## 3 Materials and Methods
### Visual Design Method
#### 3.3.3 Fusion Analysis Views  
The correlation between various NDE methods is low. Through a review of the literature on multimodal information fusion [43; 44] and an analysis of data characteristics, we use a physical layer fusion approach, combining the features of AE and DIC data during the loading process. This approach is based on the high degree of correlation observed between data from both modalities when simultaneously collected from the same material during the applied loading process. By integrating AE and DIC data, the results from individual NDE methods can be cross-verified. For example, the boundary points between damage levels II and III, determined by DIC image feature analysis, can be further confirmed through the AE feature clustering diagram. Cross-validation of the data requires a comparison and retrospective analysis of both AE and DIC data. To address this issue, we propose a method that allows relevant information to be retained after performing analysis with single-modal non-destructive testing techniques, enabling the further verification of the damage patterns (as shown in Figure 7). Figure 7: Fusion analysis visualization. (**a**) DIC view: provides the co_DIC button to generate a damage status graph in (**c**) the Dividing Pint II area; (**b**) clustering view: offers the co_AE button to generate the cluster evaluation indicator displayed in (**c**). The blank star in (**b**) 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 (**c**); (**c**) fusion view: uses the evaluation indicator choose option to fine-tune the judgment of damage force travel points, generate a damage status graph in the Dividing Point I area, and click the graph of the Dividing Point II area to interactively generate an AE damage force loading trace line in (**b**). The AE data analysis helps delineate the boundaries of the three damage stages in the material. After the user completes each feature clustering, the relevant information can be exported to a spreadsheet (Figure 7b,c). In addition to the initial clustering data, each result row includes the boundaries of each category as well as the validity indicators, facilitating the comparison of the clustering results. When integrating DIC data, to reduce visual complexity [45], we have designed composite graphics to represent the DIC states, which also allow for the preservation of the DIC state images. The inner layer of the graphic represents three types of strain information, with the stacked bar charts in polar coordinates effectively reflecting the deformation of the DIC images corresponding to different levels of damage (Figure 7a,c Dividing Point I). Each small sector in the graphic corresponds to three segmented regions, with a larger relative radius indicating a larger damaged area. To distinguish displacement from strain information, two semicircular arcs are designed in the outer layer, where the color of the arc represents the overall displacement trend. Red indicates maximum displacement and strain, while blue represents the opposite. After in-depth discussions with domain experts, we determined that using AE features to define the first boundary and DIC features to define the second boundary is the most appropriate approach. Moreover, mutual validation between the two methods can further improve the accuracy of the results. The system design compares color histograms in the vicinity of the damaged crack region and interacts with the DIC damage state graphic (Figure 7a-c Dividing Point II).