LAUSR

Abstract In this study, an effective approach was presented for the decomposition and reconstitution of ultrasonic signals and the creation of a prediction model to characterize the average grain size of materials via a nondestructive on-line evaluation technique. Aluminum specimens with diverse grain sizes were handled by different thermal treatments and detected using a laser ultrasonic technique. The ultrasonic signals were decomposed into intrinsic mode functions that were characteristic of high to low frequencies by empirical mode decomposition and ensemble empirical mode decomposition methods, and the results were compared. The ensemble empirical mode decomposition method was adopted due to its ability to reduce mode mixing. After the correlational analyses between the intrinsic mode functions and the signal, the high-frequency noise and the linear trend terms were discarded, and the remainder of the useful constituents was chosen to rebuild the ultrasonic signal. Finally, a grain size characterization model was established by an energy attenuation coefficient. This novel approach is a rapid and precise method for grain size evaluation and laser-ultrasonic technology is a promising nondestructive examination method due to its advantage of noncontact and online evaluation.