Journal of Nondestructive Evaluation最新文献

Improving detection accuracy without reducing detection depth has always been a challenge in ultrasound imaging. For multi-layer anisotropic carbon fiber reinforced polymers (CFRPs), the propagation path of ultrasonic waves becomes complex with severe attenuation. To improve the imaging accuracy of defects in CFRPs, a beam multiply and sum method suitable for full matrix data, FM-BMAS, has been proposed. This method introduces the spatial coherence of ultrasonic array signals through cross-multiplication operation. A new harmonic component is generated and high-quality imaging of side-drilled holes (SDHs) is achieved without affecting the detection depth. The FM-BMAS method can detect two SDHs with a diameter of 1 mm at a depth of 8 mm at 2.5 MHz detection frequency. In contrast, these defects cannot be visualized by the classical total focusing method due to ultrasonic attenuation even with 5 MHz detection frequency. Furthermore, FM-BMAS can achieve higher image resolution and superior noise suppression capabilities in CFRPs compared to other methods.

With increasing attention to safety, accurate detection of defects in diverse materials is crucial. Non-Destructive Evaluation (NDE) techniques play a key role in this effort. However, many approaches overlook preliminary signal analysis, which is vital for understanding fundamental sensor signal characteristics and improving advanced techniques. This study aims to investigate the fundamental signal properties from coplanar capacitive sensors (CCS) with square electrodes to gain insights into the relationship between signal properties and actual linear defect size in both conducting and non-conducting materials. The first-order derivatives (1st O.DE) of raw signals obtained from CCS through both simulations and experiments were further analysed, focusing on a circular surface defect. Nine sensor configurations were tested to examine the spreading effect. Experiment and simulation results were in good agreement, showing that the Full Width at Zero (FWZ) of the raw signals of both materials is greater than the actual defect diameter (spreading effect) whereas the signals processed using the 1st O.DE exhibited peak-trough widths greater than the actual defect diameter in non-conducting materials (spreading effect) and less than the actual diameter in conducting materials (shrinking effect). These results underscore that the spreading and shrinking effects are intrinsic characteristics of the CCS, attributed to the behavior of the CCS’s electric field and sensitivity distribution field (SDF) when interacting with different materials. By incorporating these insights into novel and advanced methods—such as imaging algorithms, machine learning approaches, and data fusion techniques—future developments can be effectively guided to enhance the accuracy, reliability, and advancement of defect detection, imaging, and sizing in coplanar capacitive sensing for NDE.

The range of application of eddy current testing (ECT) has been recently extended to non-electrically conductive materials, in which case it is called electromagnetic induction testing (EIT) given that EIT detects changes in the electromagnetic field of the displacement current. Although EIT has been reported for non-destructive characterization of non-electrically conductive materials, its detection principle is still unclear. We used finite element analysis (FEA) and experiments to evaluate the effect of cracks on the displacement current field in non-electrically conductive materials for crack detection using EIT. FEA was performed on electrically and non-electrically conductive materials with slits simulating cracks. The FEA results showed that crack detection differed between materials, as eddy currents bypassed the crack, while displacement currents passed through and formed a current path. Furthermore, the electric field intensity and displacement current induced in the non-electrically conductive materials varied significantly in the cracked area compared with the uncracked area. Experiments were conducted to detect cracks in carbon fiber reinforced thermoplastics (CFRTPs) and glass fiber reinforced plastics (GFRPs), which have isotropic electrical properties in the in-plane direction. In the CFRTP, the electromagnetic field varied significantly even at locations far from the crack, whereas it changed only slightly near the crack in the GFRP. This result demonstrates that for non-electrically conductive materials, EIT can identify cracks by detecting localized changes in the displacement current flowing through the cracks. Our findings can help clarify the principle of crack detection in non-electrically conductive materials, thereby extending the application of EIT to these materials.

High and low salinity levels play a crucial role in the vitality of organisms and affect natural ecosystems, agricultural yields and human health. To mitigate the risks associated with high blood pressure and cardiovascular diseases, the World Health Organization (WHO) advocates reducing salt consumption among adults, suggesting an intake of no more than 5 g daily. In this study, a non-invasive microwave (MW) sensing approach, that is augmented by deep neural network (DNN)  models is proposed to predict salinity levels. The MW detection measurement system, including a Horn antenna, has been developed to evaluate the salt content in bottled spring waters (BSWs). The system with DNN  model provides a novel solution for real-time water quality monitoring. The input and output dataset for DNN  model were generated using four different BSWs, each with a salt content ranging from 0 to 32 g and increased by 1 g. The developed DNN  model, designed with six fully connected layers, uses reflection coefficients (RCs) as input dataset to predict salt content in grams accurately. The accuracy performance of the DNN  model in various bandwidths was evaluated by dividing the 1–13 GHz range into 78 different bands and the lowest error rate was found to be in the 1–8 GHz bandwidth (2.18%). Furthermore, each BSW was measured five times, and the performance of the model was evaluated according to the number of measurements. In three or more measurements, the model demonstrated notable improvement(15.3%) in predicting salt content.

Carbon fiber laminates enjoy a wide range of applications from innovative architectural design to aerospace and the safety overwrap for pressure vessels. In the case of carbon fiber overwrapped pressure vessels (COPVs), the overwrap thickness can vary from 6 mm (∼ 0.25 inch) for thin-walled COPV up to 25 mm (∼ 1”) or more for thick walled COPV, depending on the vessel type. The failure mechanisms for carbon fiber are more complex than for metals and monitoring COPVs for defects or fatigue over their lifetime is further complicated by the thickness of the carbon fiber used. Traditional electromagnetic NDE methods, such as eddy current testing (ECT) for imaging defects in these structures has been severely limited, achieving accurate identification to about 4 mm in depth. In this paper, a new technique is introduced to address these shortcomings, Electro-Magnetic-Inductive-Coupling-Analysis, or EMICA, can be used to detect damage inside thick carbon fiber laminate pieces. EMICA is based on the interaction of the repeating three-dimensional structure of carbon fiber and low-frequency electromagnetic waves that are allowed to actively spread through the conductive bulk composite material highlighting defects such as delamination and fiber disruptions, well below the laminate surface. In this paper, EMICA is demonstrated in flat carbon fiber laminates up to ∼ 12 mm (0.5”) thick, made in-house, with known defects hidden through the thickness of the piece that cannot be detected via visual inspection. Delaminations, cuts/cracks, and the underlying ply layup structure can all be identified in the EMICA images. It is shown that three imbedded PTFE delaminations at varying depths (3 mm, 6 mm, 9 mm) are simultaneously imaged using EMICA in a ½” thick CF laminate [0°/90°] panel with an excitation frequency of 40 kHz. Furthermore, the electromagnetic focal point can be chosen within the depth of CF composites by intelligently selecting the excitation frequency for the ply layup being probed, while the traditional penetration depth equation does not hold true in these complex structures.

To determine optimal road maintenance and repair schedules, road agencies need to regularly evaluate asphalt pavement performance during both construction and operation. It usually involves back-calculating the pavement’s deflection responses to obtain modulus for each structural layer. However, bedrock under the subgrade can significantly affect this analysis. To enhance the accuracy of back-calculation, this study proposed bedrock depth prediction models based on pavement system transfer function (PSTF) aided by falling weight deflectometer (FWD) tests. To provide sufficient data for model development, a spectral element method with fixed-end boundary conditions (B-SEM) was used to calculate the deflection responses of various pavement structures under different bedrock conditions. Based on the transfer function (TF) theory of linear time-invariant (LTI) systems, Fourier transform (FT) was used to process time-domain data, resulting in the PSTF for each pavement structure, which was then used as the dataset. This study also analyzed the amplitude spectrum characteristics of PSTFs under different bedrock depths and proposed methods for identifying bedrock under the subgrade. A bedrock depth prediction model (PSTF-BD) based on the PSTF was developed using the results of the sensitivity analysis. The model’s performance was comprehensively evaluated using various error metrics. The results indicate that the PSTF-BD model demonstrates high accuracy in predicting bedrock depth. Specifically, the PSTF-BD (B) model achieves a correlation coefficient of 99.6%, with an average error of no more than 1.0% for the prediction results of the validated dataset. Compared to existing prediction models, the PSTF-BD model improves correlation by at least 6.4% and prediction accuracy by at least 34.1%. Furthermore, the PSTF-BD model offers superior predictive performance and is well-suited for engineering applications, showcasing significant potential for widespread adoption in road engineering projects.

The current study focuses on the examination of adhesive-bonded materials, comprising different type of flaws like brass inclusions and delamination, through the application of ultrasound and X-ray non-destructive testing (NDT) techniques. The findings from both ultrasound and X-ray inspection were used to extract unique features, contributing to a more comprehensive understanding of the distinct characteristics demonstrated by each method. Several distinct features like absolute time of flight difference, peak-to-peak amplitude, variation coefficient in time and frequency domain, mean value of amplitude in frequency domain, and absolute energy were extracted from ultrasound testing results. Similarly, features like maximum amplitude, features from accelerated segment test, dilation, watershed segmentation, wiener deconvolution, and morphological gradient extracted from X-ray data underwent fusion. Different fusion techniques were applied to combine these features into a unified data set. A quantitative evaluation was performed for the individual features and their corresponding fused features from the ultrasound and X-ray results. A systematic analysis was conducted to quantify the improvement in defect sizing within the individual features and fused features from both the X-ray and ultrasonic investigations. The minimum absolute error of 0.02 mm was achieved with average fusion of absolute energy at 2nd interface and X-ray dilate features. This research not only delves into the diverse capabilities of ultrasonic and X-ray NDT methods in identifying flaws but also emphasizes the synergistic advantages arising from the integration of their distinct features. The qualitative study of defect estimation using the proposed fusion methods demonstrate that the distinctive fusion approaches significantly highlight the complimentary benefits of ultrasound and X-ray non-destructive testing methods, resulting in a quantifiable improvement in probability of defect detection.

Reinforced concrete (RC) structures exposed to saline environments are highly susceptible to chloride-induced corrosion and estimating the service life of such vulnerable RC structures is essential for quality control and future risk assessment. Most service life estimation models rely on chloride migration coefficients, determined through destructive, time-consuming, and relatively costly rapid migration tests (RMT). This study aims to develop correlations between concrete resistivity and migration coefficients based on the silica (SiO₂) contents of the binders as a non-destructive alternative to evaluate service life of RC structure exposed to chloride induced corrosion. A wide range of used concrete mixes (for three different design strengths) with different binder types having SiO₂ content ranging from 15 to 35% has been utilized. Both fly-ash and slag were used as supplementary binders. The validity of the correlation has been established through a different set of experimental results of concrete mixes having dissimilar binder types than used in this study. From the comparison between the probabilistic service life estimated using the predicted (from developed correlations) and experimental migration coefficient values it can be concluded that the proposed correlations are considerably effective as a non-destructive and reliable approach for serviceability assessment of RC structures in saline exposures.

This study explores the sensitivity of the Magnetic Barkhausen Noise (MBN) technique in detecting various types and degrees of burning in a single sample, which is similar to what occurs in industrial processes. Using flat grinding with an aluminum oxide wheel on hardened AISI 1045 steel, eight samples with a ground area of 115 mm x 7 mm were created, varying only the ae parameter. In some samples, the effect of generating different levels of burning was observed, starting at one end (grinding wheel entrance) without damage and gradually increasing the damage until the opposite end (grinding wheel exit) with the presence of high levels of burning and the identification of a thick white layer. Results indicated that the MBN_RMS (root mean square value of the MBN signals) parameter can identify varying burning levels caused by overtempering and rehardening. Burning gradients were clearly detected by MBN and confirmed by metallographic analyses. When the white layer is generated continuously on the surface, the MBN_RMS parameter adequately tracks the variation in its thickness, varying in an inversely proportional manner.

Shearography, an effective non-destructive testing tool, is widely employed for detecting defects in composite materials. It detects internal defects by detecting deformation anomalies, offering advantages such as full-field, non-contact measurement, and high accuracy. Defect size is a critical parameter determining structure performance stability and service life. However, manual inspection is the primary method for defect size measurement in this technique, leading to inefficiency and low accuracy. To address this issue, this study established a defect recognition and high-precision automatic measurement method based on the U-Net deep learning network. First, a high-precision one-time calibration method for all system parameters was developed. Second, U-Net was employed to segment the measured image, identifying defect location and subimage. Finally, defect size was accurately calculated by combining calibration parameters and segmented defect subimage. The proposed method yielded a measurement error of less than 5% and a real-time dynamic detection rate of 14 fps, demonstrating potential for automated quantitative defect detection.