Journal of Nondestructive Evaluation最新文献

Most industries dealing with pipelines face problems resulting from the buildup of deposits therein, such as reduced efficiency, downtime and increased maintenance costs. Although solutions to this issue have been sought for decades, no widely employed technique for monitoring growth of inorganic deposits (or ‘scaling’) in pipelines exists. In this research, a means of detecting the onset of scaling growth, by processing resonance testing data, was sought. For the resonance testing measurements the pipeline segment of interest is equipped with acceleration sensors which record signals generated by impacting the pipeline with a steel tip. The signals are Fourier transformed and analysed in the frequency domain, where a clear shift in frequency peak positions can be observed as the scaling thickness changes. How best to extract quantitative information from the generated frequency data is an open question. In this research, two data analysis approaches for scaling thickness prediction are compared: a supervised (binary classification) machine learning approach as well as a comparison-based change detection approach using cross-correlation. The supervised machine learning approach yields generalizable results for different acceleration sensors and impactor diameters whilst the change detection approach is sensitive from a scaling thickness of 0.5 mm. Whilst this research is specific to the pipe–scaling geometry—and type used in the experiments conducted, resonance testing can be applied to any pipe–scaling combination. The robustness of the data processing approaches presented in this work, when applied to other pipe–scaling materials and geometries, is the next point of research.

Ultrasonic testing is utilized to ensure weld quality during the fabrication of steel bridges by identifying discontinuities that are classified as either acceptable or rejectable. The classification of a discontinuity can be affected by differences in the acoustic properties of the material under test and the reference standard used for calibration. Differences in wave velocity affect the refracted angle and amplitude of refracted shear waves. As a result, indications can be missed or incorrectly classified, or incorrectly located in the material. The objective of this research study was to characterize the acoustic wave velocities in a sample of contemporary steels to better understand the range over which velocities may vary for common steels. To address this objective, a series of velocity measurements have been conducted for shear waves propagating through different directions in steel plates of different strengths and reported manufacturing processes. The study also examines the loss of signal amplitude that results from changes in the refracted angle of shear waves used for the inspection of welds. Beam splitting that may occur in anisotropic materials and the potential impact on signal amplitudes is also presented. It was shown in the research that relatively small differences in velocity between the material under test and the reference standard cause a loss of sensitivity of the test. Data presented in the paper documents wave velocity and anisotropic ratios for a population of contemporary bridge steels used for the fabrication of steel bridges and an assessment of how velocity differences affect the amplitude of reflected shear waves.

In the timber manufacturing sector, ensuring high-quality products is crucial, but conventional inspection methods often struggle to detect internal defects non-destructively. To tackle this challenge, an innovative approach has been proposed that integrates terahertz (THz) imaging with artificial intelligence (AI) algorithms. By harnessing the unique ability of THz radiation to penetrate timber wood and AI algorithms, defect classification, segmentation, and characterization can be made possible. Here, a custom-made convolutional neural network has been optimized for the classification of the defects in timber wood into four classes – no defect, knot, small knot, and decay, yielding a classification accuracy of over 96%. Further, the custom classification model has been extended for thicker wooden samples with internal hidden defects using transfer learning and has yielded a classification accuracy of over 93%. Following classification, a U-Net-based segmentation algorithm has been developed to delineate the defect boundaries in THz images accurately with a high dice coefficient of over 0.90. Further, a YOLO-based algorithm has been utilized to characterize the defects by localizing the position of the defect using bounding boxes with a high F1 score of over 0.97. An accurate prediction of the defect dimension has been demonstrated using this algorithm with a percentage error of less than 4% for all the types of defects in the timber wood. This advanced methodology, leveraging multiple AI algorithms on the THz images, significantly boosts the efficiency and accuracy of automatic defect identification and characterization, marking a transformative step forward in timber industry quality control processes.

Gearboxes, as critical components, often operate in demanding conditions, enduring constant exposure to variable loads and speeds. In the realm of condition monitoring, the dataset primarily comprises data from normal operating conditions, with significantly fewer instances of faulty conditions, resulting in imbalanced datasets. To address the challenges posed by this data disparity, researchers have proposed various solutions aimed at enhancing the performance of classification models. One such solution involves balancing the dataset before the training phase through oversampling techniques. In this study, we utilized the Sparse Autoencoder technique for data augmentation and employed Support Vector Machine (SVM) and Random Forest (RF) for classification. We conducted four experiments to evaluate the impact of data imbalance on classifier performance: (1) using the original dataset without data augmentation, (2) employing partial data augmentation, (3) applying full data augmentation, and (4) balancing the dataset while using Kernel Principal Component Analysis (KPCA) for dimensionality reduction. Our findings revealed that both algorithms achieved accuracies exceeding 90%, even when employing the original non-augmented data. When partial data augmentation was employed both algorithms were able to achieve accuracies beyond 98%. Full data augmentation yielded slightly better results compared to partial augmentation. After reducing dimensions from 18 to 11 using KPCA, both classifiers maintained robust performance. SVM achieved an overall accuracy of 98.72%, while RF achieved 96.06% accuracy.

In this paper, the effectiveness of the eddy current methodology for crack detection in MAR 247 nickel-based superalloy with aluminide coatings subjected to cyclic loading was investigated. The specimens were subjected to force-controlled fatigue tests under zero mean level, constant stress amplitude from 300 MPa to 600 MPa and a frequency of 20 Hz. During the fatigue, a particular level of damage was introduced into the material leading to the formation of microcracks. Subsequently, a new design of probe with a pot core was developed to limit magnetic flux leakage and directed it towards the surface under examination. The suitability of the new methodology was further confirmed as the specimens containing defects were successfully identified. The changes in probe resistance values registered for damaged specimens ranged approximately from 8 to 14%.

Thermal embrittlement is a key concern for the structural integrity of engineering components. Monitoring thermal embrittlement may indicate susceptibility to crack initiation and growth and therefore act as a damage precursor. In this study the correlation between thermoelectric power (also known as the Seebeck Coefficient) and the hardness of thermally aged 2507 super duplex stainless steel was demonstrated, showing the suitability of using thermoelectric power as a proxy measurement for embrittlement. This article presents a continuous high-temperature thermoelectric power monitoring system that is suitable for installation on large engineering assets. Using temperature gradients in the sample of < 6.5 °C a measurement standard deviation of 5.8 nV/°C was possible, which was sufficient to monitor the ~ 850 nV/°C increase in thermoelectric power that occurred in this study.

Surface defect detection in mechanical processing mainly adopts manual inspection, which has certain issues including strong dependence on manual experience, low efficiency, and difficulty in online detection. A surface texture elimination method based on improved frequency domain filtering in conjunction with morphological sub-pixel edge detection is put forward in order to address the aforementioned issues with machining surface defects. Firstly, ascertain whether textures exist in the image and determine their feature values using the grayscale co-occurrence matrix. The main energy direction of the textured surface in the frequency domain was then obtained by applying the Fourier transform to the processed surface. An elliptical domain narrow stopband was designed to reduce the energy in the band region corresponding to the processed surface texture and eliminate the processed surface texture. Finally, improve morphology and sub-pixel edge fusion to extract surface defect images. Cracks and scratches have a detectable width of 0.01 mm, a detection accuracy of 97.667%, and a detection time of 0.02 s. Therefore, the combination of machine vision and texture removal technology has achieved the detection of surface scratches and cracks in machining, providing a theoretical basis for defect detection in workpiece processing.

Estimation of volume fraction percentage of the multiple phases flowing in pipes with limited access is a challenge in oil, gas, chemical processes, and petrochemical industries. In this research, the gamma backscattered spectra together with the machine learning model were used to predict precise volume fraction percentages in water-gasoil-air three-phase flows and solve the aforementioned challenge. The detection system includes a single energy ¹³⁷Cs source and a NaI(Tl) detector to measure the backscattered rays. The MCNPX code was used to simulate the setup and produce the required data for the artificial neural network. The volume fraction was calculated with mean relative error percentage 13.60% and the root mean square error 2.68, respectively. Then, the results were compared with the acquired results of transmitted gamma-ray spectra. The proposed design is a suitable, safe, and low-cost choice for industries.

The falling weight deflectometer (FWD) test is a prevalent non-destructive testing (NDT) technique in engineering that is essential for evaluating pavement conditions. In this work, the transfer function (TF) theory in frequency domain analysis was applied to address the technical challenges present in FWD research. A pavement system transfer function (PSTF) was proposed as a novel approach for evaluating pavement conditions. The spectral method with fixed-end boundary conditions (B-SEM) was employed to compute the theoretical deflection data for different pavement structures with bedrock during FWD testing. The fast Fourier transform (FFT) technique was used to convert the data into the frequency domain, enabling the construction and calculation of the PSTF. The validity of the PSTF theory was confirmed, and the pavement information contained in the PSTF spectrum was discussed. An analysis and summary are conducted on the impact of variations in pavement attributes on the spectrum. The results indicate that the proposed PSTF contains information regarding pavement system, including the structural layer modulus, structural layer thickness, and bedrock depth. The pavement conditions can be evaluated by directly analyzing the PSTF without considering external factors. The PSTF spectrum is most significantly influenced by bedrock depths between 200 and 500 cm. For every 50 cm variation in bedrock depth, the coefficient of increase and decrease (CIE) of peak frequency ranges from 8.1% to 23.1%. The PSTF spectrum is highly sensitive to variations in the subgrade modulus between 40 and 70 MPa. In this range, the CIE of peak amplitude is greater than 11% for every 10MPa variation in subgrade modulus. The impact of the modulus and thickness of both the surface layer and base layer on the spectrum is noteworthy and should not be disregarded. Spectral analysis is used to summarize the variation in pavement attributes within the PSTF spectrum, serving as a theoretical foundation for further investigations.

A new Forward Ray Tracing Instrument (FRTI) for simulating X-ray CT scanners is presented. The FRTI enables the modelling of various detector geometries to optimise instrument designs. The FRTI is demonstrated by comparing experimentally measured sphere centre-to-centre distances from two material measures with digital clones. The measured length deviations were smaller than the reconstructed grid spacing for both the experimental and simulated acquisitions. As expected the experimentally measured length deviations were larger than the simulated measurements. The results demonstrate the FRII’s capability of simulating an X-ray CT scanner and performing length measurements.