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2 results on '"HIGH-TEMPERATURE TENSILE STRENGTHS"'

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

Author

Tarasov, D. A., Milder, O. B., Tiagunov, A. G., Tarasov, D. A., Milder, O. B., and Tiagunov, A. G.

Abstract

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. © 2021 John Wiley & Sons, Ltd.

Published
2022

2. Modeling the Influence of the Composition of Refractory Elements on the Heat Resistance of Nickel Alloys by a Deep Learning Artificial Neural Network

Author

Oleg Milder, Dmitry Tarasov, and Andrei G. Tiagunov

Subjects
Materials science, General Mathematics, Alloy, chemistry.chemical_element, COST ENGINEERING, NEURAL NETWORKS, engineering.material, 01 natural sciences, BASIC REFRACTORIES, HIGH TEMPERATURE STRENGTH, law.invention, Stress (mechanics), RUPTURE STRENGTH, HEAT RESISTANCE, MODELING, Flexural strength, ALLOY COMPOSITIONS, law, DEEP NEURAL NETWORKS, Ultimate tensile strength, ENGINEERING PROBLEMS, 0101 mathematics, Crystallization, HIGH STRENGTH ALLOYS, Mathematics, REFRACTORY ALLOYS, DEEP LEARNING, 010102 general mathematics, HIGH-TEMPERATURE TENSILE STRENGTHS, Metallurgy, General Engineering, Refractory metals, TENSILE STRENGTH, REFRACTORY ELEMENTS, 010101 applied mathematics, Nickel, chemistry, NICKEL ALLOYS, TEST TEMPERATURES, engineering, Deformation (engineering), ALLOYING ELEMENTS, CHEMICAL DEGRADATION
Abstract

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. © 2021 John Wiley & Sons, Ltd.

Published
2020
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