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.