Neural classification of Lamb wave ultrasonic weld testing signals using wavelet coefficients

This paper presents an ultrasonic nondestructive weld testing method based on the wavelet transform (WT) of inspection signals and their classification by a neural network (NN). The use of Lamb waves generated by an electromagnetic acoustic transducer (EMAT) as a probe allows us to test metallic melds. In this work, the case of an aluminum weld is treated. The feature extraction is made by using a method of analysis based on the WT of the ultrasonic testing signals; a classification process of the features based on a neural classifier to interpret the results in terms of weld quality concludes the process. The aim of this complete process of analysis and classification of the testing ultrasonic signals is to lead to an automated system of weld or structure testing. Results of real-world ultrasonic Lamb wave signal analysis and classifications for an aluminum weld are presented; these demonstrate the feasibility and efficiency of the proposed method.