Journal of Nondestructive Evaluation最新文献

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.

Accurate 3D mesh registration is essential in many industrial applications of X-ray imaging, as it allows quality assessment and inspection of manufactured objects. Conventional methods rely mainly on time-consuming and expensive X-ray computed tomography (X-CT) or ancillary camera systems. Instead, we propose a novel approach for efficient 3D multi-mesh registration in few-view industrial X-ray imaging scenarios. Our approach harnesses the capabilities of CAD-ASTRA, an X-ray mesh projector, compatible with the ASTRA toolbox and popular GPU libraries such as CuPy and PyTorch, for the simulation of X-ray projec tions from a known object surface mesh. As a differentiable program, CAD-ASTRA allows iterative improvement of the objects’ position in space by back-propagation of a differentiable measure of the projection error. The potential of this approach is demonstrated through tests on simultaneous multiple object registration in a poly-chromatic imaging, even in cases where the spectral characteristics of the imaging system are unknown. Results from a diverse set of real experiments highlight the efficacy of mesh registration, achieving successful registrations even when only two projections at a 10(^circ ) angle relative to the scanning system center are available. The mesh projector facilitates resource-efficient registration in industrial applications with few viewpoints, thereby reducing the demand for resources and eliminating the need for X-CT reconstruction.

The identification of cross-sectional loss in cables due to corrosion is crucial for evaluating the remaining strength of bridge cables. To accurately determine the cross-sectional loss rate, this paper derived a three-dimensional magnetic dipole model for spatial cable damage. The study employed an independently designed self-magnetic flux leakage (SMFL) sensor array to detect corrosion on a bundle of 37 parallel steel wires. The analysis investigated the correlation between corrosion degrees and SMFL signal features. The results show that the spatial magnetic field inversion collected by the sensor array device is more accurate. The cable damage location can be pinpointed by observing abrupt changes in the B_x and B_z curves. Additionally, this paper introduces five corrosion characterization features, all correlated with the cable cross-sectional loss rate. However, recognition stability using a single characteristic value is insufficient. The cable cross-sectional loss rate identification method, utilizing a back propagation neural network in conjunction with multiple characteristic indicators, demonstrates robust quantitative and adaptive capabilities. The maximum relative error of this method is 7.6%, offering a new perspective for future cable damage detection.

This work presents the details of experimental investigations to study the effects of surface roughness on adhesive joints’ strength and establish a correlation with corresponding Nondestructive Evaluation (NDE) parameters. NDE parameters are the quantifiable properties of specimens that NDE techniques can measure. The roughness at Single Lap Joint (SLJ) interfaces was varied using different emery grades of 36, 50, 60, and 80 CAMI scale. The change in roughness was evaluated through NDE tools viz., X-ray radiography testing (XRT), Acoustic Wave Propagation, and InfraRed Thermography (IRT). While X-ray images do not show any significant variation in intensities with roughness, roughness can be visualized after histogram equalization. The change in image intensities was observed with adherend thickness. The attenuation coefficient of acoustic waves for joints with different grades of roughness evaluated using the standard Hsu-Nielsen pencil source through pitch-and-catch method shows a correlation with the surface roughness. IRT shows the variation in the cooling constant with roughness and thickness of the adherends. This paper also demonstrates the thermal conductivity evaluation of the bonded specimen with IRT and the effect of surface roughness on it. The destructive tests evaluated the shear strength, and the NDE parameters were correlated with the shear strength of the SLJ.

Period-permanent-magnet (PPM) electromagnetic acoustic transducer (EMAT) has been widely used in shear horizontal (SH) ultrasonic guided wave testing owing to its advantages, such as non-contact coupling, and convenient to excite SH waves. However, its poor transduction efficiency leads to weak signals and limits the lift-off performance. This article investigates how to improve the signal amplitude by adjusting the number of turns of the racetrack coil. The inductive coupling process of the PPM-EMAT receiver is first studied using the equivalent circuit method, and the corresponding equivalent model is obtained. Aiming at the effects of coil configurations, the equivalent impedance parameters of multilayer racetrack coils are analyzed by calculations and measurements. The proposed model can be used to predict the receiving frequency response of PPM-EMAT receivers with different coil structures, and it has been verified experimentally. It can be obtained that by choosing an appropriate coil configuration and matching resistance, the SH wave signal amplitude can be increased by 3 times.