The nickel-based superalloys are unique materials with complex doping applied to manufacturing the gas turbine engine parts. The alloys show resistance to mechanical and chemical degradation under high pressure, high temperature, and long-term isothermal exposures. One of the main alloys’ service properties is the heat resistance. Numerically, it is expressed in the tensile strength values (MPa). Simulation of the heat resistance behavior is an important engineering task, which would significantly simplify the analysis of existing and designing the new alloys. In this paper, we use results of the heat resistance simulation by an artificial neural network, as well as, experimental data for approximating the changes in the heat resistance vs isothermal exposures expressed in the complex Larson-Miller parameter by a sigmoidal function.

Topics
Artificial neural networks, Combustion engine, Materials properties, Transition metals, Alloys
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