Hot Rolling Mill Research Articles

Integration of Risk-Based Self-Maintenance Activities: Study Case for Crane Distributed System

In today's practice, the coordination of maintenance activities is steadily improved with the use of models. Among them, simulation-based models are attractive because they offer flexibility and can absorb system complexity with ease. Although we avoid state enumeration by using simulation-based models, they can sometimes become computationally demanding. To obtain efficient results, simulation-based models are often implemented on a digital platform. However, there is always concern about whether the implemented model performs adequately and delivers the expected results. In this paper, to address this concern, we propose to perform model calibration. The subject of the paper is calibration through comparison of a risk-based self-maintenance layer, which was created to be executed online without human intervention. The layer is intended for self-assessment of a critical system of overhead cranes operating in hazardous conditions. The case study of the paper is a critical system of overhead cranes operating at a continuous rate, which are part of a hot rolling mill system in a steel plant. The online self-maintenance layer is a nonlinear stochastic optimization model with bounded constraints that aims to evaluate a system-level risk indicator. The estimation of the risk indicator is based on Monte Carlo simulations. Given the features of the model, to evaluate the accuracy and robustness of the layer, only free derivative algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Nelder-Mead Modified (NMm) in our case, are used in the comparison. The presented work shows in practice common challenges when facing complex nondeterministic polynomial (NP) problems with stochastic functions.

Developing a Matheuristic for the Integrated Planning of a Cold Rolling Steel Plant

Steel-producing firms are confronted with the challenge of determining plans that coordinate production processes across a set of processing stages. While several approaches for integrated planning of hot rolling mills have been proposed, the cold rolling process is still poorly investigated. Therefore, we present a model formulation to simultaneously form campaigns of coils in cold rolling steel plants. The model considers multiple processing stages with unrelated parallel machines, sequence-dependent setup operations and order-specific due dates. To solve the model, we propose a matheuristic based on Fix-and-Relax and Fix-and-Optimize that utilizes a production line-based decomposition strategy. The results of a numerical study are presented that replicates the realistic production environment of a real cold rolling plant and a planning horizon of up to 10 days. The proposed matheuristic consistently outperforms a commercial solver in terms of solution quality and time.

Process Supervision of Long Products Hot Rolling Mill. Attack or Failure Identification.

The aim of this paper is to explore the potential capabilities of nonlinear projectors to represent the integrated behaviour of products when manufacturing involves several plants inside a typical facility of heavy industry. The interest is focused in the integrated supervision of production able to identify sudden failures or infrastructure attacks discovered from the process perspective. In this analysis, not only the feasibility but also computational capabilities are going to be explored. The selected methodology is the case analysis with action research, and the selected facility is a hot rolling mill for long steel products, which involves more than twenty plants or equipment units. The challenge is to define a monitoring procedure able to assess the product as well as the individual behaviour of each equipment. The results obtained allow to define an automatic procedure applicable in intraprocess time, which can have as an additional benefit its visual interpretation from the operator point of view.

НАБЛЮДАТЕЛЬ УПРУГОГО МОМЕНТА ДВУХМАССОВОЙ ЭЛЕКТРОМЕХАНИЧЕСКОЙ СИСТЕМЫ

To reduce the accident rate and increase the service life of electromechanical systems with shock load changes, the monitoring of elastic moments in mechanical gears is necessary. This task is relevant for the main lines of stands of hot rolling mills. The implementation of monitoring systems based on physical measuring devices is inefficient due to their complexity and low operational reliability. As an alternative, a relatively simple elastic moment observer is proposed, which is a fragment of an industrial controller software. The structure of the observer is presented and its advantages in comparison with known analogues are shown. The adequacy of the measured and reconstructed elastic moment signals is proved using the example of electromechanical systems of the stand of the thick-sheet mill 5000. A quasi-experimental analysis of emergency modes is substantiated, which allows the processing of signals stored in the form of data arrays. Based on this approach, an assessment of the dynamic moments in the emergency shutdown mode of the crate with metal in the rolls is given. The expediency of creating an online monitoring system for overloads of spindles and methods for calculating their resource is emphasized.

A learning procedure for detection of process anomalies in the production of metal long products and a new industrial case study

The highly individualized production processes for long products in the steel industry is subject to a variety of influencing variables with mutual interactions in a complex manner. To handle this complexity, modern data mining methods can be used for a highly efficient analysis of process data, to detect process anomalies in the process data, e.g. from rolling mills by statistical pattern recognition. This paper proposes a data-based strategy for detecting process anomalies within a hot rolling mill for long products. Suitable data are identified and selected from existing sensors and processed within a new database. This central database is used to train classification algorithms. The reliability of two prominent classifiers based on Principal Component Analysis (PCA) and One-Class Support Vector Machines (OC-SVM) has been evaluated. From the comparison in this respective use case, it has been concluded that satisfying results can be obtained, but PCA is highly dependent on the data distribution. The OC-SVM has also been implemented and tested and offers advantages when the data sets have a more complex distribution.

Scheduling Multiple Groups of Jobs for a Multi-Line Steel Hot Rolling Mill

This work addresses the optimal scheduling for a multi-line steel hot rolling mill with two parallel production lines. Groups of products are manufactured at one of these lines in an alternate sequence. An optimal schedule minimizes the sum of the required setup times between all products and optimally fills up retooling times necessary at one line by processing a group of products at the other line. This requires both sequence optimization and, for some groups, the optimal selection of jobs.The optimization problem is a combination of traveling salesman and orienteering problems and considers all groups simultaneously. To iteratively solve the combined problem, an algorithm consisting of a heuristic simulated annealing and a local search procedure is employed. The effectiveness of the algorithm is analyzed in a case study.

Characterization of Iron Powder Produced by Reduction of Hot-Rolled Mill Scale In Hydrogen Gas

Rolled mill scale is one of the most valuable industrial waste. The produced mill-scale briquettes are reduced by hydrogen at varying temperatures. The reduction is carried out at 800°C, 900°C, and 1000 °C and lasts for 120 minutes. The reduced samples are studied by X-ray diffraction and scanning electron microscopy. The reduction of mill scale allows the new use and development of this material to obtain sponge iron that can be reused to produce iron-based powder metallurgy components. From the SEM, EDS, and XRD analysis, it is clear that as the reduction temperature increases from 800°C to 1000°C, the amount of produced iron powder also increases at constant holding time, where the amount of iron powder produced at 800oC is 94.55%, but it is 98.22% at the reduction temperature of 1000°C. The proposed reduction process allows obtaining a sponge iron powder of an irregular morphology with different crystallite sizes as measured by the Scherrer equation, where the crystallite size increased with the reduction temperature. The minimum crystallite size value is 44.45nm at the reduction temperature of 800°C, but it equals 60.1nm at 1000°C.

Multistep networks for roll force prediction in hot strip rolling mill

Hot rolling processes consist of multiple single rolling stand operating at high temperature and speed to achieve desired steel shapes and superior properties, via exerting roll forces that need to be accurately predicted by a model. The currently used model of the mill of this study shows prediction instability and is unable to accurately accommodate changes in steel grade. In this paper, we propose a machine learning based framework to establish a model that accurately predicts roll forces at each mill stands of the hot strip rolling mill. In contrast to the traditional models, the proposed expert system considers an individual model for each rolling stand and employs rolling history when predicting roll forces. The proposed model includes both steel chemistry and physical process parameters for its predictions. Our experimental results demonstrate that the proposed framework improves both prediction accuracy and stability by 40%–50% over the currently used mill model. The enhanced prediction accuracy will greatly improve dimensional and microstructural control, as well as ensuring the avoidance of mill overloads.

A collaborative design platform for new alloy material development

To overcome the shortcomings of the conventional trial and error mode for new material development, a full-process collaborative design platform for steel rolling is developed based on an industrial internet of things (IIoT) system in this study. Equipment, process and product entities are modeled in both the physical domain and the cyber domain. A systematic data-driven Mamdani-type fuzzy modeling methodology is proposed to map the relationship between material chemical compositions, organizational structures, process parameters and mechanical performances. The proposed methodology employs a random forest (RF) algorithm to select important parameters from mechanism models, simulation models and production process variables, utilizes a K-means algorithm to merge diverse steel grades into sub-clusters, and implements a multi-objective particle swarm optimization (MOPSO) algorithm to further improve the fuzzy model in terms of both the structure and the membership function parameters. A dataset of 3500 steel coils collected by the prototype platform built in a large hot rolling mill is used to evaluate the performance of the proposed approach. Experiment results show that the proposed methodology performs well in predicting the yield strength, tensile strength and elongation, with the coverage probability over 90% under 10% deviation and about 70% under 5% deviation on average.

Development of Dynamic Monitoring System for Axial Force of Hot Rolling Mill

Development of Dynamic Monitoring System for Axial Force of Hot Rolling Mill

Semi-Automated Roller Parameters Extraction from Terrestrial Lidar

Rollers are an integral part of a hot-rolling steel mill. They transport hot metal from one end of the mill to another. The quality of the steel highly depends on the surface quality of the rollers. This paper presents semi-automated methodologies to extract roller parameters from terrestrial lidar points. The procedure was divided into two steps. First, the three-dimensional points were converted to a two-dimensional image to detect the extents of the rollers using fast Fourier transform image matching. Lidar points for every roller were iteratively fitted to a circle. The radius and center of the fitted circle were considered as the average radius and average rotation axis of the roller, respectively. These parameters were also extracted manually and were compared to the measured parameters for accuracy analysis. The proposed methodology was able to extract roller parameters at millimeter level. Erroneously identified rollers were identified by moving average filters. In the second step, roller parameters were determined using the filtered roller points. Two data sets were used to validate the proposed methodologies. In the first data set, 366 out of 372 rollers (97.3%) were identified and modeled. The second, smaller data set consisted of 18 rollers which were identified and modelled accurately.

Development of an Automatic Elastic Torque Control System Based on a Two-Mass Electric Drive Coordinate Observer

Development of control system based on digital twins of physical processes is a promising area of research in the rolling industry. Closed-loop control systems are developed to control the coordinates of two-mass electromechanical systems in order to limit the dynamic loads on the equipment of main rolling lines. These control systems are based on observers (digital shadows) that indirectly detect (reconstruct) the roll speed and the elastic torque of the shaft (spindle) in real time. Notably, observers are required to work fast in order to reconstruct transients attributable to shock (impact) loads. Literature review shows that the known observers, which use complex algorithms to compute coordinates, do not respond fast enough. The paper analyzes the kinematic diagram of Mill 5000, a plate rolling mill. It presents oscillograms that prove that the elastic torque does oscillate as the rolls grip the strip dynamically. The authors hereof have developed an observer that reconstructs the coordinates of the uncontrolled mass (the shaft) and the spindle torque from the parameters of the controlled mass, namely the torque and speed of the motor. The paper further rationalizes an approach that consists of simulating the processes on a model to further directly configure them on the object. The authors analyze the transients of the reconstructed two-mass system coordinates, which are associated with the rolls gripping the strip. The paper compares data against oscillograms recorded on the mill itself. The accuracy is satisfactory. The proposed observer has been used to developed a three-loop automatic speed control system for the uncontrolled mass. Controller configurations are substantiated. The paper shows coordinates obtained by simulation modeling as functions of time. It further presents experiments run on Mill 5000; the conclusions are that the amplitude and oscillations of the elastic torque drop significantly. The paper concludes with recommendations on industrial adoption of the observer and the novel electric drive coordinate control system. Study presented herein substantiates and implements a concept of developing algorithms that solve specific problems and are readily implementable on the existing equipment without need for additional computing devices. The contribution of the paper consists of stating and solving the problem of developing and testing an automatic elastic torque control system for the shaft of a heavy-duty rolling mill. This system has been implemented in the form of algorithms that run in the software of the existing industrial controllers (PLCs). It is simple and performs well. It does not need additional sensors or computers to be implemented, nor does it rely on complex computational algorithms. Such algorithms are based on computational tables that require a priori data on numerous process parameters. In our literature review, we have not come across any industrial implementation of such algorithms on hot-rolling mills.

Development of mathematical models of strip heat transfer in the inter-stand spacings of the finishing group during wide-strip hot rolling

The analysis of regularities of heat transfer in the finishing group of a wide-strip hot rolling mill is carried out and mathematical models are proposed to describe thermal processes in various zones of the inter-stand spacings. A significant difference in conditions of heat transfer in the middle, most extended zones of the inter-stand spacing of the finishing group was revealed, which consists in the presence of a large amount of water vapor between the stands and absorption of thermal radiation from the strip by steam. A mathematical model of absorption of radiation from a strip by water vapor is proposed, which complements the models of convective and radiant heat transfer of the strip in the spacing. The difference lies in the determination of the reduced emissivity of the medium/strip system, taking into account the absorption capacity of the vapor, depending on the temperature and partial pressure. Using an extended model of strip cooling in the spacing, a numerical simulation of the rolling process was carried out for the conditions of a 2000 wide-strip mill with various strip cooling schemes. It is shown that the prediction of strip temperatures at the exit of the finishing group without taking into account the absorption of radiation can have an error of up to 25 °C or more, which will negatively affect the prediction accuracy of the structure and mechanical properties of rolled products. It is noted that the detailing of the description of thermal processes in the finishing group makes it possible to reduce uncertainty and increase the adequacy of mathematical models when controlling the mill. The proposed model is included in the integrated model of the 2000 mill finishing group and is used when adjusting the strip cooling modes, which provided a significant reduction in the yield of inappropriate products by the “ripple” defect. The numerical implementation of the model was carried out by M. N. Oreshina, Cand. Phys.-Math. and M. R. Dabas, Master (Lipetsk State Technical University).

Optimization of continuous variable crown work roll shifting strategy using k‐nearest neighbors algorithm for hot strip rolling mill

AbstractAppropriate work roll shifting strategy in the finishing hot strip mill can make roll wear uniform and contour smooth. To eliminate the problem of roll shifting position duplication due to available roll shifting strategy, the optimized work roll shifting strategy is proposed in the hot strip rolling mill with continuous variable crown work rolls, using the k‐nearest neighbors algorithm. The self‐perpetuating parameter is used to describe the concentrated wear of the work roll. The position repeated parameter is put forward to evaluate the roll shifting position history. The relationship between concentrated shifting positions and uneven roll wear is analyzed based on in‐site data collected in a hot strip rolling mill. The program of optimization strategy is coded using language C. Off‐line simulation is made to obtain optimized shifting positions using the program. The comparison between in‐site engineering data and optimized shifting positions has been assessed using the scatter diagram and kernel density estimation. Comparison results show that the new strategy can solve the problem of shifting position duplication well. A simplified method is established to make it easy to apply the strategy to online applications, and the in‐site application shows that the optimization strategy has an excellent practical effect.

Coordinate control of strip thickness-crown-tension based on inverse linear quadratic in tandem hot rolling mill

Coordinate control of strip thickness-crown-tension based on inverse linear quadratic in tandem hot rolling mill

MATHEMATICAL MODELING OF THE SYNCHRONOUS MAIN DRIVE MOTOR OF THE HOT-ROLLED MILL 2800

This article discusses the calculation of the parameters of the mathematical model and the dynamic characteristics of a synchronous motor for the main drive of a hot-rolled mill 2800.

A new ductile iron for mechanically and thermally strained rolls Part 2: material development varying Ni, Mo, Cr content using DoE

ABSTRACT For manufacturing of steel wires, tubes and profiles in hot rolling mills, the toughest conditions occur in the front rolling stands where the mechanical load comes along with a constant change of hot rolling stock and cooling liquid. Conventional roll materials as pearlitic or acicular cast irons are not able to overcome these intense conditions because of their lack of ductility. A fine selection of alloying elements leads to a new material with a microstructure uniting bainite, austenite and martensite inducing high hardness and strength, without lacking elongation. For this purpose, a full-factorial design of experiments was created consisting of three alloying elements in two concentrations each. The alloys were cast, heat-treated and underwent extensive investigations on mechanical properties and microstructure. The results were analysed and implemented in mathematical models whose combination and optimisation led to the identification of the ideal alloy meeting the required strength, hardness and elongation.

Study of features of texture and structure evolution during hot rolling in a continuous group of stands of aluminum alloy 6016

The article presents the study results of texture and microstructure evolution during automotive 6016 aluminum alloy hot rolling in the multi-stand continuous hot rolling mill. Optical microscopy, scanning electron microscopy, X-ray structure analysis were applied for examinations. 6016 alloy is noted for specific features of structure and texture formation. The peculiarities are due to the high value of the stacking fault energy and a small amount of intermetallic particles of the second phase. The texture in the form of four incomplete pole figures {111}, {200}, {220} and {311} was investigated by the “reflection” method using a DRON-7 X-ray diffractometer in CoKα radiation. When rolling in a continuous group of stands, the coiling temperature level into a coil is 315 oC at a logarithmic deformation of 2.7 and a deformation rate in the last pass of 70 s-1 is insufficient for complete recrystallization during self-annealing. Annealing the hot rolled coil at 425 oC results in a sharp cubic texture. The amount of fine particles in alloy 6016 is very small; therefore, the main mechanism for suppressing recrystallization is an intense recovery process caused by a high stacking fault energy. The specific features are driven by high stacking fault value and low amount of the second phase intermetallic particles. The obtained data could be useful for thermomechanical treatment processes improvement, crystalline texture evolution modelling and 6ХХХ group alloys improvement.The research was conducted on the expences of the Russian Scientific Fund's grant, the project № 21-19-00548, https://rscf.ru/project/21-19-00548/.

A novel mass flow controller for tandem hot rolling mills

In tandem hot strip rolling mills, the roll speed of the mill stands is synchronized to ensure a uniform mass flow. A uniform mass flow is desirable to keep the strip tension constant and thus avoid large movements of the loopers which are located between the mill stands in order to control the strip tension. This also improves the general thickness quality and prevents the strip from necking in case of too excessive strip tension. In typical state-of-the-art implementations, the roll speed is synchronized in a cascade master–slave control structure where the very last downstream mill stand serves as speed master and the upstream mill stands operate in a slave mode to make sure that the mass flow is kept constant. While the relation between the roll speeds in the classical synchronization control concept is typically constant, a thorough analysis of the mass flow reveals that the slip between the work rolls and the strip is time varying. Therefore, in order to ensure a uniform mass flow, a novel adaptive speed synchronization controller is proposed, which can be easily added to the existing control structure and is computationally undemanding. To this end, a mathematical model of the whole tandem mill including the complete control structure is derived and validated by measurements. Simulation results for this validated model prove that the proposed mass flow controller clearly outperforms the existing solution.

Influence of the hydraulic pump unit on the quality of the rolled surface

To ensure the competitiveness of rolled metal in the modern market, the decisive factor is the quality of the rolled surface (defects with a depth of no more than 0.2 mm are allowed without stripping). Rolling processing is a source of formation of small surface defects, most of which are obtained in the process of deformation of a continuously cast billet (hereinafter referred to as CCB, billet) at hot rolling mills in the presence of areas with non-removed furnace scale. This is the main reason for the formation of such types of surface defects as rolled scale, ripple, scale shells, which reduces the quality of the rolled surface and increases the sorting of metal with surface defects. In addition, the scale not removed from the surface of the CCB has a negative impact not only on the quality of metal products, but also on the operation of rolling equipment.One of the directions that allows to achieve high quality of the surface of long products in production conditions is the effective removal of scale from the surface of the initial billet before hot deformation. Scale removal from the surface of the workpiece is carried out on the installation of a hydraulic descaling unit, but not always a satisfactory (complete) scale removal occurs.To determine the cause of poor – quality scale removal before hot deformation, the efficiency of the hydraulic descaling unit and the state of the metal – scale interface surface were investigated.