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• The ultrasonic velocity is very sensitive to a slight variation of porosity. • The ultrasonic signal peak frequency-shift occurs when the porosity increases. • The wavelet packet energy increases linearly with the increase of porosity. A noncontact and nondestructive method based on laser ultrasound is proposed for evaluation of porosity in additive manufacturing components. The 304L stainless steel samples with porosities in the range of 0.1%–5.7 % were fabricated using selective laser melting. Laser ultrasonic surface wave was used to characterize the porosity by the wave speed, peak frequency and wavelet packet energy. The result indicates that the surface wave speed, peak frequency and wavelet packet energy are sensitive to the slight variation of porosity in the samples. Good R-squares of fitting between surface wave velocity and porosity were obtained when it considers the pore irregular morphology. The peak frequency of surface wave decreases as the disappearance of high-frequency signals induced by the scattering attenuation. The wavelet packet analysis result shows that the sum of the percentage energy of the ninth layer of wavelet packet decomposition nodes 2–4 is linearly related to the porosity, and exhibits the highest coefficient of determination (R = 0.98) among the proposed characteristics. It is concluded that the laser ultrasonic surface wave based on wavelet packet energy is recommended to be a promising candidate for the quantitative nondestructive evaluation of the porosity of additive manufacturing components. [ABSTRACT FROM AUTHOR]

The random microdefects are inevitable during the Selective laser melting (SLM) process due to the principle of discrete-stacking. The rough surface induced strong background noise reduces the probability of detection of traditional laser ultrasonic testing system. In this study, an intelligent denoise laser ultrasonic imaging method was developed to inspect the micro defects on the rough surface of SLM components: (1) a non-contact laser ultrasonic scanning setup was established for data acquisition of multiple ultrasonic signals; (2) a denoising algorithm based on unsupervised machine learning was designed and trained by abundant ultrasonic data to enhance the signal to noise ratio (SNR); (3) a signal matching algorithm based on the cross-correlation and self-normalized method was established to match the amplitude and arrival time of Rayleigh waves from different scanning points. The performance of developed method was verified using micro hole defects on the rough surface of a SLM part. The results indicated that the established denoising algorithm could increase the average SNR from 27.0 dB to 35.2 dB. All holes with diameter of 50 μm and 100 μm can be detected and sized based on the high SNR image without removing the rough surface. The conclusion can be drawn that the proposed intelligent denoise laser ultrasonic imaging method is a very potential way for the online inspection of SLM. [ABSTRACT FROM AUTHOR]