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The influence of cold-rolling directions on the recrystallization texture evolution of pure iron was examined. As-received pure iron sheets were cold-rolled under two different conditions (specimens A and B). Specimen A was cold-rolled in the vertical direction against the cold-rolling direction of the as-received sheet. Specimen B was cold-rolled in the vertical direction against the cold-rolling direction of the as-received sheet, and then in the cold-rolling direction of the as-received sheet. Cold-rolled specimens were heated to each desired temperature before being quenched in water to room temperature (298 ± 2 K). Both cold-rolled specimens showed the development of γ-fiber and 100 orientation. Additionally, γ-fiber formed comparatively more in cold-rolled specimen A, while α-fiber developed comparatively more in cold-rolled specimen B. Strain distribution in cold-rolled specimen A was presumably inhomogeneous, whereas that in cold-rolled specimen B was rather uniform at the macro-scale. The formation of γ-fiber was confirmed in annealed specimen A. In annealed specimen B, however, the recrystallization texture tended to be random, and the formation of α-fiber was observed. Furthermore, the formation of Goss orientation in both annealed specimens was established. Recrystallized ferrite grains with Goss orientation nucleated in high strain regions of cold-rolled specimen. These findings show that by devising the cold-rolling direction, it is possible to discover new types of recrystallization textures.

In the cold rolling process, inaccurate rolling force settings and the resulting strip thickness fluctuations and other quality problems occur, reducing the yield and product quality. To improve the accuracy of rolling force prediction, this paper proposes three methods to combine a T-S fuzzy neural network and rolling force analytical model based on their advantages and characteristics, to construct a combined rolling force prediction model, and to fully utilize the features and benefits of each model for rolling force prediction. The model’s performance is evaluated by selecting the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The model experiments with historical production data obtained from industrial sites. The experimental results show that the combined prediction models have a more robust rolling force prediction capability than the T-S fuzzy neural network model alone, especially the combined form of using the calculated value of the rolling force analytical model as the input to the T-S fuzzy neural network without destroying the self-learning of the rolling force analytical model, which has better calculation accuracy and reliability for rolling force than other models. The model can provide an essential reference for the online prediction of cold rolling force and high precision rolling production and has high usability.

In order to meet the shape control requirements of “dead flat” transverse thickness profile of electrical steel sheet in cold rolling process, the 3D finite element model (FEM) for roll stacks and strip of 6-high tandem cold rolling mills (TCMs) was built with the developed Edge Drop Control Work Rolls for Non-shifting (EDW-N). The efficiency curves of the work roll bending (WRB), the intermediate roll bending (IRB), and the intermediate roll shifting (IRS) in cold rolling process are obtained for transverse thickness profile control performance. The control strategy of multi-stand and multi-variable profile and flatness control actuators, i.e., WRB, IRB, and IRS of stands 1 ~ 5 in 6-high TCMs is proposed based on the GA-PSO hybrid algorithm with better optimization ability. The results show that the control strategy can fully exploit the shape control ability and achieve the high precision control for transverse thickness profile of 6-high TCMs. The industrial application on the production gives remarkable results that the rate of the measured strip crown less than 7 μm increased from 38.58 to 67.74% for electrical steel sheet in the 1420-mm 6-high TCMs. The control strategy has applied to the production successfully.

Precise selective cooling control of work roll can significantly improve the cold rolled strip flatness in steel manufacturing industry. To improve the control accuracy of the coolant output of selective work roll cooling control system, a machine learning (ML) algorithm with differential evolution-gray wolf algorithm optimization support vector machine regression (DE-GWO-SVR) model has been proposed for the first time in this study. This model combines the differential evolution (DE) with grey wolf optimization algorithm (GWO) to improve the optimization performance of the algorithm. Then, the SVR model parameters are optimized with differential evolutionary gray wolf hybrid algorithm (DE-GWO) to improve the regression accuracy. Finally, the influences of data normalization methods and the selection of SVR kernel functions were systematically investigated. Compared with the test results of other regression models, the evaluation index R2 based on the DE-GWO-SVR model is greater and the RMSE, MAE, and MAPE are smaller. The DE-GWO-SVR model performs the best, with a higher regression accuracy than the other regression models. Besides, it has been successfully applied to a 1450 mm five-stand industrial cold rolling mill. The model has higher control accuracy for the thermal crown of the work roll and better control effect for the flatness deviation of the strip steel. This study provides a novel strategy with a help of ML algorithm to effectively improve the flatness quality of cold rolled strips by optimizing the selective cooling control of work roll, which exhibits a great practical application potential in steel manufacturing.

High-strength non-oriented electro-technical steels with a low thickness possess excellent isotropy of electromagnetic and mechanical properties which is highly required in the production of high-efficiency electric motors. The manufacturing process of this type of steel includes very important and technologically complex routes such as hot rolling, cold rolling, temper rolling, or final heat treatment. The final thickness is responsible for the decrease in eddy-current losses and is effectively achieved during cold rolling by the tandem rolling mill. Industrial production of thin sheets of high-strength silicon steels in high-speed tandem rolling mills is a rather demanding technological operation due to the increased material brittleness that is mainly caused by the intensive solid solution and deformation strengthening processes, making the dislocation motion more complex. The main objective of this work was to investigate the distribution of local mechanical strains through the thickness of high silicon steel hot bands, generated during the cold rolling. The experimental samples were analysed by means of electron back-scattered diffraction and scanning electron microscopy. From the performed analyses, the correlation between the material workability and the nucleation of cracks causing the observed steel strip failure during the tandem cold rolling was characterized. Specifically, the microstructural, textural, misorientation, and fractographic analyses clearly show that the investigated hot band was characterized by a bimodal distribution of ferrite grains and the formation of intergranular cracks took place only between the grains with recrystallized and deformed structures.

In the present work, the microstructure and texture evolution of ferritic stainless steel during unidirectional cold rolling were investigated, and the Visco-Plastic Self-Consistent (VPSC) polycrystal model was used for the simulation of texture during cold rolling. Comparison of different interaction models was made to obtain a model that better reproduces the texture evolution of ferritic stainless steels. The as-received hot-rolled samples were unidirectionally cold rolled in a laboratory rolling mill, and the thickness was reduced by 30%, 60% and 80%. Electron backscatter diffraction (EBSD) was used to observe the microstructure evolution and texture evolution, and micro-hardness was used to evaluate the work hardening of the sample. The important feature of the microstructure was the presence of shear bands (SBs), the frequency of which increased with the increase in cold-rolling reduction and was found to be orientation dependent. We found that the geometrically necessary dislocation (GND) density increased with cold-rolling reduction in accord with Ashby’s theory of work hardening, and higher GND density accumulates near the grain boundary. The grain fragmentation, Goss texture distribution and orientation gradient were found to be orientation dependent. The cold-rolled texture was composed of strong α-fiber and weak γ-fiber. The relative plastic compliance of grain and the homogeneous effective medium (HEM) were explored. The tangent interaction model was found to match reasonably well with the experimental texture. This work has great significance for achieving online monitoring of the texture of ferritic stainless steel under different industrial production processes and enhancing the intelligence level of ferritic stainless steel production process.

The basic control model of longitudinal thickness variability based on the graphic solution to the system of two equations is improved. The model allows regulating the longitudinal profile at skin rolling of a strip on a cold-rolling mill taking into account the most important technological parameters affecting the cold rolling force. Representation of this system as differential equations confirms the original correctness of the graphic solution, allows determining the stiffness modulus of the rolled strip, and choosing the control actions for the technological parameters of the skin rolling process. The reliability of this model is checked by comparing the profile diagrams of the calculated and measured mean values of the longitudinal thickness variability at cold rolling of strips on a skin rolling mill 2000 of Severstal Company. It is shown that an increase in the head and trail specific tensions on a skin rolling mill allows reducing the oscillations of the thickness over the entire area of the rolled strip to the required tolerance from the nominal thickness.

In the traditional rolling force model of tandem cold rolling mills, the calculation of the deformation resistance of the strip head does not consider the actual size and mechanical properties of the incoming material, which results in a mismatch between the deformation resistance setting and the actual state of the incoming material and thus affects the accuracy of the rolling force during the low-speed rolling process of the strip head. The inverse calculation of deformation resistance was derived to obtain the actual deformation resistance of the strip head in the tandem cold rolling process, and the actual process parameters of the strip in the hot and cold rolling processes were integrated to create the cross-process dataset as the basis to establish the support vector regression (SVR) model. The grey wolf optimization (GWO) algorithm was used to optimize the hyperparameters in the SVR model, and a deformation resistance prediction model based on GWO–SVR was established. Compared with the traditional model, the GWO–SVR model shows different degrees of improvement in each stand, with significant improvement in stands S3–S5. The prediction results of the GWO–SVR model were applied to calculate the head rolling setting of a 1420 mm tandem rolling mill. The head rolling force had a similar degree of improvement in accuracy to the deformation resistance, and the phenomenon of low head rolling force setting from stands S3 to S5 was obviously improved. Meanwhile, the thickness quality and shape quality of the strip head were improved accordingly, and the application results were consistent with expectations.

Flatness prediction is a critical technical concern in flatness feedforward control during strip cold rolling. This work realized a high-precision prediction of flatness for strip cold rolling by the data-driven and industrial Internet of Things (IIoT) technology and provided an effective mode for industrial data utilization. A flatness prediction model based on general regression neural network (GRNN) optimized by the differential evolutionary (DE) algorithm was proposed; an intact dataset was established by collecting data from the hot rolling and cold rolling production lines by developing a cross-process IIoT platform, and the proposed model and other common data-driven models are trained and tested based on that. The experiment results obtained based on a dataset with 50,000 samples show that the proposed model is feasible and can achieve accurate prediction of flatness during the strip cold rolling, and compared with the BP and SVM model, it has a better performance.

Three kinds of 6061 aluminum alloy thin-walled tubes rolled by Pilger cold rolling mill and three-high rolling mill were taken as the research objects. EBSD, SEM and tensile test were used to analyze the microstructure, texture and mechanical properties of the tubes under natural aging. The results show that the grain size near the mandrel in the tube wall is larger, and the grain near the roll is smaller. The grains on the transverse section are elongated and elongated along the rolling direction. Under the large deformation of 84 % ~ 87 %, the main textures of thin-walled 6061 aluminum alloy tube are V texture, Brass texture, γ fiber texture and H texture. Under natural aging, the tensile strength and yield strength of 6061 aluminum alloy tube gradually increase with time, and remain basically unchanged after 60 days. The higher processing deformation, the higher tensile strength of tube, the lower elongation.