The acoustic emissions from laser lap welds in stainless steel sheet were recorded and analyzed. The acoustic signals emanating from the weld were sensed with an instrument microphone and analyzed using short-time Fourier spectra to characterize their time-frequency distributions. It was determined that the acoustic spectrum of good-quality, full-penetration welds could be differentiated from the spectra of poor-quality welds, defined as either partial-penetration welds or welds having a gap between the sheets being joined. A novel, relatively simple classifier based on total energy in the frequency band from 1 to 2 kHz correctly discriminated full penetration from partial penetration over 90% of the time on average. Partial-penetration welds had less energy in this range than did full-penetration welds. A slightly more sophisticated approach incorporating time-averaging was found to be capable of predicting penetration with reliability approaching 100% for some welds but much less in some anomalous cases. The performance of this relatively simple classification algorithm was shown to be comparable to that found in previous work using neural network-based classifiers. A classifier based on total signal energy was shown in preliminary trials to be capable of recognizing gapped lap welds from nongapped lap welds.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Biomedical Engineering