A simplified intragranular description of dislocation density heterogeneities to improve dynamically recrystallized grain size predictions

Flipon, Baptiste; Bozzolo, Nathalie; Bernacki, Marc

doi:10.1016/j.mtla.2022.101585

Cited by 5 publications

(1 citation statement)

References 45 publications

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“…A single-phase austenitic stainless steel (316L) was selected for this study. The identification procedure of the reduced mobility product (M GB γ GB ) presented in this work required a minimal campaign of nine thermal treatments [16]. Fifteen are provided here and their conditions are detailed in Table 1.…”

Section: Experimental Datamentioning

confidence: 99%

Comparison of Grain-Growth Mean-Field Models Regarding Predicted Grain Size Distributions

Roth,

Flipon,

Bozzolo

et al. 2023

Materials

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Mean-field models have the ability to predict the evolution of grain size distribution that occurs through thermomechanical solicitations. This article focuses on a comparison of mean-field models under grain-growth conditions. Different microstructure representations are considered and discussed, especially regarding the consideration of topology in the neighborhood construction. Experimental data obtained with a heat treatment campaign on 316L austenitic stainless steel are used for the identification of material parameters and as a reference for model comparisons. Mean-field models are also applied to both mono- and bimodal initial grain size distributions to investigate the potential benefits of introducing neighborhood topology in microstructure prediction models. This article demonstrates that improvements in the predictions can be obtained in monomodal cases for topological models. In the bimodal test, no comparison with experimental data was performed as no data were available. But relative comparisons between models indicated few differences in the predictions. Although of interest, the consideration of neighborhood topology in grain-growth mean-field models generally results in only small improvements compared to classical mean-field models, especially in terms of implementation complexity.

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Section: Experimental Datamentioning

confidence: 99%