Modeling the Influence of the Composition of Refractory Elements on the
Heat Resistance of Nickel Alloys by a Deep Learning Artificial Neural
Network
- Dmitry TARASOV,
- Oleg Milder,
- Andrei Tiagunov
Oleg Milder
Ural Federal University named after the first President of Russia B N Yeltsin
Author ProfileAndrei Tiagunov
Ural Federal University named after the first President of Russia B N Yeltsin
Author ProfileAbstract
Nickel alloys are widely used in the production of gas turbine parts.
The alloys show resistance to mechanical and chemical degradation under
severe long-term stress and high temperatures. One of the major
mechanical properties of the alloys is the high-temperature rupture
strength, which is measured after a specimen is heated to a certain
temperature and held for a certain time considering deformation.
Determining the influence of certain elements on the 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 paper, we predict a high-temperature strength
based on the composition of refractory elements in alloys using a deep
learning artificial neural network. We build the model based on prior
knowledge of the composition of the alloys, information on the role of
alloying elements, type of crystallization, test temperature and time,
and the tensile strength. Successful simulation results show the
applicability of this method in practice.23 Dec 2020Submitted to Mathematical Methods in the Applied Sciences 24 Dec 2020Submission Checks Completed
24 Dec 2020Assigned to Editor
27 Dec 2020Reviewer(s) Assigned
14 Apr 2021Review(s) Completed, Editorial Evaluation Pending
14 Apr 2021Editorial Decision: Revise Major
26 Apr 20211st Revision Received
26 Apr 2021Submission Checks Completed
26 Apr 2021Assigned to Editor
26 Apr 2021Reviewer(s) Assigned
27 Apr 2021Review(s) Completed, Editorial Evaluation Pending
29 Apr 2021Editorial Decision: Accept
Oct 2022Published in Mathematical Methods in the Applied Sciences volume 45 issue 15 on pages 8809-8818. 10.1002/mma.7524