Russian Journal of Nondestructive Testing最新文献

This paper introduces a method for measuring solute particle size in solution. The working principle of this method is to characterize the particle size by using the ultrasonic extended coupled phase model combined with the acoustic attenuation coefficient of the solution. In the ultrasonic detection of highly concentrated emulsion particles, the inter-particle interaction, namely the structural loss, becomes the main factor affecting the ultrasonic attenuation. In this study, micron-sized emulsions with 10, 15, and 20% concentration were experimentally detected by the extended coupled-phase model, and the inverse calculation of the particle size distribution was carried out using a differential evolutionary algorithm. The detection results showed an average error of 4.266% compared with those of laser particle sizer. The results show that ultrasonic detection based on the extended coupled phase model combined with differential evolution inversion algorithm can achieve accurate measurement of particle size distribution of emulsions with high concentration.

In this article the methods of determining the position and size of non-flaws in albedo flaw detection are considered. Analytical and numerical solutions of the problem of determining the location of non-flaws on the basis of known parameters of the collimation system are shown. The dependence of the location of the flaw on the parameters of the collimation system is shown. It is proposed to determine not the true size of the flaw, but its equivalent area, similar to ultrasonic flaw detection.

Classification of buckwheat grains is important because the absence of defective grains is a guarantee of yield and quality. Buckwheat grains were randomly selected from a batch with grains that varied in quality. The identification and classification of buckwheat grains according to the degree of fulfillment was carried out by a combination of microfocus X-ray and hyperspectral image analysis and multivariate analysis techniques. Using microfocus radiography, buckwheat grains were categorized into groups according to the degree of fulfillment. Hyperspectral image of buckwheat grains in the range of 935–1720 nm was acquired using a Specim FX17 camera. Using the polygon selection function, the averaged spectra were obtained and a data matrix of grain samples was generated. The bands of the spectrum contributing most to the grading of the grain samples by the degree of fulfillment were identified using the principal component analysis. The classification model of grading buckwheat grain into groups by the degree of fulfillment was constructed by partial least squares discriminant analysis method. The results showed that hyperspectral image is a potential tool for rapid and accurate identification of buckwheat grains, which can be used in large-scale grain classification and grain quality determination.

Timber buildings show the exquisite skills of craftsmen in China. Under the influence of nature and human activities, damage and destruction of ancient timber structures lead to the loss of China’s cultural heritage. Therefore, it is particularly important to study the existing defect detection methods of timber members and provide excellent restoration plan for the preservation of timber structures. Defects of timber structure members were found to consist mainly of cracking, decay, insect-attack, bending and pullout of tenons, etc. These defects are the main factors that affect the mechanical properties of timber members and endanger the stability of timber structures. Pilodyn, resistograph, stress wave, radar, ultrasound, X-ray, infrared spectroscopy and piezoelectric transducers are all were studies for detection methods mentioned, which belong to semi-destructive testing and non-destructive testing. In detail, the principle, development status and application cases of wood building detection technology are elaborated to demonstrate the advantages and disadvantages of these technologies in various scenarios. New and feasible detection technology should be developed, and the development direction of damage detection technology for timber structures in the future is put forward.

An improved frequency-domain synthetic aperture focusing technique (F-SAFT) for laser ultrasonic testing (LUT) is proposed for internal defect detection of small-diameter cylindrical components. Firstly, a LUT automated detection platform is built, a pulsed laser is used to excite ultrasonic waves and a two-wave mixing (TWM) interferometer is used to detect ultrasonic waves. Since ultrasonic signals are affected by the thermal expansion of the pulsed laser, time-frequency analysis is used to obtain the frequency range for imaging longitudinal waves, and the influence of low-frequency clutter is eliminated through multiple filtering. Secondly, in order to balance signal acquisition efficiency and imaging quality, the peak signal-to-noise ratio (PSNR) is used to determine the optimal angular step size. Finally, the equivalent velocity of the longitudinal wave is corrected to compensate for the imaging position error caused by the separation of the ultrasonic excitation point and the detection point. The results show that the method proposed in this paper has high imaging accuracy, which could provide a new approach for in-service non-destructive testing of small-diameter cylindrical components.

The density of engineering plastics is a key parameter for ensuring their safety and reliability. In order to achieve rapid and high-precision on-site detection, a method based on the acoustic pressure reflection coefficient is proposed. First, finite element simulation analysis was conducted to obtain the acoustic field distribution during ultrasound propagation under water immersion conditions. The correlation between interface echo intensity and material density was determined. Optimal detection parameters were designed to reduce measurement errors caused by beam overlap and diffusion attenuation. A water immersion ultrasonic experimental system was constructed, and the measurement accuracy of the method was tested using chlorinated polyvinyl chloride pipes. The results show that, compared to the measurement results of the Archimedean drainage method, the maximum error of ultrasonic measurements does not exceed 1.7%, and the overall variance is less than 1.2%. The measurement accuracy of this method is compared with the regression results of different machine learning models. It is demonstrated that, compared to regression methods based on variable correlation, this method retains the advantages of high efficiency and low cost in ultrasonic density measurement, while achieving higher measurement accuracy. Additionally, it does not require a dataset for training support, making it promising and valuable for practical applications.

The thermal behavior of a poly(methylmethacrylate) (PMMA) pigmented with titanium dioxide (TiO₂) is studied in both Steady state and transient regimes in the present work. The numerical results of thermal conductivity, based on the finite element method, are compared to theoretical models and experimental measurements, which varies depending on the quenching temperature and pigment content. Time evolution of temperatures during the quenching of the composite is taken into account for different quenching temperatures and different pigment contents. It is noted that the heat exchange becomes slower for a pigment fraction of 0.5%, and the steady state is reached more rapidly for higher pigment content. The AFM image of the PMMA/TiO₂ composite with content equal to 3% of titanium dioxide. This demonstrates a good distribution of the particles throughout the matrix, with the individual particles being uniformly dispersed and securely embedded in the polymer matrix, thereby avoiding any clustering. An improvement in heat exchange is observed in the composite with a high content of titanium dioxide. This improvement is attributed to the increase in the thermal conductivity of the PMMA/TiO₂ composite.

Reinforced concrete (RC) structures combine steel and concrete to harness their respective advantages, making them a staple in contemporary architecture. With the aging of civil engineering structures, structural health monitoring grows increasingly critical. In this context, acoustic emission technology (AE) emerges as an effective nondestructive testing method for assessing the structural damage status. Building on this foundation, the AE technology was utilized to monitor the crack growth in the RC beam under the four-point bending test. Furthermore, a visual analysis method to assess the internal damage of the RC beam, based on the spatial b value of the AE, was introduced. This method integrates the spatial b value and the AE event density distributions to develop the T value. The results indicate that as the stirrup ratio decreases, the bearing capacity of RC beams increases; however, their ductility experiences a significant reduction, and the failure mode undergoes a transformation. Throughout each failure stage of RC beams, the AE ringing number and energy exhibit unique and easily distinguishable characteristics of change. Additionally, RA-AF correlation analysis can be applied to delve deeper into the analysis of the RC beams’ failure modes. Utilizing the spatial b value and T value facilitates the identification of damage locations within the RC beam, thereby offering a practical and feasible approach for structural damage analysis.

This study is devoted to the development of fusion techniques for data obtained by one or several nondestructive testing (NDT) methods. Experimental results were obtained by applying laser vibrometry and optical infrared thermography to evaluation of impact damage to carbon fiber composites. These NDT techniques are different by their physical nature and supply specific testing results. The proposed data fusion method allows increasing the reliability of inspection results and enables estimating defect parameters. It involves both averaging data of each single NDT technique and merging the results obtained by two methods. Vibrograms obtained by laser vibrometry are used to analyze acoustic response of the test sample to stimulation at various frequencies. In turn, infrared thermographic NDT supplies the sample response to thermal stimulation. It has been shown that the fusion of these two techniques supplies a comprehensive information on defect size and location. Also, the automation of the fusion procedure increases NDT productivity and reduces subjectivity of testing results.

Dynamic laser speckle imaging (DLSI) is an emerging nondestructive optical method used for the characterization of turbid materials. The microscopic dynamics of the turbid materials can be quantified using this imaging technique with high spatio temporal resolution. In recent decades, epoxy resins became indispensable for industries due to its fascinating mechanical properties, high chemical resistance, etc. The physical, mechanical and electrical characteristics of epoxy resins depend on the curing process. However, the techniques for monitoring the microscopic dynamics of the curing process are in-sufficient. Hence this work reports the application of dynamic laser speckle imaging to monitor the curing stages of Araldite, a 2 part epoxy resin. The computational methods incorporated with the technique include cross-correlation, inertia moment, Fujii method, etc. Conventional characterization techniques such as Differential Scanning Calorimetry and Fourier Transform Infrared Spectroscopy were also employed to confirm the results. The experimental results were also confirmed using the theoretical analysis.