Nondestructive Testing Research Articles

Estimation Model for Maize Multi-Components Based on Hyperspectral Data.

Assessing the quality of corn seeds necessitates evaluating their water, fat, protein, and starch content. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498 nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using the original spectra and extracted characteristic wavelengths, PLSR, BP, RBF, and LSSVM models were constructed to detect the content of four components. The analysis indicated that the CARS-LSSVM algorithm had the best prediction performance. The PSO algorithm was employed to further optimize the parameters of the LSSVM model, thereby improving the model's prediction performance. The R values for the four components in the test set were 0.9884, 0.9490, 0.9864, and 0.9687, respectively. This indicates that hyperspectral technology combined with the DT-CARS-PSO-LSSVM algorithm can effectively detect the main component content of corn seeds. This study not only provides a scientific basis for the evaluation of corn seed quality but also opens up new avenues for the development of non-destructive testing technology in related fields.

Feasibility of ultrasonic non-destructive testing of ancient funerary urns: Experimental and analytical parametric model

Non-invasive modalities are being developed as the computer tools and industrial non-destructive testing capabilities become more widespread. Using the same investigation methods as those applied to modern objects, we seek to study ancient funerary urns containing human bones. The exploration of ancient urns is key to increasing our understanding of the practices of burial and cremation in archaeo-anthropology. But those urns present at archaeological sites are exposed to diversified environments (wet, temperate or dry environment), and can be subjected to chemical or mechanical destructive actions over time. As ancient funeral urns containers are sometimes made of lead, stone or ceramic, the use of X-ray scanners is difficult or even impossible for in situ control. In this work, we propose an engineering technique based on ultrasonic non-destructive testing and wave propagation in urns. An analytical parametric model is developed using mathematical signal processing tools and validated by means of laboratory experiments under controlled conditions of propagation through reproductions of ancient urns for two ultrasonic frequencies (500 kHz and 1 MHz). The aim is to characterise an ancient urn by creating an analytical model, and to use a parametric identification algorithm to determine the presence or absence of bone fragments. The parametric identification algorithm based on the Levenberg–Marquardt method makes it possible to determine the geometrical (thickness) and physical (wave velocity and attenuation, mass density) parameters when the urn is filled with water, with water and bones, and with water, bones and sand. We show that the model makes it readily (processing time ≈15 sec) possible to find the different times of flight between the transducer and the different walls of the urn and the parameters with an accuracy less than 10%.

AI optimization and mathematical simulation validated by nondestructive testing for resonance frequency in advanced composite structures for bridge applications

This paper presents a novel approach integrating artificial intelligence (AI) optimization and mathematical simulation to predict the resonance frequency of nanoclay-reinforced concrete cylindrical shell structures intended for bridge applications. These composite structures, known for their enhanced mechanical properties, require precise evaluation of their vibrational behavior to ensure structural stability and longevity. Traditional methods for predicting resonance frequencies are often time-consuming and prone to inaccuracies, especially in complex materials like nanoclay composites. To address this, an AI-based optimization algorithm was developed, incorporating Particle Swarm Optimization (PSO) and a mathematical modeling to simulate resonance characteristics under varying material and geometric parameters. The mathematical modeling is validated using nondestructive testing (NDT) techniques, such as modal analysis, which provided real-world resonance data without damaging the structure. The nondestructive testing results is compared against the mathematical model to ensure accuracy and reliability. The integration of nanoclay into the concrete matrix significantly altered the vibrational properties, enhancing the stiffness and reducing damping losses, which is crucial for bridge applications where dynamic loads are prevalent. The optimized model not only predicted resonance frequencies with high accuracy but also demonstrated its potential for large-scale bridge infrastructure. This methodology offers a streamlined and robust tool for engineers, reducing the need for physical prototyping and providing enhanced design capabilities. Future research will focus on expanding the model to incorporate additional material behaviors and load conditions, furthering its application in civil engineering.

Non-destructive testing for thickness measurement of the adhesive layer and defect detection in ceramic-metal bonded structures using THz waves

Ceramics, owing to their superior thermal barrier properties, are employed by bonding them to primary structures exposed to elevated temperatures, such as the external components of aircraft and spacecraft. However, when these bonded structures are subjected to thermal impact resulting from high-temperature environments or rapid temperature variations, issues such as debonding, crack formation, and degradation may ensue. These issues can significantly undermine the reliability of the structures, necessitating nondestructive testing (NDT) to monitor the condition of the adhesive layer before and after thermal impact. In this article, we evaluated the integrity of adhesive structures before and after thermal impact using Terahertz (THz) waves, which offer exceptional penetration and resolution for NDT of nonmetallic materials without causing damage to the structure. To achieve this, ceramic-metal bonded structures were mounted on a robotic arm, and scanning was performed over a 70 mm area at 1 mm intervals and 0.5° increments. The peak-to-peak time of the THz signal through the adhesive layer was calculated to determine the thickness distribution. Based on experimental results, the average thickness of the ceramic layer before thermal shock was measured at 9.48 mm (standard deviation: 0.03) and the average thickness of the adhesive layer was 0.234 mm (standard deviation: 0.03). Additionally, four suspected defect areas within the adhesive layer were identified. After thermal shock, the ceramic layer’s thickness decreased by an average of 0.02 mm (0.17%), resulting in an average thickness of 9.46 mm, and the adhesive layer’s thickness decreased by an average of 0.002 mm (0.99%), resulting in an average thickness of 0.232 mm. The four suspected defect areas were confirmed to be in the same locations before thermal impact. To validate the NDT method using THz wave, the ceramic-metal bonded structure was cut to identify debonding defects in the suspected areas. The difference between the thickness distribution obtained using the THz wave-based NDT and that measured using optical microscopy after cutting was 4.4%, confirming the accuracy of the THz wave-based method for defect detection and thickness measurement.

A Cr3+-Yb3+ co-doped high-efficiency near-infrared luminescent phosphor for NIR-II light source applications

Phosphor conversion light-emitting diode (PC-LED) technology has become a focal point of research for portable near-infrared light sources in recent years. One of the crucial aspects is to develop phosphor materials that can be effectively stimulated by commercial available blue light. Unfortunately, efficient phosphors in the NIR-II that can be excited by commercial blue light are particularly scarce. In this study, Mg4Ta2O9:0.04Cr3+,0.03Yb3+,0.07Li+ (MTO:0.04Cr3+,0.03Yb3+,0.07Li+) phosphor was synthesized using the high temperature solid phase method, and its emission spectrum covered the first and second near-infrared windows (700–1200 nm). The internal quantum efficiency (IQE) and external quantum efficiency (EQE) are 88.7 % and 45.6 %, respectively, due to the synergistic effect of energy transfer and Li-ion modification. Additionally, the doping of Yb3+ ions and Li+ ions enhances the thermal stability of luminescence. The thermal stability of MTO:0.04Cr3+,0.03Yb3+,0.07Li+ and MTO:0.04Cr3+,0.03Yb3+ is 9.81 % and 14.43 % higher, respectively, than that of MTO:0.04Cr3+,0.03Yb3+ and MTO:0.04Cr3+. To evaluate its potential applications, we developed a prototype PC-LED utilizing MTO:0.04Cr3+,0.03Yb3+,0.07Li+ phosphor as an emission emitter which demonstrated advantages in biomedical imaging, night vision, and non-destructive testing.

Effect of micrometer bubble interfaces on the detection and measurement method of XLPE

Hidden air bubble defects in the XLPE (Cross-Linked Polyethylene) insulation will seriously affect its electrical performance, which needs to be detected timely to ensure the safe and stable operation of the power system, but the interfaces between bubbles and XLPE may affect the detection. This paper proposes a novel method for high-precision detection and measurement method of micron-sized bubbles in XLPE based on THz time-domain reflection technology. To analyze the influencing factors limiting the high-precision detection and measurement of micron-sized defects, firstly, the propagation law of THz wave in XLPE with and without defects is studied, and the unipolar and bipolar pulses reflected at the defects are analyzed by overlap, and then a new method of micron-sized defects detection and measurement is proposed based on the overlap property of the bipolar pulses, and the relationship between the minimum detecting frequency and the size of the defects in the various detecting methods is determined. And the finite element method is used to simulate the THz detection of XLPE samples with defect thicknesses of 100, 7, 5, and 3 μm, respectively, and the proposed method is validated in conjunction with experiments. The results show that micrometer defects can be detected and measured with high precision using this proposed method, and the error can be controlled within 4.39%, which is significantly better than the conventional detection method. The proposed method can provide a new idea for non-destructive testing of insulation defects.

Non-destructive testing based on Unet-CBAM network for pulsed thermography

Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe.

Modal parameters and damage evaluation of steel fiber reinforced concrete under flexural cyclic loadings

Structures may be subjected to static and dynamic loads during their service life. When it comes to the dynamic spectrum, wind action, ocean waves and seismic loads are key examples and are responsible for cracking evolution and material degradation along their lifespan. Therefore, the fatigue behavior of cementitious materials has been receiving great attention due to cyclic loading that can be of natural occurrence or induced by human activity lately. The present research aims to investigate the evolution of the modal parameters of steel fiber reinforced concretes under flexural cyclic load and to evaluate how the mechanical degradation can be monitored from a non-destructive impact modal testing over the cycles. The dynamic properties of the material, including natural frequencies, vibration modes and damping ratios, were tracked to assess the level of fatigue damage using modal testing techniques. Hence, it was possible to evaluate the material mechanical degradation as a function of the dynamic parameters. From the dynamic test, it was possible to evaluate the presence of damage, which was corroborated by the observed stiffness degradation. The first flexural mode and the following second, third and sixth vibration modes were more affected along the cycles. Finally, the proposed non-destructive methodology was effective in assessing the mechanical decay of cementitious composites.

Damage propagation and failure mechanism of single dome historical Masonry Mosque after February 6, 2023, Kahramanmaraş earthquake doublets (Mw = 7.7 and Mw = 7.6)

Many historical masonry mosques and minarets, including Milky Minaret Mosque and Ulu Mosque, which are very close to Ak Minaret Mosque in Malatya, suffered severe damage and collapsed after the Kahramanmaraş earthquake on February 6, 2023. Despite the settled loose soil properties and the presence of a small stream flow near the mosque, Ak Minaret Mosque, a historical masonry mosque with a single dome and square, remains operational even after the aforementioned earthquakes. Additionally, DEMA's strong ground motion station was out of service during both earthquakes. This situation raises questions about the mosque's ability to withstand the seismic load. The basic purpose of this paper is to investigate in detail the seismic performance, damage limits, the reason for unexpectedly less damage due to the earthquakes, the potential failure mode, and possible resisted earthquake loads of the historical Ak Minaret Mosque in Malatya, Türkiye. To achieve this goal, first, dynamic identification was performed on the mosque one year prior to the aforementioned earthquakes. Next, the material properties were determined using both non-destructive and destructive testing methods. Following the dynamic identification, a numerical model was generated by 3-D solid elements, and this 3-D model was calibrated using the dynamic identification tests. The mosque underwent both nonlinear static and nonlinear dynamic analyses. Nine seismic records were selected for the nonlinear dynamic analysis. Five of them were national, including even Kahramanmaraş earthquake records, and three of them were selected from an international database on the basis of fault characteristics and site classification. The analysis results indicate that Ak Minaret Mosque incurred less damage than expected during the Kahramanmaraş earthquakes. This could be due to soil improvement prior to the construction of Ak Minaret Mosque in 1573. Moreover, the effective restoration increased stiffness and maintained the mosque’s stability. Finally, the possible resisted PGA by the mosque was around 0.22 g.

Alternatives to Reduce Hot Cracking Susceptibility of IN718 Casting Alloy Laser Beam Welds with a Mushroom Shape

Reducing hot cracking is essential for ensuring seamless production of nickel superalloys, which are extensively used in welded structures for aircraft engines. The prevalence of hot cracking in precipitation-strengthened alloy 718 is primarily governed by two factors: firstly, the chemical composition and the coarse microstructure formed during solidification, and secondly, the activation of hot cracking mechanisms, which is particularly critical in mushroom-shaped welding morphologies. In this study, different nickel-based superalloys welded using laser beam welding (LBW), more specifically bead on plate welding (BoP), specimens are compared. The cracking susceptibility of both wrought and two investment casting 718 alloys with tailored chemical compositions is examined through the application of both continuous and pulsed LBW. Additionally, various pre-weld treatments, including with and without Pre-HIP (hot isostatic pressing), are analyzed. The influences of chemical composition, LBW parameters and pre- and post-welding treatments on both internal and external cracks determined by conventional and advanced non-destructive tests are studied. A clear reduction of hot cracking susceptibility and overall welding quality improvement was observed in a tailored 718 alloy with relatively high Ni (55.6% wt) and Co (1.11% wt) contents.

Mid-infrared optical coherence tomography and machine learning for inspection of 3D-printed ceramics at the micron scale

IntroductionIn this paper, recent developments in non-destructive testing of 3D-printed ceramics and monitoring of additive manufacturing of ceramics are presented.MethodsIn particular, we present the design and use of an inline mid-infrared optical coherence tomography (MIR-OCT) system to evaluate printed and micro-structured specimens in lithography-based ceramic manufacturing (LCM).ResultsThe proposed system helps with the detection of microdefects (e.g., voids, inclusions, deformations) that are already present in green ceramic components, thereby reducing the energy and costs incurred.DiscussionThe challenges during integration are discussed. Especially, the prospects for MIR-OCT imaging combined with machine learning are illustrated with regard to inline inspection during LCM of printed ceramics.

Fault Prediction in Resistance Spot Welding: A Comparison of Machine Learning Approaches

Resistance spot welding is widely adopted in manufacturing and is characterized by high reliability and simple automation in the production line. The detection of defective welds is a difficult task that requires either destructive or expensive and slow non-destructive testing (e.g., ultrasound). The robots performing the welding automatically collect contextual and process-specific data. In this paper, we test whether these data can be used to predict defective welds. To do so, we use a dataset collected in a real industrial plant that describes welding-related data labeled with ultrasonic quality checks. We use these data to develop several pipelines based on shallow and deep learning machine learning algorithms and test the performance of these pipelines in predicting defective welds. Our results show that, despite the development of different pipelines and complex models, the machine-learning-based defect detection algorithms achieve limited performance. Using a qualitative analysis of model predictions, we show that correct predictions are often a consequence of inherent biases and intrinsic limitations in the data. We therefore conclude that the automatically collected data have limitations that hamper fault detection in a running production plant.

A state-of-the-art assessment in developing advanced concrete materials for airport pavements with improved performance and durability

Concrete materials, as the main component of airport pavement, provide essential attributes such as strength and toughness required by airport pavement. With the rapid development of air transportation, the compression and cracking resistance performances required by airport pavement are dramatically boosted with the ever-growing weight and the frequency of aircraft. In recent years, high-performance concrete materials with excellent properties have shown great potential in improving durability and extending the service life of pavement. This review presents a-state-of-the-art assessment guided by the pavement performance requirement, material design mechanism, application cases and future application guidance of advanced cementing materials in airport pavement improvement. Firstly, the functions of each structural layer of airport pavement and their effects on the surface layer are summarized. Then, the design mechanism and application cases of advanced concrete materials were summarized with the future application guidance to be given. Finally, the application scenarios, testing mechanism and testing effect of nondestructive testing methods for airport pavement performance evaluation are summarized. This review provides a comprehensive assessment of the development and applications of advanced concrete materials for airport pavement, paving avenue to achieve high performance and long service life of airport pavement.

Calibration Method of Measuring Heads for Testing Residual Stresses in Sheet Metal Using the Barkhausen Method.

Among non-destructive testing methods, a group dedicated to the assessment of the state of residual stresses can be distinguished. The method of measuring residual stresses using the Barkhausen noise method has many advantages, as evidenced by the number of publications. The residual stresses in metal products are important for the further processing of such metal, such as laser cutting or bending. The results presented in this work are of an experimental nature, and the presented method of calibration of measuring heads shows how various research techniques can be used to correlate results. The research was carried out for structural steel due to the market share of this type of steel. The method can be used to measure the residual stresses in ferromagnetic metal products in order to assess their directions and quantify them. A prerequisite for the use of this measurement method is that the amplitude and geometry of the Barkhausen noise are adequately correlated to the specific values of the state of stress depending on the tested steel grade or other metals. In this study, a method for calibrating measuring sensors for the residual stress measurements is presented, as developed by the authors. The method involved conducting bending tests in both numerical modeling and experimental tests. During the bending tests, changes in the magnetic field (Barkhausen noise waveform) were recorded, taking into account the state of elastic stresses. Correlating the results of the numerical calculations and Barkhausen noise measurements made it possible to determine the quantitative values of the residual stresses in the steel sheets. Thanks to the method used, very accurate measurement is possible, and the obtained results are repeatable.

Balancing of Resonant Differential Coils for Broadband Inductive Sensor Systems.

Differential coils are frequently implemented in inductive sensing systems. They can be considered as a single coil that is made up of two or more subcoils, wound in series opposition. They can be used on the transmit or receive side of measurement systems, and, if designed correctly, ensure no coupling between coils under background conditions. By cancelling background coupling, the receive electronics only needs to be able to measure the change in coupling produced by a target. This allows for a more efficient use of the dynamic range, and for larger receive-side amplifier gain, thereby improving SNR. When subcoils are not electrically similar, it can be hard to engineer the coil to be perfectly balanced across a wide bandwidth. This paper presents an analytical model of a resonant differential coil pair that is tested and applied on a planar metal detector for the detection of buried objects. The model demonstrates the capability to balance an arbitrary differential coil pair, which has a broad applicability across a range of inductive sensor applications such as metal detection and non-destructive testing. The method is applied to the practical system. The results show that the correction resulting from this method ensures a stable balance across a significantly enhanced bandwidth. In the case studied here, the bandwidth of the experimental system is increased from 20 kHz to 90 kHz.

Detecting the Sigma Phase in Duplex Stainless Steel by Magnetic Noise and First Harmonic Analysis.

Non-destructive electromagnetic tests based on magnetic noise analysis have been developed to study, among others, residual stress, heat treatment outcomes, and harmful microstructures in terms of toughness. When subjected to thermal cycles above 550 °C, duplex stainless steels form an extremely hard and chromium-rich constituent that, if it is superior to 5%, compromises the steel's corrosion resistance and toughness. In the present work, a study was carried out concerning the interaction of excitation waves with duplex stainless steel. Hence, by analyzing the magnetic noise and variations in the amplitude of the first harmonic of the excitation waves, the detection of the deleterious sigma phase in SAF 2205 steel is studied. To simplify the test, a Hall effect sensor replaced the pick-up coil placed on the opposite surface of the excitation coil. Sinusoidal excitation waves of 5 Hz and 25 Hz with amplitudes ranging from 0.25 V to 9 V were applied to samples with different amounts of the sigma phase, and the microstructures were characterized by scanning electron microscopy. The results show that the best testing condition consists of applying waves with amplitudes from 1 V to 2 V and using the first harmonic amplitude. Thus, the test proved effective for detecting the formation of the deleterious sigma phase and can follow the ability to absorb energy by impact and, thus, the material embrittlement.

Conservation Treatment in the Process of Strengthening the Stone Architecture of Kazimierz Dolny (Poland)

ABSTRACT A crucial task in modern rock engineering involves studying mechanical properties of rocks used as elements of stone objects, focusing on how susceptible they are to processes that lead to degradation. In Kazimierz Dolny, several factors contribute to severe damage. Their identification made it possible to look at the effects of their action on the rock, both external — during field observations, and internal — during laboratory tests. The laboratory experiments were carried out with the aim of determining the geomechanical strength characteristics of the siliceous carbonate rock. The influence of specific climate-related factors (thermal shock and exposure to SO2 in the presence of moisture) on the deterioration of the rock was assessed. These tests were conducted on samples that were either air-dried or treated with preservative agents. The activity and effectiveness of these agents were evaluated based on results obtained from non-destructive ultrasonic tests, the percentage of weight loss, and destructive strength tests. The discussion revolves around the implications of the results obtained from the laboratory experiments. It explores the significance of the identified climate-related factors in the deterioration of siliceous carbonate rock and effectiveness of proposed preventive solutions, considering the diverse outcomes observed in tests.

Automatic defect detection in infrared thermal images of ancient polyptychs based on numerical simulation and a new efficient channel attention mechanism aided Faster R-CNN model

In recent years, the preservation and conservation of ancient cultural heritage necessitate the advancement of sophisticated non-destructive testing methodologies to minimize potential damage to artworks. Therefore, this study aims to develop an advanced method for detecting defects in ancient polyptychs using infrared thermography. The test subjects are two polyptych samples replicating a 14th-century artwork by Pietro Lorenzetti (1280/85–1348) with varied pigments and artificially induced defects. To address these challenges, an automatic defect detection model is proposed, integrating numerical simulation and image processing within the Faster R-CNN architecture, utilizing VGG16 as the backbone network for feature extraction. Meanwhile, the model innovatively incorporates the efficient channel attention mechanism after the feature extraction stage, which significantly improves the feature characterization performance of the model in identifying small defects in ancient polyptychs. During training, numerical simulation is utilized to augment the infrared thermal image dataset, ensuring the accuracy of subsequent experimental sample testing. Empirical results demonstrate a substantial improvement in detection performance, compared with the original Faster R-CNN model, with the average precision at the intersection over union = 0.5 increasing to 87.3% and the average precision for small objects improving to 54.8%. These results highlight the practicality and effectiveness of the model, marking a significant progress in defect detection capability, providing a strong technical guarantee for the continuous conservation of cultural heritage, and offering directions for future studies.

Monitoring of damage evolution in carbon fiber reinforced polymer composites by electrical impedance tomography

Electrical impedance tomography (EIT) has been widely investigated as a nondestructive testing method for carbon fiber reinforced polymer (CFRP) composites. However, the performance of EIT method on the damage process monitoring of the composites is lack of investigation. Herein, quasi-static indentation tests were utilized to introduce damage on CFRP laminates. The damage evolution process was monitored by EIT, while the potential of distinguishing the elastic deformation stage from the plastic stage was analyzed with the aid of acoustic emission. It was found that the changes in conductivity first appeared in the non-central region. With the accumulation of damage, the conductivity changes gradually extended to the center region. The reconstructed damage images were in the crossover shape, which was consist with the micro-CT results that showed the fracture of fibers in ±45° direction. This work further promotes the application of EIT in damage process monitoring of CFRP components.

Research on FPC coils with limited turns in planar electromagnetic tomography

Electromagnetic tomography (EMT) is a non-destructive testing method that reconstructs electrical parameter distribution based on boundary electromagnetic measurement data along electromagnetic rotation projection. The coil is a critical component of the EMT system, used to excite and detect changing magnetic fields. The performance of excitation-detection with specific coils directly affects the quality of the reconstructed image. The traditional EMT coils are wound coils. The excitation-detection performance of the coils is enhanced by increasing the number of turns to obtain better imaging quality. A larger coil volume has better sensitivity, but it cannot meet the space constraints of many applications. To solve this problem, this paper proposes a method using extremely thin FPC coil arrays as EMT sensors. To verify the feasibility of FPC coil imaging with a limited number of turns, firstly, this article analyses the excitation-detection capabilities of FPC coil with 1 to 12 turns through a combination of simulation and experiment. Secondly, the impact of excitation-detection capability on the EMT sensitivity matrix was studied. Finally, a 4×4 8-Turns FPC coil sensor was designed for image reconstruction experiments. The experimental results show that FPC coils with limited turns can be used for image reconstruction and to solve the problem of planar nondestructive testing in a small space.