基于深度学习的高效连续纤维复合材料结构拓扑优化方法

IF 3.8 2区 工程技术 Q1 ENGINEERING, MECHANICAL
Jicheng Li  (, ), Hongling Ye  (, ), Yongjia Dong  (, ), Zhanli Liu  (, ), Tianfeng Sun  (, ), Haisheng Wu  (, )
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引用次数: 0

摘要

本文提出了一种基于深度学习的拓扑优化方法,用于连续纤维增强复合材料结构(CFRCS)中材料布局和纤维取向的联合设计。该方法主要包括三个步骤:(1) 开发了一个由 ResUNet 参与的生成和对抗网络(ResUNet-GAN)来建立从结构设计参数到纤维增强复合材料优化结构的端到端映射,并提出了一个纤维取向色谱来表示连续纤维角度;(2) 为避免局部最优问题,利用独立连续映射法(ICM 法)和改进的主应力取向插值连续纤维角度优化(PSO-CFAO)策略构建 CFRCS 拓扑优化数据集;(3) 利用训练有素的 ResUNet-GAN 设计最优结构材料分布和相应的连续纤维取向。对基准结构的数值模拟验证了所提出的方法大大提高了 CFRCS 的设计效率和设计精度。此外,ResUNet-GAN 设计的 CFRCS 拓扑结构是通过增材制造制造的。试件的压缩实验表明,采用所提方法设计的 CFRCS 拓扑结构的刚度结构和峰值载荷均有显著提高。所提出的基于深度学习的拓扑优化方法将为工程应用中的 CFRCS 提供极大的灵活性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient deep learning-based topology optimization method for continuous fiber composite structure

This paper presents a deep learning-based topology optimization method for the joint design of material layout and fiber orientation in continuous fiber-reinforced composite structure (CFRCS). The proposed method mainly includes three steps: (1) a ResUNet-involved generative and adversarial network (ResUNet-GAN) is developed to establish the end-to-end mapping from structural design parameters to fiber-reinforced composite optimized structure, and a fiber orientation chromatogram is presented to represent continuous fiber angles; (2) to avoid the local optimum problem, the independent continuous mapping method (ICM method) considering the improved principal stress orientation interpolated continuous fiber angle optimization (PSO-CFAO) strategy is utilized to construct CFRCS topology optimization dataset; (3) the well-trained ResUNet-GAN is deployed to design the optimal structural material distribution together with the corresponding continuous fiber orientations. Numerical simulations for benchmark structure verify that the proposed method greatly improves the design efficiency of CFRCS along with high design accuracy. Furthermore, the CFRCS topology configuration designed by ResUNet-GAN is fabricated by additive manufacturing. Compression experiments of the specimens show that both the stiffness structure and peak load of the CFRCS topology configuration designed by the proposed method have significantly enhanced. The proposed deep learning-based topology optimization method will provide great flexibility in CFRCS for engineering applications.

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来源期刊
Acta Mechanica Sinica
Acta Mechanica Sinica 物理-工程:机械
CiteScore
5.60
自引率
20.00%
发文量
1807
审稿时长
4 months
期刊介绍: Acta Mechanica Sinica, sponsored by the Chinese Society of Theoretical and Applied Mechanics, promotes scientific exchanges and collaboration among Chinese scientists in China and abroad. It features high quality, original papers in all aspects of mechanics and mechanical sciences. Not only does the journal explore the classical subdivisions of theoretical and applied mechanics such as solid and fluid mechanics, it also explores recently emerging areas such as biomechanics and nanomechanics. In addition, the journal investigates analytical, computational, and experimental progresses in all areas of mechanics. Lastly, it encourages research in interdisciplinary subjects, serving as a bridge between mechanics and other branches of engineering and the sciences. In addition to research papers, Acta Mechanica Sinica publishes reviews, notes, experimental techniques, scientific events, and other special topics of interest. Related subjects » Classical Continuum Physics - Computational Intelligence and Complexity - Mechanics
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