A Flow Stress Model of 300M Steel for Isothermal Tension.
TL;DR: To eliminate the influence of sample necking on stress-strain relationship, both the stress and the strain were calibrated using the cross-sectional area of the neck zone, and a constitutive model for tensile deformation was established based on the modified Arrhenius model.
Abstract: To investigate the effect of hot working parameters on the flow behavior of 300M steel under tension, hot uniaxial tensile tests were implemented under different temperatures (950 °C, 1000 °C, 1050 °C, 1100 °C, 1150 °C) and strain rates (0.01 s−1, 0.1 s−1, 1 s−1, 10 s−1). Compared with uniaxial compression, the tensile flow stress was 29.1% higher because dynamic recrystallization softening was less sufficient in the tensile stress state. The ultimate elongation of 300M steel increased with the decrease of temperature and the increase of strain rate. To eliminate the influence of sample necking on stress-strain relationship, both the stress and the strain were calibrated using the cross-sectional area of the neck zone. A constitutive model for tensile deformation was established based on the modified Arrhenius model, in which the model parameters (n, α, Q, ln(A)) were described as a function of strain. The average deviation was 6.81 MPa (6.23%), showing good accuracy of the constitutive model.
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TL;DR: In this paper, the effects of tensile processing parameters on the hot tensile behaviors and fracture characteristics of a low-alloyed ultra-high strength (LUHS) steel are comprehensively discussed by analyzing the hot-tensor data and microstructure observation.
Abstract: The hot tensile behavior of a low-alloyed ultra-high strength (LUHS) steel is studied by performing the isothermal tensile tests under different tensile processing parameters (strain rates and tensile temperatures). The effects of tensile processing parameters on the hot tensile behaviors and fracture characteristics are comprehensively discussed by analyzing the hot tensile data and microstructure observation. For reproducing the hot tensile behaviors, the dislocation density based constitutive model is constructed and further improved by considering plastic damage. It is found that the hot tensile curves always show the high stress under low tensile temperatures or high strain rates. The ductile fracture characterized by dimples is the dominant fracture type. Meanwhile, the elongation to fracture roughly follows a decreasing trend under low strain rates or high tensile temperatures, and the variation of the reduction in cross-sectional area is opposite to that of elongation to fracture. This is because that the multiple coalescence of microvoids easily takes place with the progress of necking under low strain rates or high tensile temperatures. The relatively narrow error band (controlled in ±5.518 MPa), low mean absolute relative error (equals to 2.695%) and high correlation coefficient (higher than 0.998) indicate that the improved dislocation density based constitutive model is preferred for reconstructing the hot tensile behaviors of the studied steel.
19 citations
TL;DR: In this paper, a back-propagation artificial neural network model (BP-ANN) and a strain-compensated Arrhenius model (SCAM) were constructed for the prediction of the flow stress of annealed 7075 Al alloy.
Abstract: Hot compression experiments of annealed 7075 Al alloy were performed on TA DIL805D at different temperatures (733, 693, 653, 613 and 573 K) with different strain rates (1.0, 0.1, 0.01 and 0.001 s−1.) Based on experimental data, the strain-compensated Arrhenius model (SCAM) and the back-propagation artificial neural network model (BP-ANN) were constructed for the prediction of the flow stress. The predictive power of the two models was estimated by residual analysis, correlation coefficient (R) and average absolute relative error (AARE). The results reveal that the deformation parameters including strain, strain rate, and temperature have a significant effect on the flow stress of the alloy. Compared with the SCAM model, the flow stress predicted by the BP-ANN model is in better agreement with experimental values. For the BP-ANN model, the maximum residual is only 1 MPa, while it is as high as 8 MPa for the SCAM model. The R and AARE for the SCAM model are 0.9967 and 3.26%, while their values for the BP-ANN model are 0.99998 and 0.18%, respectively. All these reflect that the BP-ANN model has more accurate prediction ability than the SCAM model, which can be applied to predict the flow stress of the alloy under high temperature deformation.
10 citations
TL;DR: In this paper, a sine hyperbolic Arrhenius-type constitutive model was developed considering the three dimensional variation of the materials' constant with strain, strain rate and temperature.
Abstract: The present work deals with the high temperature deformation behavior of Fe-11.15Mn-5.6Al-0.07C (wt.%) triplex ferrite-based lightweight steel in the temperature range of 800–1100 °C under the strain rate of 0.001 to 0.1s−1. The compressive high temperature flow curves under the various thermomechanical conditions were accompanied by a considerable fractional softening. According to the detailed microstructural analysis, the observed flow softening was discussed relying on the occurrence of dynamic strain induced transformation and dynamic recrystallization. In this respect, a sine hyperbolic Arrhenius-type constitutive model was developed considering the three dimensional variation of the materials’ constant with strain, strain rate and temperature. This provided a proper condition for accurate assessment of the strain compensation mechanisms. The capability of the modified and un-modified constitutive models in prediction of the high temperature flow behavior of experimented low density steel were compared. According to the verified model, activation energy (Q) maps were developed and discussed in correlation with the characterized microstructure evolutions. The Q-plots were divided into three domains and a transition range was recognized at ~1100–1250 K, the extent of which decreased with increasing imposed strain. The low energy domains were attributed to the (i) activation of load transition as an effective strain compensation mechanism and the occurrence of dynamic austenite to ferrite transformation, and (ii) the high dislocation annihilation rate at high temperatures.
9 citations
TL;DR: In this article , a back propagation artificial neural network (BP-ANN) model based on supervised machine learning was employed to regress and predict flow stress in diverse deform conditions, and it was found that the BP-ANN model is superior in regressing and predicting than the Arrhenius-type constitutive equation.
Abstract: To realize the purpose of energy saving, materials with high weight are replaced by low-weight materials with eligible mechanical properties in all kinds of fields. Therefore, conducting research works on lightweight materials under specified work conditions is extremely important and profound. To understand the relationship of aluminum alloy AA5005 among flow stress, true strain, strain rate, and deformation temperature, hot isothermal tensile tests were conducted within the strain rate range 0.0003–0.03 s−1 and temperature range 633–773 K. Based on the true stress-true strain curves obtained from the experiment, a traditional constitutive regression Arrhenius-type equation was utilized to regress flow behaviors. Meanwhile, the Arrhenius-type equation was optimized by a sixth-order polynomial function for compensating strain. Thereafter, a back propagation artificial neural network (BP-ANN) model based on supervised machine learning was also employed to regress and predict flow stress in diverse deform conditions. Ultimately, by introducing statistical analyses correlation coefficient (R2), average absolute relative error (AARE), and relative error (δ) to the comparative study, it was found that the Arrhenius-type equation will lose accuracy in cases of high stress. Additionally, owning higher R2, lower AARE, and more concentrative δ value distribution, the BP-ANN model is superior in regressing and predicting than the Arrhenius-type constitutive equation.
7 citations
TL;DR: The main results have shown that the Feed-Forward Multi-Layer Perceptron architecture represents a good choice if very high accuracy is a crucial goal, and for the flow curve description the almost unused Radial Basis network offers a very easy training procedure and significantly shorter computing time under acceptable accuracy.
Abstract: In recent years, the study of the hot deformation behavior of various materials is significantly marked by an increasing utilization of artificial neural networks, which are frequently employed for a hot flow curve description. This specific kind of description is commonly solved via a Feed-Forward Multi-Layer Perceptron architecture and rarely via a Radial Basis architecture. Both network architectures are compared to assess their suitability in the process of a hot flow curve description under a wide range of thermomechanical conditions. The performed survey is also aimed on the eventual utilization of corresponding modifications of both studied networks, namely on a Cascade-Forward Multi-Layer Perceptron and Generalized Regression network. The main results have shown that the Feed-Forward Multi-Layer Perceptron architecture represents a good choice if very high accuracy is a crucial goal. However, in the case of this architecture, finding the proper parameters can be time-consuming and the hardware burdensome. On the contrary, for the flow curve description the almost unused Radial Basis network offers a very easy training procedure and significantly shorter computing time under acceptable accuracy. The results of the submitted research should then serve as a background for the selection and following application of a suitable network architecture in the process of solving future flow curve description tasks.
6 citations
References
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TL;DR: In this article, a new method of modeling material behavior which accounts for the dynamic metallurgical processes occurring during hot deformation is presented, which considers the workpiece as a dissipator of power in the total processing system and evaluates the dissipated power co-contentJ = ∫o σ e ⋅dσ from the constitutive equation relating the strain rate (e) to the flow stress (σ).
Abstract: A new method of modeling material behavior which accounts for the dynamic metallurgical processes occurring during hot deformation is presented. The approach in this method is to consider the workpiece as a dissipator of power in the total processing system and to evaluate the dissipated power co-contentJ = ∫o σ e ⋅dσ from the constitutive equation relating the strain rate (e) to the flow stress (σ). The optimum processing conditions of temperature and strain rate are those corresponding to the maximum or peak inJ. It is shown thatJ is related to the strain-rate sensitivity (m) of the material and reaches a maximum value(J max) whenm = 1. The efficiency of the power dissipation(J/J max) through metallurgical processes is shown to be an index of the dynamic behavior of the material and is useful in obtaining a unique combination of temperature and strain rate for processing and also in delineating the regions of internal fracture. In this method of modeling, noa priori knowledge or evaluation of the atomistic mechanisms is required, and the method is effective even when more than one dissipation process occurs, which is particularly advantageous in the hot processing of commercial alloys having complex microstructures. This method has been applied to modeling of the behavior of Ti-6242 during hot forging. The behavior of α+ β andβ preform microstructures has been exam-ined, and the results show that the optimum condition for hot forging of these preforms is obtained at 927 °C (1200 K) and a strain rate of 1CT•3 s•1. Variations in the efficiency of dissipation with temperature and strain rate are correlated with the dynamic microstructural changes occurring in the material.
1,121 citations
"A Flow Stress Model of 300M Steel f..." refers methods in this paper
...The modified Arrhenius model was expressed as follows [23,24]: ε ∙ exp Q RT = A(sinh(ασ)) (4)...
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...The modified Arrhenius model was expressed as follows [23,24]: ....
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TL;DR: In this article, the thermal compressive deformation behavior of GCr15 (AISI-52100), one of the most commonly used bearing steels, was studied on the Gleeble-3500 thermo-simulation system at temperature range of 950-1150°C and strain rate range of 0.1-10 s−1.
102 citations
"A Flow Stress Model of 300M Steel f..." refers background in this paper
...3, indicating that recrystallization was dominant in material softening [26,27]....
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TL;DR: In this article, the authors presented a method for obtaining the flow curve of sheet metals over a large range of strain through the combination of simple tensile test and finite element analyses, and evaluated different hardening functions for their capabilities in approximating the entire flow stress curves up to localized necking.
93 citations
"A Flow Stress Model of 300M Steel f..." refers methods in this paper
...[7] successfully calibrated the flow stress of Q195 steel, HSLA350 aluminum alloy, and AL6061 aluminum alloy....
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...[7] successfully calibrated the flow stress of Q195 steel, HSLA350 aluminum...
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TL;DR: In this paper, the effects of hot forming process parameters (strain rate and deformation temperature) on the elongation to fracture, strain rate sensitivity and fracture characteristics are analyzed.
58 citations
"A Flow Stress Model of 300M Steel f..." refers background in this paper
...[2], and tensile flow stress models for IMI834 titanium alloy and 42CrMo steel were proposed....
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TL;DR: In this article, a phenomenological constitutive model is proposed to describe the hot tensile deformation behaviors of a typical Al-Cu-Mg alloy under relatively low strain rates.
57 citations
"A Flow Stress Model of 300M Steel f..." refers background in this paper
...[3] constructed a phenomenological model to describe the influence of hot working parameters on flow stress in hot tension of Al-Cu-Mg alloy....
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