A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
PCM-BESO: a reformative multi-constraints topology optimization method using point cloud modeling oriented to additive manufacturing
This paper introduces a novel method for multi-constraints bi-directional evolutionary structural optimization (BESO) using point cloud modeling (PCM-BESO). It improves upon the primary BESO method by utilizing finite element model nodes as design variables and updating the model’s point cloud by adding or removing nodes based on sensitivity analysis. By utilizing α-shape generation and refinement technology based on Delaunay triangulation, the point cloud is geometrically reconstructed to obtain more accurate representations and smoother boundaries in optimized design. Additionally, a boundary growth technique is included in PCM-BESO to expand the point cloud outside the boundary, enabling topology optimization beyond the initial design domain. To solve multi-constraints optimization problems, this paper proposes the stress uniformity information entropy constraint that measures stress distribution uniformity while considering volume and displacement constraints. Moreover, two new dynamic evolution rate functions are introduced to enhance convergence speed and stability. Numerical results demonstrate that the method is effective for multi-constraints optimization of 2D and 3D structures and can be well combined with additive manufacturing. When breaking through the initial design domain, the method provides an optimal topology with significantly improved performance, which is particularly useful for structures with limited initial design domain.
PCM-BESO: a reformative multi-constraints topology optimization method using point cloud modeling oriented to additive manufacturing
This paper introduces a novel method for multi-constraints bi-directional evolutionary structural optimization (BESO) using point cloud modeling (PCM-BESO). It improves upon the primary BESO method by utilizing finite element model nodes as design variables and updating the model’s point cloud by adding or removing nodes based on sensitivity analysis. By utilizing α-shape generation and refinement technology based on Delaunay triangulation, the point cloud is geometrically reconstructed to obtain more accurate representations and smoother boundaries in optimized design. Additionally, a boundary growth technique is included in PCM-BESO to expand the point cloud outside the boundary, enabling topology optimization beyond the initial design domain. To solve multi-constraints optimization problems, this paper proposes the stress uniformity information entropy constraint that measures stress distribution uniformity while considering volume and displacement constraints. Moreover, two new dynamic evolution rate functions are introduced to enhance convergence speed and stability. Numerical results demonstrate that the method is effective for multi-constraints optimization of 2D and 3D structures and can be well combined with additive manufacturing. When breaking through the initial design domain, the method provides an optimal topology with significantly improved performance, which is particularly useful for structures with limited initial design domain.
PCM-BESO: a reformative multi-constraints topology optimization method using point cloud modeling oriented to additive manufacturing
Optim Eng
Yu, Jianxing (author) / Jin, Zihang (author) / Yu, Yang (author) / Huang, Kaihang (author) / Cui, Yupeng (author) / Song, Lin (author) / Ma, Jiandong (author) / Yang, Zhenglong (author)
Optimization and Engineering ; 26 ; 543-580
2025-03-01
38 pages
Article (Journal)
Electronic Resource
English
Multi-constraints topology optimization , BESO , Alpha-shape , Information entropy , Additive manufacturing Mathematics , Optimization , Engineering, general , Systems Theory, Control , Environmental Management , Operations Research/Decision Theory , Financial Engineering , Mathematics and Statistics
Combining genetic algorithms with BESO for topology optimization
| British Library Online Contents | 2009
Matlab implementation of 3D topology optimization using BESO
| British Library Conference Proceedings | 2010
Topology optimization of periodic structures using BESO based on unstructured design points
| British Library Online Contents | 2016
Topology Optimization and 3D Printing of Steel Joints by SJ-BESO and FDM Method
| Springer Verlag | 2024
Topology Optimization and 3D Printing of Steel Joints by SJ-BESO and FDM Method
| Springer Verlag | 2024