Nondestructive Evaluation Techniques Research Articles

The realisation of fast X-ray computed tomography using a limited number of projection images for dimensional metrology

Due to the merit of establishing volumetric data, X-ray computed tomography (XCT) is increasingly used as a non-destructive evaluation technique in the quality control of advanced manufactured parts with complex non-line-of-sight features. However, the cost of measurement time and data storage hampers the adoption of the technique in production lines. Commercial fast XCT utilises X-ray detectors with fast detection capability, which can be expensive and result in a large amount of data. This paper discussed a different approach, where fast XCT was realised via the acquisition of a small number of projection images instead of full projection images. An established total variation (TV) algorithm was used to handle the reconstruction.The paper investigates the feasibility of using the TV algorithm in handling a significantly reduced number of projection images for reconstruction. This allows a reduction of measurement time from 52 min to 1 min for a typical industrial XCT system with an exposure time of 1 s per projection. It also enables a reduction of data size proportionally.A test strategy including both quantitative and qualitative test metrics was considered to evaluate the effectiveness of the reconstruction algorithm. The qualitative evaluation includes both the signal to noise ratio and the contrast to noise ratio. The quantitative evaluation was established using reference samples with different internal and external geometries. Simulation data were used in the assessment considering various influence factors, such as X-ray source property and instrument noise.The results demonstrated the possibility of using advanced reconstruction algorithms in handling XCT measurements with a significantly limited number of projection images for dimensional measurements. This work lays down the foundation for conducting fast XCT measurements without the need for instrument alteration or enhancement. Although the reconstruction time required is still considerable, various possibilities to improve this have been discussed.

Two-photon electromagnetic induction imaging with an atomic magnetometer

Electromagnetic induction imaging (EMI) is a contactless, nondestructive evaluation technique based on sensing the response of a target to oscillating magnetic fields as they penetrate into materials. Leveraging the enhanced performance of radio frequency atomic magnetometers (RF-AMs) at low frequencies can enable highly sensitive through-barrier EMI measurements, which, for example, can reveal concealed weaponry or inspect subsurface material defects. However, deriving this advantage requires precise control of a well-defined, low bias magnetic field with respect to the background magnetic field texture, which presents a cumbersome challenge to stabilize in real-world unshielded scenarios. Here, we implement a two-photon RF-AM scheme in a portable setup to bypass the requirement of a low bias field and achieve stable, repeatable resonances in the sub-kHz regime. The improved accessibility to lower primary field frequencies offer greater skin-depth in target materials and facilitates an enhancement of a factor of 8 in skin penetration with this portable system, detecting features behind an Al shield of 3.2 mm. The scheme also reduces the need of large compensation coils to stabilize the bias field, facilitating the implementation of compact devices.

Reinforcement Corrosion in RC Hollow Piers: Destructive and Non-Destructive Tests

In this work, cyclic-load tests on reduced-scale corroded reinforced-concrete hollow cross-section bridge piers have been experimentally performed and compared to the results of similar non-corroded piers. Piers were aged by using an imposed electric current and sodium chloride water solution before performing a mechanical cyclic-load test. The corrosion process has been detected with Non-Destructive Evaluation techniques by means of SonReb method (to check concrete degradation) and by measuring corrosion potential (to check steel degradation). The crack pattern was recorded by dedicated cameras, and an LVDT system was set up to monitor the cyclic-load test. Experimental results focused on degradation monitoring and mechanical performance under cyclic loads. During the cyclic-load mechanical test, the first cracks on the piers surface occurred diagonally, inclined at about 45°. This is the consequence of the failure mode change from ductile failure, as expected for slender designed piers, to brittle shear failure. The flexural failure occurred in the case of non-corroded piers. Presented tests can provide a useful contribution of experimental data to analyse the behaviour of corroded reinforced concrete hollow bridge piers, scarcely tested. In particular, the cyclic response can be a useful reference for the proposition/validation of nonlinear capacity models for the evaluation of the seismic capacity of corroded bridge piers.

Classification of Impact Echo Signals Using Explainable Deep Learning and Transfer Learning Approaches

Impact echo (IE) is one of the most frequently used nondestructive evaluation (NDE) techniques for detecting subsurface defects such as delamination, honeycombing, and voids in concrete structures. In the conventional analysis of IE data, the time-domain signal is transformed into the frequency domain and the frequency content is used to estimate the presence and nature of the defect. Machine learning (ML) has been recently applied to the IE signal classification problem. However, because of the scarcity of labeled IE datasets, most existing work relies on relatively small training and test datasets without addressing the generalizability and transferability of the developed models. In this paper, we compare two approaches for automatic classification of IE signals: clustering based on expert-crafted features and deep learning (DL) from automatically extracted features. Next, we use the knowledge gained from a DL model trained on concrete specimens with available ground truth to make predictions about defects in a different specimen with completely different construction and characteristics (transfer learning). Finally, we examine our DL model to gain insights into the model working (explainability) and highlight the attributions that are significant in classifying a particular IE signal. Our findings demonstrate the utility of ML and DL for IE signal classification, but also highlight the need for high-quality labeled datasets for advancing ML and DL in NDE data analysis.

Synthesized Evaluation of Reinforced Concrete Bridge Defects, Their Non-Destructive Inspection and Analysis Methods: A Systematic Review and Bibliometric Analysis of the Past Three Decades

Defects are essential indicators to gauge the structural integrity and safety of reinforced concrete bridges. Non-destructive inspection has been pervasively explored over the last three decades to localize and characterize surface and subsurface anomalies in reinforced concrete bridges. In addition, different fuzzy set theory-based, computer vision and artificial intelligence algorithms were leveraged to analyze the data garnered from non-destructive evaluation techniques. In light of the foregoing, this research paper presents a mixed review method that encompasses both bibliometric and systematic analyses of the state-of-the-art work pertinent to the assessment of reinforced concrete bridge defects using non-destructive techniques (CBD_NDT). In this context, this study reviews the literature of journal articles and book chapters indexed in Scopus and Web of Science databases from 1991 to the end of September 2022. To this end, 505 core peer-reviewed journal articles and book chapters are compiled for evaluation after conducting forward and backward snowballing alongside removing irrelevant papers. This research study then exploits both VOSVIEWER and Bibiometrix R Package for the purpose of network visualization and scientometric mapping of the appended research studies. Thereafter, this paper carries out a multifaceted systematic review analysis of the identified literature covering tackled bridge defects, used non-destructive techniques, data processing methods, public datasets, key findings and future research directions. The present study is expected to assist practitioners and policymakers to conceive and synthesize existing research and development bodies, and future trends in the domain of the assessment of bridge defects using non-destructive techniques. It can also aid in raising awareness of the importance of defect management in bridge maintenance systems.

A Dual Mode Nondestructive Evaluation Sensor With Simultaneous Inductive and Capacitive Outputs

Conventional electromagnetic Non-Destructive Evaluation (NDE) techniques are usually sensitive to either conductors or insulators. The emerging non-destructive assessment of "insulator-conductor" composite structures is thus faced with cost and efficiency problems due to this limitation. In this paper, a novel detection technique that combines inductive and capacitive methods is proposed for the inspection of "insulator-conductor" composite structures. A new structure of a planar inductive-capacitive dual-mode sensor in which the inductive and capacitive modes can be excited simultaneously with a single excitation signal without the need for mode switching is developed. The output signal of this sensor is analyzed in relation to the spatial electromagnetic field using equivalent circuit analysis and finite element (FE) simulation. A signal conditioning module for the proposed sensor is designed and a dual-mode test system that simultaneously detects signals from both modes is built. Experimental results from the test system show that the proposed detection method is sensitive to defects in both the insulating and conducting layers of the "insulator-conductor" composite structure and thus can detect corrosion under the insulation, surface cracks on the conductor and interface debonding with the detected signals able to reflect the size of the defects. The detection information of the two modes is complementary, proving that the method is able to comprehensively evaluate "insulator-conductor" composite structures.

Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder

Ultrasonic testing (UT) is one of the most popular non-destructive evaluation (NDE) techniques used in many industries to evaluate structural integrity. The commonly used NDE techniques are basic inspection techniques, such as visual testing (VT), penetration testing (PT), and magnetic testing (MT), and advanced inspection techniques, such as UT, radiography testing (RT), eddy current testing (ECT), and phased array ultrasonic testing (PAUT). Among the numerous advanced techniques, ultrasonic testing (UT) is usually used for the inspection of welds in various industries. However, the application of UT still has some shortcomings to overcome. One major shortcoming that reduces the precision of UT is the extra signals from the geometrical interface of a specimen. UT uses the reflection indications of the ultrasonic beam. However, the reflection signals from the welding interface and geometry along with the target flaw signal produce mixed signals. The inspectors use a 1-D reflection outcome called the ultrasonic A-scan to evaluate the welding integrity. The mixed ultrasonic A-scan signals are often very difficult to analyze because inspectors must distinguish the target flaw signal of welding from the mixed ultrasonic A-scan signal, which includes the flaw indication as well as the background signal. Therefore, a method to distinguish between the flaw signal and the background signal must be developed for the efficiency of UT. Autoencoder is an artificial neural network that is made for feature extraction from the input. Denoising autoencoder (DAE) is one of the derivative models of the autoencoder which adds or eliminates random noise signals to extract the prominent features. DAE is already widely used in the denoising of images and sound data. The characteristics of DAE are used in this research to distinguish the ultrasonic flaw signal from the mixed ultrasonic A-scan signal. For the training, 2463 mixed A-scan signals were obtained from 45 different standard blocks in which 5 different types of flaws were embedded. For testing, we used 1000 mixed A-scan signals. The performance of the network was evaluated using a point-by-point comparison method. The autoencoder was trained to denoise the background signal from the mixed ultrasonic A-scan, and the target flaw signal was extracted from the original A-scan signal.

A review of non-destructive evaluation techniques for axial thermal stress and neutral temperature measurement in rail: Physical phenomena and performance assessment

Thermal buckling of continuous welded rail (CWR) has been a long-standing challenge for the railroad industry because of the high derailment rate and the associated social, economic, and environmental impacts it causes. Rail buckling is generally attributed to excessive thermally-induced axial compressive stress developed in the rail from high temperatures. Knowing the rail thermal stress or its rail neutral temperature (RNT) is critical for safe and efficient rail system operation. There has been great interest and much work on the development of nondestructive evaluation (NDE) techniques to estimate rail thermal stress and RNT in situ. This paper reviews the findings and conclusions from research about NDE approaches for estimating rail thermal stress or RNT, emphasizing the physical phenomena and performance interpretation related to each of the approaches. We identify the type of reference measurements each technique relies on and tabulate this information showing key assumptions, performance, and limitations for each technique.

Laser ultrasonic imaging of subsurface defects with the linear sampling method.

Laser ultrasonics is a remote nondestructive evaluation technique suitable for real-time monitoring of fabrication processes in semiconductor metrology, advanced manufacturing, and other applications where non-contact, high fidelity measurements are required. Here we investigate laser ultrasonic data processing approaches to reconstruct images of subsurface side drilled holes in aluminum alloy specimens. We demonstrate through simulation that the model-based linear sampling method (LSM) can perform accurate shape reconstruction of single and multiple holes and produce images with well-defined boundaries. We experimentally confirm that LSM produces images that represent the internal geometric features of an object, some of which may be missed by conventional imaging.

Miniaturized multi-modality field-ready sensing system for defect detection of CFRP materials

As infrastructure decays rapidly due to structural and material aging and failures, the development of new improved, and efficient nondestructive evaluation (NDE) techniques is vital for system health monitoring and preventive maintenance. Composite material, such as carbon fiber reinforced polymer (CFRP) plays a significant role in energy and transportation infrastructure due to the advantages of corrosion resistance, durability, and lightweight, which contributes to minimal maintenance and long service life. However, due to their unique anisotropic dielectric and mechanical characteristics, detecting composite material defects by NDE techniques is still challenging. This paper presents a novel, low-cost miniaturized multi-modality imaging system combining low-frequency capacitive shortwave and high-frequency microwave NDE technologies to detect various types of defects in CFRP materials. Based on the governing electromagnetic theory behind multi-modality electromagnetic NDE methods, the merit of combining low-frequency shortwave and near-field microwave imaging for dielectric characterization of composite materials is thoroughly investigated and demonstrated. Then, a miniaturized imaging system is developed to operate at ultra-wide bands: 10 kHz to 200 MHz for the capacitive shortwave probes and 1 GHz to 9 GHz for the microwave probes. Customized multi-material joining samples of CFRP and steel with different defect types, locations, depths, and sizes are tested by the developed imaging system, and the experimental results of the miniaturized system are compared with the existing table-top systems, which demonstrates comparatively accurate results for the developed multi-modality imaging system. The compact and practical nature of the presented imaging system makes it an optimal tool that can be utilized in field conditions with constrained operational spaces and NDE uncertainties.

Monitoring of Engineered Stones Used in Artwork Reproductions: Mechanical Characterization by Laser Vibrometry

Many museums have been producing reproductions for several years to replace artworks weakened by outdoor exhibition. Among these, in order to imitate the original aesthetic, the French consortium Réunion des Musées Nationaux–Grand Palais has chosen to work from large-format marble sculpture molds to complex composite materials based on resins comprising mineral fillers. However, similar to the original works of art, these reproductions age and deteriorate due to constant outdoor exposure. For this reason, current research focuses on the preventive conservation and monitoring of the structural health of these reconstructed objects. The goal of this paper was to study the resin/mineral powder composite materials used to produce cultural heritage reproductions of sculptures. This work is oriented toward a comparison of the mechanical properties of composite materials used in the replacement of cultural heritage sculptures (for instance, in the Garden of the Palace of Versailles or the Rodin Museum). The objectives were to first characterize the physical and mechanical properties of these materials in order to identify the most suitable material for cultural heritage reproduction, and secondly, to propose a method with minimal contact that obtained equivalent information as analyses performed with conventional ultrasonic techniques. These nondestructive evaluation techniques could be used for laboratory and in situ analyses. Samples of different polymer/mineral powder filler compositions were analyzed by compressional, shear and surface waves, generated by a 1 MHz center frequency ultrasonic transducer. Firstly, the measurements made it possible to evaluate the velocities of the bulk acoustic waves and extract the Young’s modulus of each tested material. Secondly, in order to have minimal contact with the analyzed structure, a laser interferometry system was used to detect waves at the surface and follow their propagation. The results clearly showed the possibility of using this technique to extract mechanical characteristics of composite materials, allowing for selection of material for the reproduction of large-format statues. For different types of polymer resins, the ability of ultrasonic analysis to track the impact of rock powder (marble or slate) on the mechanical properties of these synthetic materials was clearly observed, proving that this technique holds promise for monitoring the structural health of large-format artwork.

Classification and Characterization of Damage in Composite Laminates Using Electrical Resistance Tomography and Supervised Machine Learning

Electrical resistance tomography (ERT) is a nondestructive evaluation technique that uses the internal conductivity variations of materials to assess structural integrity. Due to the low instrumentation required, the widespread use of ERT in the aerospace industry for monitoring the accumulation of damage in aircraft components can lead to significant reductions in inspections and maintenance costs. However, implementing the ERT method for mapping the damage state of structural components made of carbon fiber reinforced polymeric (CFRP) composites is challenging due to the inability of this method to distinguish between damage modes such as delamination and matrix cracking. This article explores the combined use of ERT and machine learning algorithms such as neural networks, random forests, k-nearest neighbors, and support vector machines to classify and characterize delamination and matrix cracking damage in CFRP laminates. Results show that the proposed classification algorithms can successfully estimate the damage severity of delaminated composites in the presence of matrix cracking. Similarly, the classification algorithms can characterize these independent damage modes with an accuracy of 95%. The algorithms showed robustness to predict the electrical resistance variations of damaged composites and characterize delamination and matrix cracking damage even when intrinsic noise was considered. Although neural networks characterized damage with the highest accuracy, these algorithms were also the most sensitive to noise. For applications where instrumentation noise cannot be completely removed from the ERT signals, the use of nearest neighbors is thus recommended.

Composite Single Lap Shear Joint Integrity Monitoring via Embedded Electromechanical Impedance Sensors

Composite bonded structure is a prevalent portion of today’s aircraft structure. Adequate bond integrity is a critical aspect of fabrication and service, especially since many of today’s structural bonds are critical for flight safety. Over the last decade, non-destructive bond evaluation techniques have improved but still cannot detect a structurally weak bond that exhibits full adherend/adhesive contact. The result is that expensive and time-consuming structural proof testing continues to be required to verify bond integrity. The objective of this work is to investigate the feasibility of bondline integrity monitoring using piezoelectric sensors, embedded at different locations within the composite joint, and to assess the benefits of monitoring the thickness mode in addition to the radial mode. Experiments and analyses are performed on single lap shear composite joints, with and without embedded sensors, subjected to incrementally increasing tensile loads. The results indicate that the embedded piezoelectric sensors measure a change in the resonance in both the radial and thickness mode during incremental loading and that the thickness resonance shows enhanced sensitivity to impending failure. Thus, it is demonstrated that monitoring both modes of the piezoelectric sensor provides addition details for prognostic performance evaluation.

Extraction and selective promotion of zero-group velocity and cutoff frequency resonances in bi-dimensional waveguides using the electromechanical impedance method

This study showcases the electromechanical impedance (EMI) technique for extracting and promoting zero-group velocity (ZGV) and cutoff frequency resonances in a waveguide structure. We identify the mechanisms of multiple resonances in the EMI spectra via a wave propagation perspective. Both simulation and experiments reveal the fact that sharp resonances in the conductance spectra are associated with either ZGV or cutoff frequency points. Consequently, we design four test configurations to enhance local resonances by aligning induced motions with considered mode shapes. Reasonable agreement between simulation and experiment results is observed. We evaluate the performance of considered configurations in terms of mode enhancement, and configurations that can selectively promote certain mode families are summarized. This study also shines the light on the EMI technique for quantitative non-destructive evaluation (NDE) by potentially supporting the inverse characterization of mechanical properties of host structures.

Development and Calibration of Thermoelectric-Power-Based Non Destructive Evaluation (TNED) Device for Aging Analysis of Carbon Steel SA 106B

The aging analysis of nuclear power plant components and materials is crucial for the power plant’s safety, and for its upgradation and life extension. The health monitoring of power plant materials, such as steel, is mostly done by employing conventional non-destructive evaluation (NDE) techniques. Considering the limitations associated with conventional NDE techniques, thermoelectric power based NDE (TNDE) is being explored. This paper presents the first ever development and successful demonstration of TNDE in Pakistan. Moreover, the aging analysis of carbon steel grade SA 106B is also being presented for the first time. The extraction-steam pipe samples (new and aged) of SA 106B, from a 46-years-old operating nuclear power plant, are being analyzed and the sensitivity of TNDE to aging induced changes in microstructure is demonstrated. Further, the aged SA 106B is simulated via heat treatment and the equipment is calibrated for its utilization in the nuclear power plant. The correlation between thermoelectric power (TEP) and Vicker hardness (HV) [TEP (µV/oC) = ~2x10-5 Hardness (HV) + 0.008] has been analyzed. It is revealed that the technique has the potential to analyze mechanical properties of aged extraction steam piping without cutting and taking the samples to the laboratory.

Hydrogen induced cracks monitored with acoustic emission under the laboratory condition

Hydrogen induced cracking (HIC) is a common form of failure in steel exposed to hydrogen rich environments, which always occurs inside material making their detection difficult. Acoustic Emission (AE) is a promising Nondestructive Evaluation technique for crack detection. However, it is challenging to distinguish the signals generated from HIC, corrosions and bubbles. In this study, these mixed AE signals were obtained by electrochemical hydrogen charging on A516 carbon steel with the solution of 0.5 mol/L H2SO4 and 0.5 g/L NaAsO2. Microstructures of the sample’s cross-section were observed to verify the existence of HIC. moreover, separate signals of bubbles and corrosions were acquired from just exposures to 0.5 mol/L H2SO4. After analyzing, key parameters from the AE signals, i.e. the peak frequency, the spectrum gravity, the duration and energy, were identified to differentiate the source mechanisms. Results show that the peak frequency and the spectrum gravity of corrosion signals are higher than those of HIC and bubbles, while the duration and energy of HIC signals are higher than those of the other two kinds of signals. To reduce manual operations, the unsupervised learning algorithms, Principal Components Analysis (PCA) and K-means, were used for clustering, for further understanding of these clusters and their evolution with time.

Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey

AbstractApplication of deep learning (DL) for automatic condition assessment of bridge decks has been on the raise in the last few years. From the published literature, it is evident that lot of research efforts has been done in identifying the surface defects such as cracks, potholes, spalling and so forth using supervised learning methods such as deep learning. However, the health of a reinforced concrete bridge deck is jeopardized substantially due to presence of subsurface defects. Subsurface defects in bridge decks are genearlly detected using non‐destructive evaluation (NDE) methods. Interpertation of NDE data for autonomous deck evaluation requires development of DL models; however, The task of defect detection DL has not received the proper attention for subsurface defect detection in the past. The goal of this paper is to provide a review of existing DL models for analysis of NDE data of bridge decks. The authors reviewed prominent NDE techniques for subsurface defect detection of bridge decks and explored the various DL models proposed to identify these defects. First a brief overview of the working principle of NDE techniques and DL architectures is provided, and then the information about proposed DL models and their efficacy is highlighted. Based on the existing knowledge gaps, various challenges and future prospects associated with application of DL in bridge subsurface inspection are discussed.

Ultrasonic non-destructive evaluation of composites: A review

Ultrasonics is an NDT (Non-Destructive Testing) technique used to detect faults in structural components by applying a set of techniques and classifying them accordingly. This paper aims to bring together the most relevant published work on different types of techniques for Non-Destructive Evaluation (NDE) of various defects using Ultrasonic. The Ultrasonic testing techniques are relatively simple, highly sensitive and less time-consuming hence have been approved for use on various domains like composite testing, aerospace, automotive and transport. The fundamentals, their applications and constraints of various techniques are covered, along with motions to assess the performance of the techniques. Ultrasonic testing became popular, and with the progress made, new methods were discovered and corrected which resulted in defect detection and improving various material properties.

Non-Destructive Evaluation of the Quality of Adhesive Joints Using Ultrasound, X-ray, and Feature-Based Data Fusion

The aim of this work is to achieve reliable nondestructive evaluation (NDE) of adhesively bonded aerospace components by developing novel multidimensional data fusion techniques, which would combine the information obtained by ultrasonic and X-ray NDE methods. Separately, both NDE techniques have their advantages and limitations. The integration of data obtained from pulse echo immersion ultrasound testing and radiography holds immense potential to help improve the reliability of non-destructive evaluation. In this study, distinctive features obtained from single techniques, traditional ultrasonic pulse echo testing, and radiography, as well as fused images, were investigated and the suitability of these distinctive features and fusion techniques for improving the probability of defect detection was evaluated. For this purpose, aluminum single lap joints with brass inclusions were analyzed using ultrasound pulse echo and radiography techniques. The distinctive features were extracted from the data obtained, and images of features obtained by both techniques were fused together. Different combinations of features and fusion algorithms were investigated, considering the desire to automate data evaluation in the future.

Detection of defects in cellular solids using highly nonlinear solitary waves: a numerical study of the proximal femur.

This study investigates the suitability of a relatively new non-destructive evaluation (NDE) technique for the detection of non-visible defects in cellular solids using highly nonlinear solitary waves (HNSWs) in a one-dimensional granular chain. Specifically, the HNSW-based NDE approach is employed to identify the existence of micro-fractures in trabecular bone within the femoral neck (FN) and the intertrochanteric (IT) region of the proximal femur which are fracture-prone sites due to their relatively low bone density, particularly in osteoporosis patients. The availability of a HNSW-based bone quality assessment tool could not only help in early diagnosis of osteoporosis but also affect surgical decisions and improve clinical outcomes in joint replacement surgeries which motivated this study. To obtain a realistic representation of the trabecular microstructure, high-resolution finite-element (FE) models of the FN and the IT region are first constructed using a topology optimization-based bone reconstruction scheme. Then, artificial defects in the form of fractured ligaments are generated in the FN and IT models by selectively disconnecting various struts within the trabecular network. Using the FE models as the inspection medium, hybrid discrete-element/finite-element (DE/FE) simulations are performed to examine the interaction of the HNSWs with the cellular bone samples through two different inspection modes, i.e., inspection via direct contact with the sample and indirect contact through an adequately chosen face sheet inserted between the cellular sample and the granular chain. The delays and amplitudes of the HNSWs are used to estimate the effective elastic moduli of the cellular samples and these estimates were found to be reasonably accurate only in case the face sheet was applied. For the latter case, it was shown that the HNSW-based modulus estimates can be used as indicators for defect detection, allowing to discern between pristine and damaged cellular solids. These results suggest that HNSW-based NDE is a reliable and cost-effective technique for the identification of defects in cellular solids, and is expected to find applications in various fields, such as non-invasive screening of bone diseases and fractures, or damage detection in additively manufactured cellular structures.