Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network.

Autor:	Tarasov, Dmitry A., Milder, Oleg B., Tiagunov, Andrei G.
Předmět:	DEEP learning NICKEL alloys ARTIFICIAL neural networks ALLOYS CHEMICAL decomposition MECHANICAL alloying CHEMICAL resistance
Zdroj:	Mathematical Methods in the Applied Sciences; Oct2022, Vol. 45 Issue 15, p8809-8818, 10p
Abstrakt:	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]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Plný text