Non-destructive Method Research Articles

Non-invasive, pre-symptomatic detection of potato cyst nematode infection in tomato using chlorophyll fluorescence analysis.

Potato cyst nematodes (PCN) are notorious pathogens in all major potato production areas worldwide. Mainly due to the low mobility of this soil pathogen, PCN infestations are mostly observed as patches ('foci') that only cover a fraction of the acreage. In-field pre-symptomatic localization of this pathogen is valuable as it would allow for the localized application of control measures. Although the mapping of foci is technically feasible, it is unpractical as it would take the analysis of numerous soil samples. We investigated whether chlorophyll fluorescence (Chl-F) could be suitable as a rapid, non-destructive method for early PCN detection. To this end, the impact of four Globodera pallida densities on the Chl-F of tomato was investigated in a phenotyping greenhouse for 26 days. Furthermore, classical plant performance indicators biomass and root surface area were compared with Chl-F. Thermal dissipation ('NPQ_Lss') and actual photosynthetic rate ('QY_Lss') responded at 1 DPI, while QY_Lss was most sensitive to low PCN infection levels. Chl-F parameters responded more readily to PCN infection than biomass and root surface area. The efficiency of photosystem II (QY_max) and the potential activity of photosystem II (Fv/Fo) initially increased at low PCN infection levels, whereas a sharp decrease was observed at higher infestation levels. Hence, our data suggest that low PCN levels promoted plant performance before becoming detrimental at higher levels. While Chl-F allowed for early and sensitive PCN detection, it remains to be investigated whether these signals can be distinguished from those produced by other below-ground stressors in the field.

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.

Rapid identification of phycobiliproteins in Porphyra yezoensis using near-infrared combined with convolutional neural network

Phycobiliproteins (PBPs) are the main pigment proteins and important indicators for evaluating the quality of Porphyra yezoensis (nori). This study aimed to develop a non-destructive and rapid method for determining PBP content (including total PBPs, phycoerythrin, phycocyanin, and allophycocyanin) in P. yezoensis, using near-infrared spectrum technology combined with a convolutional neural network (CNN), and to explore the influence of spectral preprocessing methods and machine learning algorithms on the predictive ability of the model. First, the spectral data was standardized using a combination of standard normal variable transformation and the first derivative to improve the accuracy and predictive ability of the model. We compared various models and determined that the CNN model performed better than conventional methods. After optimizing the number of convolutional layers, dropout rate, and learning rate, the performance of the CNN model was further improved. This study demonstrates the capability of the CNN model to leverage spectral data and solve regression problems to accurately measure PBPs. Moreover, for the first time, we established a functional equation between PBPs and P. yezoensis grades. This study provides a feasible and rapid method for the quantitative detection of PBPs in P. yezoensis.

Decision Tree Clusters: Non-destructive detection of overheating defects in porcelain insulators using quantitative thermal imaging techniques

Porcelain insulators are subject to performance deterioration during operation under the influence of complex environments, thus increasing the occurrence of flashover accidents. Regular inspection of insulator condition is of great significance for the stable operation of power grids. Therefore, we propose a non-destructive detection method of insulator overheating defects using quantitative thermography. It can be used for defect detection of porcelain insulators with internal overheating defects. In this study, the relationship between the insulating properties of porcelain insulators and the heating anomalies was first analyzed. Then, Decision Tree Clusters (DTC) algorithm is used to extract the spatial temperature feature information to achieve the defective insulator location capture. Finally, Submodular-pick Local Interpretable Model-agnostic Explanations (SLIME) is used for model decision visualization to verify the feasibility of the technique. The experimental results show that DTC not only ensures the accuracy of insulator long-distance detection, but also realizes the quantitative detection of insulators.

Improved Cascade-RCNN for automatic detection of coronary artery plaque in multi-angle fusion CPR images

Coronary heart disease is a disease that seriously endangers human health and life which is caused by plaque formation due to coronary artery atherosclerosis or spasm. The detection of coronary plaques through medical imaging is a non-destructive and fast diagnostic method, which holds significant medical and clinical value for the diagnosis of coronary heart disease. In this paper, We proposed an automatic detecting method for coronary artery vulnerable plaque based on CTA image sequence. We collected CTA image sequences from 132 patients and obtained the coronary artery tree by performing three-dimensional (3D) segmentation. The coronary artery centerline was extracted using the skeleton thinning method. To address the issue of insufficient features in a single CPR image, we constructed Curved Planar Reconstructed (CPR) images from multiple directions perpendicular to the coronary artery, following the centerline. In total, we established 8797 images in the training dataset and 1553 images in the test dataset. We proposed an attentive detection method that fused a new Cascade RCNN ResNet50-FPN neural network model with a convolutional attention module (CBAM). This method achieved automatic recognition and detection of plaques on the two-dimensional multi-angle fused CPR images. The proposed ResNet50-FPN network performed feature extraction, and CBAM was added after the first and last convolutional layers to enhance the network’s attention to plaque features. Our experimental results demonstrate that deep learning neural networks can provide a feasible approach to automatically detecting coronary artery plaques. Our method achieved a plaque detection accuracy of 94.6%, outperforming the RCNN method proposed by Zriek.

NIR spectroscopy for quality assessment and shelf-life prediction of kiwifruit

In this study, a non-destructive quality testing method along with shelf-life prediction of Xu Xiang ready-to-eat kiwifruit were developed using near-infrared spectroscopy (NIR) techniques. Several traditional quality indicators (hardness, soluble solids content, and dry matter) were evaluated. Partial least squares regression (PLS) was used to predict the intrinsic quality attributes of the samples. Competitive adaptive reweighted sampling algorithm (CARS) and uninformative variable elimination (UVE) algorithm were used to select the characteristic wavelengths. Prediction models for hardness, soluble solids content and dry matter were developed. The results showed that the prediction ability of the models could be improved by screening the characteristic wavelengths of CARS and UVE. Among them, the CARS-SNV-PLS model based on soluble solids had the best prediction ability (RMSEP of 0.430 and Rp2 of 0.958). Then, an NIR-based residual shelf-life prediction model was obtained by linking the measured quality indicators to the residual shelf-life, which was well validated with an RMSEP of 1.64 and an Rp2 of 0.939. Therefore, this study demonstrated the potential of combining CARS, SNV, and PLS for the non-destructive testing of ready-to-eat kiwifruit to provide technical support and solution.

Modulus of Elasticity in Plywood Boards: Comparison between a Destructive and a Nondestructive Method

Modulus of Elasticity in Plywood Boards: Comparison between a Destructive and a Nondestructive Method

Evaluation of floatability characteristics of gastroretentive tablets using VIS imaging with artificial neural networks

Gastroretentive dosage forms are recommended for several active substances because it is often necessary for the drug to be released from the carrier system into the stomach over an extended period. Among gastroretentive dosage forms, floating tablets are a very popular pharmaceutical technology. In this study, it was investigated whether a rapid, nondestructive method can be used to characterize the floating properties of a tablet.To accomplish our objective, the same composition was compressed, and varied compression forces were applied to achieve the desired tablet. In addition to physical examinations, digital microscopic images of the tablets were captured and analyzed using image analysis techniques, allowing the investigation of the floatability of the dosage form. Image processing algorithms and artificial neural networks (ANNs) were utilized to classify the samples based on their strength and floatability. The input dataset consisted solely of the acquired images.It has been shown by our research that visible imaging coupled with pattern recognition neural networks is an efficient way to categorize these samples based on their floatability. Rapid and non-destructive digital imaging of tablet surfaces is facilitated by this method, offering insights into both crushing strength and floating properties.

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.

Steel grades classification and heating defects detection of bearing steel bars via pulse eddy current testing

Nondestructive examination of bearing steel raw material is a crucial step in ensuring the production of bearing rings. Traditional nondestructive examination methods struggle with real-time sorting, leading to potential production risks. This study proposes a pulse eddy current testing (PECT) approach combined multidimensional features and classification algorithm, to realize the recognition of steel grades and heating defects of bearing steel bars. Firstly, the two-probes differential PECT system was established. Subsequently, pulse signals were collected, and 27 features in the time domain, and frequency domain were extracted and analyzed. Finally, two classification algorithms, BP neural networking and Rindom Forest, are used to classify bearing steel. Experimental results demonstrate the approach proposed can sort bearing steel, die steel, and high-speed steel bars. Furthermore, it is capable of distinguishing between various types (brand, heat number) of bearing steel bars and identifying their overheated or burnt conditions.

Terahertz Spectroscopy in the Field of Food Safety: A Review

with the continuous improvement of people's living standards, food quality and safety problem has caused the wide attention of the whole society, the traditional food quality detection method has certain limitations, terahertz spectral technology (THZ) is a new spectral detection technology, its strong perspective, efficient spectral resolution ability, special fingerprint and nondestructive, in the field of food quality and safety detection has important application value. Due to the high efficiency of food quality and the need for accurate nondestructive testing, automated nondestructive testing for food quality and safety has aroused great interest from the food sector. Faster, accurate, reliable and simplified techniques are needed to classify good quality. With their unique low-energy, penetrating, rapid and non-destructive techniques are widely used as a non-destructive and cost-effective method in the food sector. This paper summarizes the principles and instruments of terahertz spectrum, and introduces the research progress of terahertz spectral detection in food, analyzes the problems in the field of food quality and safety detection, and discusses the future development trend of terahertz spectral technology.

Aflatoxin contamination level estimation in food using reflectance multispectral imaging based system

This study presents a non-invasive, non-destructive, and rapid method of classifying and estimating aflatoxin levels in food samples using a multispectral imaging. A cost effective, mass deployable in-house built multispectral imaging system, suitable for onsite testing and operating in range from 365 nm to 940 nm, is utilized to capture the multispectral images of the aflatoxin contaminated bread samples. Spatial and spectral corrections are performed to mitigate the imperfections of the multispectral imaging system, followed by image preprocessing steps to mitigate the effect of random noise. Linear discriminant analysis is used to reduce the dimensionality of the feature space. Ensemble classification analysis utilizing classical machine learning classifiers yielded an accuracy of 90 % when classifying samples according to aflatoxin contamination levels. A functional relationship between the Bhattacharyya distance of noncontaminated samples to contaminated samples and aflatoxin contamination levels was modeled as a first order polynomial with an R2 value of 0.9723.

Research on ultrasonic guided wave-based high-speed turnout switch rail base flaw detection

Turnout switch rail fracture detection is currently a serious issue in the field of railway transportation. Guided wave detection, a non-destructive testing method, is a good way of studying this issue and looking for a suitable solution. For this paper, a guided wave mode and an excitation position were selected based on the phase velocity dispersion curve and the wave structure. After this, a model was devised for the turnout switch area using the finite element (FE) method. This model considered the straight switch rail, curved stock rail, bolt hole, spacer block, and sub-rail foundation, and verifies the validity of the simulation model through the experiment. The propagation characteristics of guided waves in the switch rail were then simulated in different fracturing states by means of a 30-kHz excitation applied vertically to the rail base. The results showed that a single mode of the guided wave could be generated by this excitation method, showing that it could be used as an effective means for fracture detection. The growth rate of the root-mean-square (RMS) value of the time-domain acceleration signal could then be analysed to identify the state of fracture in the straight switch rail. This discrimination method is suitable for finding fractures parallel to the rail cross-section with a width of 5 mm or more at the bottom of the straight switch rail.

Rapid determination of oil content of single peanut seed by near-infrared hyperspectral imaging

Oil content is a crucial indicator for evaluating the quality of peanuts. A rapid and non-destructive method to determine oil content of individual peanut seed can provide robust technical support for breeding high-oil-content peanut varieties. In this study, we established a rapid determination method using near-infrared hyperspectral imaging and chemometrics to assess the oil content of single peanut seed. After selecting key wavelengths through competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and random frog (RF), we constructed an oil content calibration model based on partial least squares regression for single peanut seed. Validation results demonstrated that the correlation coefficient was 0.8393 with a root mean square error of 1.7771 in the calibration set, while it was 0.7915 with a root mean square error of 2.2943 in the independent prediction set. Most samples exhibited relative errors below 5%, confirming the reliability of this model in predicting oil content of single peanut seed.

INDUSTRIAL DESIGN OF HAZARDOUS PRODUCTION FACILITIES

The risk-oriented approach to industrial safety allows for the operation of hazardous production facilities in the cracks presence. There is a problem of assessing the critical size of cracks by non-destructive methods, which are currently not widely tested and standardized. Changing operating conditions and damage accumulation can transfer cracks into the category of developing cracks, and this can lead to catastrophic consequences. From this point of view, close proximity of objects containing explosive substances, as it is the case at processing plants, pumping stations, production platforms, poses a danger. Visualization of hazardous objects and their location areas warns the personnel about the need to comply with increased safety measures. This is aimed at using industrial design, which defines the principles of warning of hazardous objects by using color signs. A common trend in the coating usage is their multifunctionality. In this regard, the present research attempts to combine color warning signs with the possibility of their usage in composite coatings designed for corrosion protection, as well as showing the changes in temperature conditions of processes.

Reproduction of decarburization caused by overheating in fire tube boiler material

One of the non-destructive testing methods for analysing fire tubes is the replica technique. This technique allows the investigation of microstructure, cracking, and high-temperature creep under operational circumstances without the cutting of the components. A cellulose-acetate foil was used to make a copy from the topography of the surface, which was suitable for microscopic analysis. In this paper, decarburization was generated in P355N fire tube material. The aim of the reproduction was to investigate the effect of overheating in boilers. The decarburization of the fire tube was made at different temperatures (300 – 900 °C) in an electric heating furnace. General deterioration and decarbonization began at 700 °C. A preliminary step of the decarburization was the transformation of the pearlite, which can be easily detected by scanning electron microscope. Grain growth could not be observed, and the particle size was unrelated to the thickness of the decarbonized layer.

Investigation of End-of-Life Chrome-Magnesia Refractories Using X-Ray Computed Tomography

The lifespan of refractory linings is a major industrial concern for safety, on-line availability, and financial reasons. In copper smelting, batchwise operating matte converters are the furnaces that pose the greatest challenge when it comes to refractory wear and lining life. In this work, the structure and morphology of used magnesia–chrome bricks were studied using X-ray computed tomography and mineralogical techniques. The bricks were taken from various locations of an end-of-life brick lining of an industrial Peirce–Smith converter, after a normal campaign at Boliden Harjavalta smelter (Finland). The results show that it is possible to visualize in 3D, e.g., porosity, metal-containing phases, and refractory magnesia in the used bricks. Different digital images, such as cross-sections and average volume fractions, were used as a non-destructive method to characterize the bricks’ internal structure. The metal/matte infiltration in the open porosity was found to differ based on the location in the converter, with some bricks having no metal/matte infiltration and the tuyere line showing metal/matte infiltration at a depth of about 100 mm from the hot face.

Numerical simulation and experimental study on nonlinear ultrasonic characterization of graphite size in nodular cast iron

Graphite morphology has a significant influence on mechanical properties such as tensile strength and hardness in nodular cast iron (NCI). Compared to the detection of graphite nodularity, there is relatively less ultrasonic simulation and detection experiment research on the graphite size. Therefore, it is of great importance to study rapid nondestructive testing methods for the internal graphite size of NCI. This study establishes a simulation model of nonlinear ultrasonic penetration longitudinal wave detection for graphite size. The acoustic nonlinearity parameter (ANP) is used to characterize the graphite size. Nonlinear ultrasonic detection experiments on the internal graphite size are conducted to verify the reliability of the nonlinear ultrasonic simulation model. The simulation and experimental results show that the ANP of penetrating longitudinal waves decreases with the increase of average graphite diameter. The relationship between ANP and internal graphite size is established. Moreover, the experimental results verified the accuracy of the numerical model. The decrease in ANP may be related to the decrease in the number of grain boundaries. Therefore, the nonlinear ultrasonic technique is an effective method for characterizing the internal graphite size. The establishment of a nonlinear ultrasonic simulation model for graphite size in NCI lays the research foundation for nonlinear ultrasonic microstructure characterization experiments.

Ultrasound Control of Pickering Emulsion-Based Capsule Preparation.

Capsules with microparticle shells became of great interest due to their potential in many fields. Those capsules can be fabricated at high temperatures from particle-stabilized emulsions (Pickering emulsions) by sintering together particles that cover droplets. One of the problems with such an approach is accurately controlling whether particles are already sintered and creating the rigid capsule shell of a capsule. Here, we propose using a non-destructive ultrasound method for monitoring Pickering emulsion-based capsules prepared using heating under an alternating magnetic field. The polyethylene microparticles that were responsive to temperatures higher than 112 °C were used as droplet stabilizers together with iron oxide nanoparticles. During the coalescence of the droplets, facilitated by an external electric field, the ultrasonic attenuation increased, giving evidence that the ultrasound method detects structural changes in Pickering emulsions. The main change was the difference in the droplets' size, which was also observed via optical microscopy. The attenuation of ultrasound increased even more when measured after magnetic heating for the same concentration of particle stabilizers. Simultaneously, the values of ultrasonic velocity did not exhibit similar variety. The results show that the values of the attenuation coefficient can be used for a quantitative evaluation of the capsule formation process.

Evaluation of self-repair efficiency of polymers containing microcapsules using optical coherence tomography

Self-healing polymers are used to improve the durability and strength of materials and provide them with a longer service life. The authors propose a new optical method for evaluating the self-repair efficiency in polymers containing microcapsules. The non-destructive method measures spatial profiles of a hole series obtained after a material puncture. Spatial images of holes acquired by optical coherence tomography were processed to get a surface profile. The reduction of the average volume or depth of the holes compared to the reference material allows self-healing efficiency calculation. For the presented method, tests were carried out to measure the self-repair efficiency of test materials with 5% and 10% mass of repair capsules compared to Ethe reference polymer without capsules. The volumetric efficiencies of self-repair obtained from 30 holes of each material, acquired 8 h after the puncture, were computed as 51.6% and 58.3% for the two repaired material types, respectively.