Active disturbance rejection control for strip steering control in hot strip finishing mill

Kwon, Wookyong; Kim, Sung-Bin; Won, Sangchul

doi:10.1016/j.ifacol.2015.10.075

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…Steel strips are manufactured from cast slabs, which undergo several times between a pair of work rolls with decreasing gaps until the achievement of the intended thickness reduction [20,21,39,40]. In Steckel mill lines, during the hot rolling process, the strips are driven by the roller table in the rolling direction.…”

Section: Introductionmentioning

confidence: 99%

Automatic monitoring of steel strip positioning error based on semantic segmentation

Lemos

Silva

Nagy

2020

Int J Adv Manuf Technol

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The misalignment of steel strips in relation to the roller table centerline still is an impairment for the rolling mill production lines. Nowadays, the strip position correction remains largely in the purview of human analysis, in which the strip steering is traditionally a semi-manual operation. Automating the alignment process could reduce the maintenance costs, damage to the plant, and prevent material losses. The first step into the automatization is to determine the strip position and its referred error. This study presents a method that employs semantic segmentation based on convolution neural networks to estimate steel strips positioning error from images of the process. Additionally, the system mitigates the influences of mechanical vibration on the images. The system performance was assessed by standard semantic segmentation evaluation metrics and in comparison with the dataset ground truth. The results showed that 97% of the estimated positioning errors are within a 2-pixel margin. The method demonstrated to be a robust real-time solution as the networks were trained from a set of low-resolution images acquired in a complex environment.

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Section: Introductionmentioning

confidence: 99%

Automatic monitoring of steel strip positioning error based on semantic segmentation

Lemos

Silva

Nagy

2020

Int J Adv Manuf Technol

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“…The slab form can be modified or corrected by the technique of continuous variable crown (CVC) of the work rolls [5,6]. In finishing mill stands, however, tilt angle control and differential force control are quite sophisticated and require very fast response due to the elevated rolling speed of the thinning strip and multiple close-to-one-another mill stands [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

The Mechanism of Position-Mode Side Guide in Correcting Camber in Roughing Process of a Hot Strip Mill

Hsu

Aoh

2019

Metals

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The mechanism of the position-mode side guide in correcting slab centerline profile and camber in the roughing process of a hot strip mill (HSM) was analyzed using finite element simulation. The finite element model was established based on the actual size of the roughing mill and on the actual actuating time sequence of the roughing mill in China Steel Corporation (CSC), Kaohsiung. This work could be the first to give an insight into the mechanism of side guides in correcting the slab camber. Time sequence analysis was explored to visualize the progress of centerline profile variation and the interaction between the slab and the related roughing mill components at different moments. The history of reaction forces exerted on the slab was analyzed to explain the interaction between roughing mill components and the slab. The effect of the separation distance of side guide and the effect of the slab wedge on the centerline profile was investigated. A schematic model illustrating the reactions and the resulting moments exerted on the slab was created. By examining the force history, the cross-sectional strain/stress distribution, and the roll force across the horizontal roller, the correcting mechanism of the side guide could be elucidated. The simulation results provide further knowledge in selection and dimension design of side guide to improve the effectiveness of side guide in correcting the slab profile.

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“…Therefore, this manual adjustment should be replaced by an automatic control system with a camber control model. For this purpose, some physical control models suitable for hot rolling production are developed [8][9][10][11][12][13][14][15]: some of these provide methods for predicting slab camber in the rolling process based on the three-dimensional deformation and movement characteristics of the slab in the rolling gap, and others provide a dynamic real-time online control system based on the measurement of the shape and the position of the slab. However, due to the strong nonlinear coupling and unmeasurable time-varying variables, no simple physics-based model has been developed that includes all the influences and have a good adaptive capability.…”

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confidence: 99%

Application of Takagi–Sugeno fuzzy model for slab camber control in a hot strip rougher mill

Yang

Wang

et al. 2020

Ironmaking & Steelmaking

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Control of slab camber is an important topic in hot strip rough rolling process. In this study, a slab camber control system, composed by a camber prediction model and a gap levelling value calculation model, based on the Takagi-Sugeno (T-S) fuzzy model is proposed. The performance of the developed camber prediction model is evaluated by the estimation error between the predicted and measured values of slab camber after rough rolling with the validation set from a rougher mill. The results show that the percentage of bars with estimation error within the tolerance of ±30 mm is 94.1%. Moreover, the proposed control model is finally applied in the 1580 mm hot rough rolling process. The control model can reliably provide the suggested adjustment of gap levelling, and the percentage of slabs, of which the adjustment error meets the requirement for camber quality assurance, is 95.85%.

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Active disturbance rejection control for strip steering control in hot strip finishing mill

Cited by 9 publications

References 12 publications

Automatic monitoring of steel strip positioning error based on semantic segmentation

Automatic monitoring of steel strip positioning error based on semantic segmentation

The Mechanism of Position-Mode Side Guide in Correcting Camber in Roughing Process of a Hot Strip Mill

Application of Takagi–Sugeno fuzzy model for slab camber control in a hot strip rougher mill

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