High-temperature constitutive relationship involving phase transformation for non-oriented electrical steel based on PSO-DNN approach

Song, Chunning; Cao, Junci; Xiao, Jing; Zhao, Qianchuan; Sun, Shuangtao; Wen-hui, Xia

doi:10.1016/j.mtcomm.2022.105210

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…Particle swarm optimization (PSO) is a kind of evolutionary algorithm developed by imitating the foraging behavior of flocks of birds and fish [25]. Its concept is simple and…”

Section: The Basic Principles Of Pso-bp Integrated Modelmentioning

confidence: 99%

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Characterization of Flow Behaviors by a PSO-BP Integrated Model for a Medium Carbon Alloy Steel

2023

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In order to characterize the flow behaviors of SAE 5137H steel, isothermal compression tests at the temperatures of 1123 K, 1213 K, 1303 K, 1393 K, and 1483 K, and the strain rates of 0.01 s−1, 0.1 s−1, 1 s−1, and 10 s−1 were performed using a Gleeble 3500 thermo-mechanical simulator. The analysis results of true stress-strain curves show that the flow stress decreases with temperature increasing and strain rate decreasing. In order to accurately and efficiently characterize the complex flow behaviors, the intelligent learning method backpropagation–artificial neural network (BP-ANN) was combined with the particle swarm optimization (PSO), namely, the PSO-BP integrated model. Detailed comparisons of the semi-physical model with improved Arrhenius-Type, BP-ANN, and PSO-BP integrated model for the flow behaviors of SAE 5137H steel in terms of generative ability, predictive ability, and modeling efficiency were presented. The comparison results show that the PSO-BP integrated model has the best comprehensive ability, BP-ANN is the second, and semi-physical model with improved Arrhenius-Type is the lowest. It indicates that the PSO-BP integrated model can accurately describe the flow behaviors of SAE 5137H steel.

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“…Particle swarm optimization (PSO) is a kind of evolutionary algorithm developed by imitating the foraging behavior of flocks of birds and fish [25]. Its concept is simple and…”

Section: The Basic Principles Of Pso-bp Integrated Modelmentioning

confidence: 99%

“…Particle swarm optimization (PSO) is a kind of evolutionary algorithm developed by imitating the foraging behavior of flocks of birds and fish [25]. Its concept is simple and easy to program and implement with high operational efficiency and relatively few parameters, and it is very widely used.…”

Section: The Basic Principles Of Pso-bp Integrated Modelmentioning

confidence: 99%

Characterization of Flow Behaviors by a PSO-BP Integrated Model for a Medium Carbon Alloy Steel

2023

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“…The general tendency of roll gap profile with mechanical parameters of the mill can be investigated using these models. Song et al [ 18 ] established a 3D FE model of the rolls and strip, in which the strip is defined according to a constitutive model for the rolled steel. Liu et al [ 19 ] built a roll‐strip coupling model and investigated the effects of phase transformation and latent heat on the total roll force distribution and roll gap profile.…”

Section: Introductionmentioning

confidence: 99%

“…[ 20 ] The mechanical property of the rolled strip changes with phase transformation, which would cause significant changes in strip profile. The effects of phase transformation on strip profile have been involved in the investigations by Song et al [ 18 ] and Liu et al [ 19 ] However, thus far, the effects of the transversal temperature distributions of the strip steel on strip profile have not been fully clarified in the previous research. In this article, the characteristic of transversal temperature distributions of the strip steel is investigated.…”

Section: Introductionmentioning

confidence: 99%

Effects of Transversal Temperature Distribution and Phase State on Strip Profile During Hot Rolling Process

Dong

Liu

et al. 2023

steel research int.

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Transversal temperature distribution and phase transformation of the strip steel in hot rolling process are analyzed by thermographic observations and thermal simulation tests. The effects of transversal temperature distribution and phase state on strip profile are investigated by finite element (FE) simulations, in which the material model of the strip steel is generated from the thermal simulation tests and a constitutive model. FE calculation shows that the decreased temperature at the edge causes smaller strip crown with the dual‐phase state, while it leads to edge bulges in strip profile under single‐phase state. With the convex temperature distribution, large strip crown is witnessed under dual‐phase state; small crowns or even edge bulges appear under the single‐phase state. For the concave temperature distribution, opposite conclusions are drawn compared with the convex temperature distribution. Measures to prevent edge bulges and control the strip crown are proposed and applied to industrial production. Reheating the edge area and assigning large target crown to the former stand of the finishing mill can avoid the edge bulges. Strip crown of the steel is more significantly affected by the bending force under dual‐phase state than that under single‐phase state.

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Constitutive Modeling of High‐Temperature Deformation Behavior of Nonoriented Electrical Steels as Compared to Machine Learning

Mishra,

Pasco,

McCarthy

et al. 2024

steel research int.

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Hot rolling is a critical thermomechanical processing step for nonoriented electrical steel (NOES) to achieve optimal mechanical and magnetic properties. Depending on the silicon and carbon contents, the electrical steel may or may not undergo austenite–ferrite phase transformation during hot rolling, which requires different process controls as the austenite and ferrite show different flow stresses at high temperatures. Herein, the high‐temperature flow behaviors of two nonoriented electrical steels with silicon contents of 1.3 and 3.2 wt% are investigated through hot compression tests. The hot deformation temperature is varied from 850 to 1050 °C, and the strain rate is differentiated from 0.01 to 1.0 s−1. The measured stress‐strain data are fitted using various constitutive models (combined with optimization techniques), namely, Johnson–Cook, modified Johnson–Cook, Zener–Hollomon, Hensel–Spittel, modified Hensel–Spittel, and modified Zerilli–Armstrong. The results are also compared with a model based on deep neural network (DNN). It is shown that the Hensel–Spittel model results in the smallest average absolute relative error among all the constitutive models, and the DNN model can perfectly track almost all the experimental flow stresses over the entire ranges of temperature, strain rate, and strain.

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High-temperature constitutive relationship involving phase transformation for non-oriented electrical steel based on PSO-DNN approach

Cited by 7 publications

References 24 publications

Characterization of Flow Behaviors by a PSO-BP Integrated Model for a Medium Carbon Alloy Steel

Characterization of Flow Behaviors by a PSO-BP Integrated Model for a Medium Carbon Alloy Steel

Effects of Transversal Temperature Distribution and Phase State on Strip Profile During Hot Rolling Process

Constitutive Modeling of High‐Temperature Deformation Behavior of Nonoriented Electrical Steels as Compared to Machine Learning

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