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潘万彬, 占钰琪, 王姝钫, 王毅刚, 陶秀挺. 面向薄板刚度可定制的双分辨率晶格结构智能生成方法[J]. 计算机辅助设计与图形学学报. DOI: 10.3724/SP.J.1089.2023-00338
引用本文: 潘万彬, 占钰琪, 王姝钫, 王毅刚, 陶秀挺. 面向薄板刚度可定制的双分辨率晶格结构智能生成方法[J]. 计算机辅助设计与图形学学报. DOI: 10.3724/SP.J.1089.2023-00338
Wanbin Pan, Yuqi Zhan, Shufang Wang, Yigang Wang, Xiuting Tao. A Smart Generation Approach of Dual-resolution Lattice Structure for Customizing the Stiffness of Thin-Walled Parts[J]. Journal of Computer-Aided Design & Computer Graphics. DOI: 10.3724/SP.J.1089.2023-00338
Citation: Wanbin Pan, Yuqi Zhan, Shufang Wang, Yigang Wang, Xiuting Tao. A Smart Generation Approach of Dual-resolution Lattice Structure for Customizing the Stiffness of Thin-Walled Parts[J]. Journal of Computer-Aided Design & Computer Graphics. DOI: 10.3724/SP.J.1089.2023-00338

面向薄板刚度可定制的双分辨率晶格结构智能生成方法

A Smart Generation Approach of Dual-resolution Lattice Structure for Customizing the Stiffness of Thin-Walled Parts

  • 摘要: 晶格化是生成轻质高刚度薄板的理想方法, 具有大幅度减少材料消耗和提升产品性能的巨大潜力. 为了快速有效且通用地对薄板进行晶格化, 使其成为满足刚度需求的轻质薄板, 提出一种智能的双分辨率晶格化方法. 首先对给定的标准形状的薄板(矩形横截面)采用不同的双分辨率晶格单元数量比例, 并以均匀随机采样布局的方式构建一系列双分辨率晶格化薄板; 然后采用有限元方法对它们进行力学分析, 计算相关的刚度; 再收集上述数据, 基于人工神经网络(ANN)构建刚度预测模型, 准确地预测给定的薄板在不同双分辨率晶格结构下的刚度(相互之间具有复杂的非线性关系); 最后以生成满足刚度需求的双分辨率晶格化薄板为目标, 构造数学规划问题, 通过在粒子群优化算法中融入上述刚度预测模型对上述问题实施求解, 获得优化的双分辨率晶格结构. 实验结果表明, 所提方法能快速有效且通用地生成刚度满足要求的轻质薄板.

     

    Abstract: Lattice-generation is an ideal method for generating lightweight and high-stiffness thin plates, with great potential to significantly reduce material consumption and improve product performance. To lattice thin plates quickly, effectively, and universally into lightweight thin plates that meet stiffness requirements, a smart generation approach of dual-resolution lattice structure is proposed. Firstly, for thin plates with a given standard shape (rectangular cross-section), a series of dual-resolution lattice shaped thin plates are constructed using different ratios of dual-resolution lattice units and a uniform random sampling layout. Secondly, finite element method is used to perform mechanical analysis on them and calculate the relevant stiffness. Thirdly, the above data are collected and construct a stiffness prediction model based on an Artificial Neural Network (ANN), which accurately predict the stiffness of a given thin plates under different dual-resolution lattice structures (with complex nonlinear relationships among them). Finally, a mathematical planning problem is constructed with the goal of generating thin plates with dual-resolution lattices that satisfy the stiffness requirements. The above problem is solved by incorporating the above stiffness prediction model in the particle swarm optimization algorithm to obtain an optimized dual-resolution lattice configuration. The experimental results show that the approach can generate lightweight thin plates with the required stiffness in a fast, efficient, and general way.

     

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