Predicted Rolling Force Research Articles

Variable speed rolling force prediction with theoretical and data-driven models

The variations in thickness at the head and tail significantly impact the product quality in tandem cold rolling. Improving rolling force calculation accuracy under speed-up and speed-down conditions is essential to enhance thickness control. This paper proposes a sequential coupled model combining data-driven and theoretical models to predict rolling force under speed variation conditions. To enhance accuracy and overcome the limitations of traditional velocity field, a linear cross-section dynamic velocity field taking into account the effects of roll gap changes on the deformation zone is established. Moreover, a long short-term memory (LSTM) network is constructed to capture sequential information from production data and compensate for changes in friction state caused by speed variation and roughness attenuation in the theoretical model. 12 coils, totaling 12,000 samples, are selected from the process data acquisition system (PDA) and utilized as the dataset after normalization. Mutual information (MI) theory is employed for feature selection to improve the prediction accuracy and reduce the computing consumption. Learning rate (LR) adjustment is applied to enhance the stability of initial network and the convergence speed. Compared to evaluated models, the proposed model has relative error within ±2.5 %, R2 of it is 0.9935 and the prediction time is only around 2.5 ms. The results suggest that the proposed model demonstrates the most favorable overall performance. Additionally, some key rolling parameters whose physical relationship with rolling force remains uncertain are investigated.

Research on deformation resistance of strip steel and rolling force prediction during cold rolling based on the hot rolling parameters

ABSTRACT In view of the influence of the fluctuation of incoming material composition, finishing temperature and coiling temperature on the deformation resistance in the cold rolling process during the hot rolling process, the optimization objective function is established by combining the analytic method and big data regression. Further, the relevant characteristic coefficients are calculated through a large number of on-site actual data, and the deformation resistance model of hot-rolled finished strip and the rolling pressure prediction model of cold rolling process are obtained. Through the field test, the deviation between the predicted rolling force and the actual rolling force is less than 2%, and the accuracy of the model can meet the engineering requirements. More importantly, after using the research results described in this paper, the quality of cold rolled strip shape has been significantly improved, which can greatly improve the economic efficiency and market competitiveness of enterprises.

A new analytical model to predict the profile and stress distribution of tube in three-roll continuous retained mandrel rolling

To minimize the adjustments to the initial roll speed setting in tube manufacturing, both the tube profile and the tube stress distribution during the rolling are needed to be predicted before the actual rolling. While predicting the tube profile is to make the rolling reach to the metal flow balance state efficiently, predicting the tube stress distribution is to decrease the potential formation of the rolled defects caused by the non-ideal stress distribution in tube at each pass. This requires the rolling model to combine the prediction of tube profile with the prediction of stress distribution of tube in its formulation. A new analytical model which can meet the above requirements is presented in this paper. The mechanics of rolling tube in three-roll continuous retained mandrel rolling has been analyzed in the model. Validity of the model has been examined by hot rolling experiments at the plant. According to the experimental results, the predicted tube profiles are in good agreement with the measured, with the prediction errors of cross-sectional areas being in the range of 0.8∼2.5 %. The theoretical initial roll speed setting calculated based on the proposed model can be applied directly to tube manufacturing, with further adjustments at the mill being less than 4 %. The predicted roll forces calculated based on the proposed model have a maximum deviation from the measured less than 15 %. In general, the hot rolling experimental results at the plant have proved the validity of the proposed model.

Multi-parameter deep-perception and many-objective autonomous-control of rolling schedule on high speed cold tandem mill

High-speed cold tandem rolling process control system consists of complex mechanical and electrical equipments. The coupling association of these equipments makes multi-objective rolling process complicated to be predicted and controlled. In order to achieve higher prediction precision, a multi-parameter depth perception model is established based on a deep belief network. To get higher control precision in real time, a multi-objective rolling optimization method is introduced, which is supported by many-objective evolutionary algorithm. Five objectives are selected as rolling schedule optimization objective: equal relative power margin, slippage prevent, good flatness, total energy consumption and energy consumption per ton. Simulation results show that many-objective evolutionary algorithm based on decomposition and Gaussian mixture model achieves a set of balance solutions on these objectives. The proposed method could not only predict rolling force and rolling power in real time, but also give the solutions for many-objective reduction schedule.

Adaptive threading strategy based on rolling characteristics analysis in hot strip rolling

In order to improve the threading stability and the head thickness precision in tandem hot rolling process, an adaptive threading strategy was proposed. The proposed strategy was realized by the rolling characteristics analysis, and factors which affect the rolling force and the final thickness were determined and analyzed based on the influence coefficients calculation process. An objective function consisting of the influenced factors was founded, and the disturbance quantity was obtained by minimizing the function with the Nelder-Mead simplex method, and the proposed adaptive threading strategy was realized based on the calculation results. The adaptive threading strategy has been applied to one 7-stand hot tandem mill successfully, actual statistics data show that the predicted rolling force prediction in the range of ±5.0% is improved to 97.8%, the head thickness precision in the range of ±35 μm is improved to 98.5%, and the threading stability and the head thickness precision are enhanced to a high level.

A novel analytical approach to predict rolling force in hot strip finish rolling based on cosine velocity field and equal area criterion

In hot strip rolling process, rolling schedule setup, geometrical accuracy (thickness and profile), and even the final product homogeneity of mechanical properties are affected by the automatic control, and the rolling force and torque are the prerequisite in the control process. A new cosine velocity field is firstly proposed in this paper to get the values of the required minimum rolling force and torque. The field and equal area (EA) yield criterion are used to integrate the internal plastic deformation power. Using the co-line vector inner product method, the friction power is analyzed. Finally, the analytical expressions of rolling force, rolling torque, and stress effective factor are obtained. The theoretical predictions of rolling forces are compared with on-line measured ones in a hot strip rolling plant and other researchers’ models. Results show that the calculated rolling forces are in fair agreement with the actual measured ones, and the proposed solution is considered to be applicable for solving hot strip finish rolling.

A new model for thermo-mechanical coupled analysis of hot rolling

A new sine velocity field is proposed to analyze total power in the roll gap for rolling in hot strip finishing mill. The field and linear geometric midline yield criteria are used to calculate the plastic deformation power, and the collinear vector inner product method is used to get friction power. Then analytical equation of strip rolling power functional is obtained. Finally, the roll force and torque can be calculated by minimizing the power functional. In the model, average deformation resistance is determined by the thermo-mechanical coupled analysis. The prediction accuracy of the proposed model is examined through comparing with the on-line measured results in a hot strip finishing mill. It shows that the predicted roll forces are in good agreement with the measured ones, and the maximum error is less than 12 %. Moreover, the effects of various rolling conditions, such as thickness reduction, friction factor, and shape factor, on roll force, location of neutral angle, and stress-state coefficient are discussed systematically.

Slab analysis of large cylindrical shell rolling considering mixed friction

Large cylindrical shell rolling is an advanced plastic-forming technique that has unequal speed and radius of both its upper and lower rolls. A theoretical model for a large cylindrical shell rolling that is based on the slab method is proposed, in which the non-uniform normal and shear stresses that act on the vertical sides are considered. A mixed friction model of combined Coulomb and sticking friction is used to present interface friction, to improve the accuracy of the proposed model. The rolling pressure, rolling force, and torque as well as friction stress can be rapidly and easily calculated through the proposed model at different rolling conditions. The predicted rolling force is consistent with the experimental result. The research results provide valuable guidelines for both the design and optimization of rolling product, process, and equipment.

Strip layer method for simulation of the three-dimensional deformations of large cylindrical shell rolling

Abstract A strip layer method is presented for analyzing the three-dimensional deformations and stresses of large cylindrical shell rolling process. In this approach, the different radii and velocities of upper and lower rolls, the uneven distributions of deformations and stresses at the roll gap, are taken into account. The rolling deformation zone is divided into a number of strip and layer elements along the width and thickness directions, respectively. In order to reduce the optimization parameters and improve the computation efficiency, the exit lateral displacement distribution is expressed as polynomial function. Based on the fundamental principles of plasticity, the three-dimensional deformations and stresses of the deformation zone are formulated. The simulation results can be obtained quickly and easily. The fishtail spread is predicted satisfactorily on the free side, and the rolling pressure distribution is quite different from that of a conventional strip rolling. The predicted rolling force and average spread of the proposed method are in agreement with the experimental and FEM results.

Slab Analysis of Asymmetrical Rolling of Bonded Two-layer Sheets

In this paper, asymmetrical rolling of bonded two-layer clad sheets is studied using the slab method of analysis. It is assumed that the ingoing sheet is guided to horizontally enter the roll gap and that the work rolls may have different pressure distributions. Unlike the existing slab models, the slabs are not divided into two parts with different materials, and the whole slab is subjected to one stress field containing non-uniform normal and shear stresses acting on the vertical sides. By combining the sticking and coulomb friction models, a mixed friction model is proposed, and the work roll pressure distributions as well as the rolling force and torque are studied at different conditions. The predicted rolling force and torque of the proposed model are shown to be in a very good agreement with the analytical and experimental results of previous investigators.

Applying Deformation Resistance Estimation Method to Predict Nine Stand Hot Strip Mill Rolling Force

The rolling force of the nine stand hot continuous rolling production line is predicted on the base of the deformation resistance estimation method. The predicted error is turned out to be about 10% through the comparing analysis of the predicted rolling force and actual rolling force, which proves the feasibility of the forecasting model. One can get the deformation resistance without experiments by using this method, besides, this method works in a high speed.

Rigid Plastic FEM Model of Fast Solution to Strip Rolling

Rigid plastic finite element method (RPFEM) is one of the most efficient numerical methods during the rolling process. Realizing FEM online application has been main target for many researchers. The influence of compile method, elements number, compressible parameter, friction factor and convergent criteria were investigated and RPFEM model of fast solution to strip rolling was proposed in this work. Compile method and compressible parameter have less influence on calculated rolling force. However, the iteration steps are reduced and computational efficiency is improved greatly with compile method of release and compressible parameter 0.01. The change of calculated rolling force becomes less but iteration steps become more and more with the increment of elements number. Both accuracy and efficiency is satisfying with the change of elements number from 50 to 200. In addition, the typical rolling schedule from a certain plant has been solved with the developed program FFEM-2D by FORTRAN. The predicted rolling force has a good agreement with the measured value. The iteration steps change from 12 to 36 and computational time is less than 200(ms) with the model in one pass rolling. Therefore, the accuracy is satisfying and computational time fully meets the basic requirements of FEM online application. Keywords: Rolling; RPFEM; Fast solution; Computational time

Approximate Model for Predicting Roll Force and Torque in Plate Rolling with Peening Effect Considered

An approximate model for predicting roll force and torque in plate rolling is proposed. In this model, peening effect which unavoidably occurs in plate rolling due to a small ratio of work roll radius over mean thickness of slab (material) being deformed is considered. Besides, the proposed model does not need any iteration scheme to compute maximum reduction ratio per pass.The proposed roll force model consists of three parts: i) an ideal roll force under homogeneous deformation condition and ii) geometrical factor depending on inhomogeneous deformation state in roll gap and roll radius, material width and thickness and finally iii) the ratio of work roll radius increased by flattening over original roll radius. Multiplication of these three parts gives roll force in a given pass. A similar procedure was taken for roll torque model. A lever arm ratio which plays a vital role in computing roll torque was modified so that inhomogeneous deformation state of material in roll gap is fully covered.The validity of the proposed model is verified by applying it to No. 2 plate mill in POSCO. Results reveal that the predicted roll force and torque are in a good agreement with measured data, in comparison with conventional model being used. Accuracy of roll force is remarkably improved from 16.7 to 5.4% and that of roll torque is outstandingly enhanced from 26.0 to 6.3%.

An Analytic Model for Predicting Roll Force in Billet Rolling Process with Vertical Stands and Horizontal Stands Mounted

In this paper, we present an analytic approach for the prediction of roll force to be applicable to the billet rolling process which consists of a series of horizontal stands and vertical stands. The approach is based on the deformation shape and employs the assumption that the deformation homogeneously occurs in three directions. Strain and strain rate are calculated by the geometric relationships between those components and the prescribed deformation functions. By integrating the stress components along the rolling direction, roll force is finally obtained. The prediction accuracy of the proposed model is examined through comparison experimentally mesured roll force with computed ones.

Combined Macro–Micro Modeling for Rolling Force and Microstructure Evolution to Produce Fine Grain Hot Strip in Tandem Hot Strip Rolling

T.M.C.P. (Thermo Mechanical Control Process) has been widely used in the steel industry. We have produced fine grain hot strips industrially through high reduction and low temperature asymmetric rolling which decreases rolling force by generating a cross shear zone.We needed combined macro–micro model to predict rolling force in the finishing train and microstructure evolution of fine grain hot strips. First, deformation analysis model, an asymmetric rolling theory based on numerical analysis using Orowan's theory was proposed. Second, to obtain the material data for the kinetics of microstructure change in the low temperature austenite region, the compression tests were performed and we measured recrystallization ratio by EBSD (Electron back scattering diffraction). Third, microstructure evolution model to formulate the ferrite transformation using the intragranular nucleation was introduced.Accuracy of the new combined macro–micro model for rolling force and microstructure evolution was very excellent and it enabled us to confirm dynamic ferrite transformation occurrence during high reduction low temperature rolling at the actual tandem hot strip mill.

Study on roll force and dynamic recrystallization of high-strength interstitial-free steels in hot rolling

This paper presents a constitutive model for two interstitial-free (IF) steels that can be used for calculating the roll force during hot rolling. The constitutive models assume that the deformation behaviour of material can be described with reference to strain, strain rate, and temperature together with the volume fraction of dynamically recrystallized grains. The two IF steels are Nb-Ti-added IF steel containing 0.001 wt% B and 0.1 wt% P, and IF steel with no B and P. To verify the proposed constitutive model, a laboratory-scale hot-plate rolling experiment, together with three-dimensional finite element analysis coupled with the proposed model, has been performed. The capability of the proposed model was demonstrated through comparing predicted roll forces with measured values. A series of finite element simulations has then been carried out to study the variation in the distribution of the dynamically recrystallized volume fraction in the deformed workpiece by changing the rolling temperature and reduction ratio. Good agreement was obtained between the predicted roll forces and experimental measurements for two IF steels. The roll force of IF steel with P and B components was 11.6 per cent larger than that of IF steel without them since dynamic recrystallization was delayed owing to the alloying elements. It has been found that, as the reduction ratio increases, the effect of those alloys on the volume fraction of dynamic recrystallization becomes more dominant and leads to considerable differences between the roll force of the steels.

A study on on-line learning neural network for prediction for rolling force in hot-rolling mill

Steel manufacturers are under pressure to improve their productivity and to optimize their process parameters to maximum efficiency and quality. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI (artificial intelligence) techniques. The automation of hot-rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. The mathematical modeling of hot-rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, an on-line learning neural network for both long-term learning and short-term learning was developed in order to improve the prediction of rolling force in hot-rolling mill. This analysis shows that the predicted rolling force agrees with very close to the practical rolling force, and the thickness error of the strip is considerably reduced.

Prediction of dynamic recrystallization kinetics during hot rolling

In this study, the kinetics of dynamic recrystallization is predicted under hot rolling conditions employing a first-order rate equation. The proposed model considers the effects of temperature and strain rate variations on recrystallization kinetics by simultaneously modelling heat transfer and plastic deformation phenomena. To do so, a two-dimensional finite element method is coupled with the transformation equation to calculate the temperature distribution and the strain rate field as well as the kinetics of dynamic recrystallization within the metal, concurrently. The model developed was examined for a low carbon steel. A series of hot compression tests were carried out at different temperatures and strain rates to obtain the material parameters and then hot rolling experiments at various rolling speeds and reductions were conducted to check the reliability of predictions. Comparison between the measured and predicted roll forces as well as the microstructural studies verifies the theoretical results.

A study on genetic algorithm to select architecture of a optimal neural network in the hot rolling process

In the face of global competition, the need for the continuously increasing productivity, flexibility and quality (dimensional accuracy, mechanical properties and surface properties) has imposed a major change on steel manufacturing industries. Conventional rolling force formulas, however, provide not more than reasonably exact approximations. The mathematical modelling of the hot rolling process has long been recognised to be a desirable approach to aid in rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors (strain hardening and strain rate hardening, friction condition, flattening of rolls, deflection of the rolling mill and temperature, as well as their interactions) make the theoretical analysis of the rolling process very complex and time-consuming.This paper proposes a new genetic algorithm (GA) to select the optimal architecture of the neural network and compared with that of engineer’s experience. It is shown that learning approach with the optimal structure of neural network could be applied to predict the rolling force for lot of change in hot rolling process and compared between the predicted rolling force by calculated from developed rolling force model and actual rolling force.

New approach for prediction of roll force in rod rolling

An approximate model is presented in which the equation for computing roll force in an oval-round (or round-oval) pass rolling sequence has been formulated with a new approach. The fundamental idea considered is to introduce the concept of a weak plane strain condition which gives a two-dimensional approximation of the three-dimensional deformation problem in rod or bar rolling. The generality and robustness of the model have been examined by conducting a two-pass low speed bar rolling experiment and a four-pass continuous high speed rod rolling test with various rolling temperatures, roll gaps (pass height), rolling speeds, and steel grades. The predicted roll forces were in good agreement with those experimentally measured, although the prediction was less good when the roll gap (pass height) varied. The proposed model was very fruitful in solving the three-dimensional deformation problem in rod or bar rolling.