Sizing of flaws using ultrasonic bulk wave testing: A review

High resolution ultrasonic imaging of extended targets via combined match field and time delay beamforming

Ultrasound imaging using an active sensing array has been extensively studied in both time domain and frequency domain. Subspace decomposition methods in match field beamforming such as the multiple signal classification (MUSIC) algorithm can achieve subwavelength resolution of distinct point scatterers. However, when the size of the target is on the order of one wavelength or larger, the MUSIC type algorithms suffer from poor performance due to a tangled eigen structure. This paper proposes an adaptive match field beamformer that does not require subspace decomposition to achieve high resolution imaging of extended targets. Specifically, the broadband coherent white noise constraint (C-WNC) algorithm is utilized to achieve high focusing ability of extended targets by exploiting the cross-frequency coherence in an active sensing scheme. The dynamic range bias in the adaptive beamformer benefits the C-WNC algorithm to achieve high contrast regardless of the SNR. Both simulations and experiments show that the C-WNC images retain their resolution cells on the tips of the extended target with sizes ranging from a wavelength to sizes as large as the physical aperture width. A robust imaging scheme is then proposed to obtain high quality images by combining C-WNC images with a statistically stable delay-multiply-and-sum (DMAS) algorithm to create high-contrast and high-resolution images of extended targets in both azimuth and axial range directions.

A Multivariate Statistical Approach to Wrinkling Detection in Composites

Nondestructive inspection using ultrasound in materials such as carbon-fiber reinforced polymers (CFRPs) is challenging as the ultrasonic wave will scatter from each ply in the structure of the component. This may be improved using image processing algorithms such as the total focusing method (TFM); however, the high level of backscattering within the sample is very likely to obscure a signal arising from a flaw. Detection of wrinkling, or out-of-plane fiber waviness, is especially difficult to automate as no additional scattering is produced (as might be the case with delaminations). Instead, wrinkling changes how a signal is scattered due to the physical displacement of ply layers from their expected location. In this article, we propose a method of detecting wrinkling by examining the regional variations in image intensity, which are expected to be highly correlated between similar ply layers in the structure. By characterizing the 2-D spatial autocorrelation of an area surrounding a given location in the image of pristine components, the distribution of acceptable values is estimated. Wrinkling is observed to correspond with a significant deviation from this distribution, which is readily detected. A comparison is made with an alternative image processing approach identified from the literature, finding that the proposed method has equivalent performance for large wrinkling amplitudes and better performance for low wrinkling amplitudes.

Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data

In nondestructive evaluation (NDE), accurately characterizing defects within components relies on accurate sizing and localization to evaluate the severity or criticality of defects. This study presents for the first time a deep learning (DL) methodology using 3-D U-Net to localize and size defects in carbon fiber reinforced polymer (CFRP) composites through volumetric segmentation of ultrasonic testing (UT) data. Using a previously developed approach, synthetic training data, closely representative of experimental data, was used for the automatic generation of ground truth segmentation masks. The model's performance was compared to the conventional amplitude 6 dB drop analysis method used in the industry against ultrasonic defect responses from 40 defects fabricated in CFRP components. The results showed good agreement with the 6 dB drop method for in-plane localization and excellent through-thickness localization, with mean absolute errors (MAEs) of 0.57 and 0.08 mm, respectively. Initial sizing results consistently oversized defects with a 55% higher mean average error than the 6 dB drop method. However, when a correction factor was applied to account for variation between the experimental and synthetic domains, the final sizing accuracy resulted in a 35% reduction in MAE compared to the 6 dB drop technique. By working with volumetric ultrasonic data (as opposed to 2-D images), this approach reduces preprocessing (such as signal gating) and allows for the generation of 3-D defect masks which can be used for the generation of computer-aided design files; greatly reducing the qualification reporting burden of NDE operators.

Bottom Crack Detection with Real-Time Signal Amplitude Correction Using EMAT-PEC Composite Sensor

During electromagnetic ultrasonic testing, it is difficult to recognize small-size bottom cracks by time of flight (ToF), and the lift-off fluctuation of the probe affects the accuracy and consistency of the inspection results. In order to overcome the difficulty, a novel composite sensor of an electromagnetic acoustic transducer (EMAT) and pulse eddy current (PEC) is designed. We use the amplitude of a bottom echo recorded by EMAT to identify the tiny bottom crack as well as the amplitude of PEC signals picked up by the integrated symmetric coils to measure the average lift-off of the probe in real time. Firstly, the effects of lift-off and bottom cracks on the amplitude of bottom echo are distinguished by combining the theoretical analysis and finite element method (FEM). And then an amplitude correction method based on the fusion of EMAT and PEC signals is proposed to reduce the impact of lift-off on the defect signal. The experimental results demonstrate that the designed composite sensor can effectively detect a bottom crack as small as 0.1 mm × 0.3 mm. The signal fusion method can accurately correct the amplitude of defect signals and the relative error is less than ±8%.

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

Coded Excitation for Ultrasonic Testing: A Review

Originating in the early 20th century, ultrasonic testing has found increasingly extensive applications in medicine, industry, and materials science. Achieving both a high signal-to-noise ratio and high efficiency is crucial in ultrasonic testing. The former means an increase in imaging clarity as well as the detection depth, while the latter facilitates a faster refresh of the image. It is difficult to balance these two indicators with a conventional short pulse to excite the probe, so in general handling methods, these two factors have a trade-off. To solve the above problems, coded excitation (CE) can increase the pulse duration and offers great potential to improve the signal-to-noise ratio with equivalent or even higher efficiency. In this paper, we first review the fundamentals of CE, including signal modulation, signal transmission, signal reception, pulse compression, and optimization methods. Then, we introduce the application of CE in different areas of ultrasonic testing, with a focus on industrial bulk wave single-probe detection, industrial guided wave detection, industrial bulk wave phased array detection, and medical phased array imaging. Finally, we point out the advantages as well as a few future directions of CE.

Full-Matrix Imaging in Fourier Domain towards Ultrasonic Inspection with Wide-Angle Oblique Incidence for Welded Structures

The total focusing method (TFM) has been increasingly applied to weld inspection given its high image quality and defect sensitivity. Oblique incidence is widely used to steer the beam effectively, considering the defect orientation and structural complexity of welded structures. However, the conventional TFM based on the delay-and-sum (DAS) principle is time-consuming, especially for oblique incidence. In this paper, a fast full-matrix imaging algorithm in the Fourier domain is proposed to accelerate the TFM under the condition of oblique incidence. The algorithm adopts the Chebyshev polynomials of the second kind to directly expand the Fourier extrapolator with lateral sound velocity variation. The direct expansion maintains image accuracy and resolution in wide-angle situations, covering both small and large angles, making it highly suitable for weld inspection. Simulations prove that the third-order Chebyshev expansion is required to achieve image accuracy equivalent to the TFM with wide-angle incidence. Experiments verify the algorithm's performance for weld flaws using the proposed method with the transverse wave and the full-skip mode. The depth deviation is within 0.53 mm, and the sizing error is below 15%. The imaging efficiency is improved by a factor of up to 8 compared to conventional TFM. We conclude that the proposed method is applicable to high-speed weld inspection with various oblique incidence angles.

Laser ultrasonics for nondestructive testing of composite materials and structures: A review

This paper presents a comprehensive overview of Laser Ultrasonic Testing (LUT) and its applications in composite materials. The working principles of LUT are thoroughly explained, and an assessment of its advantages and drawbacks is provided. The mechanisms of wave generation and detection are described, along with their influence on the capabilities and limitations of LUT. The paper includes an inclusive overview of each LUT application in composite materials, highlighting their potential, challenges, and research gaps. LUT is a noncontact and nondestructive technique that utilizes lasers to generate and detect ultrasonic waves, with the material itself acting as an emitting transducer. This unique noncontact approach offers an accurate, versatile, convenient, and rapid method for inspecting and characterizing materials. However, some challenges and research gaps have hindered its widespread adoption. One significant challenge in LUT is the low signal-to-noise ratio (SNR), which becomes more pronounced in composite materials due to their low ablation threshold and high wave attenuation. Furthermore, the characterization and inspection of composite materials are more intricate due to their anisotropy and complex damage patterns. Despite these challenges, the combination of ultrasonic waves capable of characterizing and inspecting materials, coupled with the capabilities of lasers and optics for noncontact and real-time operation, presents a promising outlook for the widespread implementation of LUT in Smart Industries and harsh industrial environments, including those with high temperatures, high pressures, or radioactive conditions. This paper contributes to the understanding of LUT's potential and limitations, paving the way for further advancements in its applications.

Ultrasonic array imaging of porosity defects with contrast enhancement based on dominant response subtraction

Porosity defects can be found in many engineering structures and their inspection remains a challenge in the field of ultrasonic non-destructive testing. In this paper, ultrasonic array imaging of porosity defects has been studied with the aim of improving the image quality in the "dead zone", which is caused by the masking effects of the uppermost pores. The proposed approach first extracts contributions of the uppermost pores based on a single scattering model by adopting convolutional sparse coding. The extracted dominant contributions are then subtracted from the array data before forming an image, facilitating detection and localization of pores in the shadow zone. The performance of the proposed approach has been studied in simulation and experiments, and the mean localization errors of the pores are small (i.e., within 0.27 mm or 0.14λ). In addition, the effects of measurement noise and imaging parameters on robustness of the imaging result have been analyzed, providing guidelines for practical implementation of the proposed approach.

Shear Wave Ultrasound Inspection of Flaws in Silicon Wafers Using Focused Transducers

Silicon parts can contain micrometer-sized vertical cracks that are challenging to detect. Inspection using high-frequency focused ultrasound has shown promise for detecting defects of this size and geometry. However, implementing focused ultrasound to inspect anisotropic media can prove challenging, given the directional dependence of wave propagation and subsequent focusing behavior. In this work, back surface-breaking defects at various orientations within silicon wafers (0°, 15°, and 45° relative to the [010] crystallographic axis) are experimentally inspected in an immersion tank setup. Using 100 MHz unfocused and focused shear waves, the impact of medium anisotropy on focusing and defect detection is evaluated. The scattering amplitude and defect detection sensitivity results demonstrate orientation-dependent patterns that strongly rely on the use of focused transducers. The defects along the 45° orientation reveal two-lobe scattering patterns with maximum amplitudes less than half that of the defects in the 0° orientation, which in contrast show a one-lobe scattering pattern. The experimental results are further explored using finite element (FE) modeling and ray tracing to visualize the impact of focusing on wave propagation within the silicon. Ray tracing results show that the focused beam profiles for the 45° and 0° orientations form a butterfly wing and elliptical focusing profile, respectively, which correspond directly to experimentally found scattering patterns from defects. Additionally, the FE scattering results from unfocused transducers reveal single lobe scattering for both 0° and 45° orientations, proving the varying scattering patterns to be driven by the anisotropic focusing behavior.

Research on the 3D Reverse Time Migration Technique for Internal Defects Imaging and Sensor Settings of Pressure Pipelines

Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation of the project. Therefore, a thorough and precise characterization approach towards pressure pipelines can proactively mitigate safety risks and yield substantial economic and societal benefits. At present, the current mainstream 2D ultrasound imaging technology faces challenges in fully visualizing the internal defects and topography of pressure pipelines. Reverse time migration (RTM), widely employed in geophysical exploration, has the capability to visualize intricate geological structures. In this paper, we introduced the RTM into the realm of ultrasonic non-destructive testing, and proposed a 3D ultrasonic RTM imaging method for internal defects and sensor settings of pressure pipelines. To accurately simulate the extrapolation of wave field in 3D pressure pipelines, we set the absorbing boundary and double free boundary in cylindrical coordinates. Subsequently, using the 3D ultrasonic RTM approach, we attained higher-precision 3D imaging of internal defects in the pressure pipelines through suppressing imaging artifacts. By comparing and analyzing the imaging results of different sensor settings, the design of the observation system is optimized to provide a basis for the imaging and interpretation of actual data. Both simulations and actual field data demonstrate that our approach delivers top-notch 3D imaging of pipeline defects (with an imaging range accuracy up to 97.85%). This method takes into consideration the complexities of multiple scattering and mode conversions occurring at the base of the defects as well as the optimal sensor settings.

Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging

Analogous density foreign matter (ADFM) embedded in soy protein meat semi-finished (SFSPM) is hidden by SFSPM and has similar acoustic impedance features to SFSPM, which makes non-destructive testing techniques such as computer vision (CV), reflectance spectroscopy and ultrasound imaging inappropriate for ADFM, which not only seriously affects the quality of soy protein meat (SPM) products but also increases the safety risk to consumers. In this study, to locate and separate ADFM by using transmission hyperspectral imaging (T-HSI) technique which is sensitive to chemical composition and highlight internal contours. The optimal discrimination model SVM + PCA + MSC + SPA was constructed using transmission spectral information with an accuracy of 95.00 %. The visualization results based on the optimal model showed clearer localization results than CV and ultrasound imaging. The study demonstrated that the advantages of T-HSI technology in detecting and locating ADFM inside SFSPM, which provides a basis for improving the production quality and safety of SPM.

Suppressing artifacts in the total focusing method using the directivity of laser ultrasound

Based on a synthesized laser ultrasonic array, full matrix capture can be used to acquire data, which can then be post-processed using the total focusing method. However, this noncontact ultrasonic imaging technique has not been widely used because of the numerous artifacts in ultrasonic images and time-consuming data acquisition. To address these issues, this study proposes a post-processing algorithm, which uses the laser ultrasound directivity information to suppress the artifacts in the total focusing method's images. In particular, a weight factor is defined using the directivity information. By multiplying the image intensity of the total focusing method with this factor, the algorithm uses not only the amplitude and phase information of laser ultrasound but also its directivity information. The experimental results indicate that four types of artifacts are suppressed. Because the grating lobe artifacts can be suppressed, a larger element spacing can be used to reduce the data acquisition time.

Ultrasonic characteristics and equivalent crack width of coal and rock bodies around boreholes during progressive failure

The ultrasonic characteristics of the coal and rock bodies around boreholes during failure are closely related to the crack propagation law. To investigate the ultrasonic characteristics and crack propagation law of coal and rock bodies around boreholes, different grouting samples with boreholes were taken to carry out ultrasonic test during progressive failure. The ultrasonic amplitude, velocity and attenuation coefficient of the samples were analyzed. According to the ultrasonic time difference formula, the equivalent crack width of the sample during the failure process is calculated. The influence of grouting material on the crack propagation law is quantitatively analyzed. The results show that: (1) The peak stress, elastic energy at the peak, ultrasonic parameters and crack propagation of the coal and rock bodies around boreholes show obvious differences influenced by the strength of the grouting material. (2) During the loading process, the arrival time of the first wave of the sample with holes is 5μs later than that of the grouting sample, and the ultrasonic energy attenuates fastest in the time domain, and the coda wave is not developed. (3) During the progressive failure, the ultrasonic velocity and attenuation coefficient of all show three stages of stability(0~0.6σp), slow change(0.6σp~0.8σp) and rapid change(0.8σp~1.0σp). According to the "sudden decrease" of velocity and the "sudden increase" of attenuation coefficient to judge the crack propagation of sample. (4) The equivalent crack width of the sample increases exponentially with the increase of stress level. At the time of reaching the peak stress, the equivalent crack width of SH-BH increases about 0.027mm~0.032mm, SH-PU about 0.01mm~0.014mm, and SH-CEM about 0.002mm~0.006mm.

A Circular Total Focusing Method With Eccentricity Correction and Intensity Compensation for Endoscopic Ultrasound Imaging of Dual-Layered Media

Present endoscopic ultrasound (EUS) imaging methods for circular array (CA) suffer from the nonuniform spatial resolution in the imaging of a dual-layered media, such as the tubes' immersion EUS inspection. The problem is mainly attributed to the restricted focus and beam de-focusing at the interface. In this article, a circular total focusing method (CTFM) is proposed, which leverages the concept of the conventional total focusing method (TFM) and makes three vital improvements to overcome the challenges. First, to obtain the accurate time-of-flight (TOF) in the dual-layered media, a fourth-order equation of Snell's law is built and solved in polar coordinate system. Second, a fast geometric approximation method is derived to correct the TOF distortion caused by the transducer's eccentricity. Third, the intensity compensation is applied to flatten the imaging intensity at different positions by considering the directivity of element, transmission at interface, and divergence in media. The CTFM is validated on a tube's immersion EUS using a 10 MHz CA with 128 elements. Experimental results demonstrate that the proposed CTFM outperforms existing imaging methods. The lateral and axial resolutions are 0.71 and 0.30 mm, which are 27.5% and 33.3% higher than those of the classic delay-and-sum (DAS) method. The CTFM image shows high and uniform signal-to-noise ratio (SNR) which is 33.6% higher than that of DAS images. The CTFM provides a novel EUS imaging modality which can be applied in both medical and nondestructive testing domains.

Detection of defects in highly attenuating materials using ultrasonic least-squares reverse time migration with preconditioned stochastic gradient descent

Accurate detection and characterization of defects in high-density polyethylene (HDPE) pipe materials are very important in assessing the structural integrity of critical structures in the nuclear industry. One specific challenge here is the presence of the viscoelastic attenuation of this material, which can lead to resolution degradation and loss of detail in ultrasound imaging. In this work, an effective ultrasonic imaging technique using the least-squares reverse time migration with preconditioned stochastic gradient descent (LSRTM-PSGD) is developed to improve image quality. Compared with standard ultrasonic imaging methods which only consider the direct ray path of ultrasound, least-squares reverse time migration (LSRTM) is a powerful wave-equation-based approach and it has the ability to account for rapid spatial velocity variations and to utilize all wavefield information. The LSRTM is an inversion method, which iteratively updates the reflectivity model by minimizing the modeled data and measured data. The proposed LSRTM-PSGD combines the advantages of stochastic gradient descent (SGD) and adaptive learning rate. The SGD updates the parameter on each transmitter and the fluctuation of SGD can enable it to reach a better minimum, thus improving the imaging quality. Compared with the conventional LSRTM algorithm using a fixed step size, the proposed LSRTM-PSGD algorithm can use the adaptive moment estimation to calculate the adaptive learning rate for the parameter, thereby updating the parameter appropriately. The performance of the LSRTM-PSGD algorithm is tested with experimental data. The results show high-quality reconstructed images with good resolution for defect identification in HDPE pipe materials, especially for deep defects.

Laser ultrasonic imaging of complex defects with full-matrix capture and deep-learning extraction

Phased array-based full-matrix ultrasonic imaging has been the golden standard for the non-destructive evaluation of critical components. However, the piezoelectric phased array cannot be applied in hazardous environments and online monitoring due to its couplant requirement. The laser ultrasonic technique can readily address these challenging tasks via fully non-contact inspection, but low detection sensitivity and complicated wave mode conversion hamper its practical applications. The laser-induced full-matrix ultrasonic imaging of complex defects was displayed in this study. Full matrix data acquisition and deep learning method were adapted to the laser ultrasonic technique to overcome the existing challenges. For proof-of-concept demonstrations, simulations and experiments were conducted on an aluminum sample with representative defects. Numerical and experimental results showed good agreement, revealing the excellent imaging performance of proposed method. Compared with the total focusing method based on ray-trace model, the deep learning method could create superior images with additional quantitative information through end-to-end networks, which use the hierarchical features and generate details from all the relevant imaging and physical characteristics information. The proposed method may help assess defect formation and development at the early stage in a hazardous environment and understand the in-situ manufacturing process due to its couplant-free nature.

Automatic Thinning Detection through Image Segmentation Using Equivalent Array-Type Lamp-Based Lock-in Thermography

Among the non-destructive testing (NDT) techniques, infrared thermography (IRT) is an attractive and highly reliable technology that can measure the thermal response of a wide area in real-time. In this study, thinning defects in S275 specimens were detected using lock-in thermography (LIT). After acquiring phase and amplitude images using four-point signal processing, the optimal excitation frequency was calculated. After segmentation was performed on each defect area, binarization was performed using the Otsu algorithm. For automated detection, the boundary tracking algorithm was used. The number of pixels was calculated and the detectability using RMSE was evaluated. Clarification of defective objects using image segmentation detectability evaluation technique using RMSE was presented.

Excitation of Mechanical Resonances in the Stationary Ring of a Mechanical Seal by a Continuously Operated Electromagnetic Acoustic Transducer

Acoustic/ultrasonic testing is now a common method in the field of nondestructive testing for detecting material defects or monitoring ongoing mechanical changes in a structure during operation. In many applications, piezoelectric transducers are used to generate mechanical waves inside the specimen. Their actual operating frequency is highly dependent on the dimensions of the transducer. Larger dimensions of the piezoelectric transducer allow for a lower operating frequency. However, these dimensions limit the use of piezoelectric transducers in certain applications where the size of the transducer is restricted due to limited installation space and when low-frequency excitation is required. One application that places these requirements on the transducer is the monitoring of mechanical seals. Here, the transducer must be mounted on the stationary ring of the seal. In this paper, a continuously operated electromagnetic acoustic transducer (EMAT) is presented as an alternative to piezoelectric transducers as a transmitter. The advantage of a EMAT is that it meets the requirements of limited sensor size (sensor area < 10 × 6 mm) and can excite mechanical waves with frequencies below 10 kHz. A structural analysis of the stationary ring shows that the first two mechanical resonances occur around 4 and 5.5 kHz. An experimental study meterologically demonstrates the ability of the EMAT to excite these first two mechanical resonances of the ring. A comparative simulation agrees well with the measurement.

Defect Detection and Imaging in Composite Structures Using Magnetostrictive Patch Transducers

The use of thin magnetostrictive patches to generate and detect guided waves within the composite samples is investigated for defect detection. This approach has been implemented using SH0 shear horizontal guided waves in both CFRP and GFRP plates. A magnetostrictive patch transducer was able to generate SH0 waves with known directional characteristics. The synthetic aperture focusing technique (SAFT) was then used to reconstruct images of defects using multiple transmission and detection locations. The results for imaging defects in both types of material are presented.

Physical Simulation of Ultrasonic Imaging Logging Response

Ultrasonic imaging logging can visually identify the location, shape, dip angle and orientation of fractures and holes. The method has not been effectively applied in the field; one of the prime reasons is that the results of physical simulation experiments are insufficient. The physical simulation of fracture and hole response in the laboratory can provide a reference for the identification and evaluation of the underground geological structure. In this work, ultrasonic scanning experiments are conducted on a grooved sandstone plate and a simulated borehole and the influence of different fractures and holes on ultrasonic pulse echo is studied. Experimental results show that the combination of ultrasonic echo amplitude imaging and arrival time imaging can be used to identify the fracture location, width, depth and orientation, along with accurately calculating the fracture dip angle. The evaluated fracture parameters are similar to those in the physical simulation model. The identification accuracy of the ultrasonic measurement is related to the diameter of the radiation beam of the ultrasonic transducer. A single fracture with width larger than or equal to the radiation beam diameter of the ultrasonic transducer and multiple fractures with spacing longer than or equal to the radiation beam diameter can be effectively identified.

Extending the Incidence Angle of Shear Vertical Wave Electromagnetic Acoustic Transducer with Horizontal Magnetization

Angled shear vertical (SV) waves have been successfully employed in the non-destructive testing of welds, pipes, and railways. Non-contact meander-line coil electromagnetic acoustic transducers (EMAT) have many benefits in generating angled SV waves. The most important benefit is that the incidence angle of an SV wave can be controlled by the excitation frequency. However, the incidence angle of a traditional SV-wave EMAT is reported to be under 45 degrees in many cases. In this work, such cases are tested, and the problems of the received signal at large incidence angles are found to be due to wave interference and small signal amplitudes. An equivalent finite element (FE) model is established to analyze the problem, and the main reason is found to be the head wave. An alternative configuration of angled SV-wave EMAT with horizontal magnetization is proposed to reduce the influence of the head wave. Finally, the results from simulations and experiments show that the proposed EMAT has a larger signal amplitude and significantly reduced interference in large-incidence angle scenarios. Moreover, an incidence angle of an SV wave of up to 60 degrees can be achieved, which will help improve the performance and capability of nondestructive testing.

Focal depth localization for highly focused transducers in isotropic materials

Focusing equations aim to define the point in a solid at which a transducer beam will reach a minimum cross section. The most commonly used focusing equation relies on a small angle assumption that inherently excludes sharply focused transducers with significant curvature. In this article, a revised focusing equation is proposed for normal and oblique incidence through a fluid-solid interface. The closed-form equation is derived using ray tracing approaches similar to the conventional expression but circumvents the paraxial approximation, extending the applicability to sharply focused probes. Both conventional and modified focusing equations are compared through normal and oblique incidence ray diagrams, and the proximity to the computationally derived geometric focus is explored. The proposed modification to the focusing equation generally results in a closer approximation to the geometric focus, a smaller beam cross section, and a greater time convergence when compared to the conventional focusing equation.

3D ultrasonic imaging of surface-breaking cracks using a linear array

Ultrasonic linear arrays have great potential to generate high-quality three-dimensional (3D) images by scanning the array. However, the generated images suffer from low resolution in the elevation plane, limiting the image quality for a reliable 3D Non-Destructive Testing (NDT) inspection. Although several ultrasonic imaging methods have been implemented to inspect different types of defects, there has been limited research to characterise surface-breaking cracks (SBCs) in 3D quantitatively. To improve the characterisation of surface-breaking cracks (SBCs), a 3D hybrid imaging method is proposed by combining the Half Skip Total Focusing Method (HSTFM) and the Synthetic Aperture Focusing Technique (SAFT) using a linear array. This paper proposed the implementation of an array with a reduced element length for full matrix capture (FMC) data acquisition. In conjunction with the hybrid imaging method, a reduced element array enables the utilisation of the information from a broad ultrasonic beam in the elevation direction to achieve improved image resolution. The imaging capability is assessed via a point spread function (PSF) as well as numerical simulations. From the PSF measurements, the image resolution is shown to improve with the smaller element length of the array, which is attributable to the combination of wide beamwidth and hybrid imaging method. Thereafter, experimental validation was performed with arrays of different elevation lengths, where an excellent match with the numerical results was observed. Furthermore, the crack sizing was performed using a 6-dB-drop rule, which assisted in accurately predicting the shape and size of the SBCs and is shown to measure the depth of SBCs with greater confidence. It is shown that a reduced array elevation with the hybrid imaging method and sizing method yields improved image resolution contrary to conventional linear arrays. This approach can offer a significant improvement in manifesting complete comprehension of the spatial defect relationship, enabling NDT engineers to analyse the inspection results quantitatively in 3D for progressive reliability.

Active Ultrasonic Structural Health Monitoring Enabled by Piezoelectric Direct-Write Transducers and Edge Computing Process

While the active ultrasonic method is an attractive structural health monitoring (SHM) technology, many practical issues such as weight of transducers and cables, energy consumption, reliability and cost of implementation are restraining its application. To overcome these challenges, an active ultrasonic SHM technology enabled by a direct-write transducer (DWT) array and edge computing process is proposed in this work. The operation feasibility of the monitoring function is demonstrated with Lamb wave excited and detected by a linear DWT array fabricated in situ from piezoelectric P(VDF-TrFE) polymer coating on an aluminum alloy plate with a simulated defect. The DWT array features lightweight, small profile, high conformability, and implementation scalability, whilst the edge-computing circuit dedicatedly designed for the active ultrasonic SHM is able to perform signal processing at the sensor nodes before wirelessly transmitting the data to a remote host device. The successful implementation of edge-computing processes is able to greatly decrease the amount of data to be transferred by 331 times and decrease the total energy consumption for the wireless module by 224 times. The results and analyses show that the combination of the piezoelectric DWT and edge-computing process provides a promising technical solution for realizing practical wireless active ultrasonic SHM system.

Enhancing the spectral signatures of ultrasonic fluidic transducer pulses for improved time-of-flight measurements

Air-coupled ultrasonic (ACU) testing has proven to be a valuable method for increasing the speed in non-destructive ultrasonic testing and the investigation of sensitive specimens. A major obstacle to implementing ACU methods is the significant signal power loss at the air-specimen and transducer-air interfaces. The loss between transducer and air can be eliminated by using recently developed fluidic transducers. These transducers use pressurized air and a natural flow instability to generate high sound power signals. Due to this self-excited flow instability, the individual pulses are dissimilar in length, amplitude, and phase. These amplitude and angle modulated pulses offer the great opportunity to further increase the signal-to-noise ratio with pulse compression methods. In practice, multi-input multi-output (MIMO) setups reduce the time required to scan the specimen surface, but demand high pulse discriminability. By applying envelope removal techniques to the individual pulses, the pulse discriminability is increased allowing only the remaining phase information to be targeted for analysis. Finally, semi-synthetic experiments are presented to verify the applicability of the envelope removal method and highlight the suitability of the fluidic transducer for MIMO setups.

Deterioration Mechanisms and Advanced Inspection Technologies of Aluminum Windows

Aluminum windows are crucial components of building envelopes since they connect the indoor space to the external environment. Various external causes degrade or harm the functioning of aluminum windows. In this regard, inspecting the performance of aluminum windows is a necessary task to keep buildings healthy. This review illustrates the deterioration mechanisms of aluminum windows under various environmental conditions with an intention to provide comprehensive information for developing damage protection and inspection technologies. The illustrations reveal that moisture and chloride ions have the most detrimental effect on deteriorating aluminum windows in the long run, while mechanical loads can damage aluminum windows in a sudden manner. In addition, multiple advanced inspection techniques potential to benefit assessing aluminum window health state are discussed in order to help tackle the efficiency problem of traditional visual inspection. The comparison among those techniques demonstrates that infrared thermography can help acquire a preliminary defect profile of inspected windows, whereas ultrasonic phased arrays technology demonstrates a high level of competency in analyzing comprehensive defect information. This review also discusses the challenges in the scarcity of nanoscale corrosion information for insightful understandings of aluminum window corrosion and reliable window inspection tools for lifespan prediction. In this regard, molecular dynamics simulation and artificial intelligence technology are recommended as promising tools for better revealing the deterioration mechanisms and advancing inspection techniques, respectively, for future directions. It is envisioned that this paper will help upgrade the aluminum window inspection scheme and contribute to driving the construction of intelligent and safe cities.

3-D ultrasonic image reconstruction in frequency domain using a virtual transducer model

In ultrasonic non-destructive testing, image reconstruction is essential to restore the diffracted ultrasound signals to improve the lateral resolution of images. Some reconstruction methods, like DAS-based synthetic aperture imaging, are inefficient, especially for reconstructing three-dimensional (3-D) images. Other methods do not provide high-resolution results, because they neglect the distortion effect introduced by transducer geometry. To overcome these disadvantages, we propose a 3-D ultrasonic image reconstruction method based on synthetic aperture wavenumber algorithm. It considers wave diffraction and transducer geometry effects, and can refocus the reflectors even in non-focal zone, which suits for large depth range imaging. This method builds a virtual transducer model in frequency domain by treating the focused transducer as a virtual planar transducer on its focal plane. In addition, the method uses non-uniform fast Fourier transform and deconvolution operation to achieve the 3-D image reconstruction, which has remarkably improved the efficiency and accuracy. According to the experimental results, the lateral resolution of an image reconstructed by the proposed method can reach 290.2 μm, exceeding the lateral resolution limitation of the 15 MHz focused transducer (523.24 μm). Furthermore, the proposed method only takes 0.744 s to reconstruct a 3-D image with 1000×100×100 pixels, while the time domain SAFT takes about 1163.8 s. It shows the potential for real-time 3-D imaging under advanced hardware.

Structural Health Monitoring in Composite Structures: A Comprehensive Review

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

A Modified Wavenumber Algorithm of Multi-Layered Structures with Oblique Incidence Based on Full-Matrix Capture

Full-matrix capture (FMC)-based ultrasonic imaging provides good sensitivity to small defects in non-destructive testing and has gradually become a mainstream research topic. Many corresponding algorithms have been developed, e.g., the total focusing method (TFM). However, the efficiency of the TFM is limited, especially in multi-layered structures. Although the appearance of wavenumber algorithms, such as extended phase-shift migration (EPSM) methods, has improved imaging efficiency, these methods cannot be applied to cases with oblique incidence. Therefore, a modified wavenumber method for full-matrix imaging of multi-layered structures with oblique array incidence is proposed. This method performs a coordinate rotation in the frequency domain to adapt it to the oblique incidence. It then utilizes wave-field extrapolation to migrate the transmitting and receiving wave field to each imaging line, and a correlation imaging condition is used to reconstruct a total focused image. The proposed method can deal with any incident angle without precision loss. Moreover, it inherits the computational efficiency advantages of the wavenumber algorithms. The simulation and experimental results show that the proposed method performs better in terms of accuracy and efficiency than the TFM. Specifically, it is nearly 60 times faster than the TFM when processing an FMC dataset with a size of 4096 × 64 × 64.

Design and Performance Analysis of an Ultrasonic System for Health Monitoring of Concrete Structure

The development and research of an ultrasonic-based concrete structural health monitoring system encounters a variety of problems, such as demands of decreasing complexity, high accuracy, and extendable system output. Aiming at these requirements, a low-cost extendable system based on FPGA with adjustable system output has been designed, and the performance has been evaluated by different assessment parameters set in this paper. Besides the description of the designed system and the experiments in air medium, the residual similarity and Pearson correlation coefficients of experimental and theoretical data have been used to evaluate the submodules’ output. The output performance of the overall system is evaluated by the Pearson correlation coefficient, root-mean-square error (RMSE), and magnitude-squared coherence with 40 experimental data. The maximum, median, minimum, and mean values in three-parameter datasets are analyzed for discussing the working condition of the system. The experimental results show that the system works stably and reliably with tunable frequency and amplitude output.

Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing

Material failure may occur in a variety of situations dependent on stress conditions, temperature, and internal or external load conditions. Many of the latest engineered materials combine several material types i.e., metals, carbon, glass, resins, adhesives, heterogeneous and nanomaterials (organic/inorganic) to produce multilayered, multifaceted structures that may fail in ductile, brittle, or both cases. Mechanical testing is a standard and basic component of any design and fabricating process. Mechanical testing also plays a vital role in maintaining cost-effectiveness in innovative advancement and predominance. Destructive tests include tensile testing, chemical analysis, hardness testing, fatigue testing, creep testing, shear testing, impact testing, stress rapture testing, fastener testing, residual stress measurement, and XRD. These tests can damage the molecular arrangement and even the microstructure of engineered materials. Nondestructive testing methods evaluate component/material/object quality without damaging the sample integrity. This review outlines advanced nondestructive techniques and explains predominantly used nondestructive techniques with respect to their applications, limitations, and advantages. The literature was further analyzed regarding experimental developments, data acquisition systems, and technologically upgraded accessory components. Additionally, the various combinations of methods applied for several types of material defects are reported. The ultimate goal of this review paper is to explain advanced nondestructive testing (NDT) techniques/tests, which are comprised of notable research work reporting evolved affordable systems with fast, precise, and repeatable systems with high accuracy for both experimental and data acquisition techniques. Furthermore, these advanced NDT approaches were assessed for their potential implementation at the industrial level for faster, more accurate, and secure operations.

Towards using convolutional neural network to locate, identify and size defects in phased array ultrasonic testing

Machine learning algorithms are widely used in image recognition. In Phased Array Ultrasonic Testing (PAUT), images are typically formed through constructive and destructive superpositions of signals backscattered from flaws or geometric features. However, all PAUT data acquisition schemes require several emissions and the duration of the acquisition may be too slow in high-speed manufacturing. In this study, the Faster R-CNN was used to identify, locate and size flat bottom holes (FBH) and side-drilled holes (SDH) in an immersed test specimen using a single plane wave insonification. The training was performed on segmented and classified data generated using GPU-accelerated finite element simulations. SDH and FBH of different diameters, depths and lateral positions were included in the training set. The thickness of the test specimen was also variable. An ultrasonic phased array probe of 64 elements was simulated. All elements of the phased array probe were fired at the same time and the time traces from each element were recorded. The individual time traces were concatenated to form a matrix, which was then used in the training. This inspection scenario enables fast acquisition of data at the expense of poor lateral resolution in the resulting image. The trained neural network was initially tested using finite element simulations. Results were assessed in terms of the intersection of the union (IoU) between the ground truth geometry and the predicted geometry. With the simulated cases, the thickness of the test specimen was detected in all cases. When using a 40% IoU threshold, the detection rate of the FBH was 87% while only 20% for the SDH. The smallest detected FBH had a 0.56 wavelength depth and a lateral extent of 1.04 wavelength. Drawing a box using the -6dB drop method around the FBH always led to an IoU under 15%. On average, the lateral extent of the FBH using the -6dB method was three times larger than the diameter predicted by the proposed method. Then, the training was continued with a small augmented dataset of experiments (equivalent to 3% of the simulated dataset). In experiments, the results show that the test specimen was always correctly identified. When using a 40% IoU threshold the experimental detection rate of the FBH was 70%. The smallest detected defect in experiments had a depth of 2 wavelengths.

Optimal Extraction of Ultrasonic Scattering Features in Coarse Grained Materials

Ultrasonic array imaging is used in nondestructive testing for the detection and characterization of defects. The scattering behavior of any feature can be described by a matrix of scattering coefficients, called the scattering matrix. This information is used for characterization, and contrary to image-based analysis, the scattering matrix allows the characterization of defects at the subwavelength scale. However, the defect scattering coefficients are, in practice, contaminated by other nearby scatterers or significant structural noise. In this context, an optimal procedure to extract scattering features from a selected region of interest in a beamformed image is here investigated. This work proposes two main strategies to isolate a target scatterer in order to recover exclusively the time responses of the desired scatterer. In this article, such strategies are implemented in delay-and-sum and frequency-wavenumber forms and optimized to maximize the extraction rate. An experimental case in a polycrystalline material shows that the suggested procedures provide a rich frequency spectrum of the scattering matrix and are readily suited to minimize the effects of surrounding scattering noise. In doing so, the ability to deploy imaging methods that rely on the scattering matrix is enabled.

Improvement in the Quantification of Foreign Object Defects in Carbon Fiber Laminates Using Immersion Pulse-Echo Ultrasound

This research presents a new technique using pulse echo ultrasound for sizing foreign objects within carbon fiber laminates. Carbon fiber laminates are becoming increasingly popular in a wide variety of industries for their desirable properties. It is not uncommon for manufacturing defects to occur within a carbon fiber laminates, causing waste, either in the discarding of failed parts or the overdesign of the initial part to account for these anticipated and undetected errors. One such manufacturing defect is the occurrence of a foreign object within the laminate. This defect will lead to a localized weakness within the laminate including, but not limited to, stress risers, delamination, and catastrophic failure. This paper presents a method to analyze high-resolution c-scan full waveform captured data to automatically capture the geometry of the foreign object with minimal user inputs without a-priori knowledge of the shape of the defect. This paper analyzes twelve samples, each a twelve-lamina carbon fiber laminate. Foreign objects are made from polytetrafluoroethylene (PTFE) measuring 0.05 mm (0.002 in.) thick and ranging in diameter from 12.7 mm (0.5 in) to 1.588 mm (0.0625 in), are placed within the laminates during fabrication at varying depths. The samples are analyzed with a custom high-resolution c-scan system and smoothing, and edge detection methods are applied to the collected c-scan data. Results are presented on the sizing of the foreign objects with an average error of 6% of the true area, and an average absolute difference in the estimation of the diameter of 0.1 mm (0.004 in), an improvement over recently presented ultrasonic methods by a factor of three.

Deep Learning for Ultrasonic Crack Characterization in NDE

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing.

Deconvolution of ultrasonic signals using a convolutional neural network

Successfully employing ultrasonic testing to distinguish a flaw in close proximity to another flaw or geometrical feature depends on the wavelength and the bandwidth of the ultrasonic transducer. This explains why the frequency is commonly increased in ultrasonic testing in order to improve the axial resolution. However, as the frequency increases, the penetration depth of the propagating ultrasonic waves is reduced due to an attendant increase in attenuation. The nondestructive testing research community is consequently very interested in finding methods that combine high penetration depth with high axial resolution. This work aims to improve the compromise between the penetration depth and the axial resolution by using a convolutional neural network to separate overlapping echoes in time traces in order to estimate the time-of-flight and amplitude. The originality of the proposed framework consists in its training of the neural network using data generated in simulations. The framework was validated experimentally to detect flat bottom holes in an aluminum block with a minimum depth corresponding to λ/4.

Towards an Alternative to Time of Flight Diffraction Using Instantaneous Phase Coherence Imaging for Characterization of Crack-Like Defects

Time of flight diffraction (TOFD) is considered a reliable non-destructive testing method for the inspection of welds using a pair of single-element probes. On the other hand, ultrasonic phased array imaging has been continuously developed over the last couple of decades, and now features powerful algorithms, such as the total focusing method (TFM) and its multi-view approach to rendering detailed images of inspected parts. This article focuses on a different implementation of the TFM algorithm, relying on the coherent summation of the instantaneous signal phase. This approach presents a wide range of benefits, such as removing the need for calibration, and is highly sensitive to defect tips. This study compares the sizing and localization capabilities of the proposed method with the well-known TOFD. Both instantaneous phase algorithm and TOFD do not take advantage of the signal amplitude. Experimental tests were performed on a ¾″-thick steel sample with crack-like defects at different angles. Phase-based imaging techniques showed similar characterization capabilities as the standard TOFD method. However, the proposed method adds the benefit of generating an easy-to-interpret image that can help in localizing the defect. These results pave the way for a new characterization approach, especially in the field of automated ultrasonic testing (AUT).

Air-Coupled, Contact, and Immersion Ultrasonic Non-Destructive Testing: Comparison for Bonding Quality Evaluation

The objective of this study is to compare the performance of different ultrasonic non-destructive testing (NDT) techniques for bonding quality evaluation. Aluminium-epoxy-aluminium single lap joints containing debonding in the form of release film inclusions have been investigated using three types of ultrasonic NDT methods: contact testing, immersion testing, and air-coupled testing. Apart from the traditional bulk wave ultrasound, guided wave testing was also performed using air coupled and contact transducers for the excitation of guided waves. Guided wave propagation within adhesive bond was numerically simulated. A wide range of inspection frequencies causing different ultrasonic wavelengths has been investigated. Average errors in defect sizing per ultrasonic wavelength have been used as a feature to determine the performance of each ultrasonic NDT technique. The best performance is observed with bulk wave investigations. Particularly, the higher frequencies (10–50 MHz) in the immersion testing performed significantly better than air-coupled testing (300 kHz); however, air coupled investigations have other advantages as contactless inspection. Whereas guided wave inspections show relatively lower accuracy in defect sizing, they are good enough to detect the presence of the debonding and enable to inspect long range. Even though each technique has its advantages and limitations, guided wave techniques can be practical for the preliminary in-situ inspection of adhesively bonded specimens.

Casting Blanks Cleanliness Evaluation Based on Ultrasonic Microscopy and Morphological Filtering

The cleanliness of the casting blanks could seriously affect the quality of downstream products. Recently, ultrasound technology has been introduced to detect the inclusions in metal materials. However, due to the anisotropy of the material crystal, the ultrasonic wave has the characteristics of multiple scattering and refraction in its propagation process. This makes it difficult to evaluate the casting blanks cleanliness effectively, for the inclusion echoes are submerged in the background noise. Therefore, the ultrasonic microscope is innovatively proposed to carry out efficient scanning on the casting blanks. In the meantime, the morphological filtering algorithm has the advantages of fewer parameters and faster calculation speed which can be used to increase the signal-to-noise ratio of ultrasound images and extract the defect features more efficiently. In order to verify the effectiveness of the proposed method, specimens were taken from three strands of continuous caster for detection and analysis. The experimental results show that the second strand has the best quality and the cleanliness is 2.2/mm3, which is obviously better than the other two strands. This method will provide a new technology for the quantitative evaluation of the internal quality of the casting blanks.

Segmented analysis of time-of-flight diffraction ultrasound for flaw detection in welded steel plates using extreme learning machines

This work investigates the application of extreme learning machine, a fast training neural network model, for an ultrasound nondestructive evaluation decision support system. A novel segmented analysis of time-of-flight diffraction ultrasound signals is proposed in order to produce high flaw detection efficiency and low computational requirements, making it possible to be used in embedded applications. The frequency contents of TOFD signals temporal segments, estimated using the discrete Fourier transform, were used to feed the classification system. The test objects consisted of a set of SAE 1020 welded carbon steel plates, in which occur four types of defects. The obtained experimental results indicate that the proposed method is able to combine high accuracy, fast training and full exploration of the TOFD signal information.

Enhancing ultrasonic time-of-flight diffraction measurement through an adaptive deconvolution method

Deconvolution is generally applied to improve the temporal resolution of ultrasonic signals. However, using this process in the time-of-flight diffraction (TOFD) measurement of small and shallow defects is challenging because TOFD signals are dispersive in space-frequency distribution. Particularly, determining the reference signal for deconvolution remains a critical barrier. To this end, an adaptive deconvolution method is proposed in this study. Using wavelet transform, we firstly decompose the TOFD signals into sub-band signals to standardise the space-frequency distribution. Then, sub-band signals with strong coherences are adaptively selected on the basis of coherence coefficient metric. Upon the opted sub-band signals, a lateral wave can be readily used as the reference signal, and TOFD signals can be reconstructed with established Wiener filtering and spectral extrapolation methods. The feasibility of the proposed method is validated with the TOFD measurement of a small side-drilled hole near the surface. Results show that the proposed method effectively separates overlapping TOFD signals and improves the axial resolution of a TOFD image.

Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples

Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beam’s position, the ultrasonic transducer’s location and the echoes’ arrival times were determined, the estimation of the defect’s position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry.