De Ning Zou

Influence of Aging Time on Sigma Phase Precipitation in SAF2507 Super-Duplex Stainless Steel

Specimens of SAF2507 super-duplex stainless steel were heated at 920°C with different aging time. The phase transformation and development of microstructure in the materials were investigated by color optical microscopy, scan electron microscopy, energy-dispersive spectroscopy and X-ray diffraction. The hardness was tested by HB-3000B hardness tester. The facts that sigma phase precipitated right after 2 min. At 920°C, the grain boundaries to be the preferential precipitation sites and the sites changed from ferritic-austenitic phase interface into the inside of ferritic phase was found. Increase in aging time is proportional to the increase in sigma phase precipitation mass was most frequently observed in specimens. For phase precipitation, the values of hardness of the super-duplex stainless steel was enhanced evidently.

Microstructure, Mechanical Property and Antibacterial Characteristics of Ferritic Stainless Steel

Based on the mechanism of high antibacterial performance of copper, an antibacterial stainless steel bearing1.5wt% copper was fabricated. Processing of vacuum arc melting, hot rolling, and a series of heat treatments were applied to prepare the specimens. The relationships among the microstructure, mechanical properties, and the regime of treatment were investigated. Furthermore, the antibacterial properties of the specimens were assessed. The results showed that fine and uniformed dispersion Cu-rich phase precipitated and accumulated along the grain boundaries of the matrix during the processing of aging, and a marked variation in tensile strength and yield strength was evaluated. In addition, antibacterial result showed that the as-received material has the ability to kill Staphylococcus and coliform bacteria in the excess of 99.99%, respectively.

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.

Microstructural Evolutions and its Influence on Properties of Super-Duplex Stainless Steel

Color-optical microscopy, energy spectrum analysis, hardness measuring, tensile and corrosion testing were conducted to investigate characteristics of microstructure, mechanical properties and corrosion resistance of S32750 super-duplex stainless steel aged at 850～920°C. The results indicate that with the increase of aging temperature and aging time the content of σ phase increases, while the ferrite content decreases. The forming and growing of σ phase obtained during aging causes an increase in hardness and a reduction in ductility of the aged steel. Moreover, increasing aging time the corrosion resistance reduces, owing to a new formed austenite occurs.

Effect of Final Annealing Temperature on Antibacterial Precipitate of Copper-Bearing Ferritic Stainless Steel

In order to keep the steel with outstanding antibacterial characteristics, the relationship between the anneal temperatures and the amounts of the precipitated Cu-rich phase was investigated in this work. The SEM analysis of annealing specimens indicated that the microstructure changed with the increase of annealing temperature. At 900°C, the martensite phase appeared along the crystal boundary, but the amount of Cu-rich phase was almost no change. At 920°C, the sizes of the Cu-rich precipitates minished and the amounts of the precipitates reduced gradually. At 940°C, there was only martensite phase existing in the ferrite matrix. Therefore, the optimal final annealing temperature range was chosen from 880°C to 900°C to ensure attaining excellent antibacterial properties and favorable mechanical characteristic of copper-bearing stainless steel.

Precipitation Behavior of High-Nitrogen Low-Nickel Austenitic Stainless Steel at Intermediate Temperature

The precipitation behavior of nitrides and carbides occurred in aging process for 10Cr21Mn16NiN austenitic stainless steel at intermediate temperature was investigated by use of thermodynamic calculation, metallography and electron microscopy analysis. The precipitates evolved from chain-like initiatively along grain boundaries at lower aging temperature, to that along grain boundaries and inside the grain of austenite with more content as the temperature rising gradually. When aging at 800 °C, precipitates became layered tablet shaped and the composition was ascertained the mixture of Cr2N and M23C6. At a certain temperature, the volume fraction of precipitates for the aged testing steel by air cooling was slightly higher than that by water quenching.