A pragmatic dataset augmentation approach for transformation temperature prediction in steels

Hüter, Claas; Yin, Xu-Cheng; Vo, Trung Hung; Braun, S.

doi:10.1016/j.commatsci.2019.109488

Cited by 6 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We find that the different approximation schemes are indeed suitable to predict the bainite start temperature as function of composition. Depending on the necessary computational effort, we can reach a mean absolute error of about 14 or 4 Kelvin for the different approaches, which is in the same confidence range as the experimental measurements [ 110 ]. The trained artificial neural networks are implemented as a standalone tool as part of the bainite toolbox.…”

Section: Resultsmentioning

confidence: 99%

“…In total, 48 austenite grains were used for the analysis of the orientation relationship. Different orientation relationships are defined as follows [44]: Greninger-Trojano (G-T): [110] γ is 2.5°from [111] α ; Kurdjumov-Sachs (K-S): [110] γ [111] α ; Nishiyama-Wassermann (N-W): [211] γ [011] α and for all (111) γ (011) α applies. In Figure 6, it is clearly shown that the Greninger-Trojano orientation relationship fits better with the mean values of the experimental data than the Nishiyama-Wassermann and Kurdjumov-Sachs orientation relationships do, similarly to previous studies [45][46][47].…”

Section: Ebsd Analysismentioning

confidence: 99%

See 1 more Smart Citation

Modeling Bainitic Transformations during Press Hardening

et al. 2021

View full text Add to dashboard Cite

We revisit recent findings on experimental and modeling investigations of bainitic transformations under the influence of external stresses and pre-strain during the press hardening process. Experimentally, the transformation kinetics in 22MnB5 under various tensile stresses are studied both on the macroscopic and microstructural level. In the bainitic microstructure, the variant selection effect is analyzed with an optimized prior-austenite grain reconstruction technique. The resulting observations are expressed phenomenologically using a autocatalytic transformation model, which serves for further scale bridging descriptions of the underlying thermo-chemo-mechanical coupling processes during the bainitic transformation. Using analyses of orientation relationships, thermodynamically consistent and nondiagonal phase field models are developed, which are supported by ab initio generated mechanical parameters. Applications are related to the microstructure evolution on the sheaf, subunit, precipitate and grain boundary level.

show abstract

Section: Resultsmentioning

confidence: 99%