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Stochastic characterization of textile reinforcements in composites based on X-ray microtomographic scans

机译:基于X射线显微断层扫描的复合材料中织物增强材料的随机表征

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摘要

A stochastic method is introduced to characterize the dual-scale geometry of textile reinforcements in composites. The fiber tows are identified automatically from X-ray microtomographic scans with a machine learning algorithm, quantifying the error of the procedure. The tow geometry is then used to construct a stochastic model as a Gaussian Random Process which permits quantification of the uncertainty in the measurements of micro-scale fiber volume fraction. The hyperparameters of the model are calibrated with a custom-built multi-objective evolutionary algorithm. The approach is illustrated by the analysis of a vinyl-ester composite reinforced with a glass fiber non-crimp fabric.
机译:引入了一种随机方法来表征复合材料中织物增强材料的双尺度几何形状。使用机器学习算法从X射线显微断层扫描自动识别纤维束,从而量化程序的错误。然后,将丝束的几何形状用于构建随机模型,作为高斯随机过程,该过程可量化微尺度纤维体积分数测量中的不确定性。使用定制的多目标进化算法对模型的超参数进行校准。通过用玻璃纤维非卷曲织物增强的乙烯基酯复合材料的分析说明了该方法。

著录项

  • 来源
    《Composite Structures》 |2019年第9期|111031.1-111031.11|共11页
  • 作者单位

    Penn State Univ, Dept Civil & Environm Engn, State Coll, PA 16802 USA;

    Ecole Polytech Montreal, Res Ctr High Performance Polymer & Composite Syst, Dept Mech Engn, Montreal, PQ H3C 3A7, Canada;

    Ecole Polytech Montreal, Res Ctr High Performance Polymer & Composite Syst, Dept Mech Engn, Montreal, PQ H3C 3A7, Canada;

    Univ Lyon, MATEIS CNRS UMR5510, INSA Lyon, 7 Ave Jean Capelle, F-69621 Villeurbanne, France;

    Univ Lyon, MATEIS CNRS UMR5510, INSA Lyon, 7 Ave Jean Capelle, F-69621 Villeurbanne, France;

    Univ Technol Compiegne, Sorbonne Univ, Ctr Rech Royallieu, Lab Roberval,FRE UTC CNRS 2012, F-60319 Compiegne, France;

  • 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

    Textile composites; X-ray microtomography; Multi-scale modeling; Uncertainty quantification;

    机译:纺织复合材料;X射线显微照相;多尺度建模;不确定度量化;

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