Search

• A transient thermal grating (TTG) method is induced by pulsed laser. • The time–frequency scalograms obtained by wavelet transform are used to train a DCNN model. • The proposed method can characterize the penetration depth of cracks reliably and flexibly in automatic detection. An effective nondestructive testing technique that enables the detection and quantification of surface and subsurface defects is highly demanded for assuring the safety and reliability of materials. In this paper, we classified and regressed defects with different depths. A transient thermal grating (TTG) method induced by a pulsed laser is used to characterize the properties of surface defects. Based on thermo-elasticity theory, a theoretical model with different types of surface defects is established with the finite element method (FEM). Surface acoustic waves (SAWs) excited by the TTG method propagate at the surface of a sample with defects at different penetration depths and are measured by a vibrometer. By combining the simulation and experimental results, the time–frequency scalograms of 8200 images were obtained by wavelet transform analysis. 70 % of the images are randomly selected to train the residual network (Resnet) model, and the remaining data (2460 images), as well as 10 scalograms in an independent experiment, are used as previously unknown experimental signals to test the robustness of the Resnet model. The results indicate that the Resnet with scalograms via wavelet transform achieves a 96.21 % classification accuracy on the validation set, with a prediction accuracy of 90.00 % for the 10 scalograms. Moreover, compared with three widely used machine learning algorithms, VGG16, back propagation (BP), and support vector machine (SVM), this method achieves the highest classification accuracy. Finally, 500 data selected randomly from the previously trained experimental database and 10 data in an independent experiment are used for regression prediction. The result shows that the data are well located along or close to a straight line with a slope of 1. The statistical results suggest that 80.6 % (66.0 %) of the residuals of the experimental data fell within the size range ±10 μm (±5 μm). This work proves the feasibility and reliability of the combination of the Resnet model with wavelet transform and numerical simulation to identify the depth of surface defects using laser ultrasonics. [ABSTRACT FROM AUTHOR]

• 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]

• Defects in additive manufactured samples are characterized by laser ultrasound. • The detection accuracy can reach 300μm, confirmed by X-ray computed tomography. • Build theoretical model and simulation results are confirmed by the experimental. With the development of 3D printing technique, additive manufactured (AM) components are becoming more and more important in the fields of manufacturing industry. However, in the process of manufacturing and utilizing, it is necessary to inspect the appearance of internal defects to ensure safety and to reduce economic losses. In this paper, a pulsed laser is used to generate the ultrasonic waves inside Nickel (Ni) and Titanium (Ti) based alloy samples with internal defects of different diameters, and the propagating ultrasonic waves are detected by an optical interferometer. By analyzing the experimental results, defects with diameters greater than 300 μm in the Ni based alloy sample and that in the Ti based alloy sample can be detected, and this can be validated by X-ray computed tomography. In addition, the effects of ultrasonic waves propagating inside the samples and interacting with the defects are studied theoretically. The experimental results are consistent with the numerical simulation results. These results present that laser ultrasonic technology can evaluate the reliability of AM components, and has the potential to inspect AM components during their production as a non-contact method. [ABSTRACT FROM AUTHOR]

(TiVCrNbSiTaBY)N high entropy alloy nitride (HEAN) coatings have been deposited onto Si (100) and cemented carbide substrates by radio frequency magnetron sputtering. The deposition is carried out at different bias voltages to study its influence on microstructure, morphology, mechanical properties and tribological behaviour of the coatings. Grazing Incidence X-Ray Diffraction (GIXRD) technique shows that all coatings exhibit single NaCl-type face-centered cubic (FCC) crystal structure, while the preferred orientation of the crystal structure is greatly affected by the bias voltages. High resolution transmission electron microscopy (TEM) reveals that the structure of the coatings is a mixture of amorphous-like phase and FCC crystal structure, which is in agreement with GIXRD data. Cross-sectional Scanning Electron Microscopy (SEM) images exhibit that the columnar structure of the coatings gradually transforms into a featureless dense structure with the increase of bias voltage. SEM and Atomic Force Microscopy reveal that the surface morphology become smoother with increasing bias voltage. The (TiVCrNbSiTaBY)N HEAN coating deposited at a specific bias voltage exhibit a high hardness of 32.2 GPa. The friction coefficient is significantly influenced by the variation of the coatings surface roughness. Tribological tests for coating deposited without and at low bias voltage show that the wear behavior of the coatings is severe abrasive wear and adhesive wear, while at high bias voltage the slight abrasive wear appears. By applying the substrate bias voltage, the (TiVCrNbSiTaBY)N coatings exhibit excellent mechanical and tribological performance, which can be a promising candidate for protective coating in cutting tools or components. • Structure of coatings is a mixture of amorphous-like and FCC phases. • Columnar structure transforms into a dense structure with increasing bias. • Coating deposited at bias of −150 V exhibits high hardness of 32.2 GPa. • Wear mechanism is greatly affected by bias voltage. [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]