An urgent problem of scientific and technical development is ensuring the quality and reliability of the functioning of products made of non- metallic heterogeneous materials. Compared with traditional structural materials, the use of non-metallic heterogeneous materials allows for reducing the mass of the structure, increasing the service life, reducing material consumption, increasing the reliability and power of mechanisms, reducing the time required to manufacture parts, and simplifying the technological process of their manufacture. The possibility of developing fundamentally new structures and their operation in extreme conditions is created. An important role in solving such a problem is played by effective control methods. Promising from this point of view are methods of active thermal control, in which the informative parameter is the pattern of the thermal field of the surface under investigation. When solving the problem of developing an algorithm for flaw detection of products made of materials of the specified class, special attention should be paid to the signal-to-noise ratio and contrast characteristics of thermographic images, since it is these characteristics of the thermogram that are the main ones in ensuring the necessary reliability of control, as well as the accuracy of measuring the depth of occurrence and geometric characteristics of the detected defects. The main task of this work is to study the dependence of the signal-to-noise ratio and contrast characteristics of the thermogram of the product surface, which is controlled using the thermal imaging method, on factors such as the depth of the defect and its relative dimensions. In particular, the signal-to-noise ratio for thermal imaging control accounts for both the amplitudes of the "useful" temperature signals and the "parasitic" components characteristic of a specific measurement and/or control method.
The article addresses the urgent problem of increasing vibration resistance in the face milling process and improving the quality of the processed surfaces of parts through parametric optimization of the torsional stiffness of the main drive elements. A comprehensive analysis of the dynamic interaction of the subsystems of the metal-cutting machine is carried out, and it is substantiated that the integration of tangential displacements of the spindle unit into the design schemes allows for the elimination of the limitations of classical linear models. A mathematical model of a closed dynamical system has been developed that accounts for fluctuations in the instrument’s angular velocity and the non- stationary nature of the delay time in the trace regeneration mechanism. Using a method from the theory of random processes, the influence of torsional variations on changes in the overlap coefficient and stability parameters is estimated. The theoretical conclusions were verified by an experimental study using a vertical milling machine, in which torsional stiffness decreased by varying the diameter of the intermediate shaft of the spindle head from 35 to 10 mm. It has been experimentally confirmed that at the level of torsional oscillations (with a change in rotational speed by 8% with a shaft diameter of 12 mm), the regenerative mechanism of auto-oscillations is destroyed. This leads to a 2.5-fold decrease in the amplitude of high-frequency linear vibrations of the machine carrier system and an improvement in the parameters of the surface microrelief, with a decrease in the roughness (Ra) from 2.3... 2.5 μm to 1.9 ... 2.0 μm. The limits of rational reduction of rigidity have been determined, beyond which excessive susceptibility of the drive begins to destabilize the process of molding. The scientific results substantiate the effectiveness of optimizing the dynamic characteristics of drives in increasing the integral vibration resistance of equipment.
The operational reliability of liquid and gas transportation systems directly depends on the quality of pipeline valves. Traditional manufacturing technologies for gray cast iron castings of Grade 250 (SCh250) do not always ensure the stability of these characteristics, resulting in a significant percentage of rejects due to leakage or insufficient strength under peak loads. Therefore, searching for optimal methods of alloying and modifying cast iron is a critical task for modern foundry production. The study aims to improve the performance characteristics and reduce the scrap rate of pipeline valve castings by enhancing the technological processes for out-of-furnace melt treatment through the combined influence of alloying and modifying additives. The multi-factor experimental design method was employed in this work. To confirm the results, a comprehensive metallographic analysis of the cast iron structure was conducted. During the study, mathematical models in the form of regression equations were obtained, which adequately describe the dependence of strength and pressure tightness on additive concentrations. It was established that introducing a copper-phosphorus alloy promotes matrix pearlitization and the refinement of graphite inclusions, thereby improving casting density. The optimal range for component addition was determined. This enables stable strength values that exceed the requirements for gray cast iron Grade 250 (SCh250), while simultaneously reducing leakage-related rejects by 1.5…2 times. For the first time, the synergistic effect of the joint application of a copper-phosphorus alloy and a magnesium-containing modifier in thin-walled castings with complex configurations is quantitatively described. The influence of these elements on the formation of a dense structure capable of withstanding high hydraulic pressure without seepage of the working medium was proven. The research results were implemented in the manufacturing process for valve body parts, allowing for cost optimization of expensive additives and a significant increase in the yield of sound castings. The proposed technology is universal and can be adapted to produce other types of critical pressure-tight castings.
In this paper, the problem of evaluating the local compliance of a thin, cantilever-fixed steel plate during milling is considered, accounting for the displacement of the force application point along the machining trajectory. The relevance of the study stems from the widespread use of simplified one-dimensional models in the design of milling processes for low-rigidity parts, which systematically lead to errors in assessing local stiffness and, consequently, to excessively conservative cutting conditions. A comparison of one-dimensional (beam) and two-dimensional (plate) deformation models is performed: in the beam model, the plate is treated as a cantilever beam of constant cross-section, the deflection of which is determined analytically based on classical strength-of-materials equations; in the plate model, the Kirchhoff-Love theory is used, accounting for the two-dimensional distribution of deformations, the interaction of bending in two coordinate directions, and the actual boundary conditions of the fixation. For each position of the tool along the machining trajectory, the local compliance was determined within both models, enabling their direct quantitative comparison. It is shown that the one-dimensional model systematically overestimates deflection and underestimates local stiffness, particularly along the plate’s free edge, leading to an underestimation of the calculated limiting feed rate and overly conservative process parameters. The two-dimensional model correctly accounts for the redistribution of deformations over the plate surface, produces a spatial compliance map as a function of two coordinates, and reflects the actual stiffness characteristics with significantly greater accuracy. The comparative analysis reveals fundamental differences in the assessment of vibration-prone zones, confirms the need to apply plate models when calculating adaptive milling conditions for thin-walled cantilever elements on CNC machines, and opens the possibility of increasing machining productivity without increasing the risk of chatter.
The article examines the forecasting of reliability indicators for motor-vehicle components, assemblies, and systems during design and operation. The calculation- based forecasting framework is shown to require a hierarchical representation of the product, load spectra, physical damage models, failure and limit-state criteria, and procedures for assessing and correcting the forecast. Because reliability indicators are stochastic, an exact estimate of mean service life is often impossible, particularly when information on new designs, materials, and operating modes is incomplete. General and special forecasting methods are classified, and a combined approach integrating extrapolation, statistical, and structural-functional models is substantiated. Directed state graphs are considered for an individual vehicle and a fleet, describing operation, standby, diagnosis, maintenance, and restoration. Wear is represented as a monotonically increasing nonstationary random process whose mean and variance vary with time. For common wear approximations, relationships are obtained for the probability density of service life. The case in which the limiting wear is a random variable is investigated by Monte Carlo simulation, linearization, and numerical integration. A numerical example demonstrates close agreement between the moments of the service-life distribution obtained by the three methods. The results can support the selection of design alternatives and maintenance and repair strategies, refinement of operating parameters and modes, and planning of spare-parts demand. The proposed framework links reliability prediction with decision-making under uncertainty and therefore provides a methodological basis for managing vehicle reliability throughout the life cycle. It also improves the consistency of decisions made under risk and incomplete information.
