## 1 Introduction  
Composite materials, a novel form of lightweight, high-strength materials, have received broad applications in recent years in industries like aviation, the automotive industry, and construction [1, 2, 3]. However, due to their unique structure and manufacturing process, composite materials are prone to various types of defects and damage [4, 5], such as delamination and matrix cracking. Therefore, analyzing their damage characteristics and evolution process contributes to their safe operation and extended service life. Non-destructive examination (NDE) technology allows for the evaluation of the material's health condition without causing any damage. It has proven to be an effective method for inspecting and assessing both the internal and surface structural changes of a specimen by detecting variations in sound, light, heat, and other signals caused by anomalies or defects in the material's structure [6; 7]. To explore the defects and damage of composite materials during their usage, researchers typically employ acoustic emission (AE) testing and digital image correlation (DIC) testing [8]. Although the AE method is well-established, studies have shown that its application in material damage analysis has certain limitations [9; 10]. AE methods can typically identify the general region of material failure, but they often lack the spatial resolution necessary to precisely localize the failure points. For example, the AE tool developed by Physical Acoustics Corporation (PAC) ([[https://www.physicalacoustics.com/](https://www.physicalacoustics.com/)]([https://www.physicalacoustics.com/](https://www.physicalacoustics.com/)) (accessed on 15 December 2024)) is effective in collecting and analyzing AE signals; however, its positioning accuracy is limited, and the signal analysis process can be complex. In contrast, the DIC method [11; 12] offers a more precise approach for identifying characteristic locations on the material surface, enabling the analysis of strain evolution under applied stress. This method also facilitates the study of how different loading conditions influence material stress, deformation, and fracture failure. For instance, the VIC-3D system ([[https://www.correlatedsolutions.com/vic-3d](https://www.correlatedsolutions.com/vic-3d)]([https://www.correlatedsolutions.com/vic-3d](https://www.correlatedsolutions.com/vic-3d)) (accessed on 15 December 2024)) allows for high-precision displacement and strain field measurements. However, the accuracy of these measurements can be influenced by factors such as image quality and computational complexity. In recent years, several studies [13; 14] have proposed frameworks for multimodal data fusion in material damage analysis. Despite this progress, challenges remain in effectively extracting valuable information and achieving a comprehensive understanding of the damage evolution process. In this article, we propose an interactive visual analytics tool, NDEExplorer (Figure 1), which focuses on addressing two challenges. The first challenge is the efficient and intuitive presentation of NDE results to staff. NDEExplorer integrates technologies such as principal component analysis, an improved K-means clustering algorithm, and image segmentation to extract damage features. It allows users to perform interactive clustering experiments by continuously selecting representative features from the dataset to uncover potential damage pattern information. The second challenge is correlating the detection results from multiple NDE methods through damage features. To assist users in observing material displacement and deformation during the damage process, NDEExplorer designs an innovative slide-like view. This view allows users to visualize the displacement and deformation in DIC images, with key features extracted for each frame and linked to AE features.