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The study described in the paper was carried out through measurements taken at a feed station in a hot rolling line. The aforementioned measures are appropriate for the purpose of analyzing power quality. The measurements were taken using a three-phase power quality analyzer. Measurements were taken for voltage and current values, as well as for active, reactive, and apparent powers. The analysis of the measurements taken indicates that the power quality is affected by the nonlinearity of the load. As a consequence, the level of reactive power becomes higher, leading to a reduced power factor.

Abstract The prediction accuracy of the rolling force is crucial for the strip hot rolling process, which will significantly affect the dimensional control accuracy of the strip product. The rolling force prediction models used in actual strip hot rolling production before are mainly analytical models and simple neural network models, but with the improvement of product quality, these models can no longer meet the requirements of high-end product production. A new online model based on the gradient boosting decision tree (GBDT) method is proposed to improve the accuracy of the online prediction of rolling force, in which the random forest method based on feature importance is adopted to select feature parameters. A model self-training function was developed in the control system to ensure the accuracy of the model used online. By comparing various machine learning methods, the results show that the rolling force prediction model proposed by the GBDT is better than that based on other regression methods. The established model has been successfully applied to predict the rolling force for the finishing rolling in a 2250 mm strip hot rolling production line. Compared with the traditional model, the rolling force prediction accuracy and thickness control accuracy are significantly improved.

DP600 steel is widely used in the automotive industry due to its exceptionally favourable combination of high tensile strength and good ductility. In the hot-rolling process, DP600 steel is produced by use of controlled cooling of the rolled strip from temperature above Ar3 to temperature below Ms. In this process, it is quite difficult to control key process parameters such as the time or temperature, and the final microstructure of the material is also affected by the degree of deformation of the material at various stages of the process. This paper presents a statistical analysis of the effect of chemical composition and selected hot rolling process parameters on the microstructure of DP600 sheet with respect to its mechanical properties. Based on industrial data from hot rolling mill combined with extended microstructure analysis, it was possible to find correlations between some of the analysed parameters and material properties. Among many correlations discussed in this work, most notable are those between martensite morphology and mechanical properties, between Mn and Si concentration and martensite morphology and between rolling speed, strain, cooling rate and mechanical properties.

The issues of improving the control systems of the continuous wide-strip hot rolling mill 2000 of NLMK PJSC are considered. Modification of four control subsystems ensures the mill 2000 operation stability in the finishing group of stands, which allows switching to a higher-speed mode of rolling thin strips for cold rolling and actually increasing productivity.

This study establishes a vertical–horizontal coupling vibration model of hot rolling mill rolls under multi-piecewise nonlinear constraints considering the piecewise nonlinear spring force and piecewise nonlinear friction force constraints of the hydraulic cylinder in the vertical direction of the rolls, the piecewise stiffness constraints in the horizontal direction, and the influence of the nonlinear dynamic rolling force in the rolling process. Using the average method to solve the amplitude–frequency response equation of the coupled vibration system and taking the actual parameters of a 1780 mm hot rolling mill (Chengde Steel Co., Ltd., Chengde, China) as an example, we study the amplitude–frequency characteristics of the mill rolls under different parameter settings. The results show that the amplitude and resonance region can be reduced by appropriately reducing the external disturbance force and the nonlinear spring force of the hydraulic cylinder, appropriately increasing the nonlinear friction force, and eliminating the gap between the bearing seat and the mill housing, to avoid the amplitude jump phenomenon due to piecewise variation. Furthermore, using the singularity theory to study the static bifurcation characteristics of the coupled vibration system, we establish a relationship between the vibration parameters and the topological bifurcation solution of the coupled system. The transition sets and their corresponding bifurcation topological structure in three cases are given, and the steady and unsteady process parameter regions of the rolls are obtained. The dynamic behavior of the coupled vibration system can be controlled by varying the bifurcation parameter. This study provides a theoretical basis for restraining the vibration of hot rolling mill rolls and optimizing the process parameters.

A several processes are required for production of steel, and one of these processes is called as hot rolling. The rolling mill efficiency is most important factors that determines the steel quality that is produced. The present article provides a thorough analysis of experiments data, numerical analysis, and artificial neural network simulations for enhancement of hot rolling mill performance. For achieving this rolling mill's experimental data is collected and examined. The speed of the rollers (N), Young modulus (E), distance between a rollers (d) and fillet thickness (t) are the parameters considered in this work. The rolling process is simulated, and its effectiveness is evaluated, using finite element analysis techniques. To describe the behavior of the rolling mill while accounting for many parameters and their interactions, finite element analysis techniques are used. The performance of the rolling mill is also forecasted and optimized using simulations of artificial neural networks. The findings of this study can contribute to the development of advanced control systems and optimization methodologies for hot rolling mills, which can ultimately result in increased efficiency, cost savings, and improved product quality in the steel industry.

ZK61 magnesium-alloy plate with high tensile strength and elongation is obtained by combined multipass symmetric hot rolling and asymmetric warm rolling. Deformation history considering varying strain rate obtained from the macro-finite element analysis of the selected passes are introduced into the viscoplastic self-consistent model (VPSC) as initial boundary conditions for macro- multiscale and micro-multiscale coupling analysis. VPSC simulation results show that in the initial stage of rolling deformation, the basal slip is the dominated deformation mode, supplemented by prismatic slip and pyramidal slip. With increased rolling strain, the pyramidal slip presents competitive relationship with basal slip, and the activation amount of 101—1 compression twins is limited. During asymmetric rolling, the basal slip is dominant, followed by the pyramidal slip. Experimental results show that the basal texture is gradually strengthened after symmetric rolling, and grain size is refined due to the activation and recrystallization of twins. Asymmetric rolling makes the basal texture deflect 10° to the rolling direction and further refine the grain size. With the ongoing of symmetric rolling, the mechanical anisotropy of the plate weakens, and the yield strength, tensile strength, and plasticity of the material improves. In particular, after asymmetric rolling, the tensile strength in the RD and TD directions of the plate reaches 391.2 MPa and 398.9 MPa, whereas the elongation reaches 19.8% and 25.5%.

Control cooling is an essential method for microstructure and mechanical property control in hot rolling strip making. Therefore, it is vital to realize high-precision temperature distribution prediction and control in cooling process to ensure the industrial production. In this paper, a traditional mechanism model based on finite-difference method combined with online cycle velocity calculation strategy was introduced as one of the baseline methods estimating temperature distribution. However, considering calculation time, variable-velocity rolling makes it difficult to rapidly realize temperature and modifying water distribution of all segments in cooling zone. Herein, a temperature distribution prediction method based on recurrent neural network was proposed, by fully considering the variable-velocity rolling dynamic characteristics. And the temperature distribution prediction performance of the model with different recurrent cell and time steps was evaluated. The results indicated that the proposed model could realize temperature distribution prediction, and the model based on bi-LSTM and 48 timesteps has the highest determination coefficient value of 0.976, the lowest root mean square error of 8.03, and a mean absolute error of 5.7. Furthermore, compared with baseline model, the proposed model retained lower computational cost, making it applicable in industrial application by providing real-time temperature distribution prediction.

The ability of working rolls of rolling mills to withstand loads and wear during operation in quarto stands is affected by contact stresses in the deformation zone and at the contact surface of the working and support rolls. However, there is no assessment of the combined effect of contact stresses on the durability of working rolls in hot strip rolling, taking into account the number of cycles and loading frequency in the stands of a continuous mill. The analysis of actual rolling modes of hot-rolled strips is performed, the values of the parameters of the cyclic load of the working rolls in the inter-roll contact are determined, the contact strength of the working rolls during a rolling campaign in an operating continuous industrial hot rolling mill is investigated. It is shown that a low loading frequency in the inter-roll contact of heat-resistant rolls in the first stands of the finishing group allows for the implementation of the greatest partial reductions, ensuring a decrease in contact stresses in the last stands of the finishing group to 5% with an increase in their operational resistance to abrasive action both in the inter-roll contact and in the deformation zone. Based on the research results, recommendations are proposed to increase the durability of working rolls when rolling thin low-alloy products.

In this work, the impact of reinforcing aluminum matrix with a nano-hybrid Al 2 O 3 -GNS nanosheet and the rolling process of the prepared composites were studied. Al 2 O 3 -GNS nanocomposite powder of a 98% to 2% ratio was produced by powder mechanical milling for 24 h. Additionally, the hybrid mixture undergoes surface treatment by coating with 10% silver to improve its wettability. For the nanocomposite production, aluminum scrap was melted at 800 °C, and the hybrid nanoparticles were added to the aluminum melt in 0%, 5%, 10%, and 15% by weight, followed by mechanical stirring at 600 rpm for 10 min. Then, hot-rolling was conducted for the cast billets to ensure optimal distribution of the hybrid nanoparticles. The microstructure, hardness, and wear rate were studied before and after the hot rolling process. Scanning electron microscopy (SEM) and Raman spectroscopy were used to analyze the microstructure. For mechanical properties, hardness, tensile strength, and wear resistance were measured. Additionally, corrosion resistance was evaluated. The microstructural analysis reveals a well-distributed hybrid phase within the aluminum matrix, with strong adhesion between the nanoparticles and the base metal. The incorporation of Al₂O₃–GNS hybrid nanoplatelets led to a significant enhancement in the mechanical and corrosion properties of recycled aluminum. The hardness increased from 72.3 HV for pure aluminum to 168.7 HV for the hot-rolled composite containing 15 wt% Al₂O₃–GNS, while the ultimate tensile strength improved from 55.8 MPa to 139.9 MPa after rolling. Furthermore, the corrosion rate in 3.5 wt% NaCl solution was reduced from 3.124 mm/year for pure aluminum to 0.215 mm/year for the as-cast reinforced composite, corresponding to a 93% improvement in corrosion resistance. The findings of this work contribute to the development of advanced materials with superior performance, offering potential applications in industries where high strength and corrosion resistance are critical.