Composite
Structures, 2023, 304(Pt 1): 116397. DOI: 10.1016/j.compstruct.2022.116397.
[6] NAZIR A, ABATE K M, KUMAR A, et al. A state-of-the-art review on types, design, optimization, and additive
manufacturing of cellular structures [J]. The International Journal of Advanced Manufacturing Technology, 2019, 104(9-12):
3489–3510. DOI: 10.1007/s00170-019-04085-3.
[7] HU L L, ZHOU M Z, DENG H. Dynamic crushing response of auxetic honeycombs under large deformation: theoretical
analysis and numerical simulation [J]. Thin-Walled Structures, 2018, 131: 373–384. DOI: 10.1016/j.tws.2018.04.020.
[8] ZHANG D H, FEI Q G, LIU J Z, et al. Crushing of vertex-based hierarchical honeycombs with triangular substructures [J].
Thin-Walled Structures, 2020, 146: 106436. DOI: 10.1016/j.tws.2019.106436.
[9] NEČEMER B, GLODEŽ S, NOVAK N, et al. Numerical modelling of a chiral auxetic cellular structure under multiaxial
loading conditions [J]. Theoretical and Applied Fracture Mechanics, 2020, 107: 102514. DOI: 10.1016/j.tafmec.2020.102514.
[10] ANDREW J J, SCHNEIDER J, UBAID J, et al. Energy absorption characteristics of additively manufactured plate-lattices
under low- velocity impact loading [J]. International Journal of Impact Engineering, 2021, 149: 103768. DOI: 10.1016/j.
ijimpeng.2020.103768.
[11] MIRALBES R, RANZ D, PASCUAL F J, et al. Characterization of additively manufactured triply periodic minimal surface
structures under compressive loading [J]. Mechanics of Advanced Materials and Structures, 2022, 29(13): 1841–1855. DOI:
10.1080/15376494.2020.1842948.
[12] MA Q P, YAN Z J, ZHANG L, et al. The family of elastically isotropic stretching-dominated cubic truss lattices [J].
International Journal of Solids and Structures, 2022, 239/240: 111451. DOI: 10.1016/j.ijsolstr.2022.111451.
[13] MACONACHIE T, LEARY M, LOZANOVSKI B, et al. SLM lattice structures: properties, performance, applications and
challenges [J]. Materials & Design, 2019, 183: 108137. DOI: 10.1016/j.matdes.2019.108137.
[14] MORA S, PUGNO N M, MISSERONI D. 3D printed architected lattice structures by material jetting [J]. Materials Today,
2022, 59: 107–132. DOI: 10.1016/j.mattod.2022.05.008.
[15] TANCOGNE-DEJEAN T, SPIERINGS A B, MOHR D. Additively-manufactured metallic micro-lattice materials for high
specific energy absorption under static and dynamic loading [J]. Acta Materialia, 2016, 116: 14–28. DOI: 10.1016/j.actamat.
2016.05.054.
[16] EPASTO G, PALOMBA G, D'ANDREA D, et al. Ti-6Al-4V ELI microlattice structures manufactured by electron beam
melting: effect of unit cell dimensions and morphology on mechanical behaviour [J]. Materials Science and Engineering: A,
2019, 753: 31–41. DOI: 10.1016/j.msea.2019.03.014.
[17] WANG S H, MA Y B, DENG Z C, et al. Two elastically equivalent compound truss lattice materials with controllable
anisotropic mechanical properties [J]. International Journal of Mechanical Sciences, 2022, 213: 106879. DOI: 10.1016/j.
ijmecsci.2021.106879.
[18] DONDA K, BRAHMKHATRI P, ZHU Y F, et al. Machine learning for inverse design of acoustic and elastic metamaterials [J].
Current Opinion in Solid State and Materials Science, 2025, 35: 101218. DOI: 10.1016/j.cossms.2025.101218.
[19] XU W, LIU C, GUO Y L, et al. Problem-independent machine learning (PIML) enhanced 3D lattice composite structures
optimization via moving morphable components approach [J]. Composite Structures, 2025, 369: 119330. DOI: 10.1016/j.
compstruct.2025.119330.
[20] ZHAO S Y, ZHAO Z, YANG Z C, et al. Functionally graded graphene reinforced composite structures: a review [J].
Engineering Structures, 2020, 210: 110339. DOI: 10.1016/j.engstruct.2020.110339.
[21] ZHANG X C, SONG Z Y, LI Y N, et al. Generative inverse design of metamaterials with customized stress-strain response [J].
International Journal of Mechanical Sciences, 2025, 306: 110875. DOI: 10.1016/j.ijmecsci.2025.110875.
[22] SEPASDAR R, KARPATNE A, SHAKIBA M. A data-driven approach to full-field nonlinear stress distribution and failure
pattern prediction in composites using deep learning [J]. Computer Methods in Applied Mechanics and Engineering, 2022,
397: 115126. DOI: 10.1016/j.cma.2022.115126.
[23] PELOQUIN J, KIRILLOVA A, RUDIN C, et al. Prediction of tensile performance for 3D printed photopolymer gyroid
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