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首页> 外文期刊>Composite Structures >Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction
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Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction

机译：基于随机介观结构重构的片状模塑料复合结构多尺度有限元建模

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摘要

Predicting the mechanical behavior of the chopped carbon fiber Sheet Molding Compound (SMC) due to spatial variations in local material properties is critical for the structural performance analysis but is computationally challenging. Such spatial variations are induced by the material flow in the compression molding process. In this work, a new multiscale SMC modeling framework and the associated computational techniques are developed to provide accurate and efficient predictions of SMC mechanical performance. The proposed multiscale modeling framework contains three modules. First, a stochastic algorithm for 3D chip-packing reconstruction is developed to efficiently generate the SMC mesoscale Representative Volume Element (RVE) model for Finite Element Analysis (FEA). A new fiber orientation tensor recovery function is embedded in the reconstruction algorithm to match reconstructions with the target characteristics of fiber orientation distribution. Second, a metamodeling module is established to improve the computational efficiency by creating the surrogates of mesoscale analyses. Third, the macroscale behaviors are predicted by an efficient multiscale model, in which the spatially varying material properties are obtained based on the local fiber orientation tensors. Our approach is validated through experiments at both meso-and macro-scales, such as tensile tests assisted by Digital Image Correlation (DIC) and mesostructure imaging.
机译：预测由于局部材料特性的空间变化而导致的短切碳纤维片状模塑料（SMC）的机械性能对于结构性能分析至关重要，但在计算上具有挑战性。这种空间变化是由压缩成型过程中的材料流动引起的。在这项工作中，开发了一种新的多尺度SMC建模框架和相关的计算技术，以提供SMC机械性能的准确和有效的预测。拟议的多尺度建模框架包含三个模块。首先，开发了一种用于3D芯片封装重构的随机算法，以有效地生成用于有限元分析（FEA）的SMC中尺度代表体积元素（RVE）模型。在重构算法中嵌入了新的纤维取向张量恢复函数，以使重构与纤维取向分布的目标特征相匹配。其次，建立元建模模块以通过创建中尺度分析的替代物来提高计算效率。第三，通过有效的多尺度模型预测宏观行为，其中基于局部纤维取向张量获得空间变化的材料特性。我们的方法已通过中尺度和宏观尺度的实验验证，例如通过数字图像关联（DIC）和介观结构成像辅助的拉伸试验。

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  • 来源
    《Composite Structures》 |2018年第3期|25-38|共14页
  • 作者

    Chen Zhangxing; Huang Tianyu; Shao Yimin; Li Yang; Xu Hongyi; Avery Katherine; Zeng Danielle; Chen Wei; Su Xuming;

  • 作者单位

    Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China;

    Northwestern Univ, Evanston, IL 60201 USA;

    Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China;

    Ford Motor Co, Res & Adv Engn, Dearborn, MI 48121 USA;

    Ford Motor Co, Res & Adv Engn, Dearborn, MI 48121 USA;

    Ford Motor Co, Res & Adv Engn, Dearborn, MI 48121 USA;

    Ford Motor Co, Res & Adv Engn, Dearborn, MI 48121 USA;

    Northwestern Univ, Evanston, IL 60201 USA;

    Ford Motor Co, Res & Adv Engn, Dearborn, MI 48121 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    SMC; Multiscale; RVE; Mesostructure reconstruction; Orientation tensor;

    机译：SMC;多尺度;RVE;细观重构;取向张量;

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