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High-resolution ultrasonic imaging, which is highly demanded in nondestructive evaluation, is inherently limited by the detection wavelength. Acoustic metamaterial is an emerging technique to achieve subwavelength-resolution ultrasonic imaging beyond the diffraction limit due to its unprecedented acoustic properties. However, existing reports focus on metalenses for manipulating acoustic waves propagating in fluids like air and water, typically at a low-frequency range below 10 kHz. In this paper, a 0.5 MHz periodic column-structured metalens is designed and fabricated to realize deep-subwavelength ultrasonic imaging for quantitive visualization of subsurface defects in solid structures. The silicon-based metalens is designed based on Fabry–Pérot resonance theory. It consists of silicon columns arranged periodically with a lattice constant of 0.2 mm. The Fabry–Pérot resonance frequency is analyzed theoretically and the wave fields of the metalens at resonance mode are verified numerically. The subwavelength ultrasonic imaging performance of the proposed metalens is numerically proved and experimentally demonstrated. As a result, super-resolution ultrasonic imaging (λ/30, with λ being the wavelength) with a high resolving contrast is realized to identify two separated subsurface defects in a stainless-steel structure experimentally with the designed column-structured metalens. This work demonstrates a valuable deep-subwavelength imaging method that beyond traditional diffraction limits and paves the way for enhanced applications in nondestructive evaluation and biomedical diagnosis. [ABSTRACT FROM AUTHOR]

This paper develops an automatic and reliable nondestructive evaluation (NDE) technique that enables quantification of the width and depth of subsurface defects of metallic components simultaneously by using non-contact laser ultrasonic technique and identified machine learning (ML) algorithm. Twenty-two specimens with various subsurface defect dimensions are designed and fabricated for laser ultrasonic experiments, and a total of 220 labeled laser ultrasonic signals are obtained for training and verifying ML models. Twelve features, including four time-domain features (maximum, minimum, peak-to-peak, and |Neg|/Pos value of the laser generated Rayleigh ultrasonic waves) and eight wavelet energy features, are identified and extracted as sensitive feature vectors for establishing the dataset. The principal component analysis (PCA) is implemented as dimensionality reduction method of feature vectors to optimize the recognition algorithm and improve the detection accuracy. Three widely used ML models in NDE, adaptive boosting (Adaboost), extreme gradient boosting (XGBboost), and support vector machine (SVM), combined with the PCA are proposed and compared for detecting both the width and depth of subsurface defects. The PCA-XGBoost achieves the highest recognition rate of 98.48%, and is therefore identified as the most effective approach for analyzing laser-ultrasonic signals. Unlike published reports, the proposed model is trained and evaluated with experimental data covered various classification labels, which is more adaptive and reliable in practical application than the models established using simulated data or limited experimental data. In other applications, as long as sufficient laser ultrasonic data with regards to various defect properties (dimensions, orientations, locations, shapes, etc.) can be acquired, the developed approach can realize accurate detection of corresponding defects. [ABSTRACT FROM AUTHOR]

An effective nondestructive evaluation technique that enables the detection and quantification of subsurface defects is highly demanded for assuring safety and reliability of safety-critical structures. In this work, an improved genetic algorithm-back propagation neural network (GA-BPNN) model and non-contact laser ultrasonic technique are combined to quantify the width of subsurface defects. An experimentally validated numerical model that simulates the interaction of laser-generated Rayleigh ultrasonic waves with subsurface defects is firstly established, which is further utilized to generate a large number of labeled laser ultrasonic signals for training the GA-BPNN model. A total number of 189 data are obtained from simulation and experiments, with 173 simulated signals for training the GA-BPNN model and the remaining 13 simulated signals together with 3 experimental signals for verifying the performance of the trained GA-BPNN model. Five features including three time-domain features (maximum, minimum and peak-to-peak value of the Rayleigh ultrasonic waves) and two frequency-domain features (F c , BW -6dB), which are identified sensitive to the width of subsurface defects by both experiments and simulation, are extracted as inputs to train the machine learning algorithm. The result demonstrates that the GA-BPNN model trained with the combination of time and frequency features has the average error of 2.15%, which is substantially smaller than the errors obtained from the model trained with only time-domain features and frequency-domain features, with the average errors of 4.43% and 21.81%, respectively. This work proves the feasibility and reliability to quantify the width of subsurface defects in metallic structures using laser ultrasonic technique and the improved GA-BPNN algorithm. [ABSTRACT FROM AUTHOR]

Laser ultrasonic technique has been increasingly implemented for nondestructive testing and structural health monitoring. However, the poor signal-to-noise ratio (SNR) and low amplitude of laser generated ultrasonic signals under thermoelastic regime severely restrict its applications. In this paper, we propose a method for fabricating flexible, high-intensity and readily transplantable photoacoustic transducer (PAT) comprised of candle soot nanoparticles (CSNPs) and polydimethylsiloxane (PDMS), and utilize it to generate high SNR and amplitude ultrasonic signals for inspecting thick structures. A robotic-arm based layer-by-layer automatic scanning strategy is developed to realize candle soot nanoparticles (CSNPs) deposition with excellent homogeneity and thickness controllability, and the optimal thickness of PDMS/CSNPs nanocomposite layer to achieve high SNR and amplitude ultrasonic signal is obtained. In addition, a novel method for optimizing the PAT's structure to generate distinguishable and pure longitudinal ultrasonic signals is proposed, with amplitude over 100 times, and with center frequency (13.5 MHz) and −6 dB bandwidth (20.1 MHz) 29.8% and 35.8% higher than those generated without PAT. The optimized PAT is eventually combined with laser ultrasonic technique to successfully inspect and visualize the internal defects with various sizes (4, 2, 0.8 mm in diameter) of an aluminum component with thickness of 50 mm. With the merits of flexible and high-intensity nanocomposite PAT, the laser ultrasonics can be promisingly implemented for inspecting structure with large thickness and complex geometry under thermo-elastic mechanism. [Display omitted] [ABSTRACT FROM AUTHOR]