In-situ strain induced martensitic transformation measurement and consequences for the modeling of medium Mn stainless steels mechanical behavior

Janeiro, Ilusca; Hubert, Olivier; Schmitt, Jean-Hubert

doi:10.1016/j.ijplas.2022.103248

Cited by 12 publications

(6 citation statements)

References 54 publications

(96 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 9b shows that as the defect density increases, the martensite volume fraction distribution gradually tends to be inhomogeneous and localized. A more significant amount of the martensite phase appears in some grains than others, which is consistent with the macroscopic localization martensitic transformation reported in experimental studies [9,28,72].…”

Section: (C) Effects Of Irradiation On the Distribution Of Martensite...supporting

confidence: 90%

Model for deformation-induced martensitic transformation in irradiated materials

et al. 2023

View full text Add to dashboard Cite

Deformation-induced martensitic transformation (DIMT) is commonly reported to be easily activated in irradiated materials, but the existing transformation kinetics models of DIMT hardly capture the irradiation effect. In this work, we develop a transformation kinetics model considering the effect of irradiation hardening, shear band evolution and the role of irradiation-induced voids. A crystal plasticity framework incorporating the newly developed transformation kinetics model is established. We show that the framework is capable of capturing the accelerated evolution of martensite volume fraction in both cases with and without irradiation-induced voids. Moreover, the framework successfully simulates two post-irradiation phenomena observed in experiments, i.e. the localized distribution of martensite fraction and the evolution of the dominant transformation pathway. The present work is one of the first attempts at the theoretical modelling of DIMT in irradiated materials, and has the potential to benefit the application of irradiation in tailoring the martensitic transformation behaviour.

show abstract

Section: (C) Effects Of Irradiation On the Distribution Of Martensite...supporting

confidence: 90%

Model for deformation-induced martensitic transformation in irradiated materials

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Such a result was anticipated as the prolonged soaking causes an increase in the fraction of RA with lower stability, whose transformation promotes an enhanced strain hardening. [2] None of the samples exhibit discontinuous yielding due to the specific lath-like morphology. Both uniform elongation (UEl) and total elongation (TEl) (Figure 10) of the samples initially increase with increasing IA time demonstrating the highest UEl value of 19.5 ± 0.5 pct following a 15 min treatment and the highest TEl of 28.3 ± 0.6 pct following a 60 min treatment.…”

Section: Mechanical Propertiesmentioning

confidence: 98%

“…Figure 2(c) shows the evolution of the M s calculated using Eqs. [2][3][4] and using RA composition given in Figure 2(b). It is observed that, although the M s values differ depending on the formula used, the overall tendency is almost identical.…”

Section: A Formation and Chemical Stability Of Ramentioning

confidence: 99%

“…[1] Such strength-ductility combination may be obtained by strain-induced martensitic transformation (SIMT) of retained austenite, which enhances strain hardening. [2,3] The use of multiphase steels containing RA allows decreasing vehicle mass since complicated parts characterized by similar strength and stiffness may be produced from thinner but stronger metal sheets. Moreover, if some fraction of RA remains after the forming operations, it may be used for any subsequent event providing better energy absorption properties.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fracture Toughness Evaluation of Powder Metallurgical ASP2030 High-Speed Steels Using Flexural Specimens and Finite Element Method

Firouzi

Yazdani²,

Tavangar³

et al. 2022

Strength Mater

View full text Add to dashboard Cite

The development of superior mechanical properties in medium-Mn requires the optimization of microstructural parameters such as retained austenite (RA) stability, volume fraction, and morphology. The present work explores the possibility of using a continuous annealing approach instead of conventional batch annealing to perform an intercritical annealing (IA) treatment in a hot-rolled strip of an Al-alloyed 5Mn steel. Dilatometric studies were performed at a temperature of 680 ºC with soaking times ranging from 1 to 300 min to follow the microstructural changes as a function of time. The microstructures thus obtained were thoroughly characterized by means of X-ray diffraction, SEM and TEM, TEM-EDS microanalysis and EBSD phase and orientation maps. It was observed that with increasing soaking times, the volume fraction of retained austenite gradually increases, albeit at the cost of its stability. The comparison of martensite start temperatures (M s ) based on the chemical composition of austenite at 680 ºC with that experimentally obtained at higher process temperature revealed the effect of the grain size on the reduction of RA stability for longer process times. Accordingly, mechanical tests results showed that the yield stress, tensile strength and hardness decrease with an increase in the IA soaking time.

show abstract

“…In addition to these studies, Wang et al [20] modelled the relationship between cold deformation and the mechanical properties of austenitic AISI 316L, combining theoretical analysis, numerical simulation and experimental uniaxial tensile testing. Moreover, Janeiro et al [21] established a linear correlation between austenite deformation and martensite transformation for low-Ni ASS. The model predicts the stress-strain curve from the parameter Ms; this describes the initial temperature of the martensitic transformation and is related to the austenite stability.…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensor for Estimating the Strain-Hardening Rate of Austenitic Stainless Steels Using a Machine Learning Approach

Contreras-Fortes,

Rodríguez-García,

Sales

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This study introduces a Multiple Linear Regression (MLR) model that functions as a virtual sensor for estimating the strain-hardening rate of austenitic stainless steels, represented by the Hardening Rate of Hot rolled and annealed Stainless steel sheet (HRHS) parameter. The model correlates tensile strength (Rm) with cold thickness reduction and chemical composition, evidencing a robust linear relationship with an R-coefficient above 0.9800 for most samples. Key variables influencing the HRHS value include Cr, Mo, Si, Ni, and Nb, with the MLR model achieving a correlation coefficient of 0.9983. The Leave-One-Out Cross-Validation confirms the model’s generalization for test examples, consistently yielding high R-values and low mean squared errors. Additionally, a simplified HRHS version is proposed for instances where complete chemical analyses are not feasible, offering a practical alternative with minimal error increase. The research demonstrates the potential of linear regression as a virtual sensor linking cold strain hardening to chemical composition, providing a cost-effective tool for assessing strain hardening behaviour across various austenitic grades. The HRHS parameter significantly aids in the understanding and optimization of steel behaviour during cold forming, offering valuable insights for the design of new steel grades and processing conditions.

show abstract

In-situ strain induced martensitic transformation measurement and consequences for the modeling of medium Mn stainless steels mechanical behavior

Cited by 12 publications

References 54 publications

Model for deformation-induced martensitic transformation in irradiated materials

Model for deformation-induced martensitic transformation in irradiated materials

Fracture Toughness Evaluation of Powder Metallurgical ASP2030 High-Speed Steels Using Flexural Specimens and Finite Element Method

Virtual Sensor for Estimating the Strain-Hardening Rate of Austenitic Stainless Steels Using a Machine Learning Approach

Contact Info

Product

Resources

About