Abstract
This paper demonstrates the application of a neural network model to an alloy design process for a Ni-base polycrystalline superalloy. The network model used had the ability to predict tensile properties for polycrystalline superalloys as a function of temperature and chemical composition. Given set mechanical property targets and compositional limits it is possible to use the model to narrow down the choice of potential composition to those combinations which achieve or exceed the set levels. This process can dramatically reduce the number of melts that would need to be produced in an experimental process. Further considerations such as TCP formation or cost can be combined with this method, to further contract the number of alloy compositions requiring production for evaluation purposes. Through such an approach a more cost-effective and rapid alloy development route has been demonstrated.
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