Abstract

This research study employs Convolutional Neural Network (CNN) for the purpose of identifying damage related to cracks in aluminum plates. Successfully identifying features associated with damage is a pivotal step within the damage identification process. However, the process of identifying these features is known to be arduous and complex in its implementation. CNN, as a profound deep learning model, offers the unique capability to learn representations of high-dimensional data that traditional mathematical models often struggle to depict accurately. In order to harness this advantageous trait of CNN, the current study transforms the task of non-destructive evaluation pertaining to notched crack damage within metal plates into an image classification challenge within the domain of machine learning. Subsequently, a deep CNN model, trained on images derived from Lamb wave data transformation, is employed to tackle this issue effectively. Additionally, by utilizing the analytical formula of Lamb signals, numerical simulation techniques are utilized to generate Lamb signals, which are then coupled with the algorithm devised within this study to identify damage present within metal plate structures. Finally, the verification of aluminum plate damage identification was carried out. The research results show that the method proposed in this paper can efficiently and accurately identify the damage of metal plate structures. '''Keyword: '''Lamb waves; Deep learning; Damage localization.