Prediction of the hot flow behavior of AISI 321 stainless steel during dynamic recrystallization using sine hyperbolic constitutive equation and artificial neural network
Restricted accessResearch articleFirst published online April, 2026
Prediction of the hot flow behavior of AISI 321 stainless steel during dynamic recrystallization using sine hyperbolic constitutive equation and artificial neural network
The hot compression deformation behavior of AISI 321 austenitic stainless steel were studied under the strain rate and temperature ranges of 0.001–1 s−1 and 950–1200°C. The single peak hot flow curves indicated the occurrence of dynamic recrystallization in AISI 321 steel at applied deformation conditions. The microstructural observations showing the development of equaxed austenite grains further approved the results of flow curve analysis. The average size of austenite grains, when deformation is carried out at strain rate of 1 s−1, increases from 8 μm at 950°C to 61 μm at deformation temperature of 1200°C. After microstructure analysis, the flow behavior of AISI 321 steel was modeled using sine-hyperbolic constitutive equation and feed-forward back propagation artificial neural network. The mean absolute error related to the predicted flow stress values at different strains with the sine-hyperbolic constitutive equation is more than 2, and this value is less than unity for the neural network method. The obtained results efficiently indicate that the ANN model is more accurate in predicting the flow behavior of AISI 321 austenitic stainless steel when the dynamic recrystallization is the prevailing softening mechanism.
BaddooN. Stainless steel in construction: a review of research, applications, challenges and opportunities. J Constr Steel Res2008; 64: 1199–1206.
2.
SumitaMHanawaTTeohS. Development of nitrogen-containing nickel-free austenitic stainless steels for metallic biomaterials. Mater Sci Eng: C2004; 24: 753–760.
3.
HariharanKBalachandranGPrasadMS. Application of cost-effective stainless steel for automotive components. Mater Manuf Process2009; 24: 1442–1452.
4.
EskandariMNajafizadehAKermanpurA, et al.Potential application of nanocrystalline 301 austenitic stainless steel in lightweight vehicle structures. Mater Des2009; 30: 3869–3872.
5.
EnnisPCzyrska-FilemonowiczA. Recent advances in creep-resistant steels for power plant applications. Sadhana2003; 28: 709–730.
6.
Allan GrahamCGrahamRGReadRH. Metallurgical developments in the continuous casting of austenitic stainless steel slabs. Can Metall Q1968; 7: 255–266.
7.
ChoS-HYooY-C. Hot rolling simulations of austenitic stainless steel. J Mater Sci2001; 36: 4267–4272.
8.
RaabeDDegenhardtRSeligerR, et al.Advances in the optimization of thin strip cast austenitic 304 stainless steel. Steel Res Int2008; 79: 440–444.
9.
GuptaAKAnirudhVSinghSK. Constitutive models to predict flow stress in austenitic stainless steel 316 at elevated temperatures. Mater Des2013; 43: 410–418.
10.
MandalSSivaprasadPVenugopalS, et al.Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion. Appl Soft Comput2009; 9: 237–244.
11.
LinYChenX-M. A critical review of experimental results and constitutive descriptions for metals and alloys in hot working. Mater Des2011; 32: 1733–1759.
12.
MirzadehHCabreraJMNajafizadehA. Modeling and prediction of hot deformation flow curves. Metall Mater Trans A2012; 43: 108–123.
13.
SamantarayDMandalSBhaduriAK, et al.Analysis and mathematical modelling of elevated temperature flow behaviour of austenitic stainless steels. Mater Sci Eng A2011; 528: 1937–1943.
14.
LinYCLiQFXiaYC, et al.A phenomenological constitutive model for high temperature flow stress prediction of Al–Cu–Mg alloy. Mater Sci Eng A2012; 534: 654–662.
15.
HeAXieGYangX, et al.A physically-based constitutive model for a nitrogen alloyed ultralow carbon stainless steel. Comput Mater Sci2015; 98: 64–69.
16.
LiHYLiYHWangXF, et al.A comparative study on modified Johnson Cook, modified Zerilli–Armstrong and Arrhenius-type constitutive models to predict the hot deformation behavior in 28CrMnMoV steel. Mater Des2013; 49: 493–501.
17.
GhazaniMSEghbaliB. Modeling the flow behavior of AISI 321 austenitic stainless steel using a simple combined phenomenological method. Mech Mater2019; 137: 103108.
18.
GangCWeiCLiM, et al.Strain-compensated Arrhenius-type constitutive model for flow behavior of Al-12Zn-2.4 Mg-1.2 Cu alloy. Rare Metal Mater Eng2015; 44: 2120–2125.
19.
WangLLiuFChengJ, et al.Arrhenius-type constitutive model for high temperature flow stress in a Nickel-based corrosion-resistant alloy. J Mater Eng Perf2016; 25: 1394–1406.
20.
ZhuF-HXiongWLiX-F, et al.A new flow stress model based on Arrhenius equation to track hardening and softening behaviors of Ti6Al4V alloy. Rare Metals2018; 37: 1035–1045.
21.
GuptaAKSinghSKReddyS, et al.Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network. Mater Des2012; 35: 589–595.
22.
LiuJChangHHsuT, et al.Prediction of the flow stress of high-speed steel during hot deformation using a BP artificial neural network. J Mater Process Technol2000; 103: 200–205.
23.
ZhuYZengWSunY, et al.Artificial neural network approach to predict the flow stress in the isothermal compression of as-cast TC21 titanium alloy. Comput Mater Sci2011; 50: 1785–1790.
24.
HanYQiaoGSunJP, et al.A comparative study on constitutive relationship of as-cast 904L austenitic stainless steel during hot deformation based on Arrhenius-type and artificial neural network models. Comput Mater Sci2013; 67: 93–103.
25.
LiuJChangHWuR, et al.Investigation on hot deformation behavior of AISI T1 high-speed steel. Mater Charact2000; 45: 175–186.
26.
ZenerCHollomonJH. Effect of strain rate upon plastic flow of steel. J Appl Phys1944; 15: 22–32.
27.
PengWZengWWangQ, et al.Comparative study on constitutive relationship of as-cast Ti60 titanium alloy during hot deformation based on Arrhenius-type and artificial neural network models. Mater Des2013; 51: 95–104.
28.
HanYQiaoGSunY, et al.Modeling the constitutive relationship of Cr20Ni25Mo4Cu superaustenitic stainless steel during elevated temperature. Mater Sci Eng: A2012; 539: 61–67.
29.
YangL-CPanY-TChenI-G, et al.Constitutive relationship modeling and characterization of flow behavior under hot working for Fe–Cr–Ni–W–Cu–Co super-austenitic stainless steel. Metals2015; 5: 1717–1731.
30.
MirzaeiAZarei-HanzakiAHaghdadiN, et al.Constitutive description of high temperature flow behavior of Sanicro-28 super-austenitic stainless steel. Mater Sci Eng: A2014; 589: 76–82.
31.
SaadatkiaSMirzadehHCabreraJ-M. Hot deformation behavior, dynamic recrystallization, and physically-based constitutive modeling of plain carbon steels. Mater Sci Eng: A2015; 636: 196–202.
32.
SaniSAEbrahimiGVafaeenezhadH, et al.Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network (ANN) model. J Magnes Alloy2018; 6: 134–144.
33.
ZhaoJDingHZhaoW, et al.Modelling of the hot deformation behaviour of a titanium alloy using constitutive equations and artificial neural network. Comput Mater Sci2014; 92: 47–56.
