Strip Rolling Research Articles

Prediction and control of profile for silicon steel strip in the whole tandem cold rolling based on PSO-BP algorithm

In the rolling process of silicon steel strip of the five-stand UCMW tandem cold rolling line, the quality of finished profile is the result of cooperative control of five stands, the exit profiles of the first four stands jointly decide the exit profile of the fifth stand, so it is necessary to accurately control the strip profile in the rolling process. However, the shapemeter is only typically equipped at the exit of fifth stand, making it impossible to obtain the strip profile of the first four stands. A profile prediction model for the whole tandem cold rolling based on PSO-BP algorithm is established, compared with the finite element method, this model can satisfy the demand for fast prediction of strip profile and quickly analyzing the profile change characteristics for five stands according to the adjustment of profile control parameters in the industrial field. Firstly, a 3D elastic-plastic finite element model of rolls and strip for five stands is built, and a large number of profile calculation data are as the training sample sets to build the PSO-BP model. Through the transfer calculation of strip profile between adjacent stands, the PSO-BP model of five stands is established, and the validity is verified by comparing with the actual measured data. Secondly, to improve the quality of product profile, focusing on the unreasonable setting of production parameters, many improvement schemes of WRB and WRS for five stands are designed, and the PSO-BP model is used to quickly calculate the strip profile, then comparing the result of each scheme, the optimum improvement scheme is selected, which is furtherly carried out rolling experiments in the industrial field, and the quality of strip profile is well improved. At present, the new scheme has been successfully applied to industrial production.

Simulation and Study of Influencing Factors on the Solidification Microstructure of Hazelett Continuous Casting Slabs Using CAFE Model.

The Hazelett continuous casting and rolling process represents a leading-edge production method for cold-rolled aluminum sheet and strip billets in the world. Its solidification microstructure significantly influences the quality of billets produced for cold rolling of aluminum sheets and strips. In this study, employing the CAFE (Cellular Automaton-Finite Element) method, we developed a coupled computational model to simulate the solidification microstructure in the Hazelett continuous casting process. We investigated the impact of nucleation parameters, casting temperature, and continuous casting speed on the microstructural evolution of the continuous casting billet. Through integrated metallographic analyses, we aimed to elucidate the controlling mechanisms underlying the Hazelett continuous casting process and its resultant microstructure. The results demonstrate that the equiaxed rate of grains increases with an increase in nucleation density, and the grain size decreases under constant cooling strength. With other nucleation parameters held constant, the grain size decreases as undercooling increases, and the columnar crystal zone expands. The nucleation density of the Hazelett continuous casting aluminum alloy has been determined to range between 1011 m-3 and 1013 m-3, and the undercooling ranges between 1 °C and 2.5 °C. The solidified grain structure can be controlled between 35 μm and 72 μm. The grain size of the continuous casting billet increases with an increase in pouring temperature and decreases as the casting speed increases. Elevating the pouring temperature positively impacts the fraction of high-angle grain boundaries and promotes the dendritic to equiaxed grain transition. Moreover, there exists potential for further optimization of continuous casting process parameters.

Effect of Sodium Alkane Sulfonate Addition on Tribological Properties of Emulsion for Cold Rolling Strips: Experimental and Simulation Investigations

Effect of Sodium Alkane Sulfonate Addition on Tribological Properties of Emulsion for Cold Rolling Strips: Experimental and Simulation Investigations

Modular Finite Element Modeling of Heavy Plate Rolling Processes Using Customized Model Reduction Approaches

Heavy plates are indispensable semi-finished products. Quality is strongly linked with production, so the rolling process must be performed within well-defined narrow tolerances. To meet this challenge, adequate modeling has become a necessity. In contrast to continuous strip rolling, where the workpiece can be modeled as a semi-infinite strip and 2D modeling can be argued quite well, this strategy is insufficient for the comprehensive modeling of heavy plate rolling. The geometry of the heavy plate favors an inhomogeneous distribution of relevant state variables, such as temperature. In addition, if the process involves longitudinal and spreading passes, the required plate rotation spoils the assumption of a symmetric arrangement that might have been acceptable before rotation. Consequently, the derivation of suitably reduced models is not trivial, and modeling tailored to the specific objective of investigation is of utmost importance. Models intended to resolve the evolution of inhomogeneities in the field variables are demanding and computationally expensive. An effective modular modeling strategy was developed for such models to be used offline. Mutually complementing and interchangeable modules may constitute an efficient modeling strategy valid for the specific subject of interest. The presented approach reduces the enormous cost of complete 3D simulation as much as the model purpose allows for.

Stacking ensemble learning fused with whale optimisation algorithm based method for crown prediction of hot-rolled strip

Precision of strip shape is one of the important indexes to measure the quality of strip steel in hot rolling. Accurate prediction of strip shape can realise the timely adjustment of the production system, which is the important foundation for ensuring the quality and stable production of hot-rolled strip steel products. Given the poor accuracy of the traditional mechanism model, this paper proposes a stacking ensemble learning for crown prediction that consist of the random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), categorical boosting (CatBoost) and linear regression, whose hyperparameters are optimised by whale optimisation algorithm (WOA) Firstly, the key features are selected by analysing the feature importance and the Pearson correlation coefficient between rolling parameters. Then, the hyperparameters of the stacking ensemble learning are optimised by four meta-heuristic algorithms during modeling process, among which WOA has the best optimisation effect. Compared to RF, XGBoost, LightGBM, CatBoost and the stacking, proposed method has the highest prediction accuracy that the hit rate reaches 98.58% in the range of 5.0 μm of crown deviation. In addition, the effect of strip width, bending force, rolling force and roll shifting on crown are analysed based on Shapley additive explanations (SHAP).

IoT-based framework for digital twins in steel production: A case study of key parameter prediction and optimization for CSR

Cold-rolled strips are a pivotal product of advanced strip rolling technology in iron and steel production with stringent process standards. Over an extended period, the existence of 'information silos' between cold strip rolling (CSR) and its upstream processes, particularly hot strip rolling (HSR), causes a persistent challenge in achieving collaborative optimization throughout the entire process, thereby constraining progress in product quality. To overcome this challenge, a digital twin framework was proposed to consolidate diverse data from HSR and CSR production lines into a centralized data centre, forming a 'data continent.' This approach establishes a comprehensive dataset for model-driven training and prediction in virtual space. An improved Light gradient boosting machine (IlightGBM) is introduced, enabling high-precision rolling force prediction in unsteady-state rolling and providing essential variables for establishing the tension optimization objective function. A control function is implemented to minimize rolling force fluctuations, and three population algorithms (TPA) are devised for its solution. This strategy successfully realizes collaborative tension optimization across multiple stands, with the rolling speed acting as a follower. The proposed approach was implemented in a large-scale steel plant equipped with a complete HSR and CSR production line, effectively mitigating flatness defects and thickness deviation in the strip's head section. Thus, the proposed framework is reliable and can be flexibly applied to strip manufacturing processes.

Estimation of the Possibility of Occurrence of Two Neutral Cross Sections in the Center of Deformation during Cold Rolling of Thin Wide Strips

Estimation of the Possibility of Occurrence of Two Neutral Cross Sections in the Center of Deformation during Cold Rolling of Thin Wide Strips

Friction-mediated tuning of shear deformation and microstructure in asymmetrically rolled Mg–1Gd alloy strips

Asymmetric rolling (ASR) is distinguished from conventional symmetric rolling (SR) process as the mismatched rolling speeds of two rolls introduce additional shear deformation to the rolled strips. Friction between the rolls and strip plays an important role in the formation of shear during ASR, which further impacts the microstructures of metallic strips. In this study, finite element (FE) simulations are used to probe the effects of friction in ASR of magnesium (Mg) alloy strips with varied roll speed ratios. The results on shear deformation distributions through the thickness of the strips reveal that the increase of friction coefficient or roll speed ratio induces greater shear deformation simultaneously. It is also found that the roll speed ratio can only promote the shear formation up to a threshold value which is dictated by the friction coefficient, further increasing the mismatch of the roll speeds provides negligible improvement on the plastic deformation of the strips. Experimentally, Mg–1Gd alloy strips are processed by SR and ASR, followed by electropulse treatment (EPT) to induce static recrystallization (SRX). The predictions of the numerical models are verified via EBSD and XRD observations on the grain refinement and texture weakening in Mg–1Gd alloy, which are the direct microstructure consequences of plastic deformations during rolling. At the roll speed ratio of 1.67, the alloy shows a weakened basal texture with an average grain size of 6.0[Formula: see text][Formula: see text]m after SRX. Further increasing the roll speed ratio to 5, however, does not enhance the grain refinement or texture weakening significantly.

Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling

The concept of production stability in hot strip rolling encapsulates the ability of a production line to consistently maintain its output levels and uphold the quality of its products, thus embodying the steady and uninterrupted nature of the production yield. This scholarly paper focuses on the paramount looper equipment in the finishing rolling area, utilizing it as a case study to investigate approaches for identifying the origins of instabilities, specifically when faced with inadequate looper performance. Initially, the paper establishes the equipment process accuracy evaluation (EPAE) model for the looper, grounded in the precision of the looper’s operational process, to accurately depict the looper’s functioning state. Subsequently, it delves into the interplay between the EPAE metrics and overall production stability, advocating for the use of EPAE scores as direct indicators of production stability. The study further introduces a novel algorithm designed to trace the root causes of issues, categorizing them into material, equipment, and control factors, thereby facilitating on-site fault rectification. Finally, the practicality and effectiveness of this methodology are substantiated through its application on the 2250 hot rolling equipment production line. This paper provides a new approach for fault tracing in the hot rolling process.

Microstructure evolution and strengthening mechanism of FeCo-1.5V0.5Nb0.4 W soft magnetic alloy rolled strip with high yield strength and low coercivity

The low strength of soft magnetic alloy limits the ability to operate high speed rotors in aero-generators. Strengthening of soft magnetic alloys remains a challenge, as most methods that enhance strength lead to the deterioration of magnetic properties. In this work, a rolled strip of FeCo-1.5V0.5Nb0.4W (wt.%) alloy with high yield strength and low coercivity was developed. We demonstrated that minor additions of Nb and W has a little effect on the magnetic properties. Due to the microalloying has a negligible effect on magnetocrystalline anisotropy, and low content of the secondary phase. Additionally, solute atoms are enriched at the grain boundaries, which reduces the grain boundary energy and promotes the nucleation and growth of Laves-Co2Nb phase at the grain boundary by grain boundary diffusion. These lead to almost stagnation of grain growth during isothermal annealing at 1123 K for 3 h and stabilization at ∼6 μm. Therefore, based on the reduction of dislocation density by high-temperature annealing to restore the magnetic properties of the alloy, fine-grain strengthening coupled with solid solution strengthening, and a certain amount of Orowan bypass effect and dislocation strengthening contribution, provide for the high strength of the alloy. The yield strength of the rolled strip reached 742 MPa under the conditions of no significant decrease in the saturation magnetization of 2.39 T and an extremely low coercivity of 152 A/m. These results are of great significance for understanding the relationship between the strengthening mechanisms of metallic materials and further developing high-performance FeCo-based soft magnetic alloys.

A hybrid approach towards evaluation of average heat transfer coefficient in twin-roll strip casting mold

Continuous casting stands out as one of the most efficient and productive methods for producing billets, slabs, or thin sheets while conserving energy. Understanding the solidification dynamics and achieving defect-free sheets in continuous casting necessitates the accurate prediction of heat transfer coefficient (HTC) at the mold metal interface. Accurate prediction of HTC helps in selecting process parameters such as superheat temperature and casting velocity, which directly affect the production rate and quality of the cast component. Determining the HTC is a complex process, as it is influenced by various process parameters such as casting size, casting speed, superheat temperature, surface roughness, type of water used as a coolant, nozzle design and wettability. Therefore, the present study proposed a hybrid approach for finding HTC in a twin roll strip casting mold through a solidified shell shape, which is influenced by the factors affecting the heat transfer at the mold-metal interface. This approach integrates the experimental and simulation investigations. The numerical investigation used a three-dimensional coupled fluid flow and heat transfer equations with an assumed range of HTC (100–2000 W/(m2K)). The solidified shell shape for different HTCs was compared with the experimental solidified shape form at the outer wall of the mold using the solidification criteria. Results indicate that the initial HTC range of 100–1000 W/(m2K) does not lead to blockage of the graphite mold outlet. However, as HTC values approach 1000 W/(m2K) to 1400 W/(m2K), the melt obstructs the mold exit based on solidification criteria. The HTC values (1350 ± 150 W/(m2K)) obtained from the proposed methodology for mold, align well with the HTC values (1183 ± 593 W/(m2K)) calculated using lamellar spacing of the Al-Mg2Si composite microstructure. This hybrid approach presents a novel and straightforward method for determining HTC, offering accurate predictions that can enhance the precision of continuous casting models and provide a more nuanced understanding of solidification behavior.

Effect of Nb-Ti Microalloyed Steel Precipitation Behavior on Hot Rolling Strip Shape and FEM Simulation.

Strip shape control is a hotspot and challenge in strip rolling, where the development trend of rolling technology is towards high strength, high toughness, and a large width-to-thickness ratio. The influence of material microstructure evolution on strip shape control is being increasingly emphasized. In this paper, a Nb-Ti microalloyed steel is taken as the research object. Thermodynamic and kinetic models focusing on the precipitation of the austenite phase are established to quantify the precipitation process. A coupled model of rolls and strips is built using ABAQUS 2022 software, where the precipitation strengthening model and high-temperature constitutive model are embedded into the finite element model (FEM) through subroutines. A two-dimensional alternating differential model is employed to acquire real-time temperature differences in the width direction of the strip. The effects of precipitation inclusion and exclusion on the strip crown under different operating conditions are compared and analyzed. The results indicate that as the temperature decreases, the strengthening effect increases, reaching around 40 MPa at temperatures above 1000 °C and 96.6 MPa at 800 °C. Furthermore, the inclusion of crown in the precipitation consideration is more sensitive to overall temperature changes, but as the strip width decreases, the sensitivity of crown to temperature decreases. The research findings of this paper provide guidance for improving strip shape control and reducing abnormalities during the rolling process.

Preparation, Characterization, and Lubrication Performances of Water-Based Nanolubricant for Micro Rolling Strips.

Water-based nanolubricants are widely used in rolling processes due to their unique characteristics. As a common additive, nanoparticles could significantly improve the tribological properties of the lubricant. However, the effect of the physical properties of the particles on the anti-friction behavior is unclear. In this study, the effect of Fe3O4 nanoparticles as an additive for the prepared lubricant is studied. The tribological properties of Fe3O4 water-based nanolubricant are examined using a tribometer and a scratch meter. The absorption energy is calculated using the molecular dynamic simulation method, and the best parameters for the preparation of the nanolubricant are obtained. The developed nanolubricant is used in the rolling process. The results show that the processing quality of samples is promoted and the tribological properties of water-based lubricant can be significantly promoted by an Fe3O4 nanoparticle additive. An economical and environmentally friendly method is presented through which the water-based Fe3O4 nanolubricant can be prepared for the replacement of oil-based lubricant in cold rolling strips.

Correction to: Numerical and analytical estimation of rolling force and torque in hot strip rolling

Correction to: Numerical and analytical estimation of rolling force and torque in hot strip rolling

Modeling and Simulation of Shape Control Based on Digital Twin Technology in Hot Strip Rolling.

Focusing on the problem of strip shape quality control in the finishing process of hot rolling, a shape model based on metal flow and stress release with the application of varying contact rolling parameters is introduced. Combined with digital twin technology, the digital twin framework of the shape model is proposed, which realizes the deep integration between physical time-space and virtual time-space. With the utilization of the historical data, the parameters are optimized iteratively to complete the digital twin of the shape model. According to the schedule, the raw material information is taken as the input to obtain the simulation of the strip shape, which shows a variety of export shape conditions. The prediction absolute error of the crown and flatness are less than 5 μm and 5 I-unit, respectively. The results prove that the proposed shape simulation model with strong prediction performance can be effectively applied to hot rolling production. In addition, the proposed model provides operators with a reference for the parameter settings for actual production and promotes the intelligent application of a shape control strategy.

Fractional-order backstepping sliding mode control for the speed tension and screw down system of the reversible cold strip rolling mill

This paper investigates the robust control problem of the speed tension and screw down system of Reversible Cold Strip Rolling Mill (RCSRM). A finite-time observer is proposed to observe the uncertainties of the system. Based on the finite time observer, a fractional-order backstepping sliding mode controller is designed for each sub-system of the speed tension and screw down system of RCSRM. The Particle Swarm Optimization (PSO) algorithm is used to optimize the main parameters of the designed controller. By Lyapunov stability, the tracking error of the system can converge to the neighbourhood of the equilibrium point in a finite time. Finally, simulation research is carried out on the actual data of a 1422 mm RCSRM, and the effectiveness of the designed controller is verified.

Research on the control strategy of coning amount and ASU adjustment considering profile and high-order flatness cooperative control

ABSTRACT In the production of a 20-high mill, high-order flatness defects occur frequently. To achieve collaborative control of the profile and high-order flatness of a 20-high mill, an integrated coupling model of the roll system and strip has been established based on ABAQUS software. It has been found that as the coning amount of the intermediate taper roll increased, the profile evaluation index C25 decreased, while the work roll axis exhibited high-order deflection deformation, which could lead to the generation of high-order flatness defects. Subsequently, a novel control strategy of the coning amount and backup roll segmented reduction (ASU) control adjustment has been proposed to ensure that the profile evaluation index C25 meets the requirements while eliminating the risk of high-order flatness defects caused by roll shifting (coning amount) control. The specific combination form of the control strategy has been determined by using the particle swarm optimisation (PSO) algorithm. The Industrial application results show that the maximum compressive stress in the area 75 mm-300 mm away from the edge can be reduced by more than 40% after using the novel control strategy.

Effect of ceramic work rolls on surface roughness of rolled SUS304 ultra-thin strips

Effect of ceramic work rolls on surface roughness of rolled SUS304 ultra-thin strips

Prediction of contact pressure during cold rolling of thin strips with predominance of front tension

The results of theoretical studies of the average relative contact stress during cold rolling of thin steel strips with a predominance of front tension are presented. The transition from symmetric to the asymmetric tension mode helps to reduce the contact stress by 6.4...13.5 %. This makes it possible to increase the service life of rolling rolls. A regression equation is obtained for predicting of the average relative contact stress for the studied range of change in the main technological parameters.

Eco-friendly lubricants for improving performance and environmental impact in cold rolling

Cold rolling of strips is a well-known metal forming process, carried out below the material recrystallization temperature to improve the surface finish and the geometrical tolerance of the rolled strip. Friction plays a fundamental role in the rolling process and, even in case of a favorable roll bite condition, a non-optimal lubrication causes the rapid wear of the work rolls and high energy consumptions. Most of the currently used lubricants are based on synthetic or semi-synthetic formulations. However, the increase in the cost of petroleum-based products and the tightening of the environmental regulations is pushing the scientific and industrial community to search for more sustainable alternatives. Recently, the demand for green lubricants capable of matching non-toxicity, high biodegradability while keeping good lubricating performance is becoming of outmost importance. In the present work, the environmental impact and the lubricating performance of two innovative natural-based lubricants have been assessed using laboratory tests. An animal-based (AB) and a vegetable-based (VB) formulation were investigated and the results compared with a commercial mineral-based (MB) one. As concerns the environmental impact, emulsions at 2% oil concentration were chemically treated using a powder composed of bentonite and activated carbons and the Chemical Oxygen Demand (COD) and total Surfactants in the aqueous phase were evaluated. The lubricating performance of the emulsions at 2% oil concentration was assessed by conducting tribological tests using the Falex tribometer in accordance with the Standard. The experimental comparison revealed that it is not only feasible but also convenient in terms of both environmental impact and performance to replace the mineral-based lubricant.