Selective laser sintering (SLS) is one of the most popular 3D molding technologies; however, the manufacturing steps of SLS machines are cumbersome, and the most important step is focused on molding testing because it requires a lot of direct labor and material costs. This research establishes advanced hybrid mathematical classification models, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN), for effectively identifying the SLS yield of the sintering results from three sintered objects (boxes, cylinders, and flats) to achieve the key purpose of reducing the number of model verification and machine parameter adjustments, thereby saving a lot of manufacturing time and costs. In the experimental process, performance evaluation indicators, such as classification accuracy (CA), area under the ROC curve (AUC), and F1-score, are used to measure the proposed models’ experience with practical industry data. In the experimental results, the ANN gets the highest 0.6168 of CA, and it is found that each machine reduces the average sintering time by four hours when compared with the original manufacturing process. Moreover, we employ an oversampling method to expand the sample data to overcome the existing problems of class imbalance in the dataset collected. An important finding is that the RF algorithm is more suitable for predicting the sintering failure of objects, and its average sintering times per machine are 1.7, which is lower than the 1.95 times of ANN and 2.25 times of SVM. Conclusively, this research yields some valuable empirical conclusions and core research findings. In terms of research contributions, the research results can be provided to relevant academic circles and industry requirements for referential use in follow-up studies or industrial applications. Full article

Recent research has focused on sheet shear cutting operations. However, little research has been conducted on bar shear cutting. The main objective of the present investigation is to study bar shear cutting with numerical and experimental analysis. Bar shear cutting is an important operation because it precedes bulk metalworking processes for instance machining, extrusion and hot forging. In comparison to sheet shear cutting, bar shear cutting needs thermomechanical modelling. The variational formulation of the model is presented to predict damage mechanics in the bar shear cutting of aluminium alloys. Coupled thermomechanical modelling is required to analyse the mechanical behaviour of bulk workpieces, in which the combined effect of strain and temperature fields is considered in the shear cutting process. For this purpose, modified hardening and damage Johnson–Cook laws are developed. Numerical results for sheet and bar shear cutting operations are presented. The comparison between numerical and experimental results of shearing force/tool displacement during sheet and bar shear cutting operations proves that the use of a thermomechanical model in the case of the bar shear cutting process is crucial to accurately predict the mechanical behaviour of aluminium alloys. The analysis of the temperature field in the metal bar shows that the temperature can reach T = 388 °C on the sheared surface. The current model accurately predicts the shear cutting process and shows a strong correlation with experimental tests. Two values of clearance (c₁ = 0.2 mm) and (c₂ = 1.2 mm) are assumed for modeling the bar shear cutting operation. It is observed that for the low shear clearance, the burr is small, the quality of the sheared surface is better, and the fractured zone is negligible. Full article

Approaches presented today in the scientific literature suggest that there are no methodological solutions based on the training of artificial neural networks to predict the direction of industrial development, taking into account a set of factors—innovation, environmental friendliness, modernization and production growth. The aim of the study is to develop a predictive model of performance management of innovative industrial systems by building neural networks. The research methods were correlation analysis, training of neural networks (species—regression), extrapolation, and exponential smoothing. As a result of the research, the estimation efficiency technique of an innovative industrial system in a complex considering the criteria of technical modernization, development, innovative activity, and ecologization is developed; the prognostic neural network models allow to optimize the contribution of signs to the formation of target (set) values of indicators of efficiency for macro and micro-industrial systems that will allow to level a growth trajectory of industrial systems; the priority directions of their development are offered. The following conclusions: the efficiency of industrial systems is determined by the volume of sales of goods, innovative products and waste recycling, which allows to save resources; the results of forecasting depend significantly on the DataSet formulated. Although multilayer neural networks independently select important features, it is advisable to conduct a correlation analysis beforehand, which will provide a higher probability of building a high-quality predictive model. The novelty of the research lies in the development and testing of a unique methodology to assess the effectiveness of industrial systems: it is based on a multidimensional system approach (takes into account factors of innovation, environmental friendliness, modernization and production growth); it combines a number of methodological tools (correlation, ranking and weighting); it expands the method of effectiveness assessment in terms of the composition of variables (previously presented approaches are limited to the aspects considered). Full article

For the problem of 5G network planning, a certain number of locations should be selected to build new base stations in order to solve the weak coverage problems of the existing network. Considering the construction cost and some other factors, it is impossible to cover all the weak coverage areas so it is necessary to consider the business volume and give priority to build new stations in the weak coverage areas with high business volume. Aimed at these problems, the clustering of weak point data was carried out by using k-means clustering algorithm. With the objective function as the minimization of the total construction cost of the new base stations, as well as the constraints as the minimal distance between adjacent base stations and the minimal coverage of the communication traffic, the single-objective nonlinear programming models were established to obtain the layout of macro and micro base stations in order to illustrate the impact of the shape of the station coverage area, the circular and the “shamrock” shaped coverage areas were compared in this paper. For the “shamrock” base station, a secondary clustering was undertaken to judge the main directions of the three sector coverage areas. Then, an improved model taking the coverage overlapping into consideration was proposed to correct the coverage area of different sectors. Finally, the optimal layout was obtained by adjusting the distribution of all base stations globally. The results show that the optimal planning method proposed in this paper has good practicability, which also provides a very good reference for solving similar allocation problems of dynamic resources. Full article

The safe development of chemical industries requires adequate control of the environmental sustainability of the areas where enterprises are located. The purpose of the article is to develop and test a methodology for solving the multicriteria problem of choosing industrial zones for the development of chemical industries using the method of an additive global criterion. The novelty of the methodology lies in the multi-criteria and complexity of the tool and the presence of a statistical base, which allows it to be used for various socio-economic purposes at all levels of government. As the main research tools, the methods of multi-criteria selection of objects, one-dimensional data scaling, additive convolution of criteria, and methods of multivariate statistical analysis for verifying the results obtained and making a decision were used. The article describes the mathematical apparatus of the technique for solving the multicriteria problem of selecting objects by the method of an additive global criterion. The solution algorithm provides for a three-level integration of particular indicators using the methods of mathematical processing of an array of different-dimensional values. The procedure for selecting the vectors of the criterion space makes it possible to select industrial zones and obtain a global criterion using the additive convolution method. In order to test the methodology, the problem of choosing industrial zones for the potential development of chemical industries in the Russian region was solved. For the development of chemical production, industrial zones have been selected that are included in the above-average environmental sustainability group: Bavlinskaya, Nurlatskaya, Bugulminskaya, and Leninogorskaya. Tendencies of decrease in ecological stability of the zones, which have relatively safe industries on their territory but are adjacent to the zones of location of environmentally unfavorable industries, are revealed. The materials of the article can be used in the development of intelligent systems for monitoring and controlling the development of chemical industries, which allow monitoring the level of environmental safety of industrial zones, identifying sources of negative environmental impact with pursuing decision-making on the organization and planning of production systems in the territorial space. Full article

In this paper, we introduce a general family of distributions based on Whittaker function. The properties of obtained distributions, moments, ordering, percentiles, and unimodality are studied. The distributions’ parameters are estimated using methods of moments and maximum likelihood. Furthermore, a generalization of Whittaker distribution that contains a wider class of distributions is developed. Validation of the obtained results is applied to real life data containing four data sets. Full article

This article presents a methodological approach to modeling the processes of changing the dispersed composition of solid phase particles, such as granulation, crystallization, pyrolysis, and others. Granulation is considered as a complex process consisting of simpler (elementary) processes such as continuous particle growth, agglomeration, crushing and abrasion. All these elementary processes, which are also complex in themselves, usually participate in the formation of the dispersed composition of particles and proceed simultaneously with the predominance of one process or another, depending on the method of its organization and the physicochemical properties of substances. A quantitative description of the evolution of the dispersed composition of the solid phase in technological processes in which the particle size does not remain constant is proposed. Considering the stochastic nature of elementary mass transfer events in individual particles, the methods of the theory of probability are applied. The analysis of the change in the dispersed composition is based on the balanced equation of the particle mass distribution function. The equation accounts for all possible physical mechanisms that effect changes in particle size during chemical and technological processes. Examples of solutions to this equation for specific processes of practical importance are provided. The obtained analytical solutions are of independent interest and are in good agreement with the experimental data, which indicates the adequacy of the proposed approach. These solutions can also be used to analyze similar processes. The effectiveness has been confirmed during the analysis and calculation of the processes of granulation of various solutions and disposal of oil-containing waste to obtain a granular mineral additive. Full article

The problem of sustainable development is one of the central issues on the agenda of the global community. However, it is difficult to assess the pace and quality of sustainable development of individual economic systems—in particular, industry—due to the lack of a unified methodological approach. In this regard, the following research goal was formulated—to develop and test a methodology for forecasting sustainable development by using statistical tools. The achievement of the goal was facilitated by the application of formalization methods, factor analysis, discriminant analysis, the method of weighted sum of the criteria, and the method of comparison. The results of the study are new scientific and practical solutions that develop the ability to diagnose economic systems for the transition to environmentally friendly production. Firstly, methodological solutions are proposed to assess the nature of the transition of industry to sustainable development (low, medium, or high rate). The methodology is based on the proposed aggregated indicator of sustainable industrial development based on the results of factor analysis (by the method of principal components). As a result, the patterns of sustainable development of the extractive and manufacturing sectors of the Russian economy are revealed. Secondly, integral indicators of economic, environmental and social factors of sustainable development are calculated, and classification functions for each type of industrial transition to sustainable development (low, medium, or high) are formed through discriminant analysis. Scenarios of industrial development are developed, taking into account the multidirectional trajectories of the socioeconomic development of the country. Thirdly, the DFD model of the process of scenario forecasting of sustainable industrial development is formalized, reflecting the movement of data flows necessary for forecasting sustainable industrial development. It is revealed that the manufacturing industry is expected to maintain a low rate of transition to sustainable development. On the contrary, for the extractive industry, if efforts and resources are concentrated on environmental innovations, average transition rates are predicted. The uniqueness of the proposed approach lies in combining two types of multivariate statistical analysis and taking into account the indicators that characterize the contribution of industrial enterprises to sustainable development. Full article

Globally, assessing sustainable development methodology is kept in sustainable society index (SSI) format, but at the level of meso- and microsystems it remains undeveloped. The aim of the study is to typologize innovative mesosystems in Russian industry in the context of sustainable development based on the CART algorithm and to develop an algorithm for identifying priority areas of sustainable development. The research methods applied included formalization, a systematic approach, and the CART algorithm (calculation of the Gini index, training sample segmentation, the use of a recursive function and regression assessment). As a result of the study, the algorithm for the differentiated identification of innovative mesosystems sustainable development priority directions in industry based on the unique author’s methodology (ISDI) is proposed. The predominance of mesosystems with weak level of sustainable development requiring state support in favor of such mesosystems restructure is revealed. The novelty of the research lies in the development of new science-based solutions to ensure an accelerated transition of industry to the path of sustainable development. The difference of the author’s approach from the provisions known in science is the inclusion of environmental innovations in the mechanism for managing the sustainable development of innovative mesosystems and subsequent accounting in the process of mathematical processing of an array of data, which determines the uniqueness of the constructed decision trees. Full article

An interdisciplinary approach to management allows for the integration of knowledge and tools of different fields of science into a unified methodology in order to improve the efficiency of resource management of different kinds of systems. In the conditions of global transformations, it is economic systems that have been significantly affected by external destabilizing factors. This determines the focus of attention on the need to develop tools for the modeling and optimization of economic systems, both in terms of organizational structure and in the context of resource management. The purpose of this review study is to identify the current gaps (shortcomings) in the scientific literature devoted to the issues of the modeling and optimization of economic systems using the tools of mass service theory. This article presents a critical analysis of approaches for the formulation of provisions on mass service systems in the context of resource management. On the one hand, modern works are characterized by the inclusion of an extensive number of random factors that determine the performance and efficiency of economic systems: the probability of delays and interruptions in mobile networks; the integration of order, inventory, and production management processes; the cost estimation of multi-server system operation; and randomness factors, customer activity, and resource constraints, among others. On the other hand, controversial points are identified. The analytical study carried out allows us to state that the prevailing majority of mass service models applied in relation to economic systems and resource supply optimization are devoted to Markov chain modeling. In terms of the chronology of the problems studied, there is a marked transition from modeling simple systems to complex mass service networks. In addition, we conclude that the complex architecture of modern economic systems opens up a wide research field for finding a methodology for assessing the dependence of the enterprise performance on the effect of optimization provided by using the provisions of mass service theory. This statement can be the basis for future research. Full article