# Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding
###### Abstract  
In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwolding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwolding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval. acoustic emission; monitoring microwolding; hidden Markov model +
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pool generation. Some studies have investigated the correlation between AE signal features and defects resulting from keyhole-mode welding [10], and AE-based quality monitoring has been studied. However, it still needs more work to improve system reliability when trying to apply the AE-signal-based quality monitoring system to the production line. [PERSON] [11] was the first to apply the AE signal to the process monitoring of stainless-steel welding. The correlation between the crack and AE signal generation was reported. [PERSON] et al. [12] applied the AE signal to identify the lack of fusion in welding. [PERSON] et al. [13] installed the AE sensor on a workpiece and reported that the feature change of the AE signal could be observed for partial-penetration welding. [PERSON] et al. [14] also reported the increase in the AE signal as the plasma generated in keyhole-mode welding. [PERSON] et al. [15] reported that the energy of the AE signal decreases as the liquid state appears in the welding. [PERSON] et al. [16] observed that the frequency-domain AE signal correlated closely to the cold crack is at around 200 kHz. [PERSON] et al. [6] reviewed studies involving the AE signal for the development of the monitoring issues in welding. [PERSON] et al. [17] reported that lack-of-fusion defects and pores were successfully detected by AE signals. [PERSON] et al. [18] reported that the evidence of crack propagation after solidification of a weld is successfully captured by an AE sensor. Although the capability of the AE signal to monitor defects in welding is confirmed in most reports, the installation of AE sensors on a workpiece makes it not easy to be implemented in the production line. In 2014, close relationships between laser spot welding and AE signals were reported by [PERSON] et al. [19], and the selected features were adopted as the input features to a back-propagation artificial neural network to predict the weldability of stainless-steel sheets. Furthermore, for keyhole-mode monitoring, hybrid monitoring systems have been developed that integrate AE signals with other sound or optical signals [10; 20]. Few studies have reported on monitoring welding quality in conduction mode with AE signals. [PERSON] et al. [21] developed a quality monitoring system based on AE signals for thin-plate butt welding.