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《工程(英文)》 >> 2023年 第22卷 第3期 doi: 10.1016/j.eng.2022.08.019

数字孪生增强的复合材料质量预测

a School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
b Research Institute for Frontier Science, Beihang University, Beijing 100191, China
c Department of Automation, Tsinghua University, Beijing 100084, China
d School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China

收稿日期: 2022-03-29 修回日期: 2022-07-22 录用日期: 2022-08-24 发布日期: 2023-01-11

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摘要

复合材料以其优异的性能被广泛应用于许多领域。复合材料的质量缺陷会导致其构件的性能下降,成为潜在的事故隐患。当前国内外研究者通常采用实验或仿真的方法对复合材料的质量进行预测。然而,由于固化环境的不确定性和对动态、静态特征考虑不全面,因此难以准确预测复合材料的质量。为了解决这一问题,本文首先建立了复合材料的数字孪生(DT)模型,然后通过实现静态热压罐DT虚拟模型与可变复合材料DT虚拟模型的耦合,完成复合材料固化过程数字孪生模型的构建。基于该固化过程模型,生成模拟数据来增加动态特征,从而提高质量预测的准确性。最后基于获取的数据,使用极限学习机(ELM)构建复合材料质量预测模型,并通过结果分析验证了所提方法的有效性。

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