Hot Strip Rolling Research Articles

Predicting stress and flatness in hot-rolled strips during run-out table cooling

Flatness defects are complicated issues that occur during hot-rolled strip production. With more research on flatness control during the rolling process, flatness control technology during the rolling stage has improved. However, there is no online method for measuring strip flatness during the cooling stage after rolling. Therefore, this study focused on the evolution of stress and flatness of steel strips during run-out table cooling. Providing guidance for strip flatness controlled during run-out table cooling. A fully coupled finite element model based on temperature, phase transitions, and strain was developed. The calculated results of the model were in good agreement with the measured data. Furthermore, the patterns of evolution of stresses and flatness of the steel strip during run-out table cooling were obtained. The results showed that large compressive stresses formed along the edge region, as well as small tensile stresses in the middle region, causing edge wave defects to occur. With an increase in the initial temperature drop distance of the strip edge area, the edge wave degree also increased. Moreover, the cooling efficiencies of the upper and lower steel strip surfaces were not consistent, resulting in uneven distributions of stresses along the thickness of the material. Finally, the longitudinal and transverse warpage phenomena during the cooling process were discussed in detail.

Strip Thickness and Profile–Flatness Prediction in Tandem Hot Rolling Process Using Mechanism Model‐Guided Machine Learning

This study is focused on the multivariable, nonlinear, strong‐coupling, and time‐varying characteristics of the quality control process of hot strip rolling. Using the rolling process data from a continuously variable crown (CVC) mill of a 1580 mm hot tandem rolling line, a new prediction model of the strip thickness, profile, and flatness in hot rolling based on rolling mechanism model‐guided machine learning (ML) is developed. The weighted processing (WP) method is used to fuse the rolling mechanism and process data, to improve the relationship between the strong correlation features and the model. Combined with nondominated sorting genetic algorithm III (NSGA‐III), multiple parameters of multioutput support vector regression (M‐SVR) are optimized. The results show that the root mean square error (RMSE), mean square correlation coefficient (R 2), and mean absolute error (MAE) of the strip thickness, crown, and flatness are 2.0159, 1.3191, and 0.9355; 0.9854, 0.9895, and 0.9873; and 16.601, 1.126, and 0.604, respectively. Moreover, the established method of data fusion rolling mechanism shows strong capability to improve the model prediction accuracy, increasing it by 60%. Thus, it can offer theoretical guidance for realizing accurate control of the quality of strips and improving the quality of hot‐rolled products.

Coil shape defects prediction algorithm for hot strip rolling based on Siamese semi-supervised DAE-CNN model

Purpose Coil shape quality is the external representation of strip product quality, and it is also a direct reflection of strip production process level. This paper aims to predict the coil shape results in advance based on the real-time data through the designed algorithm. Design/methodology/approach Aiming at the strip production scale and coil shape application requirements, this paper proposes a strip coil shape defects prediction algorithm based on Siamese semi-supervised denoising auto-encoder (DAE)-convolutional neural networks. The prediction algorithm first reconstructs the information eigenvectors using DAE, then combines the convolutional neural networks and skip connection to further process the eigenvectors and finally compares the eigenvectors with the full connect neural network and predicts the strip coil shape condition. Findings The performance of the model is further verified by using the coil shape data of a steel mill, and the results show that the overall prediction accuracy, recall rate and F-measure of the model are significantly better than other commonly used classification models, with each index exceeding 88%. In addition, the prediction results of the model for different steel grades strip coil shape are also very stable, and the model has strong generalization ability. Originality/value This research provides technical support for the adjustment and optimization of strip coil shape process based on the data-driven level, which helps to improve the production quality and intelligence level of hot strip continuous rolling.

Analysis of the mechanism of chromatic aberration defects on A510L surface of high quality hot rolled pickled strip

In view of the chromatic aberration defects on the surface of hot-rolled strip A510L after pickling, the microscopic morphology features of different positions of the pickled strip surface were compared and analyzed by using laser confocal microscope (CLSM), and the generation mechanism of chromatic aberration defect was studied by using high-resolution scanning electron microscope and energy dispersive spectrometer (EDS). The results show that the main cause of surface chromatic aberration defects on the surface of pickling plate A510L is closely related to the interface state between hot-rolled raw material substrate and iron oxide scale, and there is an enrichment area of Fe2SiO4/FeO binary eutectic near the hot-rolled raw material substrate and iron oxide scale, and the proportion of Fe2SiO4 is higher as it approaches the strip steel matrix, and Fe2SiO4/FeO has a strong pinning effect, which leads to the adhesion of iron oxide scale. The existence of Fe2SiO4/FeO changes the structure of carbon steel iron oxide scale, which makes the bonding force between iron oxide scale and strip steel matrix increase the difficulty of pickling process, and finally presents the surface chromatic aberration defects after pickling.

Analysis of surface blackening defects on hot-rolled pickled steel

There are many blackening defects on the steel surface after the pickling of the hot-rolled strip. The microstructure and roughness of the picked steel surface were analyzed using SEM and LEXT OLS4000 laser scanning confocal microscope, XPS, and surface roughness measure. The experiment result shows that the blackening defects of the hot-rolled pickled steel sheet surface are aging of anti-rust oil film to the strip steel surface. In order to improve the surface quality of the hot rolling pickled sheet, the anti-rust oil coating process is improved, the thickness and uniformity of the oil film are controlled, and the management in the course of packing, storage and transportation are strengthened.

Shifting strategy optimization of the thermal work roll in hot strip rolling based on three particle swarms and differential evolution algorithm

ABSTRACT During fast-paced rolling of the same type strip of hot tandem rolling, the thermal expansion of the work rolls has a significant influence on the shape of the on-load roll gap, which needs to be considered in the free shifting of the work rolls. In this paper, the thermal crown evaluation index and the thermal expansion simulation model of the work roll are established, and the influence of different roll shifting parameters on the thermal crown of the roll in the service cycle of the work roll is analyzed. A special roll shifting strategy of the downstream stand is designed, and intelligent optimization is carried out with the goal of thermal roll shape and uniform wear of work rolls. The optimized roll shifting strategy can significantly improve the strip shape quality.

Bending Force of Hot Rolled Strip Based on Improved Whale Optimization Algorithm and Twinning Support Vector Machine

Bending control is one of the main methods of shape control for the hot rolled plate. However, the existing bending force setting models based on traditional mathematical methods are complex and have low control accuracy, which leads to poor strip exit shapes. Aiming at the problem of complex bending force setting of the traditional algorithm, an improved whale swarm optimization algorithm and twin support vector machine-based bending force model for hot rolled strip steel (LWOA-TSVR) is proposed. Based on the hot rolling field production data of a steel plant, the research group established the bending force prediction model by using the nonlinear approximation ability of the twin support vector machine. The introduction of the Levy flight improvement algorithm improves the generalization ability, prediction accuracy, and convergence speed of the whale swarm optimization algorithm with the help of the convergence of coefficient vectors, solves the problem of a random selection of the parameters of the traditional whale swarm optimization algorithm and optimizes the ability of the whale swarm algorithm to jump out of the local optimum. Based on the actual rolling database, the hit rate of the proposed method reaches 91% (from −5 to 5 KN), which fully meets the requirements of the detection accuracy on the actual production line. The model is not only able to overcome the local search to obtain the global optimal solution, but also has the advantages of fast convergence and higher prediction accuracy. A comparison of the model with twin support vector machines and traditional whale swarm algorithms shows that the prediction accuracy is higher. The experimental results also show that this model has advantages over existing bending force prediction models in terms of improving the accuracy of the strip shape control and providing theoretical guidance for practical bending force settings.

Contribution to improving hydrodynamics method for hot strip rolling application

The present work is a contribution in improving the hydrodynamic method proposed by Li et al. (Steel Res Int 87(9999) 88:2053–2059, 2016; Int J Adv Manuf Technol. Springer-Verlag, London, 2016) used to predict the pressures and the rolling speeds during hot rolling of aluminum strips. The hydrodynamic model gives good prediction. However, it is based on empirical coefficients which must be identified for each rolling case. Therefore, a critical analysis of the Si Li’s method has been first made and then a methodology for improving it has been presented. The improvement consists in coming out of the empiric coefficients and considering the variation of viscosity as a function of pressures. Finite element simulations have been conducted to validate the improved method. Much reliable results have been obtained that are in good agreement with the experimental data. The proposed approach is rapid and much easier to use within the industrial application.

Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method

Industrial big data technology has become one of the important driving forces to intelligent manufacturing in the steel industry. In this study, the characteristics of data in steel production are analyzed and an industrial big data platform for steeling process is developed to extract the quality-related parameters. A data-driven approach to construct prediction intervals (PIs) of mechanical performances for hot-rolling strips is proposed to represent the uncertainty and reliability of the prediction results. The proposed method employs a new manifold visualization method, SLISEMAP, to reduce the feature dimensions with interpretability, utilizes lower upper bound estimation (LUBE) method to obtain the PIs, in which the broad learning system (BLS) is used as the basic training network model and the artificial bee colony (ABC) algorithm is applied to optimize the weighting parameters of BLS under the LUBE framework. A hot-rolling steel coil dataset consisting of 39 variables and 1335 coil samples is used to validate the proposed method. Two Delta-based approaches, namely back propagation neural network (BPNN) and extreme learning machine (ELM); and three LUBE-based approaches, namely ABC-BPNN, ABC-ELM, and ABC-support vector regression (SVR) are compared with the proposed method. Results show that the proposed ABC-BLS in LUBE is effective and efficient in constructing the PIs with a higher coverage probability and a narrower interval width.

Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling

Aiming at the problems of large rolling deviation and low stability in limit specification of hot strip rolling, the optimal rolling suggestions were obtained based on back propagation (BP) neural network and genetic algorithm. According to equipment state and strip specification to select excellent sample set, in the sample set based on the data of application of neural network to build the mapping relationship between process parameters and the rolling stability, limit specifications of the mapping model is set up, and then using the genetic algorithm for the search of this mapping model, the search model of rolling stability of ideal point, determine a set of process parameters optimal advice accordingly. Taking the rolling of MRTRG00201_1276_3 as an example, a set of optimal process parameters are obtained by simulating rolling of MRTRG00201_1276_3. Then the sample distribution and rolling stability of each process are analyzed in turn. The results show that the process parameters obtained by optimizing the model accord with the distribution law of rolling samples, can obtain high rolling stability, and can play a guiding role in limit specification rolling.

Effect and Mechanism of Ca2+ on the Corrosion of Hot-Rolled Strip in Turbid Ring Water Containing Cl− and SO2- 4

After the hot-rolled strip is cooled by laminar flow, Cl−, SO2- 4 and Ca2+ in turbid ring water will remain on the surface of the steel plate, which makes the steel plate corrode during storage in a humid environment. In order to clarify the effect and mechanism of residual Ca2+ on the corrosion of strip, simulations were carried out by setting different Ca2+ concentrations in solutions containing Cl− and SO2- 4 with different times of alternate immersion. Through the analyses of corrosion pit morphology, corrosion rate, electrochemical characteristics and corrosion products, it was found that the corrosion rate increased first and then decreased regardless of the Ca2+ concentration. When the Ca2+ concentration is 50~300 mg/L, the increase of Ca2+ concentration will increase the adsorption rate of the matrix and increase the corrosion pits. In addition, the corrosion rate decreases, the depth of corrosion pit becomes shallow and the polarization resistance increases due to the inhibition of the calcium salt layer, and the increase of Ca2+ concentration will enhance the inhibition of corrosion. When the Ca2+ concentration is 1000 mg/L, the denseness of the calcium salt layer is easily destroyed and the corrosion is intensified due to the large amount of adsorption of ions by the matrix.

Correlation analysis between looper system and strip width narrowing in hot strip rolling

Correlation analysis between looper system and strip width narrowing in hot strip rolling

Study on Work Roll Vertical Vibration Under Periodic Spalling of the Oxide Film

The dynamic effect of work-roll oxide-film periodic spalling on vertical vibration in a hot strip rolling mill was studied. Based on dynamic monitoring test analysis, the friction of the work-roll surface under oxide-film periodic spalling is discussed. Using an average rolling pressure formula, the rolling force under the dynamic vertical vibration and the time-varying friction coefficient were found. A two-degrees-of-freedom dynamic system of the hot strip mill was established, considering the roller system symmetry. Based on the multiscale small-parameter perturbation method, the main resonance characteristic of work-roll vertical vibration has been explored. Using the 1780-mm hot strip mill in Chengde Iron and Steel Co., Ltd., the dynamic characteristics of the system are analyzed when oxide-film periodic spalling occurs on the work roll. The results are consistent with the test data, which show the effect of oxide-film spalling on vertical vibration. The simulation results for resonance characteristics show that the vibration of the system can be effectively reduced by increasing the damping and adjusting the stiffness of the system. In rolling production, it is very important to ensure the stability of the oxide film in order to reduce fluctuation. This study provides a reference for the operation and maintenance of a rolling mill in the strip production process.

Metallographic structure improvement of hot rolled strip induced by high temperature diffusion of MoS2 nano-lubricant

To improve metallographic structures and lubricating properties of hot rolled strips, a multifunctional MoS2 nano-lubricant was proposed for hot rolling process. Through hot rolling tests and relevant characterization methods of energy dispersive spectrometer (EDS) and field-emission scanning electron microscopy (FESEM), the multifunctional MoS2 nano-lubricant was proved to have both improved metallographic structures and lubricating properties. Not only the rolling forces were decreased, the thickness of oxidized scale was also reduced from 30–35 μm to 16–18 μm by using the MoS2 nano-lubricant. The grains in metallographic structure were observed to be refined obviously, and the orientations of pearlite lamellae were more multidirectional. There were also some carbide precipitates with dispersion strengthening effect appeared around grain boundary. The presence of Mo atoms within the iron matrix indicates the improved metallographic structure as a result of the diffusion of MoS2 nanosheets. This behavior is further instigated using thermal diffusion test, hot compression test, and molecular dynamics (MD) simulation. The results show that the metallographic structure improving effect of multifunctional MoS2 nano-lubricant was attributed to the interaction between MoS2 nanosheets and strips under the high-temperature and high-pressure condition of hot rolling process. In the presence of such high pressure, Mo atoms in MoS2 nanosheets could diffuse through the oxidized scale and exist in the iron matrix. The interaction between Fe and MoS2 nanosheets, meanwhile, was stronger. The diffused Mo atoms in iron matrix could form some coordination complexes with Fe atoms, which promote the nucleation of cementite, and playing a role in grain refinement. Therefore, the bifurcation growth was significantly enhanced, thus the growth directions of pearlite were also increased significantly. The researches provided not only theoretical basis for the development of multifunctional nano-lubricant with integrated metallographic structure improving effect, but also a new path for surface strengthening of strips.

Edge drop of GO steel strips at the hot rolling mill 2000 PJSC «NLMK»

One of the quality characteristics of hot-rolled strips is the transverse thickness variation. The transverse thickness difference is defined as the maximum difference between the measured values of the thickness of the strip in its middle, as well as at a distance of 10 to 40 mm from the edge, depending on the standard used. In many ways, the transverse thickness variation determines the manufacturability of subsequent processing, and also affects the breakage during subsequent cold rolling. Moreover, in recent years there has been a trend towards establishing additional requirements for the transverse thickness variation of electrical steel strips, which often leads to an increase in edge trim to meet these requirements; in particular, this refers to the value of the edge drop of the cross-sectional profile, which is defined as the difference in the thickness of the strip between the edge of the strip and 40 mm from the edge. It is known that the contour of the cross-sectional profile of a hot-rolled strip is inherited by a cold-rolled strip, while the value of the transverse thickness difference decreases in proportion to the elongation coefficient. Thus, hot rolling has a significant effect on the transverse thickness variation of the finished cold-rolled strip. The urgency of the issue of edge drop increases due to the tendency to reduce the size of the side trim of steel strips, which directly affects the consumption coefficient, especially for expensive steel grades, which include transformer steel. To minimize the edge drop value, it is necessary to know the causes of its occurrence and rely on an adequate calculation of the shaping of hot-rolled strips. To create a predictive model of edge drop, rolling parameters are taken into account that affect the shape of the active generatrix of the work rolls. In the presented work, an analysis was made of the influence of hot rolling parameters on the edge drop of rolled strips of GO (transformer) steel. The revealed patterns will form the basis of the developed mathematical model.

Stochastic model describing evolution of microstructural parameters during hot rolling of steel plates and strips

Enhancing strength-ductility synergy of materials has been for decades an objective of research on structural metallic materials. It has been shown by many researchers that significant improvement of this synergy can be obtained by tailoring heterogeneous multiphase microstructures. Since large gradients of properties in these microstructures cause a decrease of the local fracture resistance, the objective of research is to obtain smoother gradients of properties by control of the manufacturing process. Advanced material models are needed to design such microstructures with smooth gradients. These models should supply information about distributions of various microstructural features, instead of their average values. Models based on stochastic internal variables meet this requirement. Our objective was to account for the random character of the recrystallization and to transfer this randomness into equations describing the evolution of dislocations and grain size during hot deformation and during interpass times. The idea of this stochastic model is described in the paper. Experiments composed of uniaxial compression tests were performed to supply data for the identification and verification of the model in the hot deformation and static recrystallization parts. Histograms of the grain size were measured after hot deformation and at different times after the end of deformation. Identification and validation of the model were performed. The validated model, which predicts evolution of heterogeneous multiphase microstructure, is the main output of our work. The model was implemented in the finite element program for hot rolling of plates and sheets and simulations of these processes were performed. The model’s capability to compare and evaluate various rolling strategies are demonstrated in the paper.

Application of line pipe and hot induction bends in hydrogen gas

For the upcoming hydrogen economy, transport pipelines for hydrogen gas and gas mixtures of hydrogen and natural gas are one of the important components. For the application of steel in hydrogen gas it is necessary to handle the risk of hydrogen embrittlement by adapting the right knowledge. Compared to existing hydrogen pipelines, which are safely running since decades, higher gas pressures and steel strength levels are in discussion. For a safe operation and limited resource consumption it is necessary to clarify the product requirements. The semi-finished products medium and large line pipes and bends are regarded over the production chain: pre-material, hot rolled strip, welded pipes, and induction bent pipes. The interaction of hydrogen with steel is investigated since decades. For hydrogen gas, the surface reaction with steel is considerably reduced compared to other corrosion reactions i.e. with sour gas. This results in a much lower amount of introduced hydrogen atoms within the material. Results of lab trials of different materials after storage in pressured hydrogen gas will be shown to clarify this point. Furthermore, results of tests of applicability of commonly used flow coats in hydrogen atmosphere are shown. For a possible failure scenario of a hydrogen transport line the investigations must be focused on local effects of hydrogen enrichment in conjunction with mechanical loads. There are different laboratory tests possible to evaluate these material reactions. They are shown and discussed in the view of the following aspects: product qualification, further product and specification development and suitable approval tests.

Investigation and Modelling of Magnesium Alloy Grain Size during Hot Strip Rolling with Inter-Pass Annealing

Investigation and Modelling of Magnesium Alloy Grain Size during Hot Strip Rolling with Inter-Pass Annealing

High-resolution image-based surface defect detection method for hot-rolled strip steel

Existing methods for detecting surface defects in strip steel are mainly aimed at the case of low-resolution images, and little research has been done in the case of high-resolution images with a small percentage of valid targets. In response to the practical problems in the hot-rolled flattening stage, we propose a hot-rolled strip surface defect dataset called XLData-CLS, which contains a total of 5000 defective samples and 10,000 normal samples with an image resolution of 1344 × 672 pixels. Based on this dataset, this paper improves the ResNet152 model by adding a feature extraction layer at the front of the model so that it can extract information more effectively for high-resolution images. Experimental results on the XLData-CLS dataset show that the proposed method achieves 99.00% classification accuracy on the test set, which is higher than other comparable deep learning models. Validation results on another datasets also demonstrate the accuracy of our method for high-resolution defective images.

FEM simulation of thermo-mechanical stress and thermal fatigue life assessment of high-speed steel work rolls during hot strip rolling process

Work rolls used for hot strip rolling undergo the cyclic sequence of heating-cooling over the roll surface due to contact with the hot strip and the following cooling systems during every rolling revolution. Severe temperature gradient and thermal stress caused by cyclic heating-cooling loading are dominant factors for thermal fatigue failure of work rolls. Therefore, investigations on temperature and thermal stress-strain are essential for understanding the thermal fatigue of work roll. In this study, a thermo-mechanical analysis with elastic-plastic material parameters via a simplified finite element model is carried out to compute the stress-strain range experienced by the work roll during rolling and idling. Then, a model with the initial crack in the roll surface considering different depths and a model with the hot strip is conducted to better understand the thermal behaviors of the work roll during hot rolling respectively. Furthermore, the thermal fatigue life of work roll is assessed based on the modified Coffin-Manson relation, considering the effect of multi-axial stress state and temperature-dependent material properties caused by temperature fluctuations in work roll during hot rolling.