Journal of Nondestructive Evaluation最新文献

Functional performance of Expose Aggregate Concrete Pavement (EACP) such low tire-pavement noise and higher skid resistance are noticeable due to long-term durability, are influenced by wavelength and mean texture depth (MTD). EACP surface macrotexture is characterized by the MTD and exposed aggregate number (EAN) due to a higher correlation between wavelength and the EAN. Normally, the EAN is manually estimated which needs much human effort and is time-consuming. Recently, deep learning of computer vision has been employed for aiding human counting tasks in different condition. Mostly, many state-of-the-arts for counting are conducted by using RGB image which is color image. Regarding the counting techniques used for EAN, it is a challenging task to deal with some issues such as aggregate is some occluded and similar coloring to the background. Because the aggregate shows the peak characteristic, the depth value may benefit in improving the recognition. This additional information may be useful since it can be display distinguishable color between the object and background. Therefore, this study aims to evaluate the combination of RGB image and depth information, knowns as RGB-D image, for counting the EAN by adapted Faster RCNN deep learning model with four channel input images. The RGB-D dataset was newly constructed for training and testing implemented model. The result shows the accuracy slightly improve by 5% by using RGB-D compared to RGB. However, they both achieve similar MAE and RMSE. Therefore, it gives the valuable information for EAN counting. Both image datasets are acceptable for counting the EAN with a given condition.

Structural health monitoring of carbon fiber components is critical in high-stakes applications such as aerospace and prosthetics. Carbon fiber’s exceptional mechanical properties demand precise defect detection to ensure safety and longevity. This paper reviews recent advancements in monitoring carbon fiber aircraft structures using a custom optical coherence tomography (OCT) imaging system. This innovative system integrates hyperspectral imaging with automated classifiers to detect and classify both surface and subsurface defects, including roughness and cracks. By employing OCT with magnitude and quantitative phase imaging algorithms, the study introduces methods for detailed three-dimensional visualization of material defects. The high-resolution capabilities of the OCT system enable accurate and automated crack detection, enhancing reliability in critical applications. The paper also addresses challenges in deploying these advanced systems in practical scenarios, such as integration with existing maintenance protocols and data interpretation. It explores the potential of combining OCT with other non-destructive evaluation techniques to improve monitoring accuracy. These advancements contribute to more reliable, non-invasive monitoring of carbon fiber structures, with significant implications for safety and performance in various industries.

The surface of complex textured ceramic tiles contains numerous defects that exhibit low contrast with the background, making them easily confused with the textured background during detection. Traditional defect detection algorithms and convolutional neural networks are prone to texture interference in the defect detection of complex textured ceramic tiles, resulting in high false detection rates and missed detection rates. Inspired by the human eye’s ability to find surface defects on smooth objects against a high-light background, this paper proposes a new method for detecting surface defects of complex textured tiles. This method uses the high-light area generated by the reflection of the light source as the background for detecting textured tile defects, thereby increasing the threshold difference between the defect and the background and highlighting the defect. This method translates the position of the textured tiles horizontally and captures images while the reflection of the strip light source covering the surface of the tiles is in motion, thereby acquiring several tile images with light source reflections. Subsequently, after intercepting the images of the highlight areas covered by the light source reflection, the RANSAC algorithm is used to match the characteristic corners of these images, and after rigid splicing, a complete image of the textured tiles with the highlight area as the background is obtained. Finally, defects on textured tiles can be extracted through threshold segmentation and morphological filtering. Experimental results indicate that this method can ignore complex texture interference on ceramic tiles and achieve rapid detection of defects in textured ceramic tiles.

Differentiating between wheat species poses a significant challenge to the Indian grain industry. Visual inspection of wheat species has drawbacks, including inconsistency, low throughput, and labor intensiveness. In this study, near-infrared hyperspectral imaging (NIR-HSI) was utilized in conjunction with a deep learning approach to achieve precise predictions of wheat at the species level. A dataset comprising 40 different varieties from four Indian wheat species, namely Triticum aestivum (T. aestivum), Triticum durum (T. durum), Triticum dicocccum (T. dicoccum), and Triticale, was prepared using a NIR-HSI system that encompassed the wavelength ranging from 900–1700 nm. The imbalanced dataset is a common problem in the classification task, making it harder for the classifier to classify minority class data correctly. To address this issue, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic sampling (ADASYN) were employed. For the classification task, a 1D Convolutional Neural Network (1D-CNN), a 1D-ResNet, and four traditional machine learning models: Naive Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are utilized and compared. The performance of these models was assessed using both imbalanced and balanced datasets. The 1D-CNN model outperformed traditional machine learning models, achieving impressive test accuracies of 98.25% and 98.43% with the SMOTE and ADASYN approach, respectively. These findings underscore the efficacy of NIR-HSI in conjunction with an end-to-end 1D-CNN and oversampling techniques as a reliable and efficient method for the rapid, accurate, and nondestructive identification of various wheat species. The code is available at https://github.com/nitintyagi007-iitr/Wheat_species_classification

In the last few years, infrared long pulse thermography (LPT) has attained high reliability and accuracy in the non-destructive inspection of low-thermally conductive materials such as carbon fibre reinforced polymer (CFRP) composites. However, to date, research investigations of LPT have been conducted on standardised and controlled material flaws such as flat bottom holes. Non-standardised defects in CFRPs are more common in real-life operations and, because of different nature, dimensions and complex shapes, their detection poses a significant challenge. This paper provides an in-depth analysis of LPT combined to advanced statistical and machine learning-based image processing tools for detection of non-standardised damage in CFRP composites. Statistical methods such as skewness and kurtosis, and machine learning algorithms such as principal component analysis and Fuzzy-c clustering were used to post-process thermal LPT signals. Damage scenarios that are likely to occur during manufacturing and in-service operations were analysed in terms of defect mapping characteristics using the signal-to-noise ratio and the Tanimoto criterion. Experimental results revealed that Fuzzy-c and LPT produced superior damage inspection performance.

Adhesive joints are extensive used in various industrial applications. Bonded quality is crucial for ensuring structural integrity and safety. In this study, the nonlinear non-collinear wave mixing techniques were developed to nondestructive evaluate micro imperfections in adhesive joints, including adhesive degradation and bond weakening. Two testing schemes were proposed via the resonance conditions of the adhesive and substrate respectively, following the classical nonlinearity theories. Numerical simulations and experiments of non-collinear wave mixing were conducted to explore the feasibility of the proposed testing schemes for accessing two typical micro imperfections in adhesive joints. Both the simulation and experimental results demonstrate that the proposed nonlinear non-collinear wave mixing method is effective for nondestructive evaluation of the micro imperfections in adhesive joints. Moreover, the scheme via resonance conditions of adhesive exhibits a higher sensitive to the adhesive degradation, whereas the one relying on the resonance conditions of substrate exhibits a higher sensitive to bond weakening.

This study investigates the changes in electromagnetic radiation (EMR) emissions from cement-mortar subjected to impact throughout its curing process. The generation of EMR signals in hydrated samples is primarily driven by the accelerated motion of charged particles through the pore spaces and the time-dependent variation in dipole moments formed at the electrical double layer. As the hydration (curing) progresses, there is a noticeable decrease in EMR voltage, average EMR energy release rate, and dominant frequency. However, these EMR parameters exhibit an increasing trend with the application of higher mechanical impact energy. It was further observed that as hydration advances, the non-evaporable water content and degree of hydration increase, whereas the evaporable water content decreases. Additionally, EMR voltage recorded after fracture was consistently lower than that measured before fracture across all curing days, indicating that crack formation during repetitive loading suppresses EMR emissions. This suggests that cracks formed in the cement-mortar do not facilitate EMR generation. Moreover, the study found an inverse relationship between impact-dependent mechanical parameters and EMR voltage, highlighting that as mechanical resistance to impact increases, EMR voltage decreases. These findings suggest that the EMR technique has significant potential for non-contact, early-age monitoring of civil structures, providing critical insights into their mechanical integrity and performance under load.

Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation, the ‘Instance Segmentation XXL-CT Challenge of a Historic Airplane’ was conducted. The challenge aimed to explore automatic or interactive instance segmentation methods for an efficient delineation of the different aircraft components, such as screws, rivets, metal sheets or pressure tubes. We report the organization and outcome of this challenge and describe the capabilities and limitations of the submitted segmentation methods.

In industry, rolling bearing damage acoustic emission (AE) signals are interfered with by complex transmission paths and strong noise. The signal-to-noise ratio of the AE signal is low. The bearing periodic fault pulse is weak, and fault feature extraction is challenging. To address these issues, combined with the characteristics of impulsiveness and rapid attention of the AE signal, an enhancement of the bearing weak fault signal based on the autocorrelation function (ACF) and improved multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) method is proposed in this contribution. Firstly, in low signal-to-noise ratio, the target vector of the MOMEDA method is not optimal, and the diagnostic accuracy is low. To address this problem, this paper improves MOMEDA by using the gradient descent method, called GMOMEDA. Rolling bearing fault AE pulse signals are enhanced. Then, a method combination of ACF and GMOMEDA highlights the periodic elastic wave in the signal. Finally, the enhanced AE signal is processed by envelope demodulation to extract the frequency of the bearing fault signal. The experimental results show that the performance of the ACF-GMOMEDA method is better than the other five methods. The frequency features of bearing fault AE signal can be accurately extracted.