Research on Crown &amp; Flatness Allocation Strategy of Hot Rolling Mills

Sun, W. Q.; Li, B.; Shao, J.

doi:10.2507/ijsimm15(2)co6

Cited by 19 publications

(22 citation statements)

References 17 publications

(19 reference statements)

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“…Because of the model transversal symmetry, the metal transversally flows toward two opposite direction on both sides of strip center, as shown in Figure 3. The distribution of transversal flow of metal along the transverse direction can be fitted by cubic polynomial with only odd terms, as shown in Equation (11).…”

Section: Study On the Metal Transversal Flowmentioning

confidence: 99%

“…The law of metal transversal flow can be established by the finite element simulation results. Equation (12) shows the function between coefficient of first order a 1 in Equation (11) and relative crown at entry as well as similarity of profile D s . k D is the influence coefficient and shows the influence on a 1 of D s , b D remains constant when D s changes.…”

Section: Crown Setup For Hot Tandem Rollingmentioning

confidence: 99%

“…Zhang built a flatness calculation model via the ratio of crown to thickness, namely, relative crown . Shohet proposed the flatness calculation model known as Shohet theoretical, which is widely applied in traditional strip crown determination models . Bao and Sun built flatness calculation models considering the effect of flatness of uneven distribution of transducers and transversal temperature difference of strip, respectively .…”

Section: Introductionmentioning

confidence: 99%

“…[9] Shohet proposed the flatness calculation model known as Shohet theoretical, [10] which is widely applied in traditional strip crown determination models. [11][12][13][14] Bao and Sun built flatness calculation models considering the effect of flatness of uneven distribution of transducers and transversal temperature difference of strip, respectively. [15,16] During hot rolling, high temperature and deformation extent causes obvious metal transversal flow, [17] and it has great influence on strip flatness.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Flatness Analysis and Control of Strips with Different Thickness in 2250 mm Hot Tandem Rolling

Chai

Zhang

et al. 2018

steel research int.

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Aiming at the flatness difference between thick strip and thin strip under the same flatness control strategy on a 2250 mm hot tandem rolling line, a flatness calculation model is firstly established in this paper. With the unknown variable–transversal flow of metal solved by finite element methods, the flatness calculation model is used to analyze and compare the flatness and longitudinal residual stress evolution during thick strip and thin strip rolling. The result shows that under the premise of the constant exit relative crown in the downstream stands, the distribution of metal transversal flow due to larger reduction ratio in F2 and F3 stand causes the flatness of thin strip differing much from that of thick strip. In order to improve the flatness quality, a crown setup method is proposed. Based on the relationship among the distribution of metal transversal flow, entry relative crown, and the change of relative crown obtained from the finite element model, the exit crown of thin strip at each stand is optimized by genetic algorithm with a restriction of achieving the target crown at exit of F7 stand and a goal of good flatness quality during rolling.

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Section: Study On the Metal Transversal Flowmentioning

confidence: 99%

Section: Crown Setup For Hot Tandem Rollingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flatness Analysis and Control of Strips with Different Thickness in 2250 mm Hot Tandem Rolling

Chai

Zhang

et al. 2018

steel research int.

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“…3, where the steel coil can be regarded as an axisymmetric anisotropic body, that is, deformation and stress of the strip are irrelevant to the polar angle. Therefore, the equilibrium differential equation of the unit under the column coordinate can be expressed as [10,11] …”

Section: Geometric Equation and Equilibrium Differential Equation Of mentioning

confidence: 99%

Modelling the Dynamics and Secondary Deformation Behaviour of the Strip with Local Waves in Coiling Process

Sun¹,

Guan²,

Shao³

2016

Int. j. simul. model.

Self Cite

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With the introduction of 3D elastic-plastic deformation, and strip plastic flow factor concept, to analysis of the influence of strip local high points on the ridge-buckle behaviour in coiling process, an elastic-plastic coiling stress and ridge-buckle value model that can be used for online calculation was established. According to comparison and analysis of the influencing factors, uneven distribution of radial and circumferential stress caused by local waves is an important cause of strip ridge-buckle, and the ridge-buckle value increases with increases of local wave size, coil diameter and coiling tension, and significantly with decreases in the strip thickness. The influence of waves with different locations on the ridge-buckle value was analysed. Based on comparison of analysis results obtained by an elastic ridge-buckle value model and ANSYS FEM (finite element method), the accuracy and feasibility of this model have been proved, which will provide theoretical and model support for subsequent reduction of the ridge-buckle defects brought by local waves.

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Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

Zhang

Ding

Sun

et al. 2023

steel research int.

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In the hot strip process, the prediction of strip shape is a key factor to improve the quality of products. However, strip‐shape prediction models based on traditional machine learning algorithms with relatively simple network structures often rely on data preprocessing and feature selection. Herein, five strip‐shape prediction models are proposed by combining sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), extreme learning machine (ELM), and deep multilayer extreme learning machine (DELM). The prediction experiments show that the SSA‐DELM model has highest accuracy, and the determination coefficient (R2) of crown prediction and flatness prediction of SSA‐DELM model is 0.971 and 0.979. 100% of the samples have a prediction error of less than ±7 μm and ±7 IU. Furthermore, to solve the problem of concept drift affecting prediction accuracy in industrial processes, online optimization method is proposed. Especially, online sequential deep ELM model (OS‐DELM) optimized by SSA and guided by double window drift detection is proposed to fill the gap. Compared with other online optimization methods, the update time with this method can be reduced by up to 83.05% averagely, which is more suitable for online strip‐shape prediction in hot rolling process.

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Research on Crown & Flatness Allocation Strategy of Hot Rolling Mills

Cited by 19 publications

References 17 publications

Flatness Analysis and Control of Strips with Different Thickness in 2250 mm Hot Tandem Rolling

Flatness Analysis and Control of Strips with Different Thickness in 2250 mm Hot Tandem Rolling

Modelling the Dynamics and Secondary Deformation Behaviour of the Strip with Local Waves in Coiling Process

Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

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