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.

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.

Pipeline Elbow Corrosion Simulation for Strain Monitoring with Fiber Bragg Gratings.

This study addresses the limitation of traditional non-destructive testing methods in real-time corrosion monitoring of pipe elbows by proposing the utilization of fiber Bragg grating (FBG) strain sensors, renowned for their resilience in harsh environments. However, the current mathematical relationship model for strain representation of elbow corrosion is still lacking. This paper develops a finite element model to scrutinize the strain changes in the elbow due to corrosion under hydrostatic pressure and bending loads. To mitigate temperature loading effects, the corrosion degree is evaluated through the disparity between hoop and axial strains. Simulation outcomes reveal that, under hydrostatic pressure, the strain difference exhibits minimal changes with the increase in corrosion degree, while under bending moment loading, the strain difference escalates proportionally with corrosion progression. Consequently, strain induced by bending moment loading solely characterizes the corrosion degree. Moreover, the optimal placement for FBG sensors is identified at the extrados of the pipe elbow, where strain is most prominent. These insights enhance comprehension of strain-corrosion dynamics in pipe elbows, offering valuable guidance for developing an FBG-based monitoring system for real-time corrosion tracking and predictive maintenance of pipeline infrastructures.

A Proposed Non-Destructive Method Based on Sphere Launching and Piezoelectric Diaphragm.

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The pencil-lead break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection.

Eddy Current-Based Delamination Imaging in CFRP Using Erosion and Thresholding Approaches.

Carbon fiber reinforced plastic (CFRP) is a composite material known for its high strength-to-weight ratio, stiffness, and corrosion and fatigue resistance, making it suitable for its use in structural components. However, CFRP can be subject to various types of damage, such as delamination, matrix cracking, or fiber breakage, requiring nondestructive evaluation to ensure structural integrity. In this context, damage imaging algorithms are important for assessing the condition of this material. This paper presents signal and image processing methods for delamination characterization of thin CFRP plates using eddy current testing (ECT). The measurement system included an inductive ECT probe with three coil elements, which has the characteristic of allowing eddy currents to be induced in the specimen with two different configurations. In this study, the peak amplitude of the induced voltage in the receiver element and the phase shift between the excitation and receiver signals were considered as damage-sensitive features. Using the ECT probe, C-scans were performed in the vicinity of delamination defects of different sizes. The dimensions and shape of the ECT probe were considered by applying the erosion method in the damage imaging process. Different thresholding approaches were also investigated to extract the size of the defective areas. To evaluate the impact of this application, a comparison is made between the results obtained before and after thresholding using histogram analysis. The evaluation of damage imaging for three different delamination sizes is presented for quantitative analysis.

Application of Object Detection Algorithms in Non-Destructive Testing of Pressure Equipment: A Review.

Non-destructive testing (NDT) techniques play a crucial role in industrial production, aerospace, healthcare, and the inspection of special equipment, serving as an indispensable part of assessing the safety condition of pressure equipment. Among these, the analysis of NDT data stands as a critical link in evaluating equipment safety. In recent years, object detection techniques have gradually been applied to the analysis of NDT data in pressure equipment inspection, yielding significant results. This paper comprehensively reviews the current applications and development trends of object detection algorithms in NDT technology for pressure-bearing equipment, focusing on algorithm selection, data augmentation, and intelligent defect recognition based on object detection algorithms. Additionally, it explores open research challenges of integrating GAN-based data augmentation and unsupervised learning to further enhance the intelligent application and performance of object detection technology in NDT for pressure-bearing equipment while discussing techniques and methods to improve the interpretability of deep learning models. Finally, by summarizing current research and offering insights for future directions, this paper aims to provide researchers and engineers with a comprehensive perspective to advance the application and development of object detection technology in NDT for pressure-bearing equipment.

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.

Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System.

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography-mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.

Guided wave multi-frequency damage localization method in variable-thickness structures by one pair of sensors based on frequency-dependent velocity anisotropy.

Variable thickness structures are prevalent in aircraft, ships, and other machines, necessitating numerous sensors for health monitoring to reduce safety hazards. This paper presents a guided wave multi-frequency localization method based on frequency-dependent velocity anisotropy. This method achieves damage localization in variable-thickness structures with a pair of sensors and can effectively reduce the number of sensors used for monitoring. Variations in structural thickness cause a gradient in guided wave velocity that bends the propagation path. Different thickness variations with different directions cause wave velocity anisotropy. As a result, variations in thickness cause possible damage loci determined by echo time to deviate from an elliptical shape. Because the velocity anisotropy is frequency-dependent, damage loci at different frequencies are close but do not overlap and intersect only at the damage location. So, the multi-frequency method can increase the damage information acquired by a single pair of sensors, enabling damage localization. Experimental validation was conducted on a steel plate with linearly varying thicknesses. The feasibility of the multi-frequency localization method was verified by successfully locating the damage at three different locations using a pair of receiver-excitation sensors. In addition, the experiments demonstrated the capability of this multi-frequency method in improving the localization accuracy of sensor networks. The method has potential applications in monitoring systems lightweight, phased arrays, and imaging enhancement.

Innovations in seafood freshness quality: Non-destructive detection of freshness in Litopenaeus vannamei using the YOLO-shrimp model.

The relationship between freshness changes and visual images of Litopenaeus vannamei was established based on Sensory Evaluation, Total Volatile Base Nitrogen (TVB-N), Total Viable Count (TVC), and Gray Value during storage at 4 °C. A non-destructive detection system using the advanced YOLO(You Only Look Once)-Shrimp model was developed to evaluate shrimp freshness. The results revealed a gradual increase in freshness indices over time, with the gray value showing strong positive correlations with TVB-N and TVC (0.88 and 0.81). The advanced YOLO-Shrimp model demonstrated notable performance enhancements over the YOLOv8 model, as evidenced by a precision increase of 5.07 %, a recall improvement of 1.58 %, a 3.25 % rise in the F1 score, and a 2.84 % elevation in mAP50. This innovative approach offers substantial potential for enhancing food safety and quality control in the seafood industry.

Innovations in forensic science: Comprehensive review of hyperspectral imaging for bodily fluid analysis.

Hyperspectral imaging (HSI) has become a crucial innovation in forensic science, particularly for analysing bodily fluids. This advanced technology captures both spectral and spatial data across a wide spectrum of wavelengths, offering comprehensive insights into the composition and distribution of bodily fluids found at crime scenes. In this review, we delve into the forensic applications of HSI, emphasizing its role in detecting, identifying, and distinguishing various bodily fluids such as blood, saliva, urine, vaginal fluid, semen, and menstrual blood. We examine the benefits of HSI compared to traditional methods, noting its non-destructive approach, high sensitivity, and capability to differentiate fluids even in complex mixtures. Additionally, we discuss recent advancements in HSI technology and their potential to enhance forensic investigations. This review highlights the importance of HSI as a valuable tool in forensic science, opening new pathways for improving the accuracy and efficiency of crime scene analyses.

Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng.

Red ginseng (RG) has been traditionally valued in Northeast Asia for its health-enhancing properties. Recent advancements in hyperspectral imaging (HSI) offer a non-destructive, efficient, and reliable method to assess critical quality indicators of RG, such as reducing sugar content (RSC), water content (WC), and hollow rate (HR). This study developed predictive models using HSI technology to monitor these quality indicators over the spectral range of 400-1700 nm. Image features were enhanced using Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), followed by classification through Spectral Angle Mapping (SAM). The best-performing model for RSC achieved an R2 value of 0.6198 and a root mean square error (RMSE) of 0.013. For WC, the optimal model obtained an R2 value of 0.6555 and an RMSE of 0.014. The spatial distribution of RSC, WC, and HR was effectively visualized, demonstrating the potential of HSI for on-site quality control of RG. This study provides a foundation for real-time, non-invasive monitoring of RG quality, addressing industry needs for rapid and reliable assessment methods.

Cobalt-60, Barium-133, Cesium-137, and Europium-152 migration from cementitious sources through sediment under field conditions.

Safe and effective storage of radioactive waste is essential to protect human and environmental health. Due to the potential for accidental releases and the severity of the associated risks, it is imperative to further understand radionuclide transport should an accident occur. This study was the second set of measurements conducted in 2022 of an ongoing experiment that has analyzed the vadose zone migration of radionuclides from cementitious wasteforms at the Savannah River Site over the last ten years. The radionuclides introduced within the sources are prominent constituents of radioactive waste or analogs for other groups or series of radionuclides. Lysimeters were first analyzed in 2016 using a collimated high-purity germanium gamma-ray spectrometer to non-destructively measure the concentration of each radionuclide in the sediment column as a function of depth. Following these measurements, the lysimeters were redeployed for another 4 years. All radionuclides in all lysimeters were observed to transport further during the redeployment period; however, the extent of migration varied with the material used for introduction. Except for 137Cs, migration through the sediment control system increased with decreasing ionic potential (ionic charge/radius); migration order: 152Eu < 137Cs < 60Co < 133Ba. Overall, the cementitious wasteforms were observed to decrease radionuclide migration extent relative to natural vadose zone conditions. In both cementitious wasteforms, the migration extent increased in the order 152Eu < 133Ba<60Co < 137Cs. However, less migration was measured when the radionuclides were incorporated into a reducing grout wasteform. The novelty of this paper is the demonstration of a technique capable of creating non-destructive measurements over decade time scales. Ultimately, this work provides insight into the long-term migration of alkali, alkali earth, divalent transition metal, and trivalent (e.g., lanthanide and actinide element) isotopes.

Fluorescence hyperspectral imaging technology combined with chemometrics for kiwifruit quality attribute assessment and non-destructive judgment of maturity.

Dry matter content (DMC), firmness and soluble solid content (SSC) are important indicators for assessing the quality attributes and determining the maturity of kiwifruit. However, traditional measurement methods are time-consuming, labor-intensive, and destructive to the kiwifruit, leading to resource wastage. In order to solve this problem, this study has tracked the flowering, fruiting, maturing and collecting processes of Ya'an red-heart kiwifruit, and has proposed a non-destructive method for kiwifruit quality attribute assessment and maturity identification that combines fluorescence hyperspectral imaging (FHSI) technology and chemometrics. Specifically, first of all, three different spectral data preprocessing methods were adopted, and PLSR was used to evaluate the quality attributes (DMC, firmness, and SSC) of kiwifruit. Next, the differences in accuracy of different models in discriminating kiwifruit maturity were compared, and an ensemble learning model based on LightGBM and GBDT models was constructed. The results indicate that the ensemble learning model outperforms single machine learning models. In addition, the application effects of the 'Convolutional Neural Network'-'Multilayer Perceptron' (CNN-MLP) model under different optimization algorithms were compared. To improve the robustness of the model, an improved whale optimization algorithm (IWOA) was introduced by modifying the acceleration factor. Overall, the IWOA-CNN-MLP model performs the best in discriminating the maturity of kiwifruit, with Accuracytest of 0.916 and Loss of 0.23. In addition, compared with the basic model, the accuracy of the integrated learning model SG-MSC-SEL was improved by about 12%-20 %. The research findings will provide new perspectives for the evaluation of kiwifruit quality and maturity discrimination using FHSI and chemometric methods, thereby promoting further research and applications in this field.

Terahertz time-domain spectro-imaging and hyperspectral imagery to investigate a historical Longwy glazed ceramic.

In this paper, we present the potential of Terahertz Time-Domain Imaging (THz-TDI) as a tool to perform non-invasive 3D analysis of an ancient enamel plate manufactured by Longwy Company in France. The THz data collected in the reflection mode were processed using noise filtering procedures and an advanced imaging approach. The results validate the capability to identify glaze layers and the thickness of ceramic materials. To characterize the nature of the pigments, we also use with X-ray images, visible near-infrared hyperspectral imaging spectroscopy, and p-XRF (portable X-ray fluorescence) to qualitatively and quantitively identify the materials used. The obtained information enables a better understanding of the decoration chromogens nature and, thus, to determine the color palette of the artists who produced such decorative object. We also establish the efficiency of a focus, Z-tracker, which enables to perform THz imaging on non-flat samples and to attenuate artifacts obtained with a short focus lens. Then, 3D images are extracted and generated, providing a real vision. We also report the evaluation of the internal damage state through the detection of fractures.

Introduction of the prompt γ-ray neutron activation analysis system at CARR and the first pilot experiment on boron-containing high-temperature alloys.

A prompt γ-ray neutron activation analysis system has recently been developed at China advanced research reactor (CARR), the 60 MW research reactor in China Institute of Atomic Energy (CIAE). The system is set at the cold neutron beam guide with a thermal equivalent neutron flux at the sample position of 1.0 × 109 n·cm-2·s-1 with the power of 30 MW, and it is mainly composed of a neutron beam collimator, a sample chamber, a beam stopper, neutron and γ-ray shieldings and a detection system. The detection system can realize three modes of measurement: single, Compton suppression, and pair modes. The detection efficiency was calibrated up to 11 MeV using a set of radionuclides and the (n, γ) reactions of N and Cl. Boron, one of the most important elements in high-temperature alloy material studies, was analyzed in this work, as the first pilot experiment of the CARR-PGNAA system. The analytical sensitivity of 2000 cps/mg-B was obtained. The results verified the feasibility of the CARR-PGNAA system to measure boron in high-temperature alloys, and laid a foundation for the accurate quantification of boron in the next step.

Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains.

High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quantification of wheat nutrients with VIS-NIR (400-1700 nm) hyperspectral imaging is proposed in this study. Stepwise linear regression (SLR) was used to predict hundreds of nutrients accurately (R2 > 0.6); results improved when the hyperspectral data was processed with the first derivative. Knockout materials were also used to verify their practical application value. Various nutrients' characteristic wavelengths were mainly concentrated in the visible regions of 400-500 nm and 900-1000 nm. Finally, we proposed an improved pix2pix conditional generative network model to visualize the nutrients distribution and showed better results compared with the original. This research highlights the potential of hyperspectral technology in high-throughput and non-destructive determination and visualization of grain nutrients with deep learning.

Rapid, sensitive, and non-destructive on-site quantitative detection of nanoplastics in aquatic environments using laser-backscattered fiber-embedded optofluidic chip.

A definitive link between the micro- and nano-plastics (NPLs) and human health has been firmly established, emphasizing the higher risks posed by NPLs. The urgent need for a rapid, non-destructive, and reliable method to quantify NPLs remains unmet with current detection techniques. To address this gap, a novel laser-backscattered fiber-embedded optofluidic chip (LFOC) was constructed for the rapid, sensitive, and non-destructive on-site quantitation of NPLs based on 180º laser-backscattered mechanism. Our theoretical and experimental findings reveal that the 180º laser-backscattered intensities of NPLs were directly proportional to their mass and particle number concentration. Using the LFOC, we have successfully detected polystyrene (PS) NPLSs of varying sizes, with a minimum detection limit of 0.23 µg/mL (equivalent to 5.23 ×107 particles/mL). Moreover, PS NPLs of different sizes can be readily differentiated through a simple membrane-filtering method. The LFOC also demonstrates high sensitivity in detecting other NPLs, such as polyethylene, polyethylene terephthalate, polypropylene, and polymethylmethacrylate. To validate its practical application, the LFOC was used to detect PS NPLs in various aquatic environments, exhibiting excellent accuracy, reproducibility, and reliability. The LFOC provides a simple, versatile, and efficient tool for direct, on-site, quantitative detection of NPLs in aquatic environments.

Thermal lens technique's surrogacy unveiled: A novel tool for microplastic detection and quantification in water.

The escalating usage of paper cups and packaging materials with plastic coatings has evolved into a substantial environmental and health concern, evidenced by the report of microplastics in human blood. This research introduces an innovative laser-assisted thermal lens (TL) technique for the precise detection and measurement of microplastics, specifically those leaching from the inner plastic coatings of paper cups. Employing a multipronged approach encompassing scanning electron microscopy, optical microscopy, atomic force microscopy, Fourier transform infrared spectroscopy, UV-visible, and Raman spectroscopy, a comprehensive investigation is conducted into the leaching of microplastics into hot water from paper cups. The thermal diffusivity (D) of water samples containing microplastics is determined using the TL technique based on 120 observations for each temperature conducted using paper cups from three distinct manufacturers. The observation of a strong correlation between the number of microplastic particles (N) and D of the water sample enabled the setting of a linear empirical relation that can be used for computing the microplastics in water at a particular temperature. The study thus proposes a surrogate method for quantifying microplastics in water using the sensitive and non-destructive TL technique.