Ying Han

Investigation on hot deformation of 20Cr–25Ni superaustenitic stainless steel with starting columnar dendritic microstructure based on kinetic analysis and processing map

Abstract The deformation behaviour of a 20Cr–25Ni superaustenitic stainless steel (SASS) with initial microstructure of columnar dendrites was investigated using the hot compression method at temperatures of 1000–1200°C and strain rates of 0·01–10 s−1. It was found that the flow stress was strongly dependent on the applied temperature and strain rate. The constitutive equation relating to the flow stress, temperature and stain rate was proposed for hot deformation of this material, and the apparent activation energy of deformation was calculated to be 516·7 kJ mol−1. Based on the dynamic materials model and the Murty’s instability criterion, the variations of dissipation efficiency and instability factor with processing parameters were studied. The processing map, combined with the instability map and the dissipation map, was constructed to demonstrate the relationship between hot workability and microstructural evolution. The stability region for hot processing was inferred accurately from the map. The optimum hot working domains were identified in the respective ranges of the temperature and the strain rate of 1025–1120°C and 0·01–0·03 s−1 or 1140–1200°C and 0·08–1 s−1, where the material produced many more equiaxed recrystallised grains. Moreover, instability regimes that should be avoided in the actual working were also identified by the processing map. The corresponding instability was associated with localised flow, adiabatic shear band, microcracking and free surface cracks.

Phase Transformation and Its Effects on Mechanical Properties and Pitting Corrosion Resistance of 2205 Duplex Stainless Steel

The effects of phase transformation on mechanical properties and pitting corrosion of 2205 duplex stainless steel were investigated. The amount of σ phase in the test specimen varied up to a maximum of 6% by thermal treatment at 850 °C for up to 60 min. The results showed that σ phase markedly increased the hardness and decreased the impact toughness of the test steel. But the increasing tendency of the ultimate tensile strength and the yield strength was not obvious, while the total elongation abruptly decreased with the aging time from 5 to 60 min. SEM impact microfractograph analysis revealed that the types of impact fracture changed from ductile mode to transcrystalline mode when the specimens were aged for 5—60 min. Furthermore, the extent of pitting potential reducing was found to be strongly temperature dependent, more pronounced at the higher temperature. During the incubation period of σ phase nucleation, the pitting corrosion test temperature and the aging time had collaborative effects on evidently displacing the pitting potential towards less noble values. After 15 min, the higher temperature contributed more to decreasing the pitting potential than the aging time.

Corrosion Resistance and Semiconducting Properties of Passive Films Formed on 00Cr13Ni5Mo2 Supermartensitic Stainless Steel in Cl− Environment

The semiconducting properties of passive films grown on 00Cr13Ni5Mo2 supermartensitic stainless steel were investigated in comparison with conventional 2Cr13 martensitic stainless steel. Cyclic voltammetry and electrochemical impedance spectroscopy (EIS) were used for the studies. 00Cr13Ni5Mo2 steel exhibited a good corrosion resistance performance, attributing to its passive capability. The results of Mott-Schottky analysis demonstrated n-type semiconductors for the passive films with doping densities of about 1020–1021 cm−3, and the thickness of space-charge layers was also calculated. The experimental results confirmed that Mo plays an important role in improving the corrosion resistance of 00Cr13Ni5Mo2 steel due to its impact on the doping density.

On dynamic recrystallisation under hot working of superaustenitic stainless steel

Abstract Dynamic recrystallisation (DRX) behaviour in 904L superaustenitic stainless steel was studied by isothermal hot compression tests conducted in the temperature range of 900–1200°C and strain rate range of 0·001–10 s−1. The critical conditions for initiation of DRX are identified from the stress dependence of the derivative of strain hardening rate versus stress curves (dθ/dσ versus σ curves). The Z parameters combined with the temperature and strain rate effects during hot deformation are obtained by the regression analysis. Moreover, the kinetics model of DRX is established and the complete DRX grain size of 904L superaustenitic stainless steel is a power-law function of Z parameter with an exponent of –0·27.

Metadynamic Recrystallization Behavior of As-cast 904L Superaustenitic Stainless Steel

The metadynamic recrystallization (MDRX) behavior of as-cast 904L superaustenitic stainless steel was investigated by double pass isothermal compression tests at temperatures of 950 – 1150 °C, strain rates of 0. 05 — 5 s−1 and interval of 1 —100 s. The effects of working parameters (deformation temperature, strain rate, pre-strain and interval time) on the flow curves and microstructural evolution were discussed. The MDRX fraction increased obviously with the increase of deformation temperature, strain rate and interval time. The MDRX softening was controlled by the migration of grain boundary, annihilation of dislocation and dynamic recrystallization. Moreover, the kinetic model was established for the prediction of MDRX behavior of as-cast 904L superaustenitic stainless steel based on the experimental data. A good agreement between the predicted and the experimental values was achieved (correlation coefficient R2 =0. 98), indicating a satisfactory accuracy.

Artificial Neural Network to Predict the Hot Deformation Behavior of Super 13Cr Martensitic Stainless Steel

The hot deformation behavior of super 13Cr martensitic stainless steel was investigated using artificial neural network (ANN). Hot compression tests were carried out at the temperature range of 950°C to 1200°C and strain rate range of 0.1–50s–1 at an interval of an order of magnitude. Based on the limited experimental data, the ANN model for the constitutive relationship existed between flow stress and strain, strain rate and deformation temperature was developed by back-propagation (BP) neural network method. A three layer structured network with one hidden layer and ten hidden neurons was trained and the normalization method was employed in training for avoiding over fitting. Modeling results show that the developed ANN model can efficiently predict the flow stress of the steel and reflect the hot deformation behavior in the whole deforming process.

Sigma Phase Precipitation of Duplex Stainless Steel and its Effect on Corrosion Resistance

The present study concerns the influence of aging parameters on the microstructure and corrosion behavior of duplex stainless steel S31803 and S32750. It has been found that the microstructural evolutions were extremely sensitive to sigma phase precipitation during aging treatment, and sigma phase was enhanced with the increase of aging time from 2 min to 120min at its precipitation peak temperature 850 °C for S31803 and 920°C for S32750 steels respectively. The precipitation of sigma phase in S32750 is ahead of that in S31803 steel, within 10min, the sigma phase precipitation rate of S32750 is much faster than that of S31803 steel. The precipitation amount of sigma phases in S32750 steel is noticeable higher than that in S31803 steel during any aging treatment. The corrosion resistance is directly influenced by the abundant sigma phases, especially for the S32750. This result is helpful for practical aging treatment establishment of the S31803 and S32750 duplex stainless steels.

Influence of Sigma Phase Precipitation on Pitting Corrosion of 2507 Super-Duplex Stainless Steel

Specimens of 2507 super-duplex stainless steel aging at 850°C for 5 min, 15 min and 60 min were investigated to evaluate the pitting corrosion resistance in 3.5% NaCl solution at 30°C and 50°C. The results are correlated with the microstructures obtained with different aging time. The precipitation of σ phase remarkably decreases the pitting corrosion resistance of the steel and the specimen aged for 60 min presents the lowest pitting potential at both 30°C and 50°C. With increasing the ambient temperature from 30°C to 50°C, the pitting potential exhibits a reduction tendency, while this tendency is less obviously in enhancing the ambient temperature than in extending the isothermal aging duration from 5 to 60 min. SEM analysis shows that the surrounding regions of σ phase are the preferable sites for the formation of corrosion pits which grew up subsequently. This may be attributed to the lower content of corrosion resistance elements in these regions formatted with σ phase precipitation.

Modeling of the Stress in 13Cr Supermartensitic Stainless Steel Welds by Artificial Neural Network

In this paper, artificial neural networks (ANN) has been proposed to determine the stresses of 13Cr supermartensitic stainless steel (SMSS) welds based on various deformation temperatures and strains using experimental data from tensile tests. The experiments provided the required data for training and testing. A three layer feed-forward network, deformation temperature and strain as input parameters while stress as the output, was trained with automated regularization (AR) algorithm for preventing overfitting. The results showed that the best fitting training dataset was obtained with ten units in the hidden layer, which made it possible to predict stress accurately. The correlation coefficients (R-value) between experiments and prediction for the training and testing dataset were 0.9980 and 0.9943, respectively, the biggest absolute relative error (ARE) was 6.060 %. As seen that the ANN model was an efficient quantitative tool to evaluate and predict the deformation behavior of type 13Cr SMSS welds during tensile test under different temperatures and strains.

Artificial Neural Network Approach to Predict Mechanical Properties of 301 Austenitic Stainless Steel

In this study, the effect of original thicknesses of plate, the thicknesses of plate after rolling and rolling reduction on the strength in 301 stainless steel was modeled by means of artificial neural network (ANN). The experimental data were collected to obtain training set and testing set. The normalization method was employed for avoiding over-fitting. The optimal ANN method architecture was determined by according to the trial and error procedure. The results of the ANN model were in good agreement with experimental data. As can be seen from the result, we believe that the neural network model can efficiently predict the relationship between mechanical properties and rolling reduction in 301 austenitic stainless steel.