Russian Journal of Nondestructive Testing最新文献

Carbon fiber reinforced polymer (CFRP) composites are widely used in high-end manufacturing sectors such as aerospace and rail transportation due to their excellent specific strength and lightweight properties. However, delamination defects frequently occur within CFRP materials as a result of manufacturing processes or service conditions, posing a significant threat to structural integrity. Therefore, reliable nondestructive testing (NDT) of internal defects is of critical importance. In this study, an infrared thermography technique based on 13-bit Barker code pulse modulation is employed to enhance the signal-to-noise ratio (SNR) and detectability of defects. The acquired infrared image sequences are processed using fast fourier transform (FFT), principal component analysis (PCA), fast independent component analysis (Fast-ICA) and cross-correlation (CC) algorithms. A comparative analysis is conducted to evaluate the performance of each method in terms of enhancing defects contrast, suppressing background noise, and identifying deep or weak defects. Experimental results demonstrate that Barker coded thermal wave imaging, in combination with image processing algorithms, significantly improves the clarity and accuracy of defects identification in CFRP materials.

The transition from a surface forest fire to a crown fire was experimentally studied under laboratory conditions. Using noncontact infrared (IR) diagnostic methods within narrow spectral ranges of infrared wavelengths, the propagation speed of the fire front was determined, along with temperature changes at control points where the combustion transitions from a surface fire to a crown fire. The experiment was conducted under varying incoming airflow velocities and different canopy heights relative to the surface fire. In the infrared range, the radiation from the sample surfaces was recorded using a JADE J530SB thermal imaging camera equipped with an optical filter (2.5–2.7 μm), enabling temperature measurements within the range of 310–1500 K. To interpret the recorded radiation from the test samples, calibration data provided by the manufacturer of the narrowband optical filter was used.

This study introduces an ultrasonic spiral-strip waveguide sensor system with distributed reflectors, specifically designed to enhance surface contact over the measurement region through a flat sensing interface – addressing limitations associated with traditional cylindrical or wire waveguides. A thin metallic strip was configured into a spiral shape, and strategically positioned notch reflectors were integrated along its length to generate distinct reference echoes. Finite element (FE) simulations were initially employed to optimize the spiral geometry and reflector layout. The fabricated waveguide incorporated four pairs of convergent-divergent notches at calibrated radial intervals (directions), enabling seamless placement on a flat heated surface. A shear-mode ultrasonic transducer, aligned at 0° to the waveguide axis, was used to excite and receive the S₀ Lamb wave mode. Reference echoes collected at ambient temperature served as a baseline to evaluate the time-of-flight (ToF) variation as the structure was exposed to elevated temperatures. The sensor was calibrated using co-located thermocouples to establish a correlation between surface temperature and ToF shifts (δToF). The proposed sensor system addresses the need for accurate and continuous temperature monitoring in high-temperature industrial environments, including power plants, metallurgical operations, and thermal processing units in food and chemical industries. Experimental validation demonstrated that the spiral-strip waveguide could reliably measure surface temperature at multiple distributed locations, showing good agreement with thermocouple data.

The paper presents results of studying the behavior of the critical field parameter determined by the shape of the major magnetic hysteresis loop in the region of predominant displacements of 90-degree domain walls for specimens of two steel classes (20GN hull steel and 08Kh15N5D2T maraging steel) under plastic tensile deformations to various levels. The sensitivities of this parameter and other magnetic characteristics to changes in the stress-strain state of the studied steels are compared. It is established that the coercive force and the critical field of 20GN hull steel change monotonically in the entire range of plastic strain, while the sensitivity of the critical field to the value of strain is 4.8 times greater than the sensitivity of the coercive force. It has been shown that for assessing the state of products made of 08Kh15N5D2T maraging steel, a multiparameter testing is recommended that includes a combination of such parameters as critical field and residual induction.

To address the challenge of accurately quantifying surface crack depth in additively manufactured components, a novel method based on the mode conversion of laser ultrasonic surface waves is proposed. By analyzing the propagation paths and time-domain signal characteristics of mode-converted surface waves (RSR waves) at the crack location, a quantitative model relating crack depth to time is established. Finite element simulations are employed to investigate the mode conversion and propagation mechanisms of surface waves, and a non-contact laser ultrasonic testing system is developed accordingly. An optimized C-VMD algorithm is further proposed to extract the characteristic signals effectively. Experimental results demonstrate that the proposed method achieves a quantitative detection error of less than 4% for cracks with depths ranging from 0.6 mm to 1.4 mm, indicating its suitability for detecting small-sized cracks in metal additive manufacturing components.

High-entropy alloy of the CoCrFeNiAl_x system (x = 0.3, 0.6, 0.8, 1.0) was obtained by powder sintering. The influence of homogenization temperature (900, 1000, and 1100°C) on microstructure, phase composition, microhardness, and magnetic properties of the alloy was investigated. It was found that microhardness, saturation magnetization, and maximum magnetic permeability increase with increasing homogenization temperature. The changes in magnetic characteristics correlate with the phase composition. The results obtained confirm the possibility of using magnetic methods to evaluate structural changes in high-entropy alloys of this system.

The paper presents a study on the application of the Jiles–Atherton magnetic hysteresis mathematical model. The optimal model parameters were selected based on measurement data in a closed-loop magnetic circuit and used to build digital models in COMSOL Multiphysics. Experimental studies on ferromagnetic steel samples with different magnetic properties demonstrated good agreement with the design data. The results showed that the deviation of the experimental values of the key characteristics (B_max, B_r, H_c) from the simulation results did not exceed 5%. Detailed pictures of the spatial distribution of magnetic induction and field strength in samples in different parts of the magnetic hysteresis loop were obtained. The verified model will allow further optimization of the designs of magnetizing devices and the location of sensors when developing new methods and means of magnetic nondestructive testing.

Measurements using eddy current transducers in a wide frequency range are considered. Resonance properties have been investigated, hodographs of transducer signals have been constructed depending on the signal frequency and the thickness of the electrically conductive coating on an electrically conductive nonmagnetic base, and the phase method of detuning from the gap for different signal frequencies is considered. A comparative analysis of the dependencies of the amplitude of the insertion voltage and the penetration depth of the electromagnetic field into the coating material at different excitation frequencies of the transducer is performed.

The results of experimental studies on detection of defects such as cracks and impact damages in glass-reinforced plastic pump compressor pipes (GRP PCP) and glass-reinforced plastic sucker rods (GRP SR) using a thermal nondestructive testing method involving combined ultrasonic and optical stimulation are presented. It is demonstrated that ultrasonic infrared thermographic testing is appropriate for detecting cracks, especially “kissing” ones, whereas traditional thermal inspection based on optical heating is more suitable for identifying delamination and thinning. The efficiency of the inspection depends on the size of the ultrasonic stimulation zone with sufficient power (approximately 0.8 m in this study). During optical heating procedures, the testing productivity depends on the size of the heated area and the field of view of the thermal imager and can reach several square meters per hour.

Nondestructive testing of internal broken wires in wire ropes is crucial for maintenance. Existing methods focus on surface or shallow defects of exposed wires, neglecting detection at joints. The thick housing at wire rope joints makes internal broken wire detection difficult for common methods. Thus, this study uses CT technology to obtain wire rope cross sectional images and applies computer image processing for automatic defect detection. Traditional image processing methods struggle with complex cross sectional CT images, while deep learning based methods require extensive training data, which is hard to meet in practice. Therefore, a steel wire rope joint broken wire defect detection system based on template matching segmentation and gray level threshold judgment is proposed. First, the image is preprocessed, and a template is generated. Then, using constraint terms and the stochastic gradient descent(SGD), optimize the template to align with wire cross sections. Finally, an adaptive gray level threshold is calculated to judge defects at template circle locations. Experiments on generated wire rope joint CT image datasets show that the proposed system has stronger segmentation ability compared with general purpose segmentation models. And compared with object detection models, it can detect steel wire breaks more accurately and completely.