More than 25 Years of Experience in Thin Slab Casting and Rolling Current State of the Art and Future Developments

Klinkenberg, Christian; Kintscher, Björn; Hoen, Karl; Reifferscheid, Markus

doi:10.1002/srin.201700272

Cited by 29 publications

(17 citation statements)

References 10 publications

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“…The steel industry has bid farewell to traditional production modes with the extensive application and development of modern automatic control theory in the industrial field. The combination of modern equipment and advanced technology has made the strip production process increasingly complex [1]. In the strip steel production process, multiple devices are organically connected.…”

Section: Introductionmentioning

confidence: 99%

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN‐ELM

Wang

Chen

2021

Journal of Sensors

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With the continuous development of the manufacturing industry, the requirement for strip steel quality is becoming higher and higher in automobile manufacturing, mechanical processing, and electronic and electrical industries. The precise control of strip quality depends on the accurate prediction of strip quality to a certain extent. However, the data collected by a large number of sensors on the complex strip production line and generated by the computer control system presents the characteristics of high dimensionality, high coupling, and nonlinearity, which brings difficulties to the prediction of strip quality. The continuous production of massive data in the production line also forces steel enterprises to seek new data mining methods, mining the relationship between sensor data to predict and control strip quality. To solve these problems, this paper proposes a GBDBN-ELM model, which is more efficient and more accurate than other algorithms. In this model, the RBM in DBN is replaced with GBRBM, so that RBM no longer depends on the binary distribution, can handle continuity values, and retain more data features. In order to solve the problem of too long DBN training time, this article replaces the BP network in DBN with an ELM regression model. The ELM model predicts the strip quality based on the extracted data abstract features, thereby improving the model’s prediction accuracy and shortening the training time. In this paper, the GBDBN-ELM model is compared with the BP neural network, ELM, and DBN, and root mean square error, R square coefficient of determination, and training time are selected as evaluation indexes of the models. The experimental results show that the improved GBDBN-ELM model can not only improve the accuracy of strip steel quality prediction but also shorten the time of model training. The model proposed in this paper has achieved good results in prediction accuracy and performance.

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Section: Introductionmentioning

confidence: 99%

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN‐ELM

Wang

Chen

2021

Journal of Sensors

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“…Consequently, solidifying structure of slabs produced by CSP is finer, resulting in inhibiting elements' segregation. Additionally, slabs produced by CSP remain above A 3 , avoiding precipitation before rolling, which is of great advantage for chemical homogenization, such that the strips exhibit a greater uniformity as regards chemical composition, microstructure, mechanical properties, and dimensional accuracy [35,36].…”

Section: Introductionmentioning

confidence: 99%

Effect of Solidifying Structure on Centerline Segregation of S50C Steel Produced by Compact Strip Production

Liu

Cheng

et al. 2021

Coatings

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Medium-high carbon steels having a high quality are widely used in China. It is advantageous to produce high value-added hot-rolled plates with the crystal refined and chemical composition homogenized in the casting slabs. However, element segregation occurs easily during high-medium carbon steels’ production. Generally, the centerline segregation is improved by enlarging the equiaxed zone with low-superheat casting and electromagnetic stirring (EMS). Studies were conducted on centerline segregation of S50C steel slabs with a thickness of 52 mm produced by the compact strip production (CSP) process in China without EMS equipped. By sampling along the width at different position, the secondary dendrite arm spacing (SDAS) was measured after etching and picture processing, based on which the cooling rate was calculated. It was found that the cooling rate increased from the center to the surfaces of the slabs ranging in 1~20 K/s, 10 times faster than that of a conventional process. The faster cooling rate led to a refined solidifying structure and columnar dendrite through the center of the slabs. The SDAS tended to increase from surfaces to the center, ranging only 32~120 μm smaller than that of a conventional process in 100~300 μm, indicating a finer solidifying structure by the CSP process. Results by EPMA indicated that elements C, Si, and Mn distribute in dispersed spots, increasing towards the center, and the centerline segregation changed in a narrow range: for C mainly in 1.0~1.1, Si in 0.98~1.08, Mn in 0.96~1.02, respectively, meaning a more chemical homogenization than that of thick slabs. Elements’ segregation originated from solute redistribution between solid and liquid. According to thermodynamic calculation, δ region of S50C is so narrow that the solute redistribution mainly occurred between γ-Fe and liquid during solidification. As the equilibrium partition coefficient of element C was the smallest, it was easy for C to be rejected to the residual liquid in the inter-dendritic space, leading to obvious segregation, relatively. Besides, as a result of high-cooling intensity, the solidifying structure became so fine that the Fourier number increased and the volume of the residual liquid decreased, making centerline segregation alleviated effectively both in volume and degree. Although bulging was observed during the industrial experiment, the centerline segregation was still inhibited obviously as the refining solidifying structure with permeability ranged only in 0.1~2.3 μm2 from the surfaces to centerline, which showed a good resistance on the residual flow towards the centerline.

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“…Owing to low capital and operating cost, thin-slab cast direct-rolling (TSCDR) has become a major process for hot flat-rolled production since Nucor started the first thin slab caster, directly linked to a hot rolling mill, back in 1989. This process is based on a novel funnel mold caster, which can produce a thin slab of thickness from 50 to 90 mm, instead of the conventional continuous casting slab thicknesses of 200 to 250 mm [1,2]. Figure 1 is an example of the TSCDR process at Algoma Inc.…”

Section: Introductionmentioning

confidence: 99%

“…The slab is rolled in a single pass roughing mill after descaling, then goes through the heated transfer table and is rolled in the six-stand finishing mill; the resulting hot strip then passes through the run-out table with a laminar cooling system and is coiled at the down coiler [3]. TSCDR mills currently produce a variety of steel grades, including interstitial free steel, low carbon to medium carbon steel, high-strength low-alloy, and advanced high strength multiphase steel grades [1]. Recently, great effort has been placed on using this process to produce high grade micro alloyed steels that can be utilized in bridge guard rails, wind turbine towers, rail cars, and oil and gas pipelines, with stringent low-temperature ductile to brittle transition (DBTT) and drop weight tear test (DWTT) requirements to maintain structural integrity and safety over several decades of service [4,5].…”

Section: Introductionmentioning

confidence: 99%

Control of Upstream Austenite Grain Coarsening during the Thin-Slab Cast Direct-Rolling (TSCDR) Process

Zhou

O’Malley

Zurob

et al. 2019

Metals

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Thin-slab cast direct-rolling (TSCDR) has become a major process for flat-rolled production. However, the elimination of slab reheating and limited number of thermomechanical deformation passes leave fewer opportunities for austenite grain refinement, resulting in some large grains persisting in the final microstructure. In order to achieve excellent ductile to brittle transition temperature (DBTT) and drop weight tear test (DWTT) properties in thicker gauge high-strength low-alloy products, it is necessary to control austenite grain coarsening prior to the onset of thermomechanical processing. This contribution proposes a suite of methods to refine the austenite grain from both theoretical and practical perspectives, including: increasing cooling rate during casting, liquid core reduction, increasing austenite nucleation sites during the delta-ferrite to austenite phase transformation, controlling holding furnace temperature and time to avoid austenite coarsening, and producing a new alloy with two-phase pinning to arrest grain coarsening. These methodologies can not only refine austenite grain size in the slab center, but also improve the slab homogeneity.

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More than 25 Years of Experience in Thin Slab Casting and Rolling Current State of the Art and Future Developments

Cited by 29 publications

References 10 publications

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN‐ELM

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN‐ELM

Effect of Solidifying Structure on Centerline Segregation of S50C Steel Produced by Compact Strip Production

Control of Upstream Austenite Grain Coarsening during the Thin-Slab Cast Direct-Rolling (TSCDR) Process

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