Constitutive Analysis on High-Temperature Flow Behavior of 3Cr-1Si-1Ni Ultra-High Strength Steel for Modeling of Flow Stress

The isothermal tensile test of medium carbon steel material was conducted at deformation temperatures varying from 650 to 950 ∘ C with an interval of 100 ∘ C and strain rates ranging from 0.05 to 1.0 s − 1 . In addition, the scanning electron microscopy (SEM) procedures were exploited to study about the surface morphology of medium carbon steel material. Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For model training and testing purpose, the variables such as deformation temperature, strain rate, and strain data were considered as inputs and the flow stress data were used as targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination ( R 2 ) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.999 and 1.335%, respectively. For improving the model predictability, the constrained nonlinear function based optimization procedures was adopted to obtain the best candidate selections of weights and biases. By evaluating each test conditions, it was found that the average absolute relative error based on the optimized ANN-BP model varied from 0.728% to 1.775%. Overall, the trained ANN-BP models proved to be much more efficient and accurate by means of flow stress prediction against the experimental data for all the tested conditions. These optimized results displayed that an ANN-BP model is more accurate for flow stress prediction than that of the conventional flow stress models.

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Murugesan

Sajjad

Jung

2019

“…The coefficients of the relevant polynomial is shown in Table 3. Concerning the msA-type equation, the predicted stress can be obtained based on Equation (39), which is obtained by substituting Equation (38) in Equation (39). Figure 15 is shown the predicted stress from the modified strain-compensated Arrhenius-type (msA-type) equation.…”

Section: = + +mentioning

confidence: 99%

“…Therefore, 20Cr2Ni4A steel is widely applied to produce broad cross-section carburized parts. In previous research, there are many reports about the constitutive equation of metals and alloys at warm or hot temperatures [35][36][37][38][39]. The aim of the present study is that a higher-precision constitutive equation is established to describe the behavior of the 20Cr2Ni4A steel under a wider range, including warm forming and hot forming.…”

Section: Introductionmentioning

confidence: 98%

Constitutive Equations for Describing the Warm and Hot Deformation Behavior of 20Cr2Ni4A Alloy Steel

Wang

Zhai

et al. 2020

Isothermal hot compression tests of 20Cr2Ni4A alloy steel were performed under temperatures of 973–1273 K and strain rates of 0.001–1 s−1. The behavior of the flow stress of 20Cr2Ni4A alloy steel at warm and hot temperatures is complicated because of the influence of the work hardening, the dynamic recovery, and the dynamic recrystallization. Four constitutive equations were used to predict the flow stress of 20Cr2Ni4A alloy steel, including the original strain-compensated Arrhenius-type (osA-type) equation, the new modified strain-compensated Arrhenius-type (msA-type) equation, the original Hensel–Spittel (oHS) equation and the modified Hensel–Spittel (mHS) equation. The msA-type and mHS are developed by revising the deformation temperatures, which can improve prediction accuracy. In addition, we propose a new method of solving the parameters by combining a linear search with multiple linear regression. The new solving method is used to establish the two modified constitutive equations instead of the traditional regression analysis. A comparison of the predicted values based on the four constitutive equations was performed via relative error, average absolute relative error (AARE) and the coefficient of determination (R2). These results show the msA-type and mHS equations are more accurate and efficient in terms of predicting the flow stress of the 20Cr2Ni4A steel at elevated temperature.

“…A growing body of experimental evidence has clearly shown that TMP parameters have a decisive effect on the deformation behavior of materials [24][25][26][27]. This study had a major focus on the cold rolling process, where the flow of a sheet is conditioned by roll gap geometry, straining levels and friction condition.…”

Section: Correlating the Flow-line Model Parameters With Roll Gap Geomentioning

confidence: 99%

Assessment of Flow-Line Model in Rolling Texture Simulations

Sidor

2019

The nature of the thermomechanical processing of materials can be revealed by means of various numerical approaches. The accuracy of a particular model is linked to the boundary conditions employed. Intensive research activities over the past several decades in the field of finite element modeling (FEM) have enabled the development of various processing chains for particular purposes; however, this technique is computationally expensive, and in many instances, the behavior of materials during a processing step is analyzed by highly efficient analytical models. This contribution focuses on the implementation of a recently developed flow-line model (FLM), which enables the effective texture simulation of cold rolling. The results of numerous calculations, performed for a wide spectrum of roll gap geometries and various friction conditions, revealed that the deformation history predicted by the FLM employed was comparable to FEM calculations. A correlation was defined between the FLM model parameters and the rolling process quantitative indicators, implying that this analytical approach is capable of performing simulations of cold rolling without fitting constraints. It was shown that FLM coupled with a Taylor-type homogenization crystal plasticity model (Alamel) could carry out a texture simulation close to the one performed with deformation history obtained by means of FEM.