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Acta Metall Sin  2020, Vol. 56 Issue (4): 459-475    DOI: 10.11900/0412.1961.2019.00399
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Review: Relations Between Metastable Austenite and Fatigue Behavior of Steels
XU Wei(),HUANG Minghao,WANG Jinliang,SHEN Chunguang,ZHANG Tianyu,WANG Chenchong
State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China
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Abstract  

With the deepening and improvement of the research on the conventional mechanical properties of metallic materials, the long-term service properties, such as fatigue and creep, showed more and more critical influence on the development of metallic materials. As one of the most important engineering structural materials, in order to clarify the fatigue failure mechanism, the research of steels on the relationship between microstructure and fatigue properties has been a hot and difficult problem for a long time. With the rapid development of smelting technology for steels, the research on the influencing factors of fatigue gradually changes from inclusions to microstructures as metastable austenite, precipitates, etc. Therefore, in order to further analyze the feasible direction of the research on the influence of microstructure on fatigue, this paper summarizes the influence and mechanism of metastable austenite on the fatigue property of advanced steel materials. The influence mechanism of metastable austenite on fatigue property by relevant scholars under different service conditions such as low cycle fatigue and high cycle fatigue was reviewed. Based on the experimental results, the relationship between metastable austenite and fatigue properties was quantitatively evaluated by machine learning. The quantitative relationship between the content/stability of metastable austenite and fatigue life was established, which could provide the basis direction for the further study of the mechanism of fatigue for steels.

Key words:  advanced steel      metastable austenite      fatigue property      failure mechanism      machine learning     
Received:  23 November 2019     
ZTFLH:  TG111.8  
Fund: National Natural Science Fund for Excellent Young Scholars(51722101);National Key Research and Development Program of China(2017YFB0703001);Newton Advanced Fellowship(51961130389)
Corresponding Authors:  Wei XU     E-mail:  xuwei@ral.neu.edu.cn

Cite this article: 

XU Wei,HUANG Minghao,WANG Jinliang,SHEN Chunguang,ZHANG Tianyu,WANG Chenchong. Review: Relations Between Metastable Austenite and Fatigue Behavior of Steels. Acta Metall Sin, 2020, 56(4): 459-475.

URL: 

https://www.ams.org.cn/EN/10.11900/0412.1961.2019.00399     OR     https://www.ams.org.cn/EN/Y2020/V56/I4/459

Fig.1  Fatigue life is enhanced by introducing the metastable austenite structure (TRIP—transformation induced plasticity steel, DP—dual phase steel, F-B-M (R)—steel contains ferrite bainite martensite and retained austenite, T-M (F)—steel consists of tempered martensite)[41,42] (a) and the introduction of the metastable austenite structure reduces the fatigue life[43] (b)
Fig.2  Evolutions of the stress amplitude with the number of cycles (N) under different strain amplitudes for TRIP590 (a) and DP590 (b) steels[44]
Fig.3  Stress amplitudes of samples with different austenite morphologies[50](a) evolution of stress amplitude with the number of cycles (Δε/2—total strain amplitude)(b, c) TEM micrographs of the samples under 320 ℃ (b) and 395 ℃ (c) isothermal quenching in the tested steel (αb is the bainitic ferrite; γb is the blocky retained austenite; γf is the film-like retained austenite)
Fig.4  Stress amplitude with the number of cycles under different strain amplitudes (εa,p) (a, c) and development of the martensite fraction (ξ) (b, d) obtained from Ref.[53]
Fig.5  Evolutions of the stress amplitude with the number of cycles[54,55]
Fig.6  Correlations between the austenite content and fatigue strength (B—bainite, RA—retained austenite, M—martensite, F—ferrite)[4,5,8,56,57]
Fig.7  Correlations between the metastable austenite stability and fatigue strength[47,48,59,65,66]Color online
Fig.8  S-N curves for 304L (90 Hz) and 316L (150 Hz and 20 kHz) (The Md30 temperature where 50% α'-martensite formed after 30% tensile deformation)[67]Color online
Fatigue featureLow cycle fatigueHigh cycle fatigue
of austenite

Volume fraction of austenite

Positive correlation[29,33,40,42,44]

(1) austenite has advantages on plasticity[42,44];

(2) the compressive stress and shear strain produced by martensitic transformation can reduce the plastic strain[33];

(3) energy absorption during TRIP process[40,44];

(4) crack closure caused by TRIP effect[40,44];

(5) resistance of stress softening during cyclic loading[29,42,44];

(6) the crack tip passivated by martensitic transformation[42]

Negative correlation[45,46,48]

(1) martensite transformation is easy to be used as the source of crack initiation during the TRIP process[45];

(2) martensite formed by TRIP effect is easy to be used as the path of crack growth[48];

(3) remarkable cyclic hardening caused by martensitic transformation[46]

Inconclusive[47]

There is a competitive relationship between the effect of inhibiting crack growth and inducing crack initiation

Positive correlation[46,54,56,57,58,59,60,61]

(1) austenite has more slip systems, which can slow down dislocation entanglement and reduce local stress concentration, thus delaying the crack initiation[46,56,57,58,59,60];

(2) DARA effect[61];

(3) the existence of austenite would resist the dislocation moving[46];

(4) energy absorption during TRIP process[54,57];

(5) strengthening by TRIP effect[58];

(6) the higher amount of retained austenite brings more obstacles for fatigue crack growth[56,57];

(7) crack closure caused by volume expansion during the DIMT process[46,56]

Negative correlation[48,62,63]

Showed negative correlation in TRIP steel and martensitic precipitation hardening stainless steel, but lack of theoretical explanation

Stability of austenite

Positive correlation[47,49]

(1) the film-like retained austenite is beneficial to prevent crack growth[49];

(2) it can avoid the cracks caused by the stress-strain mismatch between the austenite and matrix due to its high hardness[49];

(3) the film-like austenite can also bring more RICC effect[49], and prevent the crack initiation caused by elastic mismatch between the new formed and previous martensite[47];

(4) the unstable austenite exhibits significant cycle hardening during cycle loading, which is not conducive to the stability of cycle stress[49]

Negative correlation[54]

The block retained austenite performs good compatibility deformation ability

Positive correlation[43,49,59,60,65]

(1) the highly stable austenite transformed to martensite after crack initiation which is benefit to fatigue properties[65];

(2) film-like austenite brings more RICC effect[49];

(3) production of film-like austenite would refine the microstructure[60];

(4) the calculated results of FGA show that the blocky-like austenite plays negative role on crack initiation[59];

(5) the large-size austenite is easy to transform into brittle martensite under elastic deformation, which is unfavorable to fatigue stress[43]

Negative correlation[67]

The unstable austenite performs great compatible deformation ability and plasticity

Table 1  Summary of austenite characteristics on fatigue properties[29,33,40,42,43,44,45,46,47,48,49,54,56,57,58,59,60,61,62,63,65,67]
Fig.9  Relationships between ρXY of low cycle fatigue life and γSF and Md30 of austenite under different empirical equations distinguished by subscripts a~d (ρXY—Pearson correlation coefficient, γSF—stacking fault energy, Md30—temperature of 50% austenite transformed to martensite under 30% strain)
Fig.10  Relationships between fatigue strength and volume fraction of austenite[4,5,28,57,59,60,66,83,84,85,86,87,88,89,90,91,92,93,94,95,96]
Fig.11  Mean values of correlation coefficient (R2) between the volume fraction of austenite (fv), mass fraction of C consumed by austenite (fm) and tensile properties (YS—yield strength, TS—tensile strength, EL—elongation) and fatigue strength calculated by support vector machine (SVM) (a) and back-propagation neural network (BPNN) (b) with training test random 100 times
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