Building texture evolution networks for deformation processing of polycrystalline fcc metals using spectral approaches: Applications to process design for targeted performance

Abstract Microstructure sensitive design (MSD) has thus far focused mainly on the identification of the set of microstructures that are theoretically predicted to exhibit a designer-specified combination of elastic–plastic properties. In this paper, we present the extension of the MSD methodology to process design solutions. The goal of process design is to identify a processing route to transform a given initial microstructure into a different microstructure that exhibits superior property combinations by using an arbitrary sequence of available deformation processing options (hereafter referred to as hybrid processing routes). In this paper, we have focused on orientation distribution function (i.e. the 1-point statistics of crystallographic texture in the sample) as the descriptor of microstructure, and considered only the low temperature deformation processes. We have also restricted our attention to Taylor-type crystal plasticity models. With these idealizations, it is shown that it is possible to develop efficient algorithms in the MSD framework to build texture evolution networks that cover most of the texture hull. The advantages of this approach are expounded upon in this paper with selected case studies.

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