Acoustic emission (AE) signals collected from different locations might provide various sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from sensors on workpieces has been reported in a number of papers. However, it is not feasible to implement in the production line. To study the feasibility of AE signals obtained from sensors on spindles to monitor tool wear in micro-milling, AE signals obtained from the spindle housing and workpiece were collected simultaneously and analyzed in this study for micro tool wear monitoring. In analyzing both signals on tool wear monitoring in micro-cutting, a feature selection algorithm and hidden Markov model (HMM) were also developed to verify the effect of both signals on the monitoring system performance. The results show that the frequency responses of signals collected from workpiece and spindle are different. Based on the signal feature/tool wear analysis, the results indicate that the AE signals obtained from the spindle housing have a lower sensitivity to the micro tool wear than AE signals obtained from the workpiece. However, the analysis of performance for the tool wear monitoring system demonstrates that a 100% classification rate could be obtained by using spindle AE signal features with a frequency span of 16 kHz. This suggests that AE signals collected on spindles might provide a promising solution to monitor the wear of the micro-mill in micro-milling with proper selection of the feature bandwidth and other parameters.

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding 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 microwelding 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.

This study focused on correlation analysis between welding quality and sound-signal features collected during microlaser welding. The study provides promising features for developing a monitoring system that detects low joint strength caused by a gap between metal sheets after welding. To obtain sound signals for signal analysis and develop the monitoring system, experiments for laser microlap welding were conducted on a laser microwelding platform by installing a microelectromechanical system (MEMS) microphone away from the welding point, and an acoustic emission (AE) sensor on the fixture. The gap between two metal sheet layers was controlled using clamp force, a pressing bar, and the appropriate installation of a thin piece of paper between the metal sheets. After sound signals from the microphone were collected, the correlation between features of time-domain sound signals and of welding quality was analyzed by categorizing the referred signals into eight sections during welding. After appropriately generating the features after signal analysis and selecting the most promising features for low-joint-strength monitoring on the basis of scatter index J, a hidden Markov model (HMM)-based classifier was applied to evaluate the performance of the selected sound-signal features. Results revealed that three sound-signal features were closely related to joint-strength variation caused by the gap between two metal-sheet layers: (1) the root-mean-square (RMS) value of the first section of sound signals, (2) the standard deviation of the first section of sound signals, and (3) the standard deviation to the RMS ratio of the second section of sound signals. In system evaluation, a 100% classification rate was obtained for normal and low-bonding-strength monitoring when the HMM-based classifier was developed on the basis of the three selected features.