Properties Of Hot Rolled Steel Research Articles

Development of intelligent methodologies perceiving microstructure and mechanical properties of hot rolled steels

Traditional metallographic characterization of steel microstructure can only roughly determine average grain size and phase fractions because it relies too much on researchers’ personal experiences. As a result, when the microstructure-property relationships are to be established, only the rough microstructural features can be taken into accounts, which is usually incapable of grasping the correct strengthening and toughening mechanisms. To solve this long-existed problem, an integrated system with the microstructural perception by deep learning (DL) and prediction of mechanical properties by machine learning (ML) was developed, which can quickly and precisely recognize typical ferrite-pearlite microstructures in the most used hot rolled plain carbon steels and make the predictions of their mechanical properties. In the microstructural perception, a U-shaped residual network was proposed to implement deep learning of metallographic images to obtain digital information such as the size and distribution of grains and the lengths of grain and second phase boundaries. As compared to the conventional deep learning algorithm, the pixel accuracy (PA) and frequency weighted intersection over union (FWIoU) of predictions were improved by 2.84% and 5.27%, respectively. On this basis, the relationships between mechanical properties and the detailed microstructural features were established by using the random forest (RF) algorithm. Compared with the predictions by rough features of average grain size and phase fractions, the root mean square errors (RMSE) of the yield strength (YS), tensile strength (TS), and elongation (EL) based on the test data were reduced by 71.53%, 67.51% and 53.4%, respectively. By using the accurate perceptron for microstructure and properties, changes of strengths and elongations with processing parameters and chemical compositions were analyzed in good agreement with physical laws. Also, this perceptron can be extended to high strength steels with microstructures comprised of ferrite and bainite or complex phases if the data of metallographic images and properties are available.

Features of formation of the structure of thin slabs cast at different speeds, their influence on the structure and properties of hot-rolled steel

For the first time, a complete comprehensive study of the internal quality of metal products with a carbon content of 0.3 % produced in a combined process of thin-slab casting and rolling was carried out. A feature of the combined process is the absence of a cooling and heating cycle before rolling and the small thickness of the initial slab. The zonal chemical heterogeneity of continuously cast "thin" slabs poured at different speeds has been studied. The research methods were metallographic analysis of the macrostructure and spectral method for determining the content of elements by the thickness of slabs. The results of calculations of the segregation coefficients of basic and impurity chemical elements in slabs poured at different speeds are presented. As a result of the study, it was found that an increase in the casting rate by ~ 1.2 times does not lead to a significant increase in the zonal chemical heterogeneity of the continuously cast metal produced in the process of thin-slab casting. Of particular interest was the measurement of quantitative parameters of the initial dendritic structure of "thin" slabs depending on the casting speed. The segregations formed as a result of dendritic liquation were measured. It is shown that with an increase in the casting rate, the dispersion of the dendritic structure increases and the dendritic segregation decreases by 20 %. In the course of simulation rolling, the regularity in changing the parameters of the dendritic structure during deformation of "thin" slabs into the final microstructure of rolled products is considered. When the slabs are compressed by 65 %, the distance between the primary dendrite arm spacing decreases by an average of 65 %. The comparison of the microstructure and mechanical characteristics of rolled products produced in industrial conditions from slabs poured at different speeds using thin-slab casting technology is carried out. The results of the study indicate that an increase in the casting rate increases the dispersion of the initial dendritic structure and, as a result, favorably affects the formation of the final microstructure. It is shown that hot-rolled products made from a slab with a 1.2-fold increased casting speed have a homogeneous microstructure and demonstrate an increase in the values of impact strength by an average of 30 J/cm2.

Effect of the Chemical Composition on the Structural State and Mechanical Properties of Complex Microalloyed Steels of the Ferritic Class

The most promising direction for obtaining a unique combination of difficult-to-combine properties of low-carbon steels is the formation of a dispersed ferrite microstructure and a volumetric system of nanoscale phase precipitates. This study was aimed at establishing the special features of the composition influence on the characteristics of the microstructure, phase precipitates, and mechanical properties of hot-rolled steels of the ferritic class. It was carried out by transmission electron microscopy and testing the mechanical properties of metal using 8 laboratory melts of low-carbon steels microalloyed by V, Nb, Ti, and Mo in various combinations. It was found that block ferrite prevails in the structure of steel cooled after hot rolling at a rate of 10–15 °C/s. Lowering of the microalloying components content leads to a decrease in the block ferrite fraction to 20–35% and the dominance of polygonal ferrite. The presence of nanoscale carbide (carbonitride) precipitates of austenitic and interphase/mixed types was detected in the rolled steels. It was established that the tendencies of changes in the characteristics of the structural state and present phase precipitates correlate well with obtained values of strength properties. The advantages of titanium-based microalloying systems in comparison with vanadium-based are shown.

Mechanical Properties Prediction for Hot Rolled Alloy Steel Using Convolutional Neural Network

A convolutional neural network (CNN)-based method for predicting the mechanical properties of hot rolled steel using chemical composition and process parameters is proposed. The novel contribution of this research is to introduce the prediction method of CNN into the steel properties prediction field by converting the production data into two-dimensional data images. Compared with the traditional artificial neural network method, the CNN adopts the idea of local connection and weight sharing, which reduces the complexity of the network model, and uses convolution and pooling operations to extract local features for better prediction precision. The experiments in this paper show that the proposed CNN model with the optimal structure provides higher prediction accuracy and higher robustness when compared with other prediction model reported in the literature. Finally, the metallurgical phenomena in the steel rolling processes are verified by sensitivity analysis using the proposed CNN model. The results are consistent with the metallurgical properties of the steel materials used in the experiments. Therefore, the proposed CNN model has a guiding significance in predicting the mechanical properties of hot rolled steel products in practical applications.

Mathematical modelling of controlled cooling and properties of hot rolled steel

Numerical model of controlled cooling in the production of steel hot rolled bars was developed. The numerical model and algorithm are completed to solve problems in controlled cooling of hot rolled bars in cooling beds. The controlled cooling is performed by special placement of hot rolled bars on cooling beds. By numerical model of controlled cooling is possible to predict a transient temperature field, microstructure evolution and hardness of round steel bars during their cooling in cooling beds. The numerical model of transient temperature field is based on control volume method. The hardness and microstructure distribution in steel bars has predicted by using equations of austenite decomposition kinetic. The algorithm for prediction is based the real chemical composition. Numerical model and computer program were experimentally verified by experimental work in a real industrial production of low-alloyed steel bars. The verification of developed numerical model was performed by comparison of simulated with experimentally evaluated results hardness and cooling curve.

Improving Transparency in Approximate Fuzzy Modeling Using Multi-objective Immune-Inspired Optimisation

In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, prediction accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts in eliciting models which are as transparent as possible, a ‘tricky’ exercise in itself. The proposed mechanism adopts a multi-stage modeling procedure and a variable length coding scheme to account for the enlarged search space due to simultaneous optimisation of the rule-base structure and its associated parameters. We claim here that IMOFM can account for both Singleton and Mamdani Fuzzy Rule-Based Systems (FRBS) due to the carefully chosen output membership functions, the inference scheme and the defuzzification method. The proposed modeling approach has been compared to other representatives using a benchmark problem, and was further applied to a high-dimensional problem, taken from the steel industry, which concerns the prediction of mechanical properties of hot rolled steels. Results confirm that IMOFM is capable of eliciting not only accurate but also transparent FRBSs from quantitative data.

Effect of B/N ratio on plastic anisotropy behaviour in low carbon aluminium killed steel

Effect of B/N ratio on plastic anisotropy behaviour in low carbon aluminium killed steel

Feasibility of using neural networks for real-time prediction of the mechanical properties of finished rolled products

This article examines the feasibility of predicting the mechanical properties of hot-rolled steel in flat-rolled-products shop No. 10 with the use of artificial neural networks. The mechanical properties are determined by means of a mathematical model constructed on the basis of data on the chemical composition of heats with allowance for the nominal thickness of the finished product, finishing temperature, and coiling temperature. Implementation of the findings is making it possible to reduce labor costs incurred in sample preparation, reduce metal waste, determine the soundness of batches while strip is being coiled, and eliminate the problems of the statistics-based nondestructive testing method currently in use.

Structure and mechanical properties of the nitrogen-containing austenitic plate steel 04Kh20N6G11AM2BF

Structure and mechanical properties of the nitrogen-containing austenitic plate steel 04Kh20N6G11AM2BF with a thickness of 20 and 40 mm after hot rolling, quenching from different temperatures, and final strengthening by cold or warm deformation have been studied. For these large-section plates, sufficiently high mechanical properties were obtained, namely, the yield stress σ0.2 ≥ 690 MPa, the relative elongation δ ≥ 20%, the relative reduction ϕ ≥ 50%, and the impact toughness KCV +20 ≥ 100 J/cm2. This complex of strength and plastic properties of hot-rolled steel was produced after the quenching of the plates from 1150°C and subsequent strengthening warm (600°C) or cold (20°C) rolling with a reduction of up to 15%. These properties of steel were due to several causes, namely, the presence of nitrogen in the fcc solid solution, an increased density of dislocations (≈ 1010 cm−2), the precipitation of nanocrystalline vanadium nitrides with sizes of 2–4 nm, and the absence of large amounts of coarse near-boundary particles.

Application of microstructural modelling for quality control and process improvement in hot rolled steels

The prediction of microstructures and mechanical properties is an important point for the control of steel properties and quality. The present paper describes an integrated model developed at IRSID, Arcelor group, to predict the microstructure and mechanical properties of hot rolled steel, including the microstructure of C–Mn and C–Mn–Nb steels, the strength, the cooling curve affected by heat evolution due to transformation and precipitation strengthening. Online and offline applications of this model are presented. The practical advantages of the model are numerous, including help in designing and optimising metallurgical routes or participation in plant quality control systems.

Microstructure monitor and process simulation

The knowledge of microstructure is essential to compute mechanical properties of hot rolled steel. Depending on process data and the chemical composition of the steel, a physical model is used to calculate microstructure parameters such as grain size, recrystallization and the γ-α-transformation. In addition to the physical model an artificial neural network is employed to calculate mechanical properties. New results from hot rolling mills are presented. The on-line application of the monitor system allows a quick release of strips after rolling and the saving of costs for quality control measurements. Process parameters could be adjusted to achieve desired mechanical properties of the product. Process data can either be obtained on-line from the process itself or can be simulated off-line, e.g. by Siemens’ HYBRid EXpert System HYBREX. Its central component is a high-performance flowsheet simulator, which enables complex plants to be modeled, simulated and evaluated. The architecture and capability of HYBREX will be presented as well as the newly achieved possibilities by integrating the microstructure model to the simulation tool.

Microstructure modelling for property prediction and control

The development of microstructure models to predict the final mechanical properties of hot rolled steels is reviewed. Particular emphasis is placed on the performance of these models in solving industrial problems, or for online control. While there is still ongoing activity to improve these models they are still not sufficiently accurate for many applications and there are a number of issues associated with full implementation.

A Mathematical Model to Predict the Mechanical Properties of Hot Rolled C-Mn and Microalloyed Steels.

A mathematical model has been developed that predict the final mechanical properties of hot rolled steels. It consists of submodels for static and metadynamic recrystallisation, grain growth and the transformed ferrite grain size. Each submodel was characterised for a wide range of C-Mn and HSLA steels. The total microstructure model has been integrated into process models and evaluated using production data for plate, structural, bar and strip rolling. Results to date indicate that the accuracy of the model is excellent and is suitable for the evaluation of new steel grades and the development of optimised thermomechanical processing routes.

Influencing the formation of the steel structure by suitable temperature control in the run-out sections of hot-strip mills

Increased demands on the mechanical properties of hot-rolled steel require a fixed adjustment of the steel structure. A procudure wil be introduced, which influences discrete on/off cooling position elements in the run-out section of a hot-strip mill. The procedure can also be used for continuous water-quantity readjustment. The procedure is divided into two parts: predetermination of the spraying pattern and predetermination of the control decision tables and, secondly, coiler-temperature regulation based on the decision tables

Influence of Silicon and Aluminum on the Properties of Hot-Rolled Steel

Small amounts of silicon added to steel act as a deoxidizer and lower the ductile-brittle transition temperature of the steel plate. Larger amounts, depending on the amounts of other deoxidizers already present, become an alloying addition and raise the transition temperature. Increasing aluminum contents up to 0.20 pet lowered the transition temperature at a rate depending on the amount of silicon and manganese present.