Application of damping mechanism model and stacking fault probability in Fe–Mn alloy

Huang, Shuke; Wen, Yuhua; Li, N.; Teng, Jie; Ding, Sanbo; Xu, Yu

doi:10.1016/j.matchar.2007.05.023

Cited by 35 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[13] There is an initial increase in e-martensite associated with the cfie Stage I transformation occurring at low strains (£ 4 pct) and once the system is saturated with e-martensite and all of the c-austenite is consumed, Stage II efia, transformation occurs to failure as shown in Figure 9. The strain levels associated with the transition of Stage I to Stage II are consistent with the works described by both Shin et al [52] and Huang et al [53] from their works on ferrous shape memory alloys that contain e-martensite. It was shown that beyond 4 pct strain the deformation was unrecoverable due to the formation of a-martensite from the intersection of e-martensite bands.…”

Section: Discussionsupporting

confidence: 89%

Dynamic Strain Aging Phenomena and Tensile Response of Medium-Mn TRIP Steel

Field

Aken

2018

Metall Mater Trans A

View full text Add to dashboard Cite

Dynamic strain aging (DSA) and rapid work hardening are typical behaviors observed in medium-Mn transformation-induced plasticity (TRIP) steel. Three alloys with manganese ranging from 10.2 to 13.8 wt pct with calculated room temperature stacking fault energies varying from À 2.1 to 0.7 mJ/m 2 were investigated. Significant serrations were observed in the stress-strain behavior for two of the steels and the addition of 4.6 wt pct chromium was effective in significantly reducing the occurrence of DSA. Addition of chromium to the alloy reduced DSA by precipitation of M 23 (C,N) 6 during batch annealing at 873 K (600°C) for 20 hours. Three distinct DSA mechanisms were identified: one related to manganese ordering in stacking faults associated with e-martensite and austenite interface, with activation energies for the onset and termination of DSA being 145 and 277 kJ/mol. A second mechanism was associated with carbon diffusion in c-austenite where Mn-C bonding added to the total binding energy, and activation energies of 88 and 155 kJ/mol were measured for the onset and termination of DSA. A third mechanism was attributed to dislocation pinning and unpinning by nitrogen in a-ferrite with activation energies of 64 and 123 kJ/mol being identified. Tensile behaviors of the three medium manganese steels were studied in both the hot band and batch annealed after cold working conditions. Ultimate tensile strengths ranged from 1310 to 1404 MPa with total elongation of 24.1 to 34.1 pct. X-ray diffraction (XRD) was used to determine the transformation response of the steels using interrupted tensile tests at room temperature. All three of the processed steels showed evidence of two-stage TRIP where c-austenite first transformed to e-martensite, and subsequently transformed to a-martensite.

show abstract

Section: Discussionsupporting

confidence: 89%

Dynamic Strain Aging Phenomena and Tensile Response of Medium-Mn TRIP Steel

Field

Aken

2018

Metall Mater Trans A

View full text Add to dashboard Cite

show abstract

“…Meantime, the internal friction Q À 1 at high strain amplitude (1 Â 10 À 4 -6 Â 10 À 4 ) was measured using JN-1 reversal torsion pendulum with free decay method [4,14], whose testing temperature was from RT to 300 1C and the frequency was about 1 Hz. The diameter and gauge length of testing specimens were 1 mm and 100 mm.…”

Section: Methodsmentioning

confidence: 99%

“…phase transformation. Many researches have studied this phase transformation because of its shape memory effect [1,2] and high damping capacity [3,4]. As a method, internal friction has been used to study the g2e phase transformation from 1950s [5], but the high damping capacity (internal friction) of Fe-Mn based alloys was just firstly reported by Korea's researchers in 1990s [6].…”

Section: Introductionmentioning

confidence: 99%

Internal friction mechanism of Fe–19Mn alloy at low and high strain amplitude

Huang

Liu

et al. 2013

Materials Science and Engineering: A

View full text Add to dashboard Cite

“…These processes may induce large deformations, causing atomic rearrangement; which generates new phases, modifying magnetic susceptibility and intergranular corrosion [2]- [10]. In the last 60 years, several works have been carried out with different techniques to determinate the SFE, among them are characterization by transmission electron microscopy (TEM) [1], [11]- [17]; X-ray diffraction (XRD) [18][19][20][21]; neutron diffraction [16], [22]. In the other hand, it has been developed expressions by linear multivariables analysis of experimental data [1], [12], [14], [23]- [26], and also computational thermodynamics and quantum mechanics first-principles simulations [1], [17], [27]- [31].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the stacking fault energy in austenitic stainless steels using an artificial neural network

Román¹,

Campillo²,

Sarmiento³

et al. 2019

IJETR

View full text Add to dashboard Cite

Stacking fault energy (SFE) is an important parameter to be considered in the design of austenitic stainless steels (SS) due to its influence on magnetic susceptibility, atomic order changes and intergranular corrosion resistance. An extensive review of specialized literature was examined in order to understand the different methods that have been developed for the calculation of SFE. Characterization by transmission electron microscopy (TEM), linear expressions from data processing and first-principles quantum mechanics approximations are some techniques that have been used for this objective. In the present work a feed forward backpropagation artificial neural network (ANN) was developed to predict the SFE within given specific ranges of chemical compositions of austenitic SS. The experimental data were extracted and analyzed from a research work reported by Yonezawa et al [1], for three different heat treatment conditions. The model predicts SFE values with a correlation coefficient of 0.99, which reduce the error when is compared with other works in the literature. Index Terms-Neural network, Stacking fault energy, Austenitic stainless steel.

show abstract

Application of damping mechanism model and stacking fault probability in Fe–Mn alloy

Cited by 35 publications

References 17 publications

Dynamic Strain Aging Phenomena and Tensile Response of Medium-Mn TRIP Steel

Dynamic Strain Aging Phenomena and Tensile Response of Medium-Mn TRIP Steel

Internal friction mechanism of Fe–19Mn alloy at low and high strain amplitude

Prediction of the stacking fault energy in austenitic stainless steels using an artificial neural network

Contact Info

Product

Resources

About