Hot Strip Rolling Research Articles

ОСОБЛИВОСТІ ТЕХНОЛОГІЧНИХ СХЕМ ТА ОБЛАДНАННЯ ДЛЯ ВИРОБНИЦТВА ТОНКИХ ГАРЯЧЕКАТАНИХ ШТАБ НА ШСГП

The purpose of the work is to determine rational schemes for their production by analyzing the technological and design parameters of a number of broadband mills that roll hot-rolled strips less than 2,0 mm thick. It is shown that at present there is a constant increase in the production of extremely thin hot-rolled strips (0,8-1,5 mm thick), which can be used instead of more expensive (by $ 20-30 per ton) cold-rolled strips. The development of the production of hot-rolled strips of such a thickness is limited by a number of problems, in particular, the low temperature of the end of rolling (760-820 °C), which leads to a significant decrease in the ductility of the rolled stock; limiting the rolling filling speed, which does not allow increasing the temperature of the end of rolling the complexity of controlling the cross-sectional profile and flatness of the strips. Using mathematical modeling, it was found that the strip thickness and rolling speed have the greatest influence on the temperature of the end of rolling. The thickness and temperature of the rolls at the entrance to the finishing group of stands have a lesser effect. A decrease in the number of stands in the finishing group increases the temperature of the end of rolling at a constant thickness of the rolls, but when the thickness of the rolls changes in accordance with the number of stands, the effect is significantly reduced. The most favorable technological parameters for the production of extra-thin hot-rolled strips are provided by casting and rolling units, which are characterized, in comparison with broad-strip mills, as a rule, by a greater thickness and temperature of the rolls (or continuously cast slabs) and a smaller number of stands. An increase in the thickness of billets (slabs) requires an increase in the permissible values of the energy-power parameters of rolling, as well as the use of special solutions that will ensure minimal heat loss by the rolls before entering the finishing group of stands. Calculations show that, based on the reliable maximum refueling speed of hotrolled strips (10-11.5 m / s) achieved in the industry, the minimum thickness of strips with high plastic characteristics is: for broad-strip mills - 1.9-2.0 mm; for casting and rolling units, depending on their type - 1.3-1.6 mm.

Hot workability of 420 J1 martensitic stainless steel

Steel grade 420 J1 is martensitic colonies embedded with ferritic phase stainless steel. The steel under investigation contains mainly 12.42 % chromium and 0.2% carbon.Hot workability of the 420 J1 steel grade became the most critical issue to suit the different application requirements. The present article is dealing with the parameters controlling successful hot workability of the steel grade 420 J1.4-conscutive hot rolling passes were designed for a thickness reduction from 7 to 3 mm. Hot deformation passes were designed to score two parallel goals. The first goal is to avoid instability regions, while the second one is to create dynamic softening.It was found that austenite is subjected to grain refinement to reach about 17 micron.The flow stress is calculated. Dynamic recovery is clearly noticed at the last two deformation passes. Microstructural investigation as well as mechanical properties for final hot rolled steel strips was executed.

Nonlinear Fault Detection of Batch Processes Using Functional Local Kernel Principal Component Analysis

In order to guarantee and improve the product quality, the data-driven fault detection technique has been widely used in industry. For three-way datasets of batch process in industry process (i.e., batch × variable × time), a novel method named functional local kernel principal component analysis (FLKPCA) is proposed. Since the variables' trajectories often show functional nature and can be considered as smooth functions rather than just vectors. Firstly, the variables' trajectory is expressed as the combination of smooth basis functions using functional data analysis (FDA), which means that the datasets of batches process would be transformed from the three-ways array into two-ways function matrix. Then, kernel locality preserving projections (LKPCA) is used to perform dimensionality reduction on two-way function matrix directly. Different from kernel principal component analysis (KPCA). LKPCA aims at preserving the both local and global structure of the data in a new optimization objective. Consequently, FLKPCA could more effectively seek the potential information that hidden in the three-ways datasets. Lastly, the effectiveness of the proposed approach is illustrated by the benchmark of fed-batch penicillin fermentation process and the hot strip rolling process.

Constrained dynamic matrix control strategy for a monitoring system for AGC of hot strip mills

In the hot strip rolling process, the performance of a monitoring system for automatic gauge control (MN-AGC) is influenced greatly by the model mismatch which is caused by the variation of model parameters values. A constrained dynamic matrix control (CDMC) strategy that includes a prediction model, rolling optimization, and feedback correction was used in the MN-AGC. First, the conventional Smith prediction-based control strategy for the MN-AGC was analyzed. Second, the performance index function and optimal control of the CDMC strategy were determined. Finally, simulations and industrial experiments were conducted. The results showed that both control strategies provided good control performance. When model mismatch occurred, the Smith predictor-based MN-AGC resulted in significant overshoot or even oscillations but the control performance of the CDMC-based MN-AGC was not influenced by changes in the model parameters.

Just-in-Time Learning-Based Soft Sensor for Mechanical Properties of Strip Steel via Multi-Block Weighted Semisupervised Models

Mechanical properties are important indexes to evaluate the quality of hot rolling strips. It is a research hotspot in the field of hot rolling that realizing timely and accurate soft sensing of mechanical properties. Traditional soft sensing methods have poor performance in the application of strong nonlinearity and multiple working conditions. Moreover, the utilization rate of data is relatively low, which limit the improvement of prediction accuracy. To solve the problems above, a just-in-time learning (JITL) based multi-block weighted semisupervised Gaussian mixture regression (JMWSSGMR) soft sensor is proposed in the paper. There are two stages in the soft sensor: off-line variable blocking and on-line local modeling. In the off-line phase, process variables are divided into different sub-blocks by partial least square (PLS) according to distinct principal component directions. In each sub-block, original variables with high contribution rate are retained. In the on-line phase, optimized Mahalanobis distance is constructed to select the most similar historical samples to the query sample. Next, various real-time semisupervised sub-models are built to estimate the output of the query sample. Finally, predicted values of sub-models are fused and ultimate prediction of mechanical properties is obtained. Case studies are carried out on a numerical example and a hot rolling process. The feasibility and effectiveness of proposed soft sensor are verified by the predicted results.

СУЧАСНІ ТЕНДЕНЦІЇ ВИРОБНИЦТВА КОРОЗІЙНОСТІЙКОГО І ВОГНЕСТІЙКОГО НЕНАПРУЖЕНОГО АРМАТУРНОГО ПРОКАТУ

The aim of this work is to study modern methods of production of corrosion-resistant and fire-resistant stress-free reinforcing bars in the world. Non-stressed reinforcing bars are used as working, structural and assembly reinforcement in conventional building structures, as well as in various types of prestressed structures: from openwork prefabricated to huge monolithic hydraulic structures, therefore, its assortment produced by the domestic metallurgical industry in the range from 5,5 to 40 mm in the nominal diameter of a smooth profile and from 6,0 to 40 mm for a periodic profile. Recently, in the world, much attention is paid to the design and construction of buildings and structures with an improved complex of consumer properties, intended, in particular, for work in corrosive and fire hazardous environments. Such developments are not currently being conducted in Ukraine. In the world and in Europe, to increase corrosion resistance and fire resistance, as well as for the manufacture of seismically resistant reinforcing bars (δmax≥7,5 %), hot-rolled low-alloyed (additionally alloyed with V and Mo) and microalloyed steel grades without the use of heat treatment or cold-deformed (cоld stretched) from hot rolled strip of periodic profile. In GOST 34028-2016 for the countries of the Customs Union, it is clearly stated that for the reinforcement of prefabricated reinforced concrete structures and monolithic reinforced concrete, unstressed reinforcing bars in bars or coils with a nominal diameter of 4,0 to 40 mm are also used for the production of such rolled products, low-carbon and low-alloy steel grades are used, which microalloy V, Nb, Mo or add nitride-forming elements Al, Ti, V, Nb. In this case, either hot rolling or cold deformation or heat treatment in the flow of the rolling mill is used. An alternative way of producing corrosion-resistant and fire-resistant stress-free reinforcing bars in the world is the use of stainless steel as a material. Such profiled reinforcing bars are produced in the range of 3,0 to 50 mm by hot rolling or cold deformation. The use of modern technologies for the production of stainless steel reinforcing bars, starting with the addition of effective and high-quality alloying elements and additives in the production of liquid steel and effective methods of metal pressure treatment, which allows us to guarantee the material saving from corrosion and high temperatures in case of fire. A significant disadvantage of this rolled product is its rather high cost.

Optimal Estimation and Feedforward Control of Strip-Longitudinal Hardness for Thickness Hunting Suppression of Tandem Cold Mill Process

Following the recent trend of weight reduction in car industry, producing high quality cold-rolled AHSS (advanced high-strength steel) strip becomes important. Thickness hunting (or fluctuation) problem can be more prominent for this cold-rolled AHSS strip making, which can stem from the non-uniformity of hot-rolled strip and can severely degrade product quality. In this paper, we propose a novel framework to estimate the strip-longitudinal hardness of the TCM (tandem cold mill) process and its feedforward control to substantially reduce the thickness hunting, while fully incorporating the interconnected nature and sensing sparsity of the TCM process. In particular, our estimator consists of the following two complementary loops: 1) fast real-time hardness estimation loop, which optimally fuses the process model and sensing information; and 2) slower constant process-parameter estimation loop via optimization utilizing the nonlinear process model and (stored/measured) sensor data. Efficacy of the proposed estimation and control frameworks are then validated with high-precision TCM process physics simulator.

Investigation on the control of multiphase flow behavior in a continuous casting tundish during ladle change

The transient multiphase flow behavior in a single-strand tundish during ladle change was studied using physical modeling. The water and silicon oil were employed to simulate the liquid steel and slag. The effect of the turbulence inhibitor on the slag entrainment and the steel exposure during ladle change were evaluated and discussed. The effect of the slag carry-over on the water-oil-air flow was also analyzed. For the original tundish, the top oil phase in the impact zone was continuously dragged into the tundish bath and opened during ladle change, forming an emulsification phenomenon. By decreasing the liquid velocities in the upper part of the impact zone, the turbulence inhibitor decreased considerably the amount of entrained slag and the steel exposure during ladle change, thereby eliminating the emulsification phenomenon. Furthermore, the use of the TI-2 effectively lowered the effect of the slag carry-over on the steel cleanliness by controlling the movement of slag droplets. The results from industrial trials indicated that the application of the TI-2 reduced considerably the number of linear inclusions caused by ladle change in hot-rolled strip coils.

Development and research of the cultivator frame design made of non-standard beams

The paper presents the results of the development and study of the strength of the cultivator frame made of non-standard beams. The term “non-standard beam” refers to a off-sized beam welded from steel strips. As such stripes, both standard hot-rolled strips and strips cut from standard cold-rolled or hot-rolled sheets can be used. The main advantage of the proposed frame design is the relatively low cost and availability of materials for its manufacture, as well as the potential to minimize the weight of the finished product by changing the thickness and dimensions of the strips used. Frame design and calculations were performed by the Autodesk Inventor Professional environment.

Heat treatment strategies for hot-rolled medium-Mn sheet steels

Heat treatment strategies for advanced high-strength medium-Mn sheet steels are addressed. A review of the available thermal cycles for cold-rolled and hot-rolled sheets are presented. The benefits from the intercritical annealing following hot-rolling are emphasized. The examples of intercritical annealing for the hot-rolled sheets of initial martensitic microstructure are provided. The heat treatment was performed in a temperature range of 680-720°C for various times from 1 to 5 hours. The material used in the experiment was a medium-Mn steel containing 0.16% C, 4.7% Mn, 1.6% Al, 0.2% Mo, 0.2% Si. The scope of the research included dilatometer tests, hardness tests and microstructural characterization. The effects of temperature and time on a stabilization of retained austenite are assessed. Intercritical annealing performed at 700 and 680°C allowed forming some stable retained austenite in the microstructure. Moreover, some coiling simulations for hot-rolled strips are described.

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

In the present work, to solve the problem of the lacking enough labeled training data for deep learning, a safe semi-supervised regression supporting Bayesian optimization deep neural network (SAFER-BODNN) model was proposed to establish mechanical property prediction model of hot-rolled strips. The Pearson correlation coefficient was applied to reduce the data dimension. The safe semi-supervised regression was implemented to add the pseudo labels to the unlabeled data for training dataset expansion. The deep neural network was trained with Bayesian optimization to determine the optimal hyper-parameters of the network. The results show that the SAFER-BODNN model achieves good performance for mechanical property prediction of hot-rolled strips with correlation coefficient of 0.9610 for yield strength, 0.9682 for tensile strength, and 0.8619 for elongation, respectively. Compared with the deep neural network trained on the labeled dataset, the SAFER-BODNN model obtains stable smaller predicted errors. Among all the variables, C content and Mn content have large influence on the yield strength and tensile strength, coiling temperature has the largest influence on the elongation. The investigation makes full use of unlabeled data to elevate the prediction performance of the deep neural network, and also provides a way for deep learning modeling when the data are insufficient.

The basic areas of research and actual developments of the rolling department of the Institute of Ferrous Metallurgy, Ukraine’ National Academy of sciences

In 1948 in the Institute of Ferrous Metallurgy, a department was created, the basic task of which was the perfection of equipment and technologies of rolling production. Basic research and development activity in the area of sheet and long products production, aimed at elaboration of scientific and technological fundamentals, creation of new and perfection of existing processes of continuous rolling, optimization of rolling processes and management the rolled products quality considered. The most important research and technical development of the department noted, including innovation technologies of rolled products production, expert computer systems for engineer calculations and optimization of the processes parameters, as well as systems of automatic process control. Among the basic areas of research and development achievements, the elaboration of computer simulation of hot-rolled and cold-rolled strip production processes, rebar, wire rod and profiles of high accuracy for machine-building highlighted, as well as predicting of rolled products quality indices, perfection of their production technology. The comprehensive systems elaborated and implemented at the ferrous metallurgy plants aimed at vibro-diagnostic and automatic control of speed of high-speed cold-rolling mills, automatic control of cold-rolled strip flatness, automatic alignment of plate heads, designing of rational configuration of strip mills rolls cooling collectors and prediction of working rolls temperature at their operation. In the area of long products production theoretical fundamental of the continuous long products rolling processes at using non-driven working stands and non-driven dividing facilities elaborated. Fundamental studies of regularity of temperature, deformation and energy-power parameters variation of high-speed continuous rolling process of wire rod at application of reducing-sizing blocks elaborated. The basic approaches to bands and precise profiles of small gauges by rolling-drawing method production efficiency elaborated theoretically and experimentally. A method of determination of rational structure schemes and production parameters of hot-rolled product production from continuously casted billets in the casting-rolling complexes elaborated.

P.691 Impairment of the process of thinking on different stages of schizophrenia in connection with the magnocellular and the parvocellularneural systems

Accurate prediction of rolling force is a core point during the hot-rolled strip production. In-depth research on static rolling processes has been carried out in the past. Most models, however, can only be used under static conditions. Therefore, it is challenging to research the dynamic rolling process. This paper presents a dynamic rolling model for hot rolling. Firstly, the strip dynamic velocity model and the dynamic boundary condition model are established, and then the total power equation of the dynamic rolling process is solved based on the energy method. Furthermore, by minimizing the total power function, the dynamic rolling force equation can be obtained. Finally, the effectiveness of the proposed dynamic model is verified by hot strip rolling plant experiments and finite element simulations. The results show that the dynamic model of this paper has higher prediction accuracy and can be applied to the research on the dynamic hot rolling process. Besides, the influence of the vertical velocity of the rolls on the dynamic rolling force and influencing factors of dynamic rolling force are discussed.

The Improvement and Development of Hot-Rolled Strip Bar Production Technology at NUST MISiS

This paper presents the results of the work carried out by the Department for Metal Working Processes at NUST for production technology of: high-strength automotive flat stock from bi-phase ferrite martensite steels (BPHFMS) on a broad-strip hot-rolling mill by staged laminar cooling on a runoff table, as well as K52–56 strength grade cold- and corrosion-resistant tubular strip bar. The metal’s ferrite grains rolled on the broad-strip mill are ground by combining the controlled rolling (CR) of strips with their rapid cooling (RCL). Thermal kinetic diagrams (TKDs) were created to show modeling sheet cooling, as well as to predict the resulting structure type depending on the metal cooling rate.

In-situ interfacial tribochemistry toward eliminating red-scale of silicon steel in friction process

Abstract Irregularly striped red-scale defects are often formed on hot rolled silicon steel strips, and some still remain on the surface even after pickling. In this study, an in-situ interfacial tribochemistry concept was proposed to eliminate red-scale silicon steel using ball-on-disc test, by transforming harder eutectic Fe2SiO4/FeO eutectic into other compounds with sodium polyphosphate lubricant. The results show that polyphosphate can lubricate the high speed steel/silicon steel interface effectively with significant reduction of the friction and wear by 50% and 90% respectively. And in situ formed compounds of Na3Fe2(PO4)3 and NaFePO4 at the rubbing interface can eliminate red-scale on silicon steel by weakening the adhesion of oxide scale to the substrate.

Local ductility – key parameter for predicting formability of AHSS

In order to be able to make targeted use of the advantages of AHSS grades in forming processes it is necessary to determine local material properties. Hence, in addition to edge crack sensitivity measures, such as hole expansion ratio according to ISO 16630 (HERISO), it is currently intensively discussed whether measures, that assess the local ductility of the material, should also be specified in material specifications. One representative is given by the parameter true fracture strain (TFS) that is based on the measurement of the fracture area of a cracked flat tensile specimen. By means of 16 different cold- and hot-rolled steel batches, the relation between the parameters TFS and HERISO was investigated. The outcome is that these parameters do not correlate to a satisfying degree when considering all materials and consequently are regarded as representatives of different material properties. Thus, the issue of the reliability of the measures is considered separately. For the regarded steel grades, the overall scattering of TFS, i.e. neglecting the sample orientation, is found to be 13 – 44 % and the scattering of HERISO is within 5 – 35 %. The high scattering of TFS is mainly caused by the anisotropy of the materials. In case of parallel measurements of the same batch the relative standard deviations of TFS and HERISO are on the same level for cold-rolled strips, whereas in case of hot-rolled strips the relative standard deviation of HERISO is significantly higher. By investigating the influence of the tensile specimen shape for a bainitic hot-rolled strip numerically, it could be shown that the width-to-thickness ratio, that takes the hole fracture area into account, influences the TFS value directly.

State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance

In the hot strip rolling process, many process parameters are related to the quality of the final products. Sometimes, the process parameters corresponding to different steel grades are close to, or even overlap, each other. In reality, locating overlap regions and detecting products with abnormal quality are crucial, yet challenging. To address this challenge, in this work, a novel method named kernel entropy component analysis (KECA)-weighted cosine distance is introduced for fault detection and overlap region locating. First, KECA is used to cluster the training samples of multiple steel grades, and the samples with incorrect classes are seen as the boundary of the sample distribution. Next, the concepts of recursive-based regional center and weighted cosine distance are introduced. For each steel grade, the regional center and the weight coefficients are determined. Finally, the weighted cosine distance between the testing sample and the regional center is chosen as the index to judge abnormal batches. The samples in the overlap region of multiple steel grades need to be focused on in the real production process, which is conducive to quality grade and combined production. The weighted cosine distances between the testing sample and different regional centers are used to locate the overlap region. A dataset from a hot steel rolling process is used to evaluate the performance of the proposed methods.

A novel analytical model for prediction of rolling force in hot strip rolling based on tangent velocity field and MY criterion

In order to accurately predict the rolling force in hot strip rolling process, a new tangent velocity and corresponding strain rate fields are proposed in this paper. Based on the proposed fields and the linear MY (mean yield) criterion, the plastic deformation power and shear power of rolled strip is calculated firstly, then the friction power is obtained by the co-line vector inner product method. Finally, the total power function is described by a mathematical expression. By minimizing the total power function, the analytical solution of rolling force can be obtained. By comparisons of the experimental data with the results predicted by the proposed analytical model, Sims model and А. И. Целиков model, it is found that the application of the proposed analytical model leads to a better solution to prediction of the rolling force, especially in the first stand. In addition, the rationality of proposed method has also been verified by the finite element method (FEM). Moreover, the effects of various rolling conditions such as reduction rate, friction factor and shape factor on rolling force, location of neutral plane and stress state coefficient are discussed.

Optimal feedforward control of hydraulic drive systems with long pipelines

AbstractHydraulic drives are used in many technical systems, ranging from automotive and aerospace applications to industrial production systems. They provide high forces and torques at a compact design. The typical setup of hydraulic drive systems comprises a valve which controls a hydraulic cylinder or motor. In many practical applications, the valve is connected to the hydraulic cylinder via hydraulic pipelines, whose length can amount to several meters. Typically, the distributed‐parameter characteristics of these pipelines is not considered in the controller design. For very fast operation scenarios, wave effects can be excited which may cause a significant reduction of the control accuracy or even lead to instability of the closed‐loop system. In this paper, a model‐based control strategy is proposed which allows to systematically account for long pipelines. One main element is the spatial discretization of the partial differential equations of the pipelines by means of the spectral elemental method (SEM), which yields a low‐dimensional, but yet accurate model. Based on this model, an optimal feedforward control strategy is developed, which also enables to take into account actuator constraints. The resulting optimal control problem is efficiently solved on a moving horizon, resulting in a real‐time capable moving horizon trajectory planning strategy. An observer is added to account for uncertain model parameters. The high potential of the proposed approach is demonstrated in simulation by applying the method to the actuation system of a looper in a hot strip rolling mill.

A New Data-driven Roll Force and Roll Torque Model Based on FEM and Hybrid PSO-ELM for Hot Strip Rolling

In this paper, a new Extreme Learning Machine (ELM) regression model of roll force and roll torque based on data-driven is proposed. The three-dimensional elastic-plastic finite element model (FEM) is established to solve the roll force and roll torque under different parameters (including rolling reduction rate, roll radius, rolling speed, average width of strip, entry temperature of strip). The regression model of ELM optimized by Particle Swarm Optimization (PSO) is established through using the datasets obtained by FEM. The PSO-ELM model prediction values of roll force and roll torque are compared with the single ELM and PSO-SVM model, and the error results of the prediction values are analyzed. The error results fully verify the feasibility and accuracy of the PSO-ELM model proposed. It is found that the new data-drive model of roll force and roll torque is simple in structure and it can make up for the deficiency of traditional mathematical mechanism model in dealing with nonlinear problems. The research result reveals that PSO-ELM method is suitable for parameters prediction and model optimization in strip rolling process.