Modified arrhenius-type constitutive model and artificial neural network-based model for constitutive relationship of 316LN stainless steel during hot deformation

He, Anrui; Wang, Xitao; Xie, Ganlin; Yang, Xiaoya; Zhang, Hailong

doi:10.1016/s1006-706x(15)30063-7

Cited by 36 publications

(10 citation statements)

References 48 publications

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“…In the hot deformation behavior of alloy steel, Arrhenius-type constitutive equation is widely used to describe the relationship of flow stress, strain rate, and deformation temperature. The relationship can be shown in equations (1) and (2) 33–38 :where Q is activation energy of the deformation; R is the gas constant (R=8.314 mol −1 K −1 ); A, α, β , n and n’ are the parameters, α=β/n′. The power law description of stress is more suitable for the deformation with creep, but it cannot be used to describe the deformation with high stress.…”

Section: Resultsmentioning

confidence: 99%

Predicting flow stress of Ni steel based on machine learning algorithm

Cao

Gao

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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This article builds a stress–strain prediction model based on production data from the steel industry by using machine learning algorithms. Based on the stress–strain data of 9Ni steel hot deformation behavior, the prediction model of flow stress constitutive equation of 9Ni steel is established. Four models, including Arrhenius-type model considering strain compensation, Arrhenius-type model of Stochastic Configuration Networks (SCNs) neural network, Arrhenius-type model of Multi-objective Particle Swarm Optimization (AMPSO) and Support Vector Machine (SVM) model, are adopted in this research. The results show that the Arrhenius-type model considering strain compensation can predict the stress trend under different deformation conditions, but its prediction accuracy has some deviation. The prediction model based on SVM algorithm has the best prediction accuracy. The square of Correlation Coefficient (R2), the Average Absolute Relative Error (AARE), and Mean Square Error (MSE) are 0.99996, 0.002455, and 0.1998, respectively. Based on the data of 9Ni steel hot deformation behavior, the prediction models of machine learning algorithm have good application prospects in steel industry.

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Section: Resultsmentioning

confidence: 99%

Predicting flow stress of Ni steel based on machine learning algorithm

Cao

Gao

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…When the error between the output information and the expected information exceeds the normal range, the error signal will be returned in the original way (That is, error back propagation). And the weight of each layer of neurons will be modified through the training network, and thus repeated until the output information error reaches a reasonable range, the training is completed [66,67,68].…”

Section: Resultsmentioning

confidence: 99%

Comparative Study on Constitutive Models for 21-4N Heat Resistant Steel during High Temperature Deformation

Cai

et al. 2019

Materials

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The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( ε ˙ ) of 0.01–10 s−1, the deformation temperature (T) of 1273–1453 K and the maximum deformation is 0.916. The data of the stress-strain (σ-ε) were obtained. Based on the σ-ε data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and ε ˙ of modified J-C model is considered, and the prediction accuracy is greatly improved The Arrhenius model introduces Zener-Hollomon (Z) to represent the coupling effect of T and ε ˙ , it has a fairly high prediction accuracy. And it can predict flow stress (σ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a Finite Element Method (FEM) of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.

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“…The combined effect is usually expressed by the Zener–Hollomon parameter. The Equation (2) is the Z-parametric expression and the material constitutive equation for high-temperature flow stress [30].…”

Section: Arrhenius Constitutive Equation and Dynamic Recrystallizamentioning

confidence: 99%

Hot Deformation Behavior of Q345 Steel and Its Application in Rapid Shear Connection

Zhang

et al. 2019

Materials

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The high-temperature deformation behavior of Q345 steel is detected by a Gleeble-3800 thermal simulator. The Arrhenius constitutive equation for high-temperature flow stress and the dynamic recrystallization model are constructed. With the secondary development technology, customized modifications are made on existing Deform-3D software. The constructed constitutive model and dynamic recrystallization model are embedded into Deform-3D to realize the secondary development of Deform-3D. The grain size and volume percentage distribution of dynamic recrystallization are obtained by simulating the shear connection process at high temperature and high speed. The results show that the constitutive equation and the dynamic recrystallization model constructed in this paper can be used to predict the evolution of the microstructure. The difference between the prediction results and the experimental data is about 3%. The accuracy of Arrhenius constitutive equation, dynamic recrystallization model and the feasibility of software secondary development are verified.

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Modified arrhenius-type constitutive model and artificial neural network-based model for constitutive relationship of 316LN stainless steel during hot deformation

Cited by 36 publications

References 48 publications

Predicting flow stress of Ni steel based on machine learning algorithm

Predicting flow stress of Ni steel based on machine learning algorithm

Comparative Study on Constitutive Models for 21-4N Heat Resistant Steel during High Temperature Deformation

Hot Deformation Behavior of Q345 Steel and Its Application in Rapid Shear Connection

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