Journal of Nondestructive Evaluation最新文献

Functionally gradient material (FGM) in service often experience temperature variations that can affect the propagation characteristics of guided waves. This investigation aims to study the propagation of thermoelastic guided waves in the FGM plate. A computational method for the state vector and Legendre polynomials hybrid approach, which is proposed based on the Green–Nagdhi theory of thermoelasticity. The heat conduction equation is introduced into the governing equations, and optimized using univariate nonlinear regression for arbitrary gradient distributions of the material components. To study their dispersion characteristics, a non-hierarchical calculation for the dispersion curves of FGM plates versus temperature is realized. In addition, a frequency domain simulation model is developed and compared with theoretical data to evaluate the accuracy and feasibility of the proposed theory. Then, the influence of Legendre orthogonal polynomial cut-off order on dispersion curve convergence is investigated. Subsequently, the shift of the gradient index and temperature variation on the fundamental mode in dispersion curve is analyzed. The results indicate that changes in both gradient index and temperature lead to a systematic shift in the phase velocity of fundamental modes in the low frequency range. Meanwhile, anti-symmetric modes exhibit higher sensitivity. On this basis, the study can provide theoretical support for the acoustic non-destructive characterization of FGM plates versus temperature.

This work develops a polarization-dependent analytical model using terahertz time-domain spectroscopy (THz-TDS) for computing strain in materials. The model establishes a correlation between volumetric strain and the change in time of arrival for a THz pulse by using the dielectrostrictive properties, variations in doping particle density, and changes in the thickness of the sample resulting from Poisson’s effects. The analytical model is validated through strain mapping of polydimethylsiloxane (PDMS) doped with passive highly dielectrostrictive strontium titanate (STO). Two experiments, using an open-hole tensile and a circular edge-notch specimen are conducted to show the efficacy of the proposed. The stress relaxation behavior of the composite is measured and accounted for to prevent changes in strain during the measurement window. The THz strain mapping results are compared with the finite element model (FEM) and surface strain measurements using the digital image correlation (DIC) method. The experimental findings exhibit sensitivity to material features such as particle clumping and edge effects. The THz strain map shows a strong agreement with FEM and DIC results, thus demonstrating the applicability of this technique for surface and sub-surface strain mapping in polymeric composites.

The detection of steel surface defects is of great significance to steel production. In order to better meet the requirements of accuracy, real-time, and lightweight model, this paper proposes a highly efficient and lightweight steel surface defect detection method based on YOLOv5n. Firstly, ODMobileNetV2 composed of MobileNetV2 and ODConv is used as the backbone to improve the defect feature extraction capability. Secondly, GSConv is utilized in the neck to achieve deep information fusion through channel concatenation and shuffling, enhancing the ability of feature fusion. Finally, this paper proposes a spatial-channel reconstruction block (SCRB) designed to suppress redundant features and improve the representation ability of defect features through feature separation and reconstruction. Experimental results show that this method achieves 84.1% mAP and 109 FPS on the NEU-DET dataset, and 72.9% mAP and 110.1 FPS on the GC10-DET dataset, enabling accurate and efficient detection. Furthermore, the number of parameters is only 5.04M, which has a significant lightweight advantage.

Carbon fiber reinforced polymer (CFRP) is an important composite material widely used in aerospace and other industries. However, long-term service in harsh environments can lead to various defects such as debonding, delamination, water, cracks, etc. Therefore, it becomes imperative to conduct non-destructive testing (NDT) on CFRP to ensure its structural integrity and safety. Infrared thermography was employed for defect classification in CFRP laminate and CFRP honeycomb sandwich composites (HSC) by applied a convolutional neural networks (CNN). The proposed automatic defect classification method based on CNN is one of the goals of NDE 4.0 to apply advanced technologies (such as deep learning and AI) to improve NDT efficiency and accuracy. The infrared detection dataset consisted of five classes: debonding, water, delamination, crack, and health. To effectively expand the dataset, offline data augmentation technique were employed. A deep learning technique of Dense convolutional neural network (DCNN) were proposed to defect classification. AlexNet, VGG-16, ResNet-50 and DenseNet-121 based on transfer learning fine-tuning model was applied to classify debonding, water, delamination, crack and health. The classification results were analyzed by using a confusion matrix. The results shown that the accuracy of AlexNet, VGG-16, ResNet-50 and DenseNet-121 were 92.34%, 82.86%, 88.30%, 98.48%, respectively. DenseNet-121 demonstrates good performance in defect detection and recognition with an accuracy of 98.48%, and DenseNet-121 has high application potential in accurately classify and recognize defects in deep learning technique.

This paper introduces a vision-based displacement measurement method for wind turbine blades in biaxial fatigue testing. Instead of relying on existing strain data, this method collects displacement data to control the loading system. The main idea of this method is to update the pixel radius of the target point. The ratio of the pixel radius of the target point to the actual radius is used as a reference to update the displacement conversion coefficient λ of the next frame image in real-time. Through both static and dynamic experiments, the accuracy and superiority of this method have been verified, and the feasibility of using displacement instead of strain to control fatigue loading has been validated. The data demonstrates that the measurement error between the proposed method and the electronic total station remains within 10%. Compared to the results obtained by the traditional methods, the proposed method has shown significant improvement. The vision-based displacement measurement method not only ensures accuracy but also reduces the complexity of testing, providing more possibilities for fatigue testing of wind turbine blades.

This study investigates the correlation between various micromagnetic signature patterns and the yield and tensile strengths of carbon steel (Cr12MoV steel as per Chinese standards). For this purpose, back-propagation neural network (BP-NN) models are established to quantitatively predict the yield and tensile strengths of carbon steels. The accuracy of prediction models is significantly affected by the presence of redundant micromagnetic signature patterns. By carefully screening the input parameters, it is able to effectively mitigate prediction errors arising from unreasonable model inputs. In the field of micromagnetic nondestructive testing (NDT), prediction models calibrated for a specific instrument or sensor cannot be directly applied to another instrument or sensor. In the study, a joint distribution adaptation transfer learning strategy based on auxiliary data is proposed to enhance the generalization of prediction models for cross-instrument applications. When auxiliary data accounts for 30% of the source domain data, the joint distribution adaptation transfer learning method based on auxiliary data improves the robustness of the model. The accuracy of the yield strength and tensile strength calibration models witnesses remarkable improvements of approximately 91.4% and 93.5%, respectively.

The X-ray Talbot–Lau interferometer (TLI) has been introduced as a device to measure the X-ray interference using an ordinary X-ray source rather than coherent X-ray sources. For nondestructive testing, the advantage of TLI is its capability to obtain darkfield images, where fibers in fiber-reinforced plastics can be distinguished from the matrix. From darkfield images, 3D tomographic reconstruction techniques have been investigated to visualize the distribution of fiber orientations. However, previous approaches assume that X-ray scattering occurs only along the predefined scattering directions that are shared within the entire volume of a test sample. In contrast, a novel technique that we introduce in this paper optimizes the predominant scattering directions independently at each voxel location. The proposed method employs an alternating optimization scheme, where it first calculates the scattering intensities along the scattering directions and then updates these scattering directions, accordingly. Owing to this alternative optimization scheme, our method demonstrates promising performance, particularly when the predominant scattering directions are indeterminate. This advantage of our proposed technique is validated with the sample made of carbon fiber-reinforced plastic (CFRP) and glass fiber-reinforced plastic (GFRP). For these samples, reference fiber orientations are determined in advance using micro-focus CT scanning. To our knowledge, we are the first to optimize both the scattering intensity and scattering directions in reconstructing fiber orientations in industrial-purpose darkfield tomography. The findings presented in this paper potentially contribute to advancing applications in industrial nondestructive testing.

Training data is crucial for any artificial intelligence model. Previous research has shown that various methods can be used to enhance and improve AI training data. Taking a step beyond previous research, this paper presents a method that uses AI techniques to generate CT training data, especially realistic, artificial, industrial 3D voxel data. This includes that material as well as realistic internal defects, like pores, are artificially generated. To automate the processes, the creation of the data is implemented in a 3D Data Generation, called SPARC (Synthetized Process Artificial Realistic CT data). The SPARC is built as a pipeline consisting of several steps where different types of AI fulfill different tasks in the process of generating synthetic data. One AI generates geometrically realistic internal defects. Another AI is used to generate a realistic 3D voxel representation. This involves a conversion from STL to voxel data and generating the gray values accordingly. By combining the different AI methods, the SPARC pipeline can generate realistic 3D voxel data with internal defects, addressing the lack of data for various applications. The data generated by SPARC achieved a structural similarity of 98% compared to the real data. Realistic 3D voxel training data can thus be generated. For future AI applications, annotations of various features can be created to be used in both supervised and unsupervised training.

Manufacturing and online service of ferromagnetic materials easily induce local stress concentrations and then generate cracks. Research on in-service inspection of stress status is an important criterion for healthy monitoring in steel components and structures. There are inherent limitations for stress analysis by using a single feature from a single sensor source. In this work, a multisensor feature fusion network based on combining principal component analysis (PCA) and the XGBoost algorithm is proposed to analyze the Barkhausen noise sensor and magneto-acoustic emission sensor for assessing and predicting the stress state in ferromagnetic materials. PCA combined with feature correlation analysis is conducted for feature selection by eliminating redundant information and reducing the dimensionality of the dataset. In addition, a machine learning service was used to create an XGBoost model to predict the stress state. Compared with other single sensor feature fusion methods, our proposed electromagnetic-acoustic sensing-based multi-feature fusion network outperforms other models in terms of accuracy and repeatability. Specifically, we discuss why the proposed model is superior to others from the physical mechanism of the stochastic behavior of magnetic domain wall dynamics. Experimental studies on pure iron are further carried out to verify the effectiveness and robustness of our proposed method.

Computed tomography has evolved as an essential tool for non-destructive testing within the automotive industry. The application of robot-based computed tomography enables high-resolution CT inspections of components exceeding the dimensions accommodated by conventional systems. However, large-scale components, e.g. vehicle bodies, often exhibit trajectory-limiting elements. The utilization of conventional trajectories with constant Focus-Detector-Distances can lead to anisotropy in image data due to the inaccessibility of some angular directions. In this work, we introduce two approaches that are able to select suitable acquisitions point sets in scans of challenging to access regions through the integration of projections with varying Focus-Detector-Distances. The variable distances of the X-ray hardware enable the capability to navigate around collision structures, thus facilitating the scanning of absent angular directions. The initial approach incorporates collision-free viewpoints along a spherical trajectory, preserving the field of view by maintaining a constant ratio between the Focus-Object-Distance and the Object-Detector-Distance, while discreetly extending the Focus-Detector-Distance. The second methodology represents a more straightforward approach, enabling the scanning of angular sectors that were previously inaccessible on the conventional circular trajectory by circumventing the X-ray source around these collision elements. Both the qualitative and quantitative evaluations, contrasting classical trajectories characterized by constant Focus-Detector-Distances with the proposed techniques employing variable Focus-Detector-Distances, indicate that the developed methods improve the object structure interpretability for scans of limited accessibility.