# Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding
## 4 Results and Discussions
However, unlike those in Figure 8a, the energy levels of the frequency-domain AE signals obtained for the first three 1 ms intervals of welding (Figure 8b) differed for normal and low bonding strength, especially for signals of >200 kHz. Therefore, the frequency signals for the first millisecond of welding were considered suitable features for the development of a monitoring system to identify the loss of bonding strength caused by gaps generated during welding. To determine the ability of each feature to identify joint bonding conditions, an index J, based on the ratio of between-class to in-class distribution, was obtained for each of the three aforementioned features for each of the first three 1 ms intervals. The index calculated for all frequency-domain features generated from the AE signals collected during laser operation (Figure 9). The feature with the highest J was obtained at 300 kHz. Figure 10 presents J for signals collected only in the first millisecond of laser operation. The feature with the highest J was located at 300 kHz; moreover, its J was higher than that presented in Figure 9 for the same frequency. This result agrees well with the findings presented in Figures 7 and 8. The AE features' signals for each millisecond of welding are shown in Figure 11; the root mean square (RMS) and gradient of the signals for each 1 ms interval were calculated (Figures 12 and 13, respectively). The average RMS and gradient of the signals were obtained from 20 samples for each 1 ms interval. The difference between the RMS of the cases with high and low bonding strength was obtained for the first four intervals. The gradient of signals from the first interval seems to be the most suitable for identifying low bonding strength. Therefore, the RMS of the AE signal in the first 4 ms and the gradient of the signal in the first millisecond were considered candidates for identifying the joint strength. To determine the ability of each feature to identify joint conditions, the index J for both time-domain features for each interval was recalculated. The RMS and gradient of the signals in the first millisecond exhibited the highest J (Figure 14) and thus were considered the most suitable candidates, along with the frequency features of 300 kHz, for inputs for classifiers. To evaluate the performance of the selected features for classifying the joint bonding strength from laser lap microwelding, an HMM classifier was implemented with those features. The model was trained on 10 data sets obtained from 10 samples and evaluated using another 10 data sets. Classification accuracy of 100% was obtained  
Figure 8: Frequency-domain AE signals collected during laser microwelding for different 1 ms intervals of the welding process: (**a**) low and (**b**) normal bonding strengths. (Table 3) with the features of the AE signal gradient from the first millisecond. Furthermore, by combining the 300 kHz frequency and RMS features of the first millisecond together, 100% classification accuracy was achieved based on the conditions in this study. Figure 10: _Cont._  
Figure 9: Raw signal and classification index for frequency features (**a**) raw signal for index calculation (**b**) classification index (signals collected throughout laser operation). Figure 11: Intervals of AE signals for (**a**) low and (**b**) normal joint strength. Figure 10: Raw signal and classification index for frequency features (**a**) raw signal for index calculation (**b**) classification index (signals collected in the first millisecond of laser operation). Figure 14: Classification index for each 1 ms interval: (**a**) RMS and (**b**) gradient of signals. Figure 12: RMS of AE signals at eight 1 ms intervals for joints with normal and low bonding strength. Figure 6:0 Gradient of AE signals for eight 1 ms intervals for joints with normal and low bonding strength.