摘要
针对以固体各向同性材料法(SIMP)、水平集法(Level-set)等为代表的传统连续体拓扑优化算法存在的计算代价昂贵、生成结构几何隐式等缺陷,结合深度学习与可移动变形组件法(MMC),提出一种基于深度学习的两阶段实时显式拓扑优化方法。在第一阶段使用深度学习模型预测取代有限元分析的多数费时迭代计算,第二阶段对深度学习模型预测所得结构进行少量迭代微调,形成最终的带有显式几何特征的优化结构。在相对一般的数据集下定量与定性地验证了本文方法的可行性与有效性,并研究了第一阶段深度学习模型的训练程度与最终生成结构质量及总体耗费时间的关系。结果表明:与传统连续体拓扑优化算法相比,本文方法能在保证拓扑优化结构生成质量的同时节约90%以上的计算时间。
To overcome the shortcomings like computational expensive and inability to generate structures with explicit geometry of traditional topology optimization algorithms represented by the Solid Isotropic Material with Penalization(SIMP)method and the level-set method,a deep-learning-based two-stage approach for real-time explicit topology optimization was proposed,which combined deep learning with the moving morphable components(MMC)method.In the first stage,a deep learning model was used to replace most of the time-consuming finite element analysis.The second stage performed a small number of iterative fine-tuning of the structure predicted by the deep learning model to generate the final optimized structure with explicit geometry.A relatively general data set was used to verify the feasibility and effectiveness of the framework quantitatively and qualitatively,and the relationship between the training degree of the deep learning model in the first stage and the quality of the structure generated as well as the total time consumed was studied.Experimental results show that this approach can save more than 90%of the computing time while maintaining the quality of the topology optimization structures generated.
作者
孙舒杨
程玮斌
张浩桢
邓向萍
齐红
SUN Shu-yang;CHENG Wei-bin;ZHANG Hao-zhen;DENG Xiang-ping;QI Hong(College of Computer Science and Technology,Jilin University,Changchun 130012,China;Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2023年第10期2942-2951,共10页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(U20A20285,62072211)。
关键词
计算机应用
拓扑优化
深度学习
可移动变形组件法
计算机辅助设计
computer application
topology optimization
deep learning
moving morphable components method
computer-aided design
