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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]

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]