Engineering interleaves of composite laminates with carbon nanotubes (CNTs) improves interlaminar fracture toughness, creating also conductivity, which can be employed for damage identification. The paper explores machine learning (ML) solution of the inverse problem of the defect identification for interleaves with anisotropic conductivity (aligned CNTs). The electrical and geometrical properties of the interleave are assigned based on the synchrotron X-ray computer tomography of glass fibre / epoxy laminates with nanostitch. Several machine learning (ML) models are applied (XGBoost, fully connected (FCNN) and convolution neural (CNN) networks). XGBoost and FCNN algorithms performed poorly, failing to detect smaller defects and giving significant errors for larger ones. CNN algorithm detects defects well: It predicts the geometric characteristics of the defect with error below 16 %.

中文翻译：

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用于各向异性导电纳米工程复合材料与真实几何交错中裂纹检测的机器学习


使用碳纳米管 （CNT） 的复合材料层压板的工程交错提高了层间断裂韧性，还产生了导电性，可用于损伤识别。本文探讨了具有各向异性电导率（对齐的 CNT）的交错缺陷识别逆问题的机器学习 （ML） 解决方案。交错的电学和几何特性是根据纳米缝合的玻璃纤维/环氧树脂层压板的同步加速器 X 射线计算机断层扫描分配的。应用了多种机器学习 （ML） 模型（XGBoost、全连接 （FCNN） 和卷积神经网络 （CNN） 网络）。XGBoost 和 FCNN 算法性能不佳，无法检测到较小的缺陷，而较大的缺陷会给出重大错误。CNN 算法可以很好地检测缺陷：它可以预测缺陷的几何特征，误差低于 16%。