Abstract
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.
Highlights
Laser welding is a key technology that has been used for decades to fuse various components, ensuring a low heat-affected zone on components during manufacturing
A noise-reduction method was proposed by Huang et al [14] to reduce the noise effect and to ensure that sound signals can be used in monitoring joint quality during laser welding
To analyze sound signals obtained from laser microlap welding of joints with various qualities, a number of experiments were conducted on a laser-microwelding research platform integrated with a microelectromechanical system microphone (SPM0408LE5H-TB, Knowles, Itasca, IL, USA) that had a frequency range of 100–10 kHz, and an AE sensor (8152B121, Kistler, Winterthur, Switzerland) that had a frequency range of 50–400 kHz
Summary
Laser welding is a key technology that has been used for decades to fuse various components, ensuring a low heat-affected zone on components during manufacturing. Sun [3] studied a correlation between sound-signal features and defects generated during keyhole-mode welding. Shimada et al [4] confirmed that the energy level of a sound signal generated through welding has a close relationship with laser-power density and welding-penetration condition. To study systems that monitor the quality of keyhole-mode welding, we developed a sound-based quality-monitoring system based on a neural network [10,11,12]. A noise-reduction method was proposed by Huang et al [14] to reduce the noise effect and to ensure that sound signals can be used in monitoring joint quality during laser welding. Chien et al [15] developed a quality-monitoring system on the basis of audible-sound signals for thin-plate butt welding. A hidden Markov model (HMM)-based monitoring system was applied to evaluate the quality-monitoring performance of the selected sound features
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