Journal of Nondestructive Evaluation最新文献

The safety aspects of key components made from ferromagnetic materials are directly influenced by stress and hardness, thus necessitating the detection and evaluation of material hardness and stress state in the applications of such materials. This study proposes a highly sensitive method for characterizing mechanical properties (stress and hardness) using the time–frequency information of magnetic Barkhausen noise (MBN) technique. The MBN amplitude spectrum is obtained using the Short Time Fourier Transform (STFT) method under various mechanical conditions. The relationship between the amplitude spectrum and stress (or hardness) is analyzed in a point-by-point manner within the spectrums. A linear equation is employed to fit the dependence of mechanical property on the amplitude spectrum. Subsequently, the slope of the linearized equation is calculated to construct a sensitivity matrix. The optimal region is selected based on the amplitude spectrum region exhibiting high sensitivity. Goodness of fit, rate of change, and absolute error are employed as indicators to assess the parameters’ capability in characterizing mechanical properties. This study compares the time–frequency domain parameters obtained through the proposed methodology with the conventional time-domain parameters. Time–frequency information of MBN could play a valuable role for the non-destructive evaluation of mechanical properties.

In this paper, electrical resistivity monitoring using carbon screen-printed electrodes (SPEs) was successfully implemented to characterize the early age properties of cement and fly ash blended pastes, showing good agreement with other conventional methods (such as heat evolution and Vicat needle penetration). Resistivity changes were attributed to the material hydration physical and chemical changes, identifying four critical points in early hydration. These critical points correlate with different hydration phases, including ionic dissolution, early product formation, percolation of solid hydrates, and the final setting phase. The findings suggest that SPE can effectively track hydration evolution, providing an alternative to traditional setting time and calorimetry measurements. Limitations associated with contact resistivity methods found in macroelectrodes were addressed in this research using printed microelectrodes, in which due to their small size, the required electrical currents are very small, preventing the negative effects of ohmic drop (IR drop), noise, and temperature increase at the electrode/material interface. Through this method, sensitive measurements of the hydration process of cement pastes were carried out. Finally, As paste resistivity at early age is governed by the pore solution conductivity and the solid cementitious microstructure development, this research also includes the results of a neural network model designed to predict the early pore solution conductivity, offering researchers a practical tool to model and analyze the behavior of fly ash-blended cement mixtures, providing insights into the material’s early behavior.

In this paper, we propose an approach for determining the two-dimensional electrical conductivity mapping of an anisotropic unidirectional carbon fiber reinforced polymer (UD CFRP). The approach consists of subdividing the UD CFRP ply into several identical virtual zones and determining the longitudinal and transverse electrical conductivity components for each zone by applying an inverse problem technique with multiple cost functions. Following a clearly defined measurement protocol, experimental measurements were carried out on the UD CFRP ply to obtain DC electrical resistances between pairs of electrodes placed at the ends of the bordering zones. This was followed by a numerical calculation of the DC resistances for all electrode pairs using a finite element model (FEM). A multi-objective simulated annealing algorithm was applied to minimize the cost functions which are expressed as the difference between the calculated resistances and those measured for all electrode pair placement positions. The numerical model and experimental setup were validated using a conductive material with known electrical conductivity. The proposed multi-zone conductivity evaluation approach was first verified using numerical results and then successfully applied to a UD CFRP ply.

This paper introduces a novel, non-destructive, and preventive method for monitoring chloride ion penetration in concrete, replacing conventional techniques. While previous research has focused on using sensors to detect rebar corrosion, this study prioritizes preventive measures to monitor chloride ion penetration depth and prevent corrosion. We utilized carbon-based substrate (CPS) sensors, cement pseudo-reference electrodes (GS1, GS3, GSAg), and steel 316 ladder (L316) sensors. The results demonstrate that these sensors effectively provide real-time data on chloride ion penetration via an Internet of Things (IoT) platform. This system can prevent further ion infiltration, thus reducing corrosion risks. The sensors’ performance was validated by comparing them with chloride profile tests. The penetration rate was also compared to Fick’s second law diffusion rate, showing a 21% difference, confirming the reliability of the sensors for long-term structural health monitoring.

Magnetic particle inspection, a widely used nondestructive testing method, is employed to inspect surface defects in ferromagnetic materials due to its ease of operation, low cost, and high efficiency. In this study, Fe₃O₄ hollow nanospheres were synthesized by a solvothermal method. Lemon yellow (LY) pigments were successfully encapsulated on the surface of these magnetic nanospheres using E51 epoxy resin. The synthesized Fe₃O₄/E51/LY composite material was characterized in terms of its microscopic morphology, physical phase, and structural properties. The adsorption mechanism of the fluorescent materials on the particle surface was analyzed. Additionally, the photoluminescence and magnetic properties of the composite were tested and evaluated. A magnetic particle inspection test bench was then established to detect defects in the workpiece. The composite exhibited a saturation magnetization of 53.22 emu/g and emitted yellow-green fluorescence at 525 nm under ultraviolet light. The surface defects of the workpiece were accurately detected using magnetic fluorescent particles.

Graphical Abstract

In Additive Manufacturing (AM), precise rigid three-dimensional (3D) image registration between X-ray Computed Tomography (XCT) scans and Computer-Aided Design (CAD) models is an important step for the quantification of distortions in produced parts. Given the absence of standardized benchmarks for image registration in AM, we introduce a gold standard for 3D image registration, using a reference base plate beneath the build structure. This gold standard is used to quantify the accuracy of rigid registration, with a proof of concept demonstrated in PBF-LB/M. In this study, we conduct a comparative analysis of various rigid 3D registration methods useful for quality assurance of PBF-LB/M parts including feature-based, intensity-based, and point cloud-based approaches. The performance of each registration method is evaluated using measures of alignment accuracy based on the gold standard and computational efficiency. Our results indicate significant differences in the efficacy of these methods, with point cloud based Coherent Point Drift (CPD) showing superior performance in both alignment and computational efficiency. The rigidly registered 3D volumes are used to estimate the deformation field of the printed parts relative to the nominal CAD design using Digital Volume Correlation (DVC). The quality of the estimated deformation field is assessed using the Dice score metric. This study provides insights into methods for enhancing the precision and reliability of AM process.

Electromagnetic eddy current testing is a well recognised and widely used non-destructive evaluation (NDE) technique for detecting subsurface defects, thickness and conductivity measurements, corrosion studies etc., in conducting structures over several decades. The eddy current NDE techniques operated in frequency domain or in time domain are based on the eddy current induction in the target which is under investigation and acquiring its response. In both cases, the eddy current induced in the target is due to the physical phenomenon of Faraday’s Law of electromagnetic induction and the mode of measurement is different. Since from the beginning of the discovery of eddy current, there are only two magnetic fields such as primary and secondary magnetic fields have been playing a role in eddy current NDE. Here, it is proposed that the contribution of an additional magnetic field in the process of eddy current induction is the tertiary magnetic field and it has been explicitly observed through transient eddy current NDE measurements. The proposed novel hypothesis for the generation of tertiary magnetic field has been experimentally verified through specially designed pulsed eddy current NDE experiments using suitable targets. The proposed hypothesis explains the existence of self induced tertiary magnetic field in the absence of the target which is altered by not only with the target and also with lift-off. In this work, a set of NDE measurements have been carried out with targets of different thicknesses. The response of the tertiary magnetic field has also been observed by repeating the experiments with targets located at different lift-offs. The experimental data and subsequent analysis confirmed the proposed hypothesis that the contribution of an additional tertiary magnetic field in transient or pulsed eddy NDE measurements is responsible for the observation of peculiar PEC signal for the lift-off variations and it must be taken into account while analysing the data. This observation pave the way to develop novel methods on sub-surface defect detection, thickness measurements, corrosion studies, etc. in conducting structures, particularly on the most commonly used structural materials by various industries.

Inspection of aircraft structures usually requires the disassembly of structures. The intrusive nature of the disassembly process is a major cause of unreliability. Thus, inspection without disassembly is favored, but it causes decreased structural reliability due to increased uncertainties in crack size measurement. This paper proposes and validates a Bayesian method for estimating the initial size of cracks hidden by the fastener head. A shifted probability of detection method was used to provide a reasonable likelihood function for inspection with a fastener. Combined with Monte Carlo simulation and backpropagation, the initial size of the hidden crack was estimated. The resultant crack size distribution was applied to structural reliability analysis, and the result was compared with experimental results. The method yielded slightly conservative risk, but a reasonable inspection interval. The decreased uncertainty benefits the operator, who enjoys an increased inspection and maintenance schedule.

As a promising reference technique for non-destructive evaluation of both electrowelded and butt-fused polyethylene (PE) assemblies, Phased Array Ultrasonic Testing (PAUT) is still studied extensively in several laboratories worldwide and is supported by the technical standard ISO TS 16943. During the last 10 years, several joint projects have been completed aiming at evaluating the acuity of PAUT applied to both pipes and electrofused assemblies either exhumed from the field or prepared in laboratory. More recently, a focus has been made on fixing some acceptance criteria combining PAUT data and long term resistance of the laboratory joints. This paper presents the updated data obtained on electrofused assemblies—63 mm saddles and 110 mm sockets—containing different types of defects such as: insufficient heating time, pipe under-penetration in the socket, excessive localized scraping, pollutants and calibrated thin strips, in both mass and cross configuration, put at the interface pipe-saddle. PAUT scanning on the different specimens, both during the welding phase and after cooling, confirms the capability of the technique to visualize and size the Heat Affected Zone (HAZ), which can be revealed and compared afterwards on sample sections. Moreover, most of the defects are detected and sized, confirming the fairly good performance of PAUT, except for the smallest strips which are located in non accessible zones, due to the particular design of the saddle. Long term resistance of the welds is then evaluated by Hydrostatic Pressure Tests (HPT) followed by a decohesion test after rupture, according to the requirements of both the ISO 13956 and NF EN 1555 standards. Under such test conditions, every joints comply with the requirements of the standards (rupture time greater than 1000 h at 80 °C and 5 MPa), even those violating the critical proportions of non-welded zones.

The determination of void fraction in various two-phase flows holds great significance across a range of industries, including gas, oil, chemical, and petrochemical sectors. Scientists have proposed a wide array of methods for measuring void fractions. In comparison to other methods, capacitive-based sensors stand out as a good choice due to their affordability, nondestructively, robustness, and reliability. However, one of the factors that can affect the accuracy of these sensors is changes in the fluid composition. For instance, even a minor alteration in the fluid within the pipe can result in a significant void fraction measurement error. To address this issue, regular calibration is necessary, which can be a laborious task. In this paper, an Artificial Neural Network (ANN) is employed in order to make sensor measurements independent of fluid changes, which allows for more reliable and precise measurements without the need for frequent calibration. Our focus is on studying stratified two-phase flow. In this research, four different combinations of electrodes of a four-concave sensor are utilized as the input of an ANN. As a result, the ANN’s output accurately quantifies the void fraction. COMSOL Multiphysics software is utilized to simulate the behavior and measure the capacitance value of different combinations of this sensor. Additionally, a Multilayer Perceptron (MLP) neural network in MATLAB is designed and implemented, which can forecast the gas percentage within a two-phase fluid containing different liquids, achieving a remarkable mean absolute error of only 0.0031.