A Laser-Induced Audible Metal Defect Detection Method Based on Spectral Discriminative Weights

This paper proposes a metal defect detection method based on laser-induced audible sound testing (LAST). Defective and defect-free martensitic stainless-steel cubes were used as study samples, and the spectral characteristics of the acoustic signals generated under laser irradiation were comparatively analyzed. Based on F-ratio analysis, weighting curves characterizing the discrimination capability of each frequency band were calculated. Subsequently, nonlinear filter banks were designed according to the spectrum discrimination weights, tailored to the degree of spectrum discrimination. Finally, a globally weighted cepstral coefficient (GWCC) extraction algorithm for laser-induced acoustic signals was developed to determine whether defects are present in metals. Experimental results show that the recognition rate of defective samples based on GWCC features reached 94%, higher than that of traditional acoustic features, effectively enhancing feature discriminability. The results of this study demonstrate that applying LAST to metal defect detection is feasible. This method leverages laser-generated acoustic signals from a more comprehensive and economical perspective, pioneering a new solution for non-destructive testing of metal defects.