基于拓扑优化和深度学习的新型结构生成方法

计算机辅助设计已广泛应用于结构计算和分析,但如何利用计算机智能生成最佳的新型结构还面临巨大挑战。针对这一问题,提出了一种基于拓扑优化和深度学习的新型结构智能生成方法。该方法首先通过结构拓扑优化分析获得不同参数下的优化结果制作训练集图片,并将训练集标签定义为相应的工况类型,然后应用最小二乘生成对抗网络(LSGAN)深度学习算法进行训练并生成大量的新型结构,最后建立评价指标和评估体系对生成的模型进行评价比较,根据需求选择最佳结构设计方案。结合一个铸钢支座节点底板设计的工程案例,详细阐述了上述方法的应用过程,并借助三维重构技术和增材制造技术实现结构模型的一体化制造。研究结果表明,基于拓扑优化和深度学习的新型结构智能生成方法不仅可以自动生成新的结构,而且可以进一步优化结构的材料用量和力学性能。

The generation method of innovative structures based on topology optimization and deep learning

Computer-aided Design has been widely used in the calculation and analysis of structures,but there are still challenges in intelligently generating optimized innovative structures automatically.Aiming at solving this issue,a new method was proposed for the intelligent generation of innovative structures based on topology optimization and deep learning.Firstly,the topology optimization results under different optimization parameters were extracted using topology optimization analysis to produce the training picture set,and the training set labels were defined as the corresponding load cases.Then,the Least Squares Generative Adversarial Networks (LSGAN) deep learning algorithm was used to train for generating numerous innovative structures.Finally,the generated designs were evaluated and compared by building evaluation indexes and an evaluation system,and the optimal structure was selected according to the design requirements.Combined with an engineering case of designing a baseplate in a cast-steel support joint,the application process of the above method was described in detail.The 3D reconstruction and additive manufacturing techniques were subsequently applied to manufacture the structural models.The results show that the proposed intelligent structure generation method based on topology optimization and deep learning not only can generate innovative structures intelligently but also can optimize structural material consumption and mechanical performance further.