Journal of Nondestructive Evaluation最新文献

Simulations of XCT systems, as employed in the context of the manufacturing and design process, represent a time-saving, cost- and resource-efficient alternative to repeated experimental measurements. This article is dedicated to the development and evaluation of various metrics that should enable an adequate verification and optimization of a XCT simulation of an experimental XCT system. The present study employed statistical evaluation as a methodological approach. The present article makes a significant contribution to the optimization of the development process of a XCT simulation and provides a foundation for future research activities in this field.

In lab experiments, it has been observed that the stress–and time-dependent elastic properties of a complex material at a structural scale perform accordingly to its composition at a microstructural level. We seek complementary practices to the current wavefield-based non-destructive testing techniques to assess not only the integrity level of civil structures but also the microstructural elements that contribute to it. In this paper, we study the systematic evolution of elastic properties of concrete as an alternative to investigate the density of micro imperfections in an outdoor-conditioned concrete structure. We estimate 5-second relative velocity changes in four locations on a Test bridge subjected to the action of vertical impulsive sources, at different prestressing levels (dynamic effects at different static conditions). We describe the structure’s stress- and time-dependent elastic response by means of acoustoelastic effect and Slow-dynamic processes, respectively. We also estimate the conventional ultrasound pulse velocity and perform a cooperative integrity analysis of the structure using the three elastic phenomena. Our findings reveal: 1) The presence of soft microstructures and their orientation’s influence on the acoustoelastic effect and Slow-dynamics in field-conditioned concrete structures. 2) The relation of low ultrasound pulse velocities with high acoustoelastic effect and high magnitudes and variability of Slow-dynamics. 3) Different elastic behaviours on the north and south spans of the bridge, suggesting different heterogeneity levels on the analysed locations of the concrete beam.

Inductive thermography is a non-destructive testing (NDT) method used for checking friction stir welding (FSW) joints, which can have defects like tunneling. In this research, inductive thermography was used to find tunneling defects in three FSW samples that had already been looked at with radiography and ultrasonic testing. Using thermal signal reconstruction (TSR) techniques in MATLAB made the thermography images clearer, helping to identify defects that were hard to see otherwise. To make defect detection more accurate, an image fusion method was used. This combined thermography and radiographic images and then checked them against ultrasonic images to confirm the findings. The fusion process in MATLAB helped combine different types of data to give a fuller view of the defects, thus improving the identification of defects like tunneling in FSW joints. The study shows that inductive thermography when paired with image fusion, provides quicker, safer, and cheaper defect detection compared to classical methods like radiography. Merging multiple NDT methods through data fusion improves accuracy in finding defects, leading to better reliability and safety in welded structures.

This paper presents the development of a monitoring system using acoustic emission (AE) analysis for the prediction of micro- and initial cracks in fatigue-stressed steel structures such as bridges, cranes, offshore, or industrial constructions. Initial experimentation suggests a relationship between microscopically observed crack length and AE intensity, further data is required to establish a definitive correlation. As part of an ongoing research project, AE measurement techniques and evaluation are to be further developed to create a monitoring concept for micro-crack prediction in more complex fatigue-stressed steel components. The focus of this research is not on the localization and detection of crack growth or structural changes but on micro-crack detection using AE. Existing acoustic emission analysis systems can thus be extended to measure and detect micro-cracks for the earliest possible identification of damage events. This paper describes the first results of the innovative research idea.

To maximize power generation and enhance energy conversion efficiency, wind turbine blades have been increasingly scaled up. As the primary component responsible for capturing wind energy, these blades are particularly vulnerable to damage under harsh environmental conditions. Additionally, due to the remote locations, expansive areas, and unmanned operations of wind farms, regular inspections are crucial to maintaining safe operation. This paper presents a lightweight small object detection algorithm (LSOD-YOLO) based on YOLOv8, designed for detecting surface damage on wind turbine blades using drone aerial imagery. To tackle the challenge of detecting small objects on wind turbine surfaces, LSOD-YOLO incorporates Omni-dimensional Dynamic Convolution (ODConv) into the C2f module. The neck network is subsequently improved with the Scale Sequence Feature Fusion (SSFF) module and the Triple Feature Encoder (TFE) module. Furthermore, a small object detection layer is introduced to capture additional shallow feature information. These refinements enhance the algorithm’s capacity to detect small objects while preserving accuracy for other target sizes. To achieve a lightweight model design, a strategy involving parameter sharing and partial convolution is employed to optimize the detection head structure. This approach significantly reduces computational load while preserving accuracy. Experimental results on the wind turbine surface damage dataset demonstrate that the proposed LSOD-YOLO algorithm surpasses the baseline in both detection accuracy and model size, facilitating low-latency real-time inference with a notable performance enhancement.

In this study, computer vision and deep learning was combined to develop a rapid method for quantifying the astaxanthin isomer content in krill meal. A total of 310 Antarctic krill meal samples were collected and their astaxanthin isomer content was determined as observed values using high-performance liquid chromatography. A computer vision system was then used to acquire images of the krill meal samples, which were subsequently preprocessed and fed into a Convolutional Neural Network (CNN) to establish a predictive model; its performance was compared with that of a feature-based artificial neural networks model. The results showed that the 13-cistrine (13-Cis) astaxanthin, all-trans astaxanthin, and 9-cistrine (9-Cis) astaxanthin content were distributed in the range of 0–2.05 mg/kg, 0.09–62.97 mg/kg, and 0–7.58 mg/kg, respectively. For the test set, CNN achieved an R² of 0.96 in predicting all-trans astaxanthin and an R² of 0.88 for 9-Cis astaxanthin. In out-of-sample validation, the CNN achieved mean relative errors of 5.20% and 11.35% for all-trans and 9-Cis astaxanthin, respectively. In conclusion, computer vision combined with CNN offers an efficient, precise, and non-destructive technique for quantitatively analysing astaxanthin isomers in krill meal.

The belt conveyor is an important continuous transport device in modern industrial production. The conveyor belt, a crucial part of the belt conveyor, is vulnerable to damage since it works for lengthy periods of time at high speeds and large loads. If these damages are not detected and addressed in a timely manner, they may hasten the conveyor belt’s wear and even lead to safety accidents. This paper suggests a conveyor belt damage detection and segmentation network, BDSE-YOLO, based on an enhanced YOLOv11, to address the problems of low detection accuracy, poor real-time performance, and insufficient adaptability to complex backgrounds in the current conveyor belt damage detection methods. First, the YOLOv11 architecture is optimized by introducing the ACmix module in the feature extraction module. A new C2PSA_ACmix module is designed to leverage the self-attention characteristics of the ACmix module, enhancing the network’s capacity to extract both local and global characteristics, thereby improving the performance of damage segmentation and detection, particularly for small or complex damages. Additionally, the iRMB module is added to the backbone network to enhance information flow. This module captures long-range dependencies while maintaining the lightweight nature of the network, enhancing the efficiency and accuracy of segmentation tasks. On this basis, a damage evaluation method based on geometric features and size quantification is proposed. The rupture direction is determined using an ellipse fitting algorithm, while size quantification techniques are employed to accurately analyze the damage morphology and eight quantification indicators are established. Experimental results on a self-made dataset and two public datasets demonstrate that the suggested model attains 96.2%, 81.0% and 92.7% accuracy rates, respectively, outperforming the comparison models and demonstrating high detection accuracy and robustness. The model exhibits strong adaptability in complex industrial environments, and the eight proposed evaluation indicators provide reliable criteria for evaluating rupture propagation trends and the severity of damage. The proposed network and method offer an effective solution for the intelligent detection and evaluation of damage to conveyor belts.

Spectral X-ray computed tomography enables material decomposition by leveraging energy-dependent X-ray attenuation properties. However, material decomposition with spectral CT requires a longer acquisition time to obtain sufficient numbers of photons in each energy bin. Sparse-view offers a practical solution to reduce acquisition time, but it introduces ill-posedness, degrading decomposition accuracy. This study introduces a material decomposition framework based on Neural Radiance Fields where material maps are represented using a multilayer perceptron (MLP). The material maps are then optimized through a spectral forward projection process based on the Lambert–Beer’s law, while a partition of unity (PoU) loss ensures the physical constraint on material maps. Our method was evaluated using simulated and real spectral CT datasets and compared with a traditional statistical approach. The results demonstrated that our method performs well in material decomposition under sparse-view conditions. The results suggest that our “neural material composition fields” framework offers accurate material decomposition robust to sparse-view conditions without requiring labeled training data.

Carburization is a critical degradation mechanism in high-performance (HP) steel furnace tubes, impairing structural integrity during prolonged high-temperature service. This study proposes a machine learning-assisted ultrasonic testing framework to classify four levels of carburization damage in Cr‒Ni‒Nb HP steel alloys. A total of 80 A-scan signals were acquired per frequency (2.25 and 5 MHz) across four distinct damage classes, with spectral features extracted via discrete cosine transform (DTC). Microstructural analysis confirmed a linear increase in the volumetric fraction of chromium carbides from 9.5% (SP01, low carburization) to 40.5% (SP04, severe carburization). Among the classifiers evaluated, the K-Nearest Neighbors (KNN) and Quadratic Support Vector Machine (QSVM) achieved 100% accuracy (AUC = 1.00) at 2.25 MHz for advanced damage levels. However, early-stage detection remained challenging, with GNB attaining only 83.1% accuracy and AUC = 0.91 for SP01. Classification performance deteriorated significantly at 5 MHz due to increased signal attenuation and noise, with accuracy falling to 47.3–53.5%. These findings underscore the influence of ultrasonic frequency on damage detectability and model reliability. The integration of frequency-optimized ultrasonic inspection with machine learning delivers a scalable approach for real-time, non-destructive monitoring of carburization in industrial HP steel components, offering critical insights for predictive maintenance and structural health assessment.

This paper presents a method to determine thermal parameters, which can neither be calculated analytically nor measured directly. Specifically, the convective heat transfer coefficient is discussed, which is usually determined using empirical models, namely the Nusselt correlations. To overcome the lack of information about the relation between temperature and parameters, the methodology of data assimilation is applied. Therefore, a dynamic model is combined with measurement data from a thermography experiment avoiding interference with the actual process enabling inline parameter identification. The method is first applied on artificially created simulation data and second on real measurement data. This paper shows that the developed method estimates the heat transfer coefficient in agreement with the well-known Nusselt correlations. Moreover, the present work compares different estimation strategies and gives a recommendation regarding state-parameter, pure parameter or combined estimation, including a detailed analysis of the variation of a smoothing parameter.