ABSTRACT

Carbon fibre reinforced plastics (CFRPs) are replacing metals in fields such as aerospace due to their high mechanical strength and low weight. They have an anisotropic behaviour, which hinders the analysis of structural impairment caused by damages like impacts. Optical lock-in thermography (OLT) can be used to assess CFRP integrity and image processing tools can be applied to measure the area affected by impacts on the thermal images. There are several alternatives for segmenting those images and this work proposes a transfer learning approach with a U-Net neural network used in characterisations of neuronal structures in microscopy for segmenting OLT images of CFRP plates with impact damages. After training and testing this tool with OLT images, using as ground truth their manual segmentation, the results were compared with four image processing combinations of methods: a filter based on two-dimensional Fast Fourier Transform with an adaptive threshold tool; an absolute thermal contrast (ATC) with a global threshold (GT) tool; the image overflow difference with GT; and principal component analysis (PCA) with GT. The results show that the U-Net was the most reliable for the proposed conditions for defective area assessment, allowing a higher safety in maintenance tasks.

摘要

碳纤维增强塑料 (CFRP) 由于其高机械强度和低重量，正在航空航天等领域取代金属。它们具有各向异性行为，这阻碍了对撞击等损害造成的结构损伤的分析。光学锁定热成像 (OLT) 可用于评估 CFRP 完整性，图像处理工具可用于测量受热图像影响的区域。有几种分割这些图像的替代方法，这项工作提出了一种使用 U-Net 神经网络的迁移学习方法，用于表征显微镜中的神经元结构，用于分割具有冲击损坏的 CFRP 板的 OLT 图像。在使用 OLT 图像训练和测试此工具后，使用他们的手动分割作为基本事实，将结果与四种图像处理组合方法进行了比较：基于二维快速傅立叶变换的滤波器和自适应阈值工具；具有全局阈值 (GT) 工具的绝对热对比度 (ATC)；与GT的图像溢出差异；和主成分分析 (PCA) 与 GT。结果表明，对于缺陷区域评估的建议条件，U-Net 是最可靠的，在维护任务中具有更高的安全性。