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Nickel‐based superalloys are unique materials with complex doping that demonstrate excellent resistance to mechanical and chemical degradation. Over a long period of use in industry, a variety of information has been accumulated about their possible chemical compounds and features corresponding to a certain composition. One of the main service properties of the alloy is the tensile strength (σ). For a more convenient comparison of the characteristics of the alloys with different chemical compositions, the temperature and holding time of the metal during testing are often combined into a complex Larson–Miller parameter (PLM). The availability of experimental or simulated data on the alloys' properties in the entire range of temperatures and exposures would significantly expand the possibilities of the alloy applications and would allow a more accurate evaluation and comparison of the alloys. In this work, we use a machine learning method for modeling the properties of the alloys according to their composition. A Bayesian regularized artificial neural network was engaged to simulate missing tensile strength values for 278 superalloys. Special data preprocessing and the use of an ensemble of networks during training reduced the model error. Comparison of the predicted and experimental data showed excellent convergence. The method made it possible to obtain enough data to approximate the relation σPLM with a sigmoid function. The slope coefficient is considered as a quantitative expression of the thermal stability of the superalloys. [ABSTRACT FROM AUTHOR]

As an economic development determinant, transport needs to be improved on an ongoing basis. This is due to complex dynamic processes taking place both within the state and internationally. Social and economic transformations and scientific progress of the 21st century in the national economic sectors challenge the national policy of any state. The readiness for macroeconomic turbulence and the level of adaptability depend, to a decisive extent, on the economic potential of the state, where transport is the connecting link between the production, research and technical, agricultural and social components. As a backbone industry, the transport complex is based on infrastructure. The development of transport infrastructure is fundamental and is considered a priority not only at the national level, but also within the framework of globalization processes. Thus, the importance of resolving the issues of transport infrastructure development is due to the rapid development of the national economy as a whole. In this regard, the article emphasizes the importance of the integrated innovative development of Russia’s transport complex infrastructure and reveals promising directions for its development in conditions of market constraints.

The strategy for the development of the supply chain should ensure a high level of fault tolerance of all links when exposed to adverse factors. The article analyzes the impact on the stability of the supply chain of two types of influences: failure and disruption. The low stability of the supply chain appears in the stoppage of work in case of any disruptions and failures. With moderate stability, disruptions do not give up a significant impact on the operation of the supply chain, and failures lead to an increase in operating costs to maintain the stability of work processes. With a high level of stability, failures can cause disruptions in the operations of individual links. In case of disruptions, response models are applied based on the control of process parameters, the subsequent analysis of the causes of disruptions and the development of measures to restore the normal operation of the links in the supply chain. Effective disruption response involves the use of proactive response models. For this, it is necessary to ensure flexibility and transparency of processes in all links of the supply chain based on digital services for material flow control and mining of big data.

Among numerous melt structure model representations, the most relevant for liquid heat-resistant nickel alloys description is the quasicrystalline model of a microinhomogeneous structure, in which it is assumed that multicomponent nickel melts consist of clusters and intercluster space. Clusters inherit the short-range order of the atomic structure from various phases of the initial solid metal crystalline structure. Heating the melt to a certain temperature and/or increasing a period of its isothermal holding at constant pressure led to a second-order phase liquid–liquid phase transition (LLT). As a result, atomic associations that are more balanced and uniformly distributed over the melt volume are formed. Structural changes in nickel superalloy melts are irreversible and have a significant effect on the formation of the structure and properties of a solid metal during crystallization. Structural LLT changes in multicomponent nickel melts are the basis for a scientific substantiation of the technological modes of smelting, which contributes to an improvement in the technological properties of melts, a reduction of metallurgical defects, a rational use of expensive elements and foundry waste, as well as a significant improvement in the quality of metal products. This work is devoted to the experimental determination of the LLT transition in superalloy melts by the noninvasive electromagnetic method. [ABSTRACT FROM AUTHOR]

Nickel alloys are widely used in the manufacture of gas turbine parts. The alloys show resistance to mechanical and chemical degradation under prolonged loads and high temperatures. The properties of an alloy are determined by its composition and are subject to careful modeling. A set of basic refractory elements makes a special contribution to the parameters of the alloy. One of the main mechanical properties of the alloys is the high‐temperature tensile strength. Determining the influence of certain elements on certain properties of an alloy is a complex scientific and engineering problem that affects the time and cost of developing new materials. Simulation is a great chance to cut costs. In this article, we predict high temperature strength based on the composition of refractory elements in alloys using a specially designed deep learning artificial neural network. We build the model based on prior knowledge of alloy composition, information on the role of alloying elements, type of crystallization, test temperature and time, and tensile strength. Successful simulation results show the applicability of this method in practice. [ABSTRACT FROM AUTHOR]