## 4 Results and Discussions
### Scenario II: Identification of Gap Size
#### 4.3.1 Classification Accuracy  
Figure 7a shows the average accuracy of 50 RLT iterations over varying segment durations. Overall, the accuracy of quantifying the gap size is lower compared to that of gap detection. This is particularly true for the structure-borne sensor (SB1), whose highest accuracy is around 90%. On the other hand, the highest accuracy of the airborne sensor (AB) is slightly below 92.5%. Nevertheless, in a similar manner to that observed with gap detection, the accuracy of identifying gap size increases with duration and starts to saturate from a certain point. Contrary to gap detection, both sensors have similar saturation points at around 25 ms when it comes to identifying the gap size. This indicates that signal segments can be shortened to 25 ms while maintaining their informativeness about the gap size. Noticeably, the accuracy of airborne sensor signals (AB) is consistently higher  
Figure 6: Exemplary illustration of segments separability for different segment durations \(T\). Since the dimension of the input vectors \(\mathbf{x}\in\mathbb{R}^{2048}\) is reduced to 2 (=number of classes minus one) after NCA clustering, the results shown here are the output vectors \(\mathbf{y}=[c_{1},c_{2}]^{\mathrm{T}}\in\mathbb{R}^{2}\) in two dimensional images. The cluster regions are represented by different colors (purple for Zero Gap, blue for Gap and yellow for Noise). For exemplary purpose, these results are obtained by running a single training-test split for each duration. The channels mentioned in the results are the structure-borne ultrasound sensor at the beginning (SB1) and the airborne ultrasound sensor (AB). than that of structure-borne sensor signals (SB1). The possible reasons for this performance difference will be discussed in this section. For further insights, the average confusion matrices are provided in Figure 7b for three different durations. Compared to other classes, classification accuracy for identifying a 0.3 mm gap (Gap 0.3) remains remarkably high under varying durations for both sensor types. This implies that an adequate-sized gap results in signal characteristics that are observable in a very short duration of acoustic process emissions. Interestingly, if segment duration is 1 ms, the Gap 0.3 segments can be better predicted than the Noise segments,  
Figure 7: Illustration of how the classification accuracy of gap size changes depending on the segment duration \(T\). The equivalent weld seam length is provided on top (in purple), which indicates that weld seam is progressed for 0.2 mm in 1 ms. The results are obtained by conducting repeated learning-testing validations for 50 iterations based on Scenario II, where the segments are classified into five classes (Zero Gap, Gap 0.1, Gap 0.2, Gap 0.3 and Noise). Top row (**a**): average accuracy with error bars (standard deviation) and bottom row (**b**): average confusion matrices for selected segment duration. The channels mentioned in the results are the structure-borne ultrasound sensor at the beginning (SB1) and the airborne ultrasound sensor (AB). where the Noise segments are misclassified more as Zero Gap segments than Gap 0.3 ones. This suggests that the acoustic emissions induced by a large-sized gap are not merely the lack of emissions and a decrease in signal energy but also have distinctive signal characteristics. When the results of both sensor types are compared, the accuracy of classifying Gap 0.2 is significantly different. If segments are longer than 25 ms, airborne sensor signals (AB) yield 94% accuracy of correctly classifying the Gap 0.2 segments, whereas the accuracy of structure-borne signals (SB1) is low around 75%. This tendency continues when segments become shorter. A possible explanation for the impaired accuracy is the difficulty in maintaining the same measurement condition for the structure-borne sensors. The structure-borne sensors were mounted anew every time a new specimen was set.