High-strength steels are being found of great applications in different industries because of their high strength-to-weight ratio. In order to achieve a high quality weld with full weld penetration during the laser welding process of the high strength steel, acoustic monitoring based on a microphone is applied to sense the weld penetration in this study. To overcome the obstacle posed by intensive background noise, a noise reduction method called spectral subtraction is used to maximally denoise the acquired acoustic signals. Based on these denoised signals from the full weld penetration and partial weld penetration, the analyses in the time domain and frequency domain are carried out. The results indicate that the full weld penetration produces a higher sound pressure than the partial weld penetration and the corresponding acoustic signals also have different frequency distributions from 500 Hz to 1500 Hz. Based on these differences both in the time domain and frequency domain, two acoustic signatures are extracted from the denoised signals and the relationship between these extracted acoustic signatures and the depth of weld penetration is characterized by a neural network and a multiple regression analysis.

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