Neural network assisted multiscale analysis for the elastic properties prediction of 3D braided composites under uncertainty
Neural network assisted multiscale analysis for the elastic properties prediction of 3D braided composites under uncertainty
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DOI：
10.1016/j.compstruct.2017.06.037
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发表时间：
2018-01-01
影响因子：
6.3
通讯作者：
Rolfes, Raimund
中科院分区：
文献类型：
--
作者：
Balokas, Georgios;Czichon, Steffen;Rolfes, Raimund
近20年来，纺织复合材料的刚度预测得到了广泛的研究。复杂的纱线结构增加了特殊的性能，但也需要计算昂贵的方法来精确解决均匀化问题。编织复合材料是航空航天和汽车工业特别感兴趣的材料，因此引起了许多研究者的关注，研究和开发了提取其有效弹性性能的分析和数值方法。本文旨在研究自动化制造过程中产生的不确定性对编织复合材料弹性性能的影响。在此方向上，提出了一种快速的基于有限元的多尺度算法，允许在蒙特卡罗框架内引入不确定性并计算三维编织复合材料宏观性能的响应变异性。人工神经网络用于进一步减少计算工作量，因为它们允许在训练时快速生成大样本。在此基础上，应用基于方差的全局敏感性分析方法，通过昂贵的Sobol指数来识别最关键的不确定参数是可行的。该方法简单、准确，突出了现实不确定度量化的重要性。(C) 2017 Elsevier Ltd.版权所有。
The stiffness prediction of textile composites has been studied intensively over the last 20 years. It is the complex yarn architecture that adds exceptional properties but also requires computationally expensive methods for the accurate solution of the homogenization problem. Braided composites are of special interest for the aerospace and automotive industry and have thus drawn the attention of many researchers, studying and developing analytical and numerical methods for the extraction of the effective elastic properties. This paper intends to study the effect of uncertainties caused by the automated manufacturing procedure, to the elastic behavior of braided composites. In this direction, a fast FEM-based multiscale algorithm is proposed, allowing for uncertainty introduction and response variability calculation of the macro-scale properties of 3D braided composites, within a Monte Carlo framework. Artificial neural networks are used to reduce the computational effort even more, since they allow for rapid generation of large samples when trained. With this approach it is feasible to apply a variance-based global sensitivity analysis in order to identify the most crucial uncertain parameters through the costly Sobol indices. The proposed method is straightforward, quite accurate and highlights the importance of realistic uncertainty quantification. (C) 2017 Elsevier Ltd. All rights reserved.