Laser-induced thermal damage detection in metallic materials via acoustic emission and ensemble empirical mode decomposition

• A thermal detection approach was proposed using AE and EEMD.
• A soft de-noising method was proposed based on EEMD.
• A feature extraction method based on the mean period of IMF was proposed.
• RMS of the reconstructed AE signal evidenced a strong correlation with thermal damage.
• We provide a potential and indirect approach for thermal damage detection.

A detection method of laser-induced thermal damage–surface burn, rehardening and residual stress, was studied in this work. Artificial thermal damage was produced to various steels, e.g., AISI 1045, ASTM A36 and AISI 304, by virtue of laser irradiation. The aim of the present work is to identify thermal damage through sensor and feature extraction techniques. Acoustic emission(AE) sensor and ensemble empirical mode decomposition(EEMD) method were employed for this purpose. A de-noising method was proposed to eliminate noises from original AE signals, based on EEMD. Quantified thermal damage features were obtained. Results evidenced a strong correlation between AE features, i.e., RMS value of the reconstructed acoustic emission signal, and surface burn, residual stress value, as well as hardness of steels. The present work could be used as a potential and indirect approach for thermal damage detection.