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A compact five-element integrated ultrasonic transducer was designed and manufactured for the inspection of defects in the split pins of control rod guide tubes in nuclear power plants. The transducer consists of three types of ultrasonic elements to detect cracks in the entire volume of the split pins. In the transducer design, two main factors were investigated: the coupling of elements in confined space and measurement sensitivity. The experimental results demonstrated that the developed transducer has good acoustic performance and defect response capabilities and can detect 10 mm × 2 mm × 0.5 mm notches in the three areas of the split pins. This work provides a foundation for applications in nuclear site inspections.

• 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]

• 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]

Defects or discontinuities are inevitable during the melting and consolidation process of metal additive manufacturing. Online inspection of microdefects during the processing of layer-by-layer fusion is urgently needed for quality control. In this study, the laser ultrasonic C-scan imaging system is established to detect the surface defects of selective laser melting (SLM) samples that have a different surface roughness. An autosizing method based on the maximum correlation coefficient and lag time is proposed to accurately measure the defect length. The influences of the surface roughness on the laser ultrasound signal-to-noise ratio distribution and defect sizing accuracy are also studied. The results indicate that the proposed system can detect notches with a depth of 50 µm and holes with a diameter of 50 µm, comparable in size to raw powder particles. The average error for the length measurement can reach 1.5% if the notch is larger than 2 mm. Meanwhile, the sizing error of a 1 mm length notch is about 9%. In addition, there is no need to remove the rough surface of the as-built SLM samples during the detection process. Hence, we propose that the laser ultrasonic imaging system is a potential method for online inspection of metal additive manufacturing.