Integrated Computational Alloy Design of Nickel-Base Superalloys

Abstract

The nickel-base superalloys are unsurpassed for hot sections of the gas turbine engines whose efficiency can be improved by developing new superalloys with enhanced properties. The artificial neural networks (ANN) method is utilized in alloy design of single-crystal nickel-base superalloys. The ANN method is especially powerful in applications where developing a physically sound model is challenging such as alloy design with multiple components. A design space of 45,000 alloy compositions is generated, and then a multilayer perceptron (MLP) feed-forward (FF) artificial neural network is used to predict the density and creep lives of these alloys. The ANN method is coupled with physics-based (PHACOMP) calculations to predict and then correlate the creep rupture life to the composition, precipitate volume fraction, topologically closed packed phases, temperature, and stress. Then, an experimental alloy composition is selected from the design space, and its solidification behavior is investigated by a thermodynamics (CALPHAD)-based software. Moreover, single-crystal rods of the experimental alloy are grown and machined into creep samples, which are creep tested at three different stress–temperature couples. A good match between the experimental results and the ANN predictions is displayed in scatter plots and in Larson–Miller plots for the experimental alloy and for selected, well-known commercial single-crystal alloys. As the correlation of the microstructure to mechanical properties is still in its infancy by thermodynamics and mechanics-based software, an integrated ANN modeling is shown to be a powerful tool for finding a composition and establishing relationships between the microstructure and properties of alloys. This, of course, can help reduce the material development cycle time as aimed by the Integrated Computational Material Engineering (ICME).