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The cross-sectional profile of hot-rolled silicon steel strips has a great impact on the transverse thickness difference of cold-rolled strips. The genetic algorithm - Levenberg Marquardt - backpropagation neural network model was developed for the analysis of heredity effect of the cross-sectional profile of hot-rolled strip. The feature importance was analysed based on random forest and extreme gradient boosting methods. Furthermore, the influence law was analysed by data visualization. The coupling term of the strip crown and thickness , the edge drop and the coupling term of the strip wedge and thickness are recommended as key indexes of the hot-rolled silicon steel strip. As the transverse thickness difference must be controlled below 7 μm, the corresponding , , should be controlled no more than 60, between -10∼10 μm and no more than 7.5 , respectively. Meanwhile, the requirement for the cross-sectional profile indexes is synchronous. [ABSTRACT FROM AUTHOR]

This study presents an advanced intelligent control method that integrates ABAQUS simulation with the Derivative Dynamic Time Warping (DDTW) algorithm to enhance lateral deviation control in hot-rolled steel strips. Tested on a 2250 mm seven-stand tandem mill, the method incorporates a simulation model developed to assess control effectiveness under varying rolling conditions, including steel grades, strip widths, and thicknesses. The DDTW algorithm was employed to track the strip's centerline and accurately correct tail-end deviations, achieving over 96% accuracy. By combining real-time deviation data with the simulation, control precision was significantly enhanced, reducing deviation at the finishing mill exit by 20% compared to manual methods. Additionally, operator intervention was reduced by over 80%, and incident rates dropped by more than 60%, improving both process stability and product quality. These findings demonstrate the method's potential to optimize production consistency and minimize operational risks. [ABSTRACT FROM AUTHOR]

Tata Steel UK manufactures hot rolled strip products designed for a wide range of market applications. Typical mechanical properties for hot rolled strip products comprise: yield strength (Rp0.2), ultimate tensile strength (UTS) and elongation (A80). Physical tensile testing of a representative sample taken from each coil is generally used to measure these mechanical properties. As an alternative, artificial neural network (ANN) models have been developed to predict these tensile properties. This article describes the development and integration of ANN models for two hot rolled strip products: a low carbon steel for cold forming, DD11, and a high strength low alloy, S355MC. The current work proposes to combine the ANN model with a product release criteria to ensure high prediction confidence after deployment into the industrial environment. Following a period of 6-month implementation of the ANN models, predicted mechanical properties for both products were shown to be well within product specification requirements. The average predicted mechanical properties for DD11 were 241 MPa for Rp0.2, 351 MPa for UTS and 38.1% for A80. The average predicted mechanical properties for S355MC were 400 MPa, 488 MPa and 31.4% for Rp0.2, UTS and A80, respectively. Input parameters with relatively high Pearson correlation coefficient value showed that Rp0.2 increases monotonically as Mn content increases or coiling temperature decreases for DD11. Whereas for S355MC, Rp0.2 increases as Nb content increases, but coiling temperature did not show a linear relationship with Rp0.2. [ABSTRACT FROM AUTHOR]

Spheroidizing annealing is commonly carried out to increase the deformation capability of hot rolled steels. In this work, the influence of heat treatments at 690°C and 720°C was studied on a low-carbon boron steel. The aim of this work is to relate the mechanical characteristics of the as-spheroidized steel with the as rolled prior microstructures and spheroidizing parameters. The paper suggests a tool for selecting the best combination of the as rolled microstructure and spheroidizing parameters (temperature and time) to obtain the requested deformability. Microstructure and mechanical characteristics were studied varying the spheroidizing soaking time. Different prior microstructural conditions were obtained modifying the thermo-mechanical rolling and the Stelmor cooling parameters. Other microstructural conditions were prepared by laboratory heat treatments. The results remarked the importance of the prior microstructure on the shape and the distribution of the carbides after spheroidizing. Moreover, also the spheroidization rate was influenced by the prior microstructure. [ABSTRACT FROM AUTHOR]

The present work describes the development of microstructures in multi-phase steels with minimum ultimate tensile strength (UTS) in the range of 750–800 MPa. A relatively leaner composition was designed to achieve the strength, and with two compositions, the UTS values of about 750 MPa and 800 MPa were achieved. In both cases, multi-phase microstructures were developed, which consisted of ferrite, bainite and martensite. Bainite was the major phase in these microstructures. The cooling rate played a vital role. Ferrite was observed to form as the major phase along with islands of bainite at a cooling rate of 2 °C/s or less than that. The desired amount of bainite phase could be obtained at and above a cooling rate of 20 °C/s. A double step cooling strategy was applied to achieve the desired multi-phase microstructure. After the completion of hot rolling at around 850 °C, a fast cooling to a temperature of 580 °C and holding there for a duration of 5–12 s led to the formation of ample bainite in the microstructure, followed by transformation of remaining austenite into martensite during the second step cooling at 250 °C. [ABSTRACT FROM AUTHOR]

Peeling defects induced by inclusion were investigated by scanning electron microscope and energy dispersive spectrometer. Mechanical parameters of steel matrices and Al2O3 inclusion were studied using nano-indentation. Inclusion movement in the sheet thickness direction during cold rolling was simulated by 2D finite element (FE) method. The boundary conditions of inclusion induced peeling defects were obtained under different inclusion initial position and size. When 10 μm inclusion is located in the position of 1/16 of the sheet thickness direction, it has potential to be the source of peeling defects. While the diameters of inclusion are 50 and 100 μm, the initial position boundaries are 1/10 and 1/9, respectively. The defects around inclusion calculated by FE simulation are consistent with that by experimental results. Finally, the relationship between inclusion position and peeling defects is discussed. [ABSTRACT FROM AUTHOR]

A heavily cold-rolled (80%) high manganese austenitic steel was subjected to isochronal and isothermal heat treatments, in order to study the texture development during the recovery stage. Progress of recovery was negligible after annealing at 500°C for 60 min, but annealing for 60 min at 800°C led to complete recrystallisation, and appearance of the final texture was very similar to that of a copper type deformation texture. Isothermal heat treatments at 600°C revealed that at the very early stage of recovery, there was a weakening of brass type deformation texture, but it was followed by a sharpening of Bs component. At the same time, there was a shift of peak intensity towards Bs/Goss location. After this stage of intermediate sharpening, the texture sharpness experienced a continuous weakening with further progress of recovery. [ABSTRACT FROM AUTHOR]

The dimensional quality of cold rolled sheets is very important for its usage in automotive, packaging and white goods industries. To predict strip shape a partial least square (PLS) regression model has been developed and validated with actual plant data. The model was then used to optimise the process parameters to reduce the magnitude of shape errors. The most important parameters responsible for shape defects have been identified from a large number of input process parameters. With the help of the model it has been found that apart from the tensions and the speeds at different stands, the levelling of the last stand plays an important role in controlling the strip shape. The model can be used online, and is useful for plant operation. [ABSTRACT FROM AUTHOR]

Elastic modulus anisotropy with respect to sheet rolling direction was calculated, using X-ray diffraction determined texture components and their known individual anisotropies, for a deep drawing grade steel in the cold rolled as received and three heat treated conditions. The calculated elastic modulus anisotropy was compared to mechanically measured modulus values, at 0, 45 and 90° to the rolling direction, and ultrasonic velocity anisotropy, measured using an electromagnetic acoustic transducer (EMAT) system. It was found that the EMAT velocity anisotropy matched the measured modulus variation with angle, with differences between the as rolled and heat treated conditions being observed. However the predicted modulus variation did not show much difference between the as rolled and heat treated conditions, despite different textures being measured, resulting in a slight discrepancy with the EMAT velocity and measured modulus values at 90° for one heat treatment condition. [ABSTRACT FROM AUTHOR]

Geometric quantities of rolled products can be measured online in the manufacturing process using laser measurement technology. Examples of laser inspection systems, which are in routine use in rolling mills, their performance, and the operational experience will be discussed. One to three-dimensional geometric quantities are measured, such as the thickness of cold rolled sheets, the cross-sectional profile of rails and flatness defects of heavy plates. The features of the laser method will be compared to those of conventional measuring methods. Precision and measurement capability achieved under production conditions will be described. [ABSTRACT FROM AUTHOR]

In the manufacture of rolled steel from a hot strip mill, the final mechanical properties, such as yield strength, ultimate tensile strength and elongation to fracture, are important requirements specified by the customer. With the advent of ever more powerful computational hardware and software, non-linear modelling techniques such as artificial neural networks can result in the creation of acceptably accurate predictive models. In the present paper, the application of known metallurgical knowledge on the selection of model input data, along with the excellent non-linear mapping capabilities of neural networks, combines to create a solution for the prediction of final mechanical properties throughout the length of rolled coils from the Corus Port Talbot hot strip mill. Predictive models are trained on two data sets (one from 2003 and the other from 2005) of a structural steel grade S275 from the high strength steel family. Actual through coil tensile test results from a validation coil (rolled in 2005) were used to certify the models. The model results clearly show that when an optimised neural network architecture is trained on coil samples taken from the specific grade of steel and within a reasonable timeframe relative to the unseen validation data, an acceptably accurate model is created for the prediction of final mechanical properties. [ABSTRACT FROM AUTHOR]