Introduction. The development of coatings capable of reproducing the structural and functional properties of dental enamel is of considerable interest for dental materials science and biomimetic surface engineering. Despite the progress achieved in biomimetic calcium-phosphate systems, the most common approaches still rely on multistep protocols that are highly sensitive to interfacial-layer formation conditions and do not always ensure simultaneous reduction of deposition time, control of coating morphology, and reproducible surface mechanical response. The objective of this study was to experimentally evaluate the feasibility of one-step formation of a biomimetic hybrid nHAp/PDA coating in an electric field using isolated electrodes, and to determine the effect of the deposition mode on surface morphology and the surface microhardness of the “coating–substrate” system.Materials and Methods. Segments of native human permanent tooth enamel were used as a model of a natural apatite-containing substrate. Four surface conditions were compared: native enamel, an nHAp/AA layer formed after acid conditioning, a PDA/nHAp coating obtained by sequential deposition, and a hybrid coating formed via simultaneous electric-field-assisted mineralization and accelerated dopamine polymerization. Deposition was performed in a potentiostatic cell with isolated copper electrodes. Surface morphology was evaluated using scanning electron microscopy and atomic force microscopy (AFM). Surface mechanical response was assessed by Vickers microhardness testing at a 50 g load, AFM mapping of indentation imprints, and local nanoindentation.Results. The one-step electric-field-assisted mode was found to produce the densest and most uniform surface layer, approximately 1 μm thick, with a minimum roughness of about 20 nm. Sample D demonstrated the highest surface microhardness values, reaching approximately 310 VHN, whereas native enamel showed values of approximately 280 VHN, sample B — about 120 VHN, and sample C — about 190 VHN. One-way ANOVA confirmed a statistically significant effect of sample type on microhardness (p < 0.001). AFM mapping of the indentation imprints confirmed the accuracy of optical diagonal measurements on the textured surface.Discussion. The increased surface microhardness of the electric-field-assisted sample appears to be associated with more organized interfacial interactions involving polydopamine and a denser packing of the mineral component of the coating. At the same time, Vickers microhardness testing and AFM nanoindentation characterize different scale levels of the mechanical response and should therefore be interpreted as complementary methods.Conclusion. It is shown that one-step formation of a hybrid nHAp/PDA coating in an electric field using isolated electrodes makes it possible to obtain a morphologically organized layer with a surface mechanical response comparable to that of intact enamel. The proposed approach appears promising for the accelerated formation of functional organomineral coatings on apatite-containing substrates.
Introduction. Methods for processing information contained in variations in the optical flow intensity during object motion are widely used in numerous technical applications, including space research, technical diagnostics, machine (technical) vision, object tracking in digital images, autonomous navigation of unmanned vehicles, etc. Among these methods, monocular techniques for estimating the parameters of the video camera own motion have demonstrated the greatest practical efficiency, both based on a cartographic analysis of the underlying surface and using various algorithms for estimating optical flow (velocity field) parameters. Although existing velocity field estimation algorithms offer advantages such as operability in the absence of terrain maps and computational costs easily implemented in onboard processors, their practical application is significantly complicated by the inevitable noise in optical measurements, which can have a wide variety of physical origins and reduce dramatically the accuracy of optical flow parameter estimation. Therefore, the objective of the research discussed in this paper is to address the problem of simultaneous high-precision estimation of the intensity of optical flow and identification of its parameters under conditions of measurement noise with unknown probabilistic characteristics. A theoretical solution to this problem enables the development of a new general approach to the synthesis of robust algorithms for high-precision optical flow processing in video monitoring systems. This, when applied in practice, will ensure noise immunity and the required accuracy characteristics for machine vision systems, autonomous navigation systems of unmanned vehicles, and other applications.Materials and Methods. The solution was obtained by monocular methods for determining the camera proper motion and minimizing a regularized quadratic criterion. The starting-point for the solution was the formulation of the problem as one of stochastic estimation and parametric identification of a discrete linear nonstationary system while observing its state vector under interference with an unknown probability distribution. The synthesis of the “estimation-identification” algorithm in this formulation was implemented as a procedure that guaranteed the highest accuracy in the minimax sense. Minimizing the resulting minimax criterion allowed us to construct an algorithm for estimating and parametrically identifying the optical flow as a stable vector recurrent procedure, easily implemented in onboard computers of moving objects.Results. The problem of simultaneous high-precision estimation of the optical flow intensity and identification of its parameters under measurement noise with unknown probabilistic characteristics is solved. This problem has not been considered in the scientific literature to date. The solution will enable the development of an approach to the synthesis of robust algorithms for high-precision processing of optical flows in video monitoring system. In practical use, it will ensure noise immunity and the required accuracy characteristics of machine vision systems, autonomous navigation systems of unmanned vehicles, etc. The assessment of the practical applicability of the developed algorithm for estimating and identifying optical flow parameters was performed under conditions of non-Gaussian measurement noise through numerical simulation. Despite the specified high level of measurement noise, the errors in estimating the optical flow intensity at all tested coordinate points proved to be both rapidly converging to steady-state values and very small in the steady-state mode (a few percent of the maximum value of the optical flow intensity).Discussion. The obtained data confirm that the proposed algorithm has such advantages over known optical flow processing algorithms as the ability to estimate the intensity of the optical flow and identify its parameters under noise conditions, whose probability distributions are a priori unknown. It is characterized by high accuracy and robustness, and it does not require high computational costs.Conclusion. The practical significance of the developed algorithm consists, firstly, in the possibility of high-precision stable processing of the optical flow under the conditions of uncertain probabilistic nature of measurement errors, and secondly, in the computational efficiency of the developed “estimation-identification” procedure. This, in turn, provides its successful practical application in solving optical information processing problems in vision systems, navigation of autonomous robotic systems, space exploration, technical diagnostics, and other fields.
Introduction. Reliable identification of the probabilistic and temporal features of switching nodes is required for assessing the quantitative characteristics of software-defined networks. Widely used queuing theory (QT) methods only approximately specify and model the processes in an OpenFlow switch and its buffer memory. This results in understated and unrealistic performance estimates for the designed network equipment, causing switch buffer overloading and packet loss. A different modeling approach can solve this problem. The objective of this paper is to develop and study an analytical model for the buffer memory of an OpenFlow switch in an SDN using advanced techniques.Materials and Methods. The discrete Laplace-Stieltjes transform was used. Statistical characteristics of packet flows and the throughput of communication channels for a given packet loss probability were taken into account. The OpenFlow switch buffer memory model was based on the mathematical apparatus of the QT. It was constructed under the assumption of recurrence of input data flows with batch arrivals. The model was based on schematic representations of the switch structure, its record set, and a graph description of the transmission of network packets leaving the switch. We started with schematic representations of the switch structure, its record set, and a graph description of the transmission of network packets exiting the switch. Two model assumptions were taken as acceptable:arbitrary distribution of the relationship between the volume of data flows and their service time;discreteness of the distribution of the information flow structure.Results. The developed model integrated the probability of packet flow loss, their statistical characteristics, the throughput of computing devices, and the multiphase service procedure. When testing the model performance, we assumed that the switch load increased from 0.1 to 0.9, and the loss probability — from 10–3 to 10–6. For these metrics, we determined how the switch load affected the buffer memory size and latency. In the first case, the minimum value (memory capacity) was 0.201, the maximum — 10564. In the second, they were 0 and 470 ms, respectively. For simulation modeling, the minimum time was 0 ms, the maximum — 2300 ms. The simulation and analytical modeling indicators were close at loads below 50% and increased several times at loads above 50%. The indicators increased sharply with loads up to 70%, and then increased exponentially.Discussion. At low network loads, queues did not overflow, packets were not lost, and linear dependences were maintained. At medium and high loads, packet flow processing was described by nonlinear dependences. The results of analytical and simulation modeling diverged due to the explosive nature of self-similar network traffic and its approximate description by the Pareto distribution. Switch load determined the feasibility of the proposed approach. The model is suitable for designing elements of software-defined networks to analyze their resilience under various information impacts.Conclusion. The proposed SDN analytical model determined the values and variances of the switch buffer memory size, as well as the memory capacity for constructing address flow tables. The solution performance was tested with switch loads ranging from 0.1 to 0.9. It is planned to create a model that takes into account request flows from both the external network and the server.
Introduction. Modern technologies of tool and mold production increasingly use metal-composite systems (MCS), which combine additively manufactured metal shells and metal-polymer fillers. This corresponds to priority areas of scientific and technological progress, such as digitalization and additive manufacturing (in accordance with the Federal Project “Development of Materials and Production Technologies” within the framework of the national program “Scientific and Technological Development”). The scope of application of MCS in industry is growing: according to industry reviews, their share in the production of high-precision components for the aerospace and automotive industries has increased by 25–30% over the past five years, providing economic benefits due to a 15–20% reduction in the weight of structures and improvement of the energy efficiency of processes. Such systems combine the strength and thermal conductivity of metal with the damping properties of polymers, yet exhibit high sensitivity to overheating during machining. Consequently, the temperature at the metal–MCPM (metal-polymer composite material) interface during turning may exceed the thermal stability threshold (170 °C), resulting in thermal degradation, loss of adhesion, and shell deformation. In the literature, the problem of MCS thermal stability in turning is addressed only fragmentarily: existing studies focus on monolithic composites or general heat‑transfer models, lacking detailed analysis of interfacial heating in additively manufactured systems featuring low‑conductivity fillers. Therefore, research is needed to quantify the thermal response during the machining of such systems and to determine the cutting parameters that provide their thermal stability. The objective of this work is to experimentally study the temperature response during turning of MCS with a shell thickness of δ = 3.5 mm and to construct a second-order regression model linking the temperature at the metal – MPCM interface with the cutting parameters.Materials and Methods. A hardware-software measurement unit simulating the MCS structure was developed for the study. It included a replaceable bushing made of 12Kh18N10T steel, an internal insert made of Ferro-Chromium metal-polymer, three built-in type K thermocouples, and a data acquisition module based on an ESP32-WROOM microcontroller with MAX6675 converters, providing temperature recording at 5 Hz and data transmission via Wi-Fi. The accuracy of the measurements was confirmed by thermal imaging verification using FLUKE Ti400. The experiment was conducted according to the full factorial design (FFD) 2³ + n0, in which cutting speed V, feed S and cutting depth t were varied. Data processing was performed by the least-squares method with adequacy validation using Fisher's F-test and coefficient significance by Student's t-test. Based on the results of processing in real physical units, a second-order regression model was constructed — model 3.5TP, designed for engineering prediction.Results. The analysis of the experimental data showed that the thermal response of the metal–composite system was nonlinear. The depth of cut t was the dominant factor increasing the temperature, whereas within the investigated range, an increase in the feed rate S and cutting speed V led to a decrease in the interface temperature due to a shorter thermal exposure time and more intensive heat removal with the chip flow. The resulting 3.5TP model was characterized by the coefficient of determination R² = 0.9513, Fisher criterion value F = 364.31 and the significance level p < 10⁻⁵, which validated its adequacy. Interpretation of the regression coefficients indicated that the depth of cut (t) had the strongest impact on the temperature rise, the feed rate (S) showed a moderate effect, and the cutting speed (V) had the least sensitivity within the investigated range. The constructed response surfaces and contour maps identified the “safe zones” of cutting conditions that satisfied the constraint T ≤ 170°C, corresponding to the thermal stability limit of the metal–polymer filler. The average deviation between the experimental and calculated data did not exceed 7 °C, that confirmed the high accuracy and predictive capability of the proposed model.Discussion. The constructed 3.5TP model revealed the relationship between geometric and technology factors that determine the thermal load of the MCS during turning. The dominant impact of the depth of processing was due to the increase in the volume of the cut layer and heat generation in the contact zone, while the increase in feed and cutting speed was accompanied by compensating effects due to a decrease in the time of thermal contact and more intense heat removal with the chips. The results obtained indicated the need to optimize processing modes taking into account the shell thickness δ. Directions for further research were identified.Conclusion. The conducted study demonstrates that the developed experimental setup reproduces accurately the thermal behavior of a metal–composite system composed of an additively manufactured metal shell and a metal–polymer filler. The constructed 3.5TP regression model adequately describes the temperature response during turning and can be used for engineering prediction of mechanical processing modes.
Introduction. The wear rate of a cutting tool can be controlled by introducing additional vibrations into the cutting zone. The effect of vibration parameters on tool wear appears to be well-studied. However, the conclusions of some such studies are contradictory. It is noted that vibrations of varying amplitudes can both increase and decrease wear. There are no analytical models in the literature that resolve this contradiction or reflect the nonlinear relationship between the tool and workpiece subsystems under cutting. Furthermore, the fact that wear on different tool faces requires different force interaction models is not taken into account. The present research fills these gaps. The objective of the study is to determine the patterns of impact of high-frequency vibrations (HFV) on tool flank wear.Materials and Methods. The data from mathematical modeling of the dynamic cutting system in Simulink were used, taking into account the forces on the back face, effective parameters, and the HFV. Equipment: 16K20 machine tool, vibration control measuring stand with a frequency range of 0.4–15000 Hz, computer, E20-10 analog-to-digital converter, acoustic system, and STD.201-1 cutting force testing stand. Workpieces made of 10GN2MFA steel with a diameter of D = 84 mm were machined using tools with brazed T15K6 plates without lubrication.Results. The effect of the HFV on the contact interaction forces along the tool flank and the phase trajectory of the tool deformation displacements are demonstrated for different HFV amplitudes: from 0.5 ⋅ 10–2 to 2 ⋅ 10–2 mm. It is established that power N of irreversible energy transformations (IET) depends on the direction of the introduced vibrations. The dependence of tool wear rate on additional vibrations with amplitudes of 5 and 10 µm in different directions at cutting speeds of 1 m/s, 1.4 m/s, and 2 m/s is shown. The results obtained are compared with wear trajectories without disturbances.Discussion. The optimal amplitude of additional vibrations in the feed direction depends on the tool clearance and decreases with wear stage. The maximum wear value drops from 0.55 mm to 0.35 mm when introducing vibrations with an amplitude of 5 µm and to 0.26 mm — at 10 µm. With additional vibrations in the tangential direction, wear rate depends weakly on the amplitude of the introduced vibrations, as it is many times smaller than the velocity of the tool vibrational displacements. The maximum wear value decreases from 0.65 mm to 0.6 mm at 5 µm and to 0.48 mm — at 10 µm. With increased wear, there is no optimal amplitude for additional vibrations.Conclusion. The developed models allow for a quantitative assessment of the impact of HFV on the tool flank wear rate and the appropriate selection of vibration parameters introduced into the cutting zone. This allows for the creation of:virtual models of the cutting process and the selection of modes to minimize wear rate;wear monitoring systems with a comprehensive approach to prediction.Next, it is required to study the dynamics of the cutting process at HFV amplitudes greater than 10–15 µm.
