.png){kind=icon}
返回
Constructing self-supporting structures in biscale topology optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s00371-021-02068-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The self-supporting requirement is very necessary in additive manufacturing so that the printed structure will not collapse during fabrication. Imposing the self-supporting constraint on topology optimization allows for designing a performance optimized structure that is ready-to-print. However, although biscale topology optimization has been widely studied, conducting self-supporting topology optimization separately for the macro-structure and for each micro-structure is not sufficient to produce an overall self-supporting structure, as first observed in this study. The issue is resolved via an approach to bridge the gap between the requirements of self-supporting at the two scales via distinguishing the macro-cells based on their relative locations. In addition, the self-supporting constraint is expressed as a simple quadratic function included in the topology optimization in both scales, and a convolution operator is designed to efficiently implement its detection. Ultimately, a completely self-supporting overall structure is generated within a biscale topology optimization framework and extends its potentiality to produce design to be directly fabricated via additive manufacturing. Performance of the approach is demonstrated via various 2D and 3D examples.
Keyword:
Biscale topology optimization
Self-supporting
Print-ready
Additive manufacturing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.9
论文数:
4.6K
被引数:
6.5K
机构
引用论文
.png){kind=icon}
SURF: A connectivity-based space filling curve construction algorithm in high genus 3D surface WSNsSURF: 一种基于连通性的高属三维曲面WSNs空间填充曲线构建算法
PREAI
分享收藏Design of materials using topology optimization and energy-based homogenization approach in Matlab
PREAI
分享收藏
查看更多内容