The increasing complexity of structures and materials, coupled with ever more stringent demands for safety and cost reduction in maintenance operations, has driven the need to develop advanced techniques for structural integrity monitoring, known as Structural Health Monitoring (SHM). In this context, this study investigates the use of image processing techniques of mode shapes in a composite sandwich panel, employing various Convolutional Neural Network (CNN) models, with the purpose of identifying damage. The main objective is to classify the type of damage, whether it is in the core, at the interface, or in the laminate, followed by the precise location of the flaw and determination of damage dimensions in terms of length and width. To achieve these goals, the finite element method was used to create a representative database, and subsequently, an efficient data management system and model implementation were established to optimize computational costs. The results obtained show that, in the classification task, a high accuracy in damage type identification (99%) was achieved, even when images had high levels of noise. Regarding the regression task for damage localization, satisfactory results were obtained, while the dimensioning of damage length proved acceptable, albeit with some limitations, and inclination identification presented challenges. This study represents a significant advancement in the field of SHM and explores both the advantages and limitations of using Convolutional Neural Networks for this purpose. The results obtained provide valuable insights for the practical application of these techniques in the detection and assessment of damage in sandwich structures, thus contributing to enhancing the safety and efficiency of maintaining such structures.

中文翻译：

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使用卷积神经网络识别夹层结构的损伤


结构和材料的复杂性日益增加，加上对维护操作的安全性和降低成本的要求越来越严格，推动了开发结构完整性监测先进技术（称为结构健康监测（SHM））的需求。在此背景下，本研究研究了模态图像处理技术在复合材料夹芯板中的应用，采用各种卷积神经网络（CNN）模型，以识别损坏。主要目标是对损坏类型进行分类，无论是在芯部、界面还是层压板中，然后精确定位缺陷并确定损坏尺寸（长度和宽度）。为了实现这些目标，使用有限元方法创建代表性数据库，随后建立高效的数据管理系统和模型实现以优化计算成本。获得的结果表明，在分类任务中，即使图像具有高水平的噪声，也能实现损伤类型识别的高精度（99%）。关于损伤定位的回归任务，获得了令人满意的结果，而损伤长度的尺寸被证明是可以接受的，尽管有一些限制，并且倾斜识别提出了挑战。这项研究代表了 SHM 领域的重大进步，并探讨了为此目的使用卷积神经网络的优点和局限性。获得的结果为这些技术在夹层结构损伤检测和评估中的实际应用提供了宝贵的见解，从而有助于提高维护此类结构的安全性和效率。