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• 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]

• Contact surface boundary conditions between the two surfaces of the vertical crack were considered to study contact acoustic nonlinearity (CAN) from the breathing contact crack in conjunction with the modal decomposition method, Fourier transform, and variational principle-based algorithm. • The clapping effects when the crack closed due to the different ratios of incident wave amplitude to crack width with the contact boundary conditions between the crack surfaces were studied and analyzed to get the quantitative correlation between analytical calculations and FE simulation. • When the crack state was modulated at a low frequency (much lower than the one of the probing ultrasonic waves), the related mode conversion coefficients were modulated accordingly both in the fundamental frequency and harmonic frequency. To characterize fatigue crack, an analytical calculation and finite element (FE) simulation of Lamb wave propagating through the region of a breathing crack in a two-dimensional(2D) isotropic plate were studied. Contact surface boundary conditions between the two surfaces of the vertical crack were considered to study contact acoustic nonlinearity (CAN) from the breathing contact crack in conjunction with the modal decomposition method, Fourier transform, and variational principle-based algorithm. Reflection and transmission coefficients in the fundamental frequency and second harmonic frequency were calculated and analyzed quantitatively. Different ratios of incident wave amplitude to crack width were studied to calculate CAN results related to micro-crack width. In addition, a low-frequency (LF) vibration(10 Hz) excitation was introduced to perturb the free surface vertical crack to close, and an interrogating Lamb wave(1 MHz) was used to study crack-related CAN in different conditions for interpreting the modulation mechanism. The contact boundary conditions between two surfaces of vertical crack were set which were dynamically changed due to the low frequency modulation. The clapping effects when the crack closed due to the modulation of the contact boundary conditions between the crack surfaces were studied and analyzed to get the quantitative correlation between CAN and LF modulation. The results obtained from the analytical model were compared with those from the FE simulation, showing good consistency. Knowledge of these effects is essential to correctly gauge the severity of surface cracks in the plate, which can be spotlighted in its application to quantitative evaluation of micro fatigue cracks in structural health monitoring(SHM). [ABSTRACT FROM AUTHOR]

Compared with traditional ultrasonic nondestructive testing technology, the laser ultrasonics method (LU) has the advantages of high precision, non-contact, wide-band response, suitable for non-destructive testing (NDT) of materials having complex shapes or working in extreme environments such as the additive manufacturing (AM) process, etc. However, for a purely optical LU system, it is still limited by the poor reflection of rough/absorption surface of the material. In order to increase the signal-to-noise ratio (SNR) of the signal detected on the rough surface of a sample, and to overcome the limitation of the resolution restricted by half wavelength, a modified Synthetic Aperture Focusing Technique (SAFT) that can be used for internal defect detection of the material is proposed. The modified SAFT is based on LU centering transmission detection and it improves the resolution of the detection system by using the characteristics of ultrasonic propagation and its mode conversion. It is firstly studied in theory with a numerical model based on finite element method (FEM), and then it is applied in experiments to reconstruct the defect according to the signals detected by LU method. In our experiment, the artificial defects buried in an AM sample with diameter from 0.2 mm to 0.75 mm are successfully characterized by the SAFT imaging of ultrasonic signals. The results prove that the position of the defect in 3D and its size can be successfully obtained by the proposed SAFT. • A modified SAFT based on centering transmission detection is proposed. • An AM sample with defect in different size is characterized by a purely optical LU system. • The position in 3D and the size of buried defects in AM sample are identified by proposed SAFT. • The resolution can be improved by using multi-mode scattering method of bulk waves. [ABSTRACT FROM AUTHOR]

• We design an underwater acoustic touchscreen that can identify the pressing position and pressing pressure. • The deep learning algorithm is introduced into the design of underwater acoustic touchscreen, which has a higher accuracy. • We compared the accuracy of traditional machine learning algorithm and deep learning algorithm in identification of the pressing position and pressing pressure. With the improvement of living standards, the way of human-computer interaction has gradually changed from the original mouse and keyboard to touchscreen. Unfortunately, most traditional touchscreens lose their accuracy when working in water due to the influence of the fluid-solid interface. In this paper, based on the theory of leaky-Lamb wave, an acoustic touchscreen used underwater is proposed. The proposed acoustic touchscreen device is composed of three piezoelectric transducers and a sheet of quartz glass. Pressing position and pressing pressure on this underwater touchscreen can be successfully identified by the trained convolutional neural network. According to the experiments, the recognition accuracy of the pressing position of the testing set is 96.90 % with the spatial resolution of 1 cm × 1 cm. When it comes to recognizing the pressing pressure, the recognition accuracy of the testing set is 91.84 %. [ABSTRACT FROM AUTHOR]

• 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]