Probabilistic defect analysis of fiber reinforced composites using kriging and support vector machine based surrogates

Davidson Paul; Waas Anthony M.

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Probabilistic defect analysis of fiber reinforced composites using kriging and support vector machine based surrogates

机译：基于Kriging和支持向量机的替代物对纤维增强复合材料的概率缺陷分析。

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In this study a framework for analyzing defects and their effect on the performance of composites, using wavy fiber composites as a test case, is presented. The focus is on predicting the composite strength and failure mode in the presence of through thickness waviness. The framework utilizes surrogate models, derived from a high fidelity validated Finite Element analysis, to perform Monte Carlo simulations. The final analysis product that is developed is versatile enough to solve the forward and inverse problem, while considering partial or uncertain data as input.

机译：在这项研究中，提出了一个使用波浪纤维复合材料作为测试案例的分析缺陷及其对复合材料性能影响的框架。重点是在存在贯穿厚度波纹的情况下预测复合材料的强度和破坏模式。该框架利用从经过高保真度验证的有限元分析得出的替代模型来执行蒙特卡洛模拟。开发的最终分析产品具有足够的通用性，可以解决正向和反向问题，同时将部分或不确定数据视为输入。

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来源
《Composite Structures》 |2018年第7期|186-198|共13页
作者
Davidson Paul; Waas Anthony M.;
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作者单位

Univ Washington, William E Boeing Dept Aeronaut & Astronaut, Seattle, WA 98195 USA;

Univ Washington, William E Boeing Dept Aeronaut & Astronaut, Seattle, WA 98195 USA;

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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
Composite compression; Defects; Kinking failure; Wavy composite; Surrogate modeling; Inverse analysis; Non Destructive Inspection;

机译：复合材料压缩;缺陷;扭结失败;波浪复合材料;替代模型;反分析;无损检测;

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5. Efficient sampling-based RBDO by using virtual support vector machine and improving the accuracy of the Kriging method [D] . Song, Hyeongjin 2013

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6. Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements [O] . Christopher Bowd, Felipe A. Medeiros, Zuohua Zhang, -1

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8. Probabilistic Analysis for the Mechanical Properties of Cross-Ply Fiber- Reinforced Composite Laminate (Postprint) [R] . Chuang, S. 2006

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Probabilistic defect analysis of fiber reinforced composites using kriging and support vector machine based surrogates

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