This study investigates the chemical and mineralogical composition, particle size distribution, and physical and chemical properties of raw dune sands from the Kushkupyr district of Uzbekistan. The particle size distribution indicates a predominance of particles larger than 0.007 mm, with a fineness modulus of less than one, which classifies these sands as fine-grained. Scanning electron microscopy (SEM) reveals that the sand particles consist primarily of irregularly shaped quartz grains. This work proposes compositions for producing sodium silicate based on a microsilica – dune sand – caustic soda system.
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.
This article is devoted to the key aspects of optimizing urban bus routes in small and medium-sized cities. It examines the criteria that make it possible to assess the effectiveness of the existing route, as well as the main economic indicators, without which it is impossible to make an informed decision whether to maintain, change or close the route.
The article examines the issues of automating academic process management in higher education institutions under the conditions of digital transformation of the university environment. A comparative analysis of the most widely used information systems for supporting academic administration, including 1C:University, MMIS University, and TANDEM.University, is presented. The study is of a scientific-methodological and applied nature and is aimed at identifying the functional, organizational, and technological characteristics of the considered solutions. The research analyzes the capabilities of the systems in the areas of curriculum management, workload calculation, timetable generation, student records management, integration with external services, as well as reporting and analytical support. Additional attention is paid to issues of scalability, adaptability to the internal processes of a university, maintenance costs, and dependency on software vendors. The comparison was carried out using a set of criteria reflecting the practical requirements associated with the operation of information systems in educational organizations. The study demonstrates that the examined software solutions differ significantly in terms of functional completeness and are focused on different models of academic process organization. The 1C:University system demonstrates a high level of support for regulated administrative procedures and document management; MMIS University is effective in timetable scheduling and curriculum management; TANDEM.University is characterized by advanced integration mechanisms and analytical data processing capabilities. At the same time, several limitations related to the adaptation of standard software solutions to the specific requirements of individual universities were identified. The obtained results may be used in the selection, implementation, and modernization of academic management information systems, as well as in the design of a university’s own digital infrastructure.
A decision support system has been developed to select the optimal structure and parameters of a group of mobile robots, the configuration of which is determined by the mobile platform and auxiliary components. Within the framework of the decision support system, a method of multi-criteria optimization of the group composition and agent parameters has been developed. Two concepts for the selection of importance coefficients in the course of multi-criteria optimization are proposed. The first concept evaluates regions of a parametric space by “intensity” (how many configurations have been tested) and “quality” (how dominant the configurations are). Areas with a high total score are considered the most promising to search for. The second concept evaluates the voids between non-dominant configurations in the criteria space, where a greater distance between them makes the area more promising. A methodology has been developed for analyzing the preference of options obtained as a result of multi-criteria optimization using sensitivity assessment of non-dominant options. Various ways of normalizing the values of criteria in sensitivity analysis, as well as various sensitivity metrics, have been investigated. The influence of parameter uncertainty on the choice of the best solution when using various sensitivity metrics is investigated.
