摘要
本文针对SLA 3D打印技术中支撑结构材料浪费、后处理复杂、零件重量增加及力学性能不足等问题,研究提出一种基于蜂窝仿生原理的结构设计与工艺约束驱动的轻量化结构优化方法。通过构建螺旋蜂窝结构与分形树状自适应支撑网络,实现了模型内部自支撑特性与力学性能的协同优化。该方法突破了传统镂空结构自支撑性差与材料效率低的瓶颈,为复杂模型的跨尺度轻量化提供了新解决方案。
This article proposes a lightweight structural optimization method based on cellular biomimetic principles for structural design and process constraint driven optimization,aimed at addressing issues such as material waste,complex post-processing,increased part weight,and insufficient mechanical properties in SLA 3D printing technology.By constructing a spiral honeycomb structure and a fractal tree adaptive support network,the synergistic optimization of the internal self-supporting characteristics and mechanical performance of the model was achieved.This method breaks through the bottleneck of poor self-supporting and low material efficiency in traditional hollow structures,providing a new solution for cross scale lightweighting of complex models.
作者
赵军
ZHAO Jun(Zhejiang Industry Polytechnic College,Shaoxing Zhejiang 312000,China)
出处
《机电产品开发与创新》
2026年第1期55-57,68,共4页
Development & Innovation of Machinery & Electrical Products
基金
浙江省教育厅2022年度高校国内访问工程师“校企合作项目”(FG2022215)。
