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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
lattices using structural porosity, base material properties, and machine learning [J]. Materials & Design, 2023, 232: 112126.
DOI: 10.1016/j.matdes.2023.112126.
[24] GLAESENER R N, KUMAR S, LESTRINGANT C, et al. Predicting the influence of geometric imperfections on the
mechanical response of 2D and 3D periodic trusses [J]. Acta Materialia, 2023, 254: 118918. DOI: 10.1016/j.actamat.
2023.118918.
[25] YU G J, XIAO L J, SONG W D. Deep learning-based heterogeneous strategy for customizing responses of lattice structures [J].
International Journal of Mechanical Sciences, 2022, 229: 107531. DOI: 10.1016/j.ijmecsci.2022.107531.
[26] SANTOSA S P, WIERZBICKI T, HANSSEN A G, et al. Experimental and numerical studies of foam-filled sections [J].
International Journal of Impact Engineering, 2000, 24(5): 509–534. DOI: 10.1016/S0734-743X(99)00036-6.
[27] LE V T, DINH D M, TRAN V C, et al. Modelling, analysis, and multi-objective optimization of single weld bead
characteristics in wire arc additive manufacturing of Inconel 625 based on machine learning and NSGA-Ⅱ [J]. Materials
Today Communications, 2025, 49: 113831. DOI: 10.1016/j.mtcomm.2025.113831.
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