A New Mill Design for the Production of a Strip with Zero Strip Crown

Zamanian, Ahmad; Nam, Seung Yeon; Hwang, S.M.

doi:10.1002/srin.201970111

Cited by 5 publications

(5 citation statements)

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“…The whole tandem mill (20) = 0.88855 − 0.91753 * exp(−1.49848E − 5 * Q) Fig. 17 The efficiency curves of entry strip crown on exit strip crown Fig. 18 The strip crown inheritance factor from S1 to S5 can be divided into two parts: upstream stands (S1, S2, S3) and downstream stands (S4, S5).…”

Section: The Formation Mechanism Of Strip Shape Under Different Entry...mentioning

confidence: 99%

“…Cao et al [16] applied the FEM to design new roll contours to improve the edge drop and strip crown in four-high and sixhigh tandem cold mills. Zamanian et al [17] evaluated the strip crown control performance of a new uniform thickness control mill using a 3D symmetrical elastic-plastic FE model. Wang et al [18] explored the effects of IRS on strip edge drop in a six-high tandem cold mill employing a 3D elastic-plastic FE model based on the explicit method.…”

Section: Introductionmentioning

confidence: 99%

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Numerical analysis of the strip crown inheritance in tandem cold rolling by a novel 3D multi-stand FE model

Xie

Liu

et al. 2022

Int J Adv Manuf Technol

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The strip shape inheritance model is widely applied to improve the strip shape quality in tandem cold rolling (TCR). However, the inheritance mechanism is still currently unclear. To bridge this gap, this paper presents a new numerical method for calculating the strip crown inheritance factor. In addition, the effects of the entry strip crown on exit strip crown and flatness were quantitatively analysed at each stand in the TCR using a novel three-dimensional (3D) multi-stand elastic–plastic finite element (FE) model. The results show that the strip crown inheritance factor increases slowly from S1 (stand 1) to S3 (stand 3), while rising sharply from S3 to S5 (stand 5), reaching a peak value of 0.495 μm/μm at S5. This trend coincides with that of strip plastic rigidity, which verifies that the strip crown inheritance factor is dependent on the strip plastic rigidity. Furthermore, the variation of strip crown and flatness under different entry strip crowns from S1 to S5 is jointly influenced by the pass reduction and strip plastic rigidity. Moreover, the strip crown inheritance factor increases with the deformation resistance of the strip at both S1 and S5. These findings not only offer a fresh perspective to understand the mechanism of strip crown inheritance, but also provide an important basis for optimising the strip shape control in the TCR process.

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Section: The Formation Mechanism Of Strip Shape Under Different Entry...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical analysis of the strip crown inheritance in tandem cold rolling by a novel 3D multi-stand FE model

Xie

Liu

et al. 2022

Int J Adv Manuf Technol

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show abstract

“…So far, many studies have been made to further comprehend the strip crown control capacity of hot rolling mills in the rolled procedure [13]. Zamanian et al [14] designed a type of new mill for the product of a strip with zero strip crown. Zhao et al [15] proposed a high precision shape model for hot strip rolling.…”

Section: Hot Rolled Strip and Description Of Strip Crown 21 Hot Rollmentioning

confidence: 99%

Application of support vector machine for the prediction of strip crown in hot strip rolling

Song

Yuan

et al. 2020

Preprint

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In order to enhance the prediction accuracy of the strip crown and improve the quality of final product in the hot strip rolling, an optimized model based upon support vector machine (SVM) is proposed firstly. Meanwhile, for purposes of enriching data information and ensuring data quality, the actual data from a hot-rolled plant are collected to establish prediction model, as well as the prediction performance of models was evaluated by using multiple indicators. Besides, the traditional SVM model and the combined prediction models with the particle swarm optimization (PSO) and the cuckoo search (CS) optimization algorithm are also proposed. Furthermore, the prediction performance comparisons of the three different methods are discussed and validated. The results show that the CS-SVM has the highest prediction accuracy compared to the other two methods, and the root mean squared error (RMSE) of the proposed CS-SVM is 2.05µm, and 98.11% of prediction data have an absolute error below 4.5μm. In addition, the results also demonstrated that the CS-SVM not only with faster convergence speed and higher prediction accuracy but can be well applied to the actual hot strip rolling production.

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“…So far, many studies have been made to further comprehend the strip crown control capacity of hot rolling mills in the rolling procedure [4]. ZAMANIAN et al [5] designed a new mill for the product of a strip with zero strip crown. ZHAO et al [6] proposed a high-precision shape model for hot strip rolling.…”

Section: Introductionmentioning

confidence: 99%

Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

Song

Sun

et al. 2021

J. Cent. South Univ.

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To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown, an optimized model based on support vector machine (SVM) is put forward firstly to enhance the quality of product in hot strip rolling. Meanwhile, for enriching data information and ensuring data quality, experimental data were collected from a hot-rolled plant to set up prediction models, as well as the prediction performance of models was evaluated by calculating multiple indicators. Furthermore, the traditional SVM model and the combined prediction models with particle swarm optimization (PSO) algorithm and the principal component analysis combined with cuckoo search (PCA-CS) optimization strategies are presented to make a comparison. Besides, the prediction performance comparisons of the three models are discussed. Finally, the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed. Furthermore, the root mean squared error (RMSE) of PCA-CS-SVM model is 2.04 µm, and 98.15% of prediction data have an absolute error of less than 4.5 μm. Especially, the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.

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A New Mill Design for the Production of a Strip with Zero Strip Crown

Cited by 5 publications

References 0 publications

Numerical analysis of the strip crown inheritance in tandem cold rolling by a novel 3D multi-stand FE model

Numerical analysis of the strip crown inheritance in tandem cold rolling by a novel 3D multi-stand FE model

Application of support vector machine for the prediction of strip crown in hot strip rolling

Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

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