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- 저자 : Yu Huangchao (검색결과 2 건)
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Stress-constrained topology optimization for material extrusion polymer additive manufacturing
Liu Jikai,Yan Jingjing,Yu Huangchao 한국CDE학회 2021 Journal of computational design and engineering Vol.8 No.3
This paper presents a comprehensive numerical and experimental study on stress-constrained topology optimization for Fused Deposition Modeling (FDM) additive manufacturing. The qp method is employed to avoid the singularity issue of stress-constrained problems. The P-norm function with stability transformation is adopted to build the global stress constraint with iterative corrections to eliminate the gap between the maximum local stress and the P-norm stress. The Heaviside projection is employed to generate clear-cut 0–1 designs. Two benchmark examples have been studied with the numerical algorithm. Experiments are performed on the topologically optimized MBB beam to investigate the impact of the FDM process parameters, including deposition path direction, building direction, and slicing layer height, on the resulted structural strength. The stress-constrained designs without and with Heaviside projection are comparatively tested with experiments. The stress-minimization designs subject to different P-norm parameters are compared both numerically and experimentally. Experiments show that the deposition path direction and the building direction evidently affect the derived structural strength. Moreover, overthin structural members may severely degrade the structural strength due to manufacturing and loading uncertainties.
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Wang Yifan,Wu Tao,Liu Jikai,Yu Huangchao 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.6
This paper presents a lightweight support design method for material extrusion-type three-dimensional printed panel structures that innovatively involves the deposition path curvature information for support point determination. Specifically, this support design method provides a robust segmentation algorithm to divide the filament deposition paths into segments based on the curvature sign alternating condition, and then searches for the fewest support points for the filaments counting on the experimentally calibrated relationship between the maximum allowable self-support distance and the local mean curvature. The proposed method features in generating thin-walled skeleton-ray styled support structures that are lightweight while providing firm support for the panels. More importantly, the support design method provides a new type of self-support criterion for structural topology optimization involving non-designable planar panels, i.e., only a sparse point set would be sufficient to support the panel. Consequently, more materials could be spent on enhancing the load-bearing capacity instead of being wasted on oversupporting. The achievable structural performances from self-support topology optimization with this new self-support criterion can improve significantly. Support design and printing tests were conducted on a few panel structures that validated the improved support effect compared with equal-volume supports generated by commercial software. Equidistant and gap-free deposited filaments, no filament collapse due to insufficient support, and no isolated voids reflect the improved support effect. The improved self-support topological design was also validated through a comparative numerical case study, and a compliance reduction of 7.76% was achieved.
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