Abstract
In order to predict hot deformation behavior of superalloy nimonic 80A, a back-propagational artificial neural network (BP-ANN) and strain-dependent Arrhenius-type model were established based on the experimental data from isothermal compression tests on a Gleeble-3500 thermo-mechanical simulator at temperatures ranging of 1050–1250 °C, strain rates ranging of 0.01–10.0 s−1. A comparison on a BP-ANN model and modified Arrhenius-type constitutive equation has been implemented in terms of statistical parameters, involving mean value of relative (μ), standard deviation (w), correlation coefficient (R) and average absolute relative error (AARE). The μ -value and w -value of the improved Arrhenius-type model are 3.0012% and 2.0533%, respectively, while their values of the BP-ANN model are 0.0714% and 0.2564%, respectively. Meanwhile, the R-value and ARRE-value for the improved Arrhenius-type model are 0.9899 and 3.06%, while their values for the BP-ANN model are 0.9998 and 1.20%. The results indicate that the BP-ANN model can accurately track the experimental data and show a good generalization capability to predict complex flow behavior. Then, a 3D continuous interaction space for temperature, strain rate, strain and stress was constructed based on the expanded data predicted by a well-trained BP-ANN model. The developed 3D continuous space for hot working parameters articulates the intrinsic relationships of superalloy nimonic 80A.
Highlights
Nimonic 80A, as a nickel-based superalloy, has been widely used in jet engines for aircraft, gas turbines for power plant and marine diesel engines because of its high creep strength, superior oxidation resistance and strong resistance to corrosions at high temperature [1,2,3]
The results indicate that the back-propagational artificial neural network (BP-artificial neural network (ANN)) model can accurately track the experimental data and show a good generalization capability to predict complex flow behavior
Flow stress increases with the increasing of the strain while forevolution a fixed temperature, thethe true strain‐stress can be summarized in three distinct stages rate of the stress with which isstrain owing to an increase of the dislocation multiplication rate and dislocation
Summary
Nimonic 80A, as a nickel-based superalloy, has been widely used in jet engines for aircraft, gas turbines for power plant and marine diesel engines because of its high creep strength, superior oxidation resistance and strong resistance to corrosions at high temperature [1,2,3]. The modified Arrhenius-type equation was precise for describing the elevated temperature flow stress of Aermet100 steel [10], Ti60 titanium alloy [11], Al–Zn–Mg–Er–Zr alloy [12], etc Such constitutive equations are typically only applicable to the limited materials with specific conditions due to the poor adaptability for the new experimental data. Haghdadi et al developed a feed-forward back propagation ANN with single hidden layer to predict the flow behavior of an A356 aluminum alloy [15] Several such works reveal that the predicted results are well consistent with experimental results; the neural network is an effective tool to predict the hot deformation behavior of non-linear characteristic materials. As described previously, a 3D continuous interaction space within the temperature range of 950–1250 ̋ C, strain rate range of 0.01–10 s1 , and strain range of 0.1–0.9 was constructed
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