## 4 Evaluation and Discussion
### User Study  
Our visual analytics system offers a promising method for analyzing multimodal data in the context of material damage. The purpose of this study was twofold as follows: to evaluate the effectiveness of our system in facilitating the proposed visualization tasks, and to obtain user feedback on the usefulness of our visual design. We recruited a cohort of 10 students between the ages of 22 and 27 (7 male, 3 female) with a mean age of 24.5 years (standard deviation of 1.23). These participants consisted of both master's and doctoral students in the field, with a background in materials science and normal or corrected-to-normal vision. Each session lasted approximately 20 min and was conducted on a 15.6-inch ASUS TUF Gaming F15 laptop. The first 5 min were spent explaining the visualization system, followed by 15 min of free exploration. Participants then had 10 min to complete a series of six tasks designed to utilize all visual components and reflect the full range of visualization tasks outlined in Section 4. We recorded the time it took participants to complete each task from the start to write down their responses. The visualization system was reset before each task. Finally, we administered a survey to each participant. Figure 11: Comparison of different force loading values. (**a**) DIC view and color feature view of \(E_{xx}\) at a force loading of 0.80; (**b**) DIC view and color feature view of \(E_{xx}\) at a force loading of 0.95; (**c**) DIC view and color feature view of \(E_{xx}\) at a force loading of 0.98; (**d**) AE boundary verification line in (**c**); (**e**) dividing point graph in (**c**). 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 (**c**). In Figure 12, we present a description of each user's tasks and completion times. All participants completed each task within 53 s, with an average completion time of less than 34 s. Specifically, for UT1 and UT3, participants quickly grasped the essence of the problem and completed the tasks quickly, while for UT2, UT4, and UT6, they needed further familiarization with the operational flow before completing the tasks. We observed a significant increase in completion time for UT5 compared to the other tasks, primarily due to its greater complexity. The questionnaire was designed to evaluate the usefulness of our system and to collect explicit feedback on the visual design. Using the suggestions provided by E0, we developed six questions as shown in (Figure 13) to ensure validity. We used histograms with error bars to show the mean and standard deviation of each question. Overall, all participants agreed that our system is useful for multimodal analysis tasks related to material damage (Q1). For AE analysis (Q2, Q3), the average rating for our system was 4.2 out of 5, which is very encouraging. Our participants were satisfied with the feature selection and clustering processes, and the evaluation of the DIC view (Q5) was also positive. However, there was mixed feedback on the views within the fusion analysis area (Q6), with some debate as to whether AE features should be included in a glyph design. Participants' satisfaction with the color feature view corresponding to the damage (Q4) was low. They stated that they were not very familiar with the color feature view and somewhat misunderstood damage tracing. Regarding the representation of AE features using glyphs, we conducted an analysis. The original glyphs consisted of DIC features, and users interacted with the glyphs to observe the AE feature information associated with the corresponding damage degree in the cluster view.