Journal of Nondestructive Evaluation最新文献

Magnetic field measurements play a vital role in various industries, particularly in the detection of cracks using magnetic field images, also known as magnetic field leakage testing. This paper presents an approach to automate the extraction of crack signals in magnetic field imaging by using neural networks. The proposed method relies on simulation-based training using the lightweight Python library Magpylib to calculate the three-dimensional static magnetic field of permanent magnets with surface defects. This approach has numerous advantages. It allows control of training data set variance by tuning simulation input parameters such as sample magnetization, measurement parameters, and defect properties to cover a wide range of cracks in size and position. Starting data acquisition before system operation allows investigating potential changes in sample shape or measurement parameters. Importantly, simulation-based data generation eliminates the need for physical measurements, leading to significant time savings. The study presents and discusses results obtained on two different ferromagnetic samples with surface cracks, a hollow cylinder and a steel sheet.

In this work, we present a system architecture that is especially designed for remote ultrasound testing inspections (UT). The system itself is realized using a real-time session service and a web service based on a REST API (REST: Representational State Transfer; API: application programming interface). This web service is used to store data persistently, e.g., sample geometries, raw nondestructive testing (NDT) data and derived inspection results in a shared dataspace. In the current development state, the results consist of textures mapped onto the sample geometry. This approach allows us to display the UT results directly on the real sample using mixed reality technologies. We also implemented a feature to assist the inspector remotely by making use of the availability of this digital representation. Hence, it is necessary to share additional data like the current UT-signal, temporary position marks, user and device positions, etc. between the different participants. A real-time distribution of this highly dynamic data is required to create an effective assistance environment. Therefore, a separate session service is used. The inspection data generated in this temporary session can also be transferred to the afore mentioned dataspace to be saved persistently. The system features mixed reality visualization for the inspector and optionally a virtual reality or a 3D Desktop environment for one or more remote assistants.

The experimental study of ultrasonic transmission coefficient measurement method was carried out for the problem of bonding quality inspection of bonded structures of composites with thick adhesive layer. For the uniaxial CFRP bonded specimens, the ultrasonic waves’ phase velocity distribution and the ultrasonic transmission coefficient spectrums were measured by the water-immersion ultrasonic transmission method. Then their complex elastic constants were inversed by the particle swarm optimization algorithm based on simulated annealing. Based on this, CFRP bonded specimens in perfect bonding status and having single weak bonding interface, dual weak bonding interfaces were manufactured. Then the ultrasonic transmission coefficient spectrums were measured, and the distinction of bonding quality between different bonding specimens was predicted. At the same time, according to the shift characteristics of the measured ultrasonic transmission coefficient spectra, the influence of the symmetry of the CFRP bonded structures with thick adhesive layers on the ultrasonic transmission coefficient spectrums was investigated experimentally. In addition, by comparing the time-domain and coefficient spectra characteristics of transmitted waves, the advantage of using the coefficient spectrum measurement method in bonding quality detection was expounded.

In this paper, the vibration-based image representation and data fusion demonstrates distinctive benefit in feature extraction, yielding superior performance for damage identification in railway engineering. Specifically, based on vehicle-track coupled dynamics, the rail vibration datasets under diverse fastener damage conditions are generated. By converting 1-D vibration signals into 2-D grayscale images with recurrence plots (RPs) and the aid of conditional variational autoencoder (CVAE), the acceleration RPs and displacement RPs are fused for enhancing feature extraction. It is demonstrated that detecting the variation in texture patterns and color distribution of the vibration-based images facilitates effective damage identification, mitigating the sensitivity of damage recognition to the deterioration of track irregularity. The results show that the displacement RPs characterised by quasi-static features are more suitable for fastener damage identification. Further, by employing the data fusion that combines both the random dynamic features of the acceleration RPs and quasi-static features of the displacement RPs, the tolerance of measurement range for accurate fastener damage identification can be extended. The robustness of the proposed method is validated after testing different sampling frequencies and additional noise.

This study presents a Decision Support System (DSS) designed for Non-Destructive Online Evaluation in welding. Based on the Multi-Scale Dense Cross Block Network (MDCBNet), it is able to detect, classify, and recommend remedial actions to prevent surface defects in welding. The performance of the network architecture is enhanced with synthetic defect samples generated through image augmentation techniques. By employing gradient attribution and t-SNE plot methods, we gained insights into the network’s predictions and comprehensively analyzed decision-making process. Comparative evaluations against pre-trained deep learning techniques revealed that our proposed model exhibits significant improvements, ranging from 2 to 10% across various performance metrics. Extensive comparisons with state-of-the-art methods underscored the effectiveness of our approach in detecting and classifying weld defects. Notably, our network, initially trained on Gas Tungsten Arc Welding images, demonstrated remarkable adaptability and versatility by effectively classifying images from Gas Metal Arc Welding processes. These findings emphasize the potential of the MDCBNet-based DSS to enhance welding practices, thereby contributing to producing high-quality weldments. The successful implementation of our DSS recommendations further supports its capacity to optimize the welding process and facilitate improved weld quality.

The ice formation over the aerofoil structure of the aircraft wing has been an obstruction as they abrupt the airflow, acting as drag. The investigation will intend to determine ice accumulation on carbon fiber-reinforced polymer (CFRP), approximated as ice build-up on aircraft wings. The observation is carried out over quasi-isotropic composite laminates using ultrasonic-guided waves with a central working frequency regime of 100 kHz. The three-dimensional (3D) finite element (FE) simulations are performed to observe the scattering effect to explore the reflection site in the far field. This effect was quite prominent for different thicknesses of Glaze ice (G-Ice) and was found to be strongly linked with the wave propagation and dispersion effect. The scattering results for the reflection of Lamb mode, when it interacted with the G-Ice interface, were quite noteworthy along the angular region rather than on the center line, indicating that the scattering was more prominent due to the presence of a 45° or (− 45)-degree fiber orientation in that laminate. A similar but complex scattering phenomenon was observed for different stacking sequences where the wave propagation angle and its amplitude at the receiver nodes are found to be closely bound with the exponential decay in group/phase velocity for the ice thicknesses studied. The FE approach is verified, and the results are validated analytically. Analytically, we have investigated a much-closed approximation with the detectability obtained from three-dimensional studies. Where the dispersion study performed has also contributed to verifying the present investigation in the long wavelength limits. This study can reveal the various optimized locations for placing the sensor for ice detection and quantification, which can be further helpful for practical guided wave inspection in ice detection and its removal.

Computer-aided weld defect recognition is transforming the field of Non-Destructive Testing by addressing the shortcomings of slow and error-prone manual inspections. This technology provides a reliable solution for detecting changes in pipeline conditions and structural damage. While conventional neural networks fall short in precise fault localization, deep learning-based object detection techniques step in to fill the gap. Addressing a real-industrial problem, particularly visually inspecting an X-ray welding database, without relying on a pre-existing benchmark presents a significant challenge in this field. Additionally, the poor quality of our welding data, which is riddled with small, sticky porosity in each image, poses several issues related to selecting the appropriate deep neural network object detector. This is yet another challenge that needs to be tackled. To direct these challenges, we introduced a novel approach based on the renowned Faster RCNN architecture to develop a model specifically designed for weld defect detection and recognition. This study dives deep into the inner workings of this newly adopted methodology. In our research, we have thoroughly parameterized, trained, tested, and validated this model. Our approach stands out through a comparative analysis with YOLO and DCNN models, highlighting the superiority of our Faster RCNN-based system. By evaluating its robustness and efficiency, our study reveals that the Faster RCNN model outperforms its counterparts in weld defect detection and localization for this specific small and sticky porosity defect type. This stands as a testament to effectively setting a new standard in this area.

Lead-based water pipelines pose a significant public health risk in the US. The challenge lies in locating these pipelines, as current identification technologies have limitations. This study discusses potential and challenges of identifying the water Service Line (SL) material through a stress wave propagation methodology. Since buried service lines are surrounded by soil and contain water, the stress wave propagation is non trivial. This work presents numerical simulations to investigate the applicability of the proposed method. First, authors consider wave propagation properties that could be used in a stress wave approach to identify buried lead based pipelines. For instance, dispersion curves are quite different for steel, copper, Lead, and PVC pipes filled with water. While the soil surrounding pipes causes a decrease in wave propagation energy due to the energy leakage into the soil medium, this phenomenon can enable the detection of leaked waves with sufficiently sensitive sensors installed near the soil surface. The received signals vary for different types of pipe materials, allowing to differentiate among service line materials. This study’s numerical simulations and lab experiments suggest that stress wave propagation could become a valuable tool for identifying buried lead-based water SL materials.