## 2 Related Work
### NDE Method  
The understanding of the damage mechanism of composite materials and the identification of damage modes are crucial for ensuring safe design. In the field of materials engineering, NDE is an important damage detection technology that can be used to monitor the condition of various industrial materials in real time. Since its first use in 1987, the AE method has been widely applied to evaluate the failure features of composite materials [15], and it can be performed without interrupting the test. Recent studies [16; 17; 18; 19] have shown that AE signals can be associated with damage mechanisms using clustering methods based on one or more features. [PERSON] et al. [20] performed a cluster analysis using fuzzy clustering algorithms on AE signals generated during tensile tests on composite materials, extracting corresponding damage source features. [PERSON] et al. [21] used K-means clustering algorithms for the multi-parameter analysis of AE signals generated during the tensile process of composite materials, thereby revealing the different damage mechanisms of composite materials. However, due to the complex mechanical behavior of composite materials, relying on the AE method alone may not be sufficient to identify their damage  
Figure 1: NDEExplorer’s system interface. The system is organized into the following three distinct analysis areas: the AE analysis area (**a**–**d**), the DIC analysis area (**e**,**f**), and the fusion analysis area (**d**,**g**). The blank star in (**d**) is the clustering center of the horizontal dotted line closest to it. And these four lines are trend lines for clustering of AE events generated based on the values of the set clustering method. The dots and dotted lines of each color correspond to same color, indicating the clustering trend of a type of AE event. The red vertical line represents the location of the damage status traced according to the Dividing Point II of (**g**). modes. [PERSON] et al. [22] studied the influence of energy attenuation on AE signal features to demonstrate the limitations of relying solely on AE to identify damage. Therefore, it is crucial to equip the AE method with additional measurement technologies to accurately distinguish damage modes. For composite materials, another widely-used NDE method is DIC [23; 24], which evaluates sample damage and defects by collecting speckle images before and after material deformation. [PERSON] et al. [25] proposed a gray-scale change detection method based on DIC to monitor the damage state of materials by studying the features of gray-scale changes caused by damage during zero-strain experiments. [PERSON] [26] proposed a semantic segmentation technique based on convolutional neural networks (CNN) to classify DIC strain field images at the pixel level, which is used for the structural health monitoring of fiber-reinforced composite materials (CFRP). Both AE and DIC are widely used NDE methods used for evaluating damage in composite materials. The AE method facilitates the real-time, non-invasive monitoring of damage progression and can detect failure features without interrupting testing. However, its reliance on clustering methods and the complex behavior of composites may limit its accuracy in distinguishing specific damage modes. In contrast, the DIC method offers high spatial resolution by capturing surface deformation through speckle images, but it requires extensive image processing and is limited to surface-level damage detection. Therefore, combining the AE method with the DIC method can enhance the accuracy of damage identification. Most studies have shown that the damage process of composite materials can be divided into three stages as follows: damage initiation, damage evolution, and damage instability. [PERSON] et al. [13] compared the damage initiation and propagation of CFRP and found that DIC methods can more accurately identify the boundary points of the latter two stages compared to the AE method. Meanwhile, [PERSON] et al.