Non-destructive Measurement Technology Research Articles

Development and experimental evaluation of hybrid K-edge/X-ray fluorescence densitometer for uranium solution measurement

The hybrid K-edge/X-ray fluorescence densitometer (HKED) is a combination of K-edge absorption technology (KED) and characteristic X-ray fluorescence (XRF), which has the advantages of direct, fast and non-destructive determination, and is an ideal non-destructive measurement technology for uranium and plutonium concentrations. In this paper, a new HKED was developed, primarily utilizing an X-ray tube from COMET, alongside high-purity germanium (HPGe) and cadmium telluride (CdTe) detectors from AMETEK ORTEC. This manuscript delves into several variables that influence measurement outcomes under predefined experimental conditions and operational prerequisites to pinpoint critical parameters. It was discerned that the adoption of a 160 kV high voltage setting markedly diminishes experimental interferences, while the beam current, optimally set at 2 mA, not only ensures a linear correlation with the count rate but also maximizes the effective count detected. The incorporation of a 2 cm fixed-length iron rod along the trajectory between the sample and the detector, complemented by an additional 3 mm external absorber before the KED detector, effectively mitigates direct X-ray exposure, thereby enhancing transmittance values to attainable extents. Subsequent to the determination of these pivotal parameters, validation of the HKED system's efficacy was conducted via performance evaluation tests on a laboratory-scale HKED setup. Measurements undertaken for both KED and XRF across an interval ranging from 300 to 3000 s fell within the 2σ boundary, affirming the system's stability. Repeated measurements of 50 g/L and 150 g/L uranium solutions yielded KED precision rates of 0.56% and 0.19%, respectively. Moreover, linear regression analyses linking transmittance, characteristic X-ray fluorescence, and uranium concentrations across a spectrum of 0–150 g/L underscored the laboratory HKED instrument's robust analytical capabilities. Notably, the relative discrepancy between theoretical predictions and empirical findings for the 150 g/L uranium sample was minimized to a commendable 0.58%.

Nondestructive detection of key phenotypes for the canopy of the watermelon plug seedlings based on deep learning

Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding, management, and quality testing. The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient, subjective and destroys samples. Therefore, the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning. The Azure Kinect was used to shoot canopy color images, depth images, and RGB-D images of the watermelon plug seedlings. The Mask-RCNN network was used to classify, segment, and count the canopy leaves of the watermelon plug seedlings. To reduce the error of leaf area measurement caused by mutual occlusion of leaves, the leaves were repaired by CycleGAN, and the depth images were restored by image processing. Then, the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud. The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray. Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height. The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages. The average relative error of measurement is 2.33% for the number of true leaves, 4.59% for the number of cotyledons, 8.37% for the leaf area, and 3.27% for the plant height. The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.

Hyperspectral prediction of pigment content in tomato leaves based on logistic-optimized sparrow search algorithm and back propagation neural network

Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from leaves of tomato seedlings cultured at different nitrogen concentrations. The visible/near-infrared(VIS/NIR) hyperspectral imaging (HSI) non-destructive measurement technology, 430-900 nm and 950-1650 nm, with total variables of 794, was used to obtain the reflection spectra of leaves. An improved strategy of the sparrow search algorithm (SSA) based on Logistic chaotic mapping was proposed and optimized the back propagation (BP) neural network to predict the pigment content of leaves. Different pretreatment methods were used to effectively improve the prediction accuracy of the model. The results showed that when the nitrogen concentration in the nutrient solution was 302.84 mg·L-1, the pigment content of leaves reached the maximum. Meanwhile, the inhibition effect of high concentration was much stronger than that of low concentration. To address the problem that the SSA is prone to get in premature convergence due to the reduction of population diversity at the end of the iteration, the initialization of the SSA population by Logistic chaotic mapping improves the initial solution quality, convergence speed and search capacity. The root mean squared error (RMSE), coefficient of determination (R2) and relative percent deviation (RPD) of chlorophyll a were 0.77, 0.77 and 2.08, respectively. The RMSE, R2 and RPD of chlorophyll b were 0.30, 0.66 and 1.71, respectively. The RMSE, R2 and RPD of chlorophyll were 0.88, 0.81 and 2.28, respectively. The RMSE, R2 and RPD of carotenoid were 0.14, 0.75 and 2.00, respectively. The HSI technology combined with machine learning algorithms can achieve rapid and accurate prediction of crop physiological information, providing data support for the precise management of fertilization in facility agriculture, which is conducive to improving the quality and output of tomatoes.

Development and Dissemination of Compact Neutron Systems and of Non-destructive Measurement Technology for Infrastructures

Development and Dissemination of Compact Neutron Systems and of Non-destructive Measurement Technology for Infrastructures

Nondestructive Thickness Measurement of Thermal Barrier Coatings for Turbine Blades by Terahertz Time Domain Spectroscopy

Owing to its high penetrability with dielectric materials, terahertz time domain spectroscopy (THz-TDS) is a promising nondestructive measurement technology. The coating thickness deviation and defect of thermal barrier coatings (TBC) will affect its thermal insulation performance and lifetime. In this work, THz-TDS was applied to measure the coating thickness distribution of TBC. The refractive index was obtained by THz-TDS transmission mode. To avoid the normal incidence THz signal loss, the THz signal was reflected from the TBC with a 10° incident angle, which also made the measurement result insensitive to the unevenness and tilt of the TBC sample. In the experiment, the yttria-stabilized zirconia (YSZ) TBC was measured by THz-TDS to estimate the thickness distribution. To validate the thickness measurements, metallography was introduced to correlate the TBC thickness result. The measurement deviation was within 12.1 µm, i.e., 3.45% for the THz-TDS and metallography result. A piece of turbine blade was measured by THz-TDS and a eddy current test. The maximum deviation was 8.48 µm, i.e., 2.36% of these two methods. Unlike the eddy current test, the THz-TDS thickness result was not affected by the cooling holes. The effectiveness of the nondestructive thickness measurement of TBC for turbine blades by THz-TDS was verified.

Remote estimation of rice LAI based on Fourier spectrum texture from UAV image

BackgroundThe accurate estimation of rice LAI is particularly important to monitor rice growth status. Remote sensing, as a non-destructive measurement technology, has been proved to be useful for estimating vegetation growth parameters, especially at large scale. With the development of unmanned aerial vehicles (UAVs), this novel remote sensing platform has been widely used to provide remote sensing images which have much higher spatial resolution. Previous reports have shown that the spectral feature of remote sensing images could be an effective indicator to estimate vegetation growth parameters. However, the texture feature of high-resolution remote sensing images is rarely employed for this purpose. Besides, the physical mechanism between the texture feature and vegetation growth parameters is still unclear.ResultsIn this study, a Fourier spectrum texture based on the UAV Image was developed to estimate rice LAI. And the relationship between Fourier spectrum texture and rice LAI was also analyzed. The results showed that Fourier spectrum texture could improve the accuracy of rice LAI estimation.ConclusionsIn conclusion, the texture feature of high-resolution remote sensing images may be more effective in rice LAI estimation than the spectral feature.

Magnetoimpedance behavior and its equivalent circuit analysis of Co/Cu/Co/Py pseudo-spin-valve with a nano-oxide layer

Magnetoimpedance behaviors and thermal effects of a Co/Cu/Co/Py pseudo-spin-valve (PSV) with a nano-oxide layer (NOL) were studied. The PSV can be regarded as a combination of resistances, inductances, and capacitances. In addition, equivalent circuit theory can be used to analyze the ac behavior of this system. The imaginary part of the magnetoimpedance (magnetoreactance) ratio is more than 1700% at the resonance frequency (fr)=476 kHz at room temperature (RT). The dc magnetoresistance (MR) ratio decreases as the annealing temperature increases because the NOL is formed at the interface between the spacer and the magnetic layer. The NOL deteriorates the differential spin scattering and reduces the dc MR ratio. Impedance spectroscopy was utilized to analyze the capacitance effect from NOL after annealing. The effective capacitance of the PSV was 21.8 nF at RT and changed to 11.8 nF after annealing at 200 °C. The useful equivalent capacitor circuit not only is a nondestructive measurement technology but can also explain the experimental results and prove the formation of the NOL.