Phase Transformation Kinetics in Medium-Mn Third-Generation Advanced High Strength Steels
Notice bibliographique
Résumé
Medium-Mn third-generation advanced high strength steels (3G AHSS) are promising candidates for vehicle lightweighting due to their desirable combination of specific strength and ductility. However, the mechanical properties of medium-Mn (med-Mn) steels are strongly influenced by the amount and stability of any retained austenite present. It is, therefore, crucial to understand the effect of the initial microstructure and heat treatment on the amount of retained austenite. In the current study, the effects of starting microstructure and intercritical annealing temperature (IAT) on the phase transformation kinetics in a prototype Fe-0.15C-5.56Mn-1.1Si-1.89Al med-Mn third-generation advanced high strength steel (3G AHSS) were determined. The growth kinetics of intercritical austenite were simulated using DICTRA for both cold-rolled (CR) and martensite–ferrite (MF) starting microstructure during intercritical annealing. In the CR microstructure, austenite nucleates at ferrite grain boundaries apart from cementite, while in the MF microstructure, it directly grows from pre-existing inter-lath retained austenite (RA). Accelerated austenite reversion kinetics were observed for the MF microstructure due to the direct growth of intercritical austenite from pre-existing inter-lath RA films and rapid partitioning of C from the martensitic matrix. The difference in growth kinetics is more significant at 665 °C IAT due to the presence of cementite. The slow dissolution of cementite effectively reduces the growth rate of austenite. To further develop and validate the modeling approach, this study conducted in situ measurements of austenite transformation kinetics using in situ High-Energy X-ray Diffraction (HEXRD) and dilatometry. Additionally, ex situ microstructural analysis was performed using Atom Probe Tomography (APT) to characterize elemental partitioning, and Electron Backscatter Diffraction (EBSD) was used to determine morphology and phase distribution in intercritically annealed samples at room temperature. The HEXRD experiments, combined with microstructural characterization, verified the austenite growth model. The overall kinetics at 665 ℃ and 710 ℃ were determined through HEXRD and dilatometry, aligning well with DICTRA results. These kinetics can be effectively modeled using the simple diffusion model outlined in DICTRA. The partitioning of substitutional alloying elements at the interface, as measured by APT, aligns with thermodynamic predictions, with the exception of Si. While DICTRA predicted Si enrichment in austenite, experimental data revealed a flat Si profile between ferrite and austenite. A comparison between intercritical austenite and RA based on HEXRD results revealed that ~1% of austenite transformed into martensite during quenching in the MF, compared to ~5% in the CR sample. These results support the argument that the lath-shaped RA in the MF sample is more chemically stable. A set of process maps were developed for a prototype med-Mn steel with the generic composition of 0.2C-xMn-1.0Si-1.5Al wt.% (x = 4 – 12 wt.%). These maps predict the intercritical austenite vol.% formed during IA, as a function of the intercritical annealing parameters and starting microstructure employing a combination of microstructural analysis and DICTRA-based modeling. The maps cover an intercritical annealing temperature (IAT) range of 600 °C to 740 °C and an IA holding time of 120 s, suitable for industrial continuous galvanizing lines. The DICTRA-based model, in conjunction with the modified Koistinen–Marburger model was employed to enhance the estimation of RA vol.% in med-Mn steels. In situ HEXRD experiments, along with complementary microstructural characterization verified the intercritical austenite growth model during IA. However, the calculated results represent the austenite vol.% before cooling to room temperature, where a portion is expected to transform into martensite. To address this limitation, RA maps have been computed using the Koistinen–Marburger model considering both starting microstructures. To validate this method, the experimentally determined RA fractions obtained through XRD for various prototype med-Mn steels were considered. The values predicted by the model closely correspond with the experimentally measured RA vol.% reported in the literature. A comparison between process maps of both starting microstructures (M and TM) also highlighted the role of martensite starting microstructure in enhancing the mechanical properties of med-Mn steels. This enhancement is attributed to its ability to promote a higher vol.% of intercritical austenite during IA and results in a higher RA fraction after cooling to room temperature.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».