Hot Deformation Behavior and 3D Processing Maps of Mn18Cr18N Steel

Generally, steels with a yield strength more than 1400 MPa are defined as ultrahigh-strength steels. [1] Ultrahigh-strength steels own exceptional mechanical qualities and are utilized to create large key load-bearing components, [2][3][4] which are commonly treated by hot forging with considerable strain. During the hot forging process with large strain, the mechanisms of dynamic recovery and dynamic recrystallization occur, which make it difficult to control the flow behaviors. [5,6] In addition, flow behaviors closely depend on strain, strain rate, and deformation temperature during the hot deformation process. The flow characteristics are dramatically different at various deformation parameters. The numerous features make it challenging to forecast flow characteristics and optimize hot processing parameters. [7] Constitutive modeling is a powerful method to simulate the real deformation process. [8] So, it is vital to analyze flow characteristics and develop an appropriate constitutive model for ultrahigh-strength steels during the hot forging process with large strain.Constitutive models include artificial neural network models, physical-based constitutive models, and phenomenological constitutive models. [9,10] Artificial neural network models can accurately capture the intricate link between flow characteristics and multiple deformation factors. Wan et al. [11] utilized the methods of back propagation neural network and particle swarm optimization to predict the flow behaviors of Zr-4 alloy during hot deformation, and the correlation coefficient between predicted and experimental flow stress is 0.9981. Li et al. [12] employed a deep neural network to characterize the flow characteristics of Ti-2Al-9.2Mo-2Fe beta titanium alloy during hot deformation. The prediction accuracy of the constitutive model is 0.9995. Murugesan et al. [13] explored the accuracy of different supervised machine learning methods in predicting flow stress and pointed out that the random forest regression model has the highest accuracy. The flow behaviors can be correctly predicted by artificial neural network models. However, as stated by Savaedi et al., [14] the successful application of artificial neural network models depends on high-quality data. Furthermore, compared to the computation efficiency of analytical models, the computation efficiency of artificial neural network models is lower. [15] Physical-based constitutive models may express the physical meanings of material during the deformation process. Buzolin et al. [16] constructed a dislocation-based constitutive model to predict the flow behaviors and microstructure evolution during the hot deformation process of Ti5553 alloy. Zeng et al. [17] built a unified constitutive model

“…Generally, the relationship between flow stress and strain rate can be described by Equation (6). [ 52,53 ]

σ = K {\overset{ε}{true}}^{m}

where K is the material constant, and m is the strain rate sensitivity coefficient. According to Equation (6), the strain rate sensitivity coefficient can be calculated by Equation (7).…”

Section: Resultsmentioning

confidence: 99%

“…Generally, the relationship between flow stress and strain rate can be described by Equation ( 6). [52,53] σ ¼ Kε…”

Section: Analysis Of Strain Rate Sensitivitymentioning

confidence: 99%

Flow Stress Characteristics and Constitutive Modeling of Typical Ultrahigh‐Strength Steel under High Temperature and Large Strain

Zhao

Huang

et al. 2022

“…Xi et al [ 17 ] studied instability of 317LN‐Cu medical stainless steel during processing, the processing maps were established based on the DMM to predict the suitable hot processing interval. Zhang et al [ 18 ] examined the deformation behavior of Mn18Cr18N steel. It was observed that the DRX would progressively rise as the deformation temperature increased, and an increase in strain rate was identified as result in a significant reduction in both the DRX grain size and DRX fraction.…”

Section: Introductionmentioning

confidence: 99%

Hot Deformation Behavior of 301L Stainless Steel and Dissolution Behavior of δ‐Ferrite During Heat Treatment

Liu

Wang

et al. 2023

The hot deformation behavior of 301 L stainless steel is investigated at 1000–1250 °C and 0.1–50 s−1. Deformation characteristics are studied through compressive stress–strain curves and constitutive equations. According to the dynamic material model and a series of diffusion annealing experiments, the hot processing map of 301 L stainless steel is established to determine the suitable processing domain. The results show that the stable domain is located in 1085–1240 °C and high strain rate range of 4.5–50 s−1, and the strain rate is the main factor affecting the machinability. Based on the hot compression tests, δ‐ferrite gradually forms a network structure with the increase of temperature; however, it can be inhibited at high strain rate. Therefore, it is necessary to reduce the temperature and increase the strain rate in the actual processing of the machinable area. It is found that diffusion annealing can reduce the content of δ‐ferrite from 12% to 0.67%, and the dissolution of δ‐ferrite is controlled by the diffusion of Cr and Ni, especially noticeable at the interface between γ and δ phase. Finally, the optimum diffusion annealing process parameter is determined at 1300 °C and 10 min.

Hot Deformation Behavior and Processing Map of Cr–Mn–Ni–Mo–N Austenitic Stainless Steels

Wang,

Liu,

Teng

et al. 2024

In the present work, the hot deformation behavior of two austenitic stainless steels with different nitrogen is studied by conducting compression tests at the strain rates in the range 0.01–7 s−1 and temperatures in the range 950–1150 °C using a Gleeble thermomechanical simulator. Simultaneously, the constitutive relationship is based on the Arrhenius hyperbolic sine equation and constructs the corresponding thermal processing maps based on dynamic material modeling (DMM).The 23Cr7MnNi2Mo0.93N (0.93 N) steel possesses a higher hot deformation resistance and a higher activation energy of deformation (628.39 kJ mol−1) compared to the 23Cr7MnNi2Mo0.45N (0.45 N) steel (315.79 kJ mol−1). Dynamic recrystallization occurs predominantly in 0.93 N steels under the conditions tested, the samples with 0.93% nitrogen content exhibit higher peak stresses and more complex rheological behavior. According to the thermal processing maps, the hot working region for 0.45 N steel is 1020 to 1150 °C at strain rates of 0.01 to 0.1 s−1, while 0.93 N steel is not suitable for processing in the experimental parameter range.