Computational modeling of the nonlinearity parameter β in steel alloys
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Fuchs, Brian Matthew
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Abstract
Nonlinear ultrasound (NLU) inspection techniques have been shown to be effective in detecting second-phase precipitation in steel alloys. These nondestructive techniques could provide valuable information about the state of a microstructure prior to the development of secondary macroscale defects that stem from the development of nano- or microscale defects. However, predictive models are needed to quantitatively link measurements of nonlinearity parameters to changes in the microstructure. One such example is the development of grain boundary carbides in austenitic stainless steels associated with an increase in measured nonlinearity.
The aim of this research is to develop a model that quantitatively links the growth of grain boundary precipitates in steel to the evolution of the classical nonlinearity parameter β. The model is developed for the growth of M23C¬6 carbides in austenitic stainless steel, linking the development of misfit dislocations at the semicoherent phase-boundary interface to growth in β. The predictions generated through precipitate growth modeling in conjunction with physics-based acoustic models are confirmed by microstructural characterizations and experimental measurements of nonlinearity in 304L and 316L stainless steels. This overall approach is then demonstrated in Fe-1%Cu alloys and 9Cr-1Mo martensitic stainless steel alloys, and the misfit dislocation model is assessed for each alloy. This work results in the generation of a model to link grain boundary precipitation to nonlinearity in stainless steels and provides an approach towards future model generation and characterization.
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2022-04-20
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Dissertation