跨尺度结构智能优化方法与快速设计
Intelligent Optimization Method and Rapid Design of Cross-scale Structure
霍树林 1江和昕 1宋贤海 2周恩临 1何智成1
作者信息
- 1. 湖南大学汽车车身先进设计制造国家重点实验室,长沙,410082
- 2. 南昌航空大学材料科学与工程学院,南昌,330036
- 折叠
摘要
跨尺度拓扑优化设计极大激发了结构轻量化潜力,在先进装备的开发中具有重要作用.然而基于传统有限元的结构拓扑优化算法难以适应产品快速迭代的需求.为此,本文提出了一种基于耦合深度学习的跨尺度拓扑优化方法,通过集成残差神经网络(Resnet)、U-net架构及SEnet中的注意力机制,建立快速生成双尺度拓扑结构的深度学习模型.模型训练数据利用双向渐进结构优化算法产生,并用一组全新的数据对模型进行测试.数值算例表明,本文提出的深度学习模型可以高效且准确的生成基于各种边界下的宏观材料分布与微观拓扑结构.
Abstract
The multiscale topology optimization design has greatly stimulated the lightweight potential of the structure and play an important role in the development of advanced equipment.However,the structural topology optimization algorithm based on traditional finite element is difficult to meet the needs of rapid product iteration.To this end,this paper proposes a coupled deep learning-based cross-scale topology optimization method to establish a deep learning model for fast generation of dual-scale topologies by integrating residual neural networks(Resnet),U-net architecture and attention mechanism in SEnet.The training data are generated using a bidirectional evolutionary structure optimization algorithm,and the model is tested with a completely new set of data.Numerical examples show that the proposed deep learning model can efficiently and accurately generate macroscopic material distribution and microscopic topology based on various boundaries.
关键词
跨尺度拓扑优化/耦合深度学习/微结构/轻量化Key words
multiscale topology optimization/coupled deep learning/micro structure/light weigh引用本文复制引用
基金项目
国家重点研发计划(升力翼)(2020YFA0710904-03)
国家自然科学基金联合基金(U20A20285)
湖南省杰出青年基金(2021JJ10016)
高新技术产业科技创新引领计划(2020GK4062)
出版年
2024
国家科技期刊平台
NSTL
万方数据