工艺与制造英文2024被引 12
树脂传递模塑中基于神经网络代理模型的实时贝叶斯反演
Real-time Bayesian inversion in resin transfer moulding using neural surrogates
M.E. Causon, Marco Iglesias, M.Y. Matveev, A. Endruweit, M. V. Tretyakov · University of Nottingham
摘要整理
在树脂传递模塑（RTM）工艺中，增强材料局部性能的变异（孔隙率和渗透率）以及增强材料边缘间隙的形成导致树脂流动模式不均匀，可能引发复合材料制品缺陷。集合卡尔曼反演（EKI）算法已被用于反演工艺过程数据以估计局部增强材料性能。然而，该算法在某些应用中的实施受到需要运行数千次计算成本高昂的树脂流动仿真的限制。本研究采用机器学习方法训练代理模型，可近乎实时地模拟树脂流动仿真。通过将流动域划分为低维表示，人工神经网络（ANN）代理模型能以简单的网络结构实现精准预测。当ANN集成于EKI算法中时，可实时获得局部增强材料渗透率和孔隙率的估计值，虚拟实验和室内实验均验证了该方法的有效性。由于EKI采用贝叶斯框架，估计值以置信区间形式给出，可在线评估增强材料各区域缺陷的概率。该框架在室内实验中表现出良好的预测能力，增强材料性能估计值的计算时间均在1 s以内。
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