Anton Yu. Poroykov

The paper is devoted to the study of acoustic emission (AE) under cyclic loading of dissimilar welded joints. The object of the study is carbide and decarburized diffusion interlayers that are formed during the welding of pearlitic and austenitic steels, as well as during the subsequent operation of such dissimilar joints under high temperatures. The paper presents the results of an experimental study on cyclic loading of samples of dissimilar welded joints with the registration of AE data and measurement of local deformation fields using the digital image correlation (DIC) method. Loading was carried out in the elastic region at a stress close to the operational values. Analysis of the deformation distribution fields over the surface of the samples showed that welded joints with diffusion interlayers have increased deformation values in the interlayer zone. According to the values of the AE parameters, the level of activity and amplitudes of AE hits for samples with diffusion interlayers are higher than in defect-free samples.

Subject of study. This study investigates the influence of optical-system parameters on the error in determining the orientation and position of fiducial markers. Aim of study. This study determines the dependencies of absolute error in position and orientation on various influencing factors. Method. The error in a machine-vision system is assessed based on fiducial markers using computer-image modeling in the Unity 3D graphics system. Main results. Over 100,000 images of AprilTag markers in different positions and orientations were synthesized and processed during the simulation. The results of this simulation yielded the dependencies of absolute position and orientation errors on the distance between the camera and marker, the rotation angle of the marker, and the focal lengths of the camera. Practical significance. The obtained results may be utilized to optimize the placement of markers on the platform, select the optimal video camera positions and lens focal lengths, and implement adjustments in the image-processing algorithm. These changes can improve measurement accuracy in systems used for developing orientation algorithms for microsatellites.

Close-range photogrammetry methods are widely used for non-contact and accurate measurements of surface shapes. These methods are based on calculating the three-dimensional coordinates of an object from two-dimensional images using special digital processing algorithms. Due to the relatively complex measurement principle, the accurate estimation of the photogrammetric measurement error is a non-trivial task. Typically, theoretical estimations or computer modelling are used to solve this problem. However, these approaches cannot provide an accurate estimate because it is impossible to consider all factors that influence the measurement results. To solve this problem, we propose the use of physical modelling. The measurement results from the photogrammetric system under test were compared with the results of a more accurate reference measurement method. This comparison allowed the error to be estimated under controlled conditions. The test object was a flexible surface whose shape could vary smoothly over a wide range. The estimation of the measurement accuracy for a large number of different surface shapes allows us to obtain new results that are difficult to obtain using standard approaches. To implement the proposed approach, a laboratory system for the error estimation of close-range photogrammetric measurements was developed. The paper contains a detailed description of the developed system and the proposed technique for a comparison of the measurement results. The error in the reference method, which was chosen to be phasogrammetry, was evaluated experimentally. Experimental testing of the stereo photogrammetric system was performed according to the proposed technique. The obtained results show that the proposed technique can reveal dependencies that may not be detected by standard approaches.

Close-range photogrammetry is widely used to measure the surface shape of various objects and its deformations. The classic approach for this is to use a stereo pair of images, which are captured from different angles using two digital video cameras. The surface shape is measured by triangulating a set of corresponding two-dimensional points from these images using a predetermined location of cameras relative to each other. Various algorithms are used to find these points. Several photogrammetry methods use cross-correlation for this purpose. This paper discusses the possibility of replacing the correlation algorithm with neural networks to determine displacements of small areas in the images. They allow increasing the calculation speed and the spatial resolution of the measurement results. To verify the possibility of using convolutional networks for photogrammetry tasks, computer and physical modeling were carried out. For the first test, a set of synthetically generated images representing images of the Particle Image Velocimetry method was used. The displacements of particles in the images are known, it allows to estimate the accuracy of processing of such images. For the second test, a series of experimental images with surfaces with different deformation was obtained. Computational experiments were performed to process synthetic and experimental images using selected neural networks and a classical cross-correlation algorithm. The limitations on the use of the compared algorithms were determined and their error in reconstructing the three-dimensional shape of the surface was evaluated. Computer and physical modeling have shown the operability and efficiency of neural networks for processing photogrammetry images.

Fatigue crack in rails and cyclic-loaded train parts is a contributory factor in multiple railroad accidents. We address the problem of crack detection and measurement at early stages, when total failure has not yet occurred. We propose to combine acoustic emission (AE) testing for prediction of crack growth with digital image correlation (DIC) for its accurate quantitative characterization. In this study, we imitated fatigue crack appearance and growth in samples of railway rail and two train parts by cyclic loading, and applied these two techniques for inspection. Experimental results clearly indicate the efficiency of AE in the early detection of fatigue cracks, and excellent DIC capabilities in terms of geometrical measurements. Combination of these techniques reveals a promising basis for real-time and non-destructive monitoring of rails and train parts.

The research is devoted to steel structure diagnostics by the acoustic emission (AE) method. The existing regulatory documents for AE diagnostics of metals and alloys do not take into account some critical factors, among which one is the scale factor should be highlighted. As a result, this can lead to an unreliable assessment of the danger degree of defects in structures when using standard AE diagnostic criteria. This paper presents a quantitative assessment of the scale factor impact on the AE data during the static tension test of steel specimens to failure. Experimental studies were carried out on flat specimens of various thicknesses with a side notch made of high-quality alloyed steel 30 KhGSA. It was established that AE data changed (rise in the AE signals amplitudes and AE activity) within the increase of specimen thickness. Growth in the recorded AE signals cumulative energy was registered with a greater specimen thickness. Partial correlation dependences of the mean count frequency and cumulative energy of AE signals on the specimen thickness were obtained. It was shown that such an effect occurred due to both a general increase in the deformed metal volume and greater strain intensity during the tension of thick specimens. The obtained dependences may contribute to the development of AE diagnostics of metallic materials which is invariant to the scale factor impact.

Fiducial markers are widely used in machine vision systems. One of the main ways of their application is to estimate the relative position of the camera and marker in space. The latter are visual artificial objects, which are designed for their robust detection among other objects in the environment of the images. Examples of fiducial marker accuracy estimation are found in many studies. Usually such studies are conducted for one or two or three code marker systems. And accuracy estimates are given only for specific parameters of the machine vision system, which is designed by the authors of the work. This approach is not universal, and for other parameters it is necessary to carry out the error estimation again. This paper presents an approach to determining the error for comparing more fiducial marker systems with each other using more universal metrics. For this purpose, computer simulations of marker images were performed for five different systems with different spatial orientations. The images were processed using detection algorithms proposed by the authors of the marker systems. The processing results were compared with the specified parameters in the simulations. Relative error estimates were obtained for the five different code marker systems.