• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2024, Vol. 60 ›› Issue (1): 85-95.doi: 10.3901/JME.2024.01.085

• 特邀专栏:高性能制造专栏 • 上一篇    下一篇

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数据驱动的指定变形非均质多胞结构优化设计

许思远1,2, 张卫东3, 毛玉明3, 牛斌1,2   

  1. 1. 大连理工大学高性能精密制造全国重点实验室 大连 116024;
    2. 大连理工大学机械工程学院 大连 116024;
    3. 上海宇航系统工程研究所 上海 201109
  • 收稿日期:2023-03-16 修回日期:2023-05-08 发布日期:2024-03-15
  • 作者简介:牛斌(通信作者),男,1981年出生,教授,博士研究生导师。主要研究方向为材料结构功能一体化设计制造,结构动力学拓扑优化。E-mail:niubin@dlut.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(52375232,51975087)。

Data-driven Design Optimization of Heterogeneous Cellular Structures for Realizing the Specified Deformation

XU Siyuan1,2, ZHANG Weidong3, MAO Yuming3, NIU Bin1,2   

  1. 1. State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, Dalian 116024;
    2. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024;
    3. Shanghai Institute of Aerospace Systems Engineering, Shanghai 201109
  • Received:2023-03-16 Revised:2023-05-08 Published:2024-03-15

摘要: 针对包含大量异质微结构的非均质多胞结构设计难题,提出了可指定结构变形方式的多胞结构宏微观优化设计方法。该方法在宏观结构设计中采用梯度类方法优化属性分布,引入可行性约束保证微结构确实存在,在微结构设计中引入数据驱动的方法,利用深度学习实现微结构逆向设计并映射到宏观结构,同时采用微结构偏移和兼容性算法解决相邻微结构连接不兼容的问题,最终通过指定变形问题的多个数值算例验证了该方法的有效性,并讨论了单元尺寸、微结构偏移次数以及微结构映射密度对结构性能的影响。建立的多胞结构设计方法引入数据驱动方法和考虑了微结构连接兼容性要求,成功实现多样化指定变形的非均质多胞结构优化设计。

关键词: 多胞结构, 指定变形, 深度学习, 微结构偏移

Abstract: Regarding the challenge of realizing the design optimization of heterogeneous cellular structures, a design optimization method is proposed for designing the heterogeneous cellular structures with specified deformation. In the macro design part, the gradient-based optimization method is used to optimize the distribution of equivalent properties, and the feasibility constraint is introduced to ensure the realizability of microstructures. The data driven method is introduced into the microstructure design, where the deep learning is used to realize the inverse design of the microstructure. Then, the microstructures are mapped to the macro structure, which finally results into non-uniform design. At the same time, the offset and compatibility algorithm of microstructures are used to tackle the problem of incompatibility between adjacent microstructures. Finally, the effectiveness of the method is verified by several numerical examples of the specified deformation problem, the effects of size of elements, the number of microstructural offsets, and the microstructure mapping density on the structural performance are discussed. The proposed method successfully realizes the requirement of specifying the deformation mode of the heterogeneous cellular structure by introducing the data driven method and the compatibility of adjacent microstructures.

Key words: heterogeneous cellular structure, specified deformation, deep learning, microstructure offset

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