Mill scale structure and damp heat corrosion performance of four 510L hot-rolled strips

In this study, the matrix structure, state and composition of the mill scales of four typical domestically made 510L hot-rolled strips were observed and analyzed by means of optical microscopy （OM） ,scanning electron microscopy （SEM） and X-ray diffraction （XRD）. The corrosion behavior of the steels with and without mill scales were investigated by means of hot-humid corrosion tests under the condition of relative humidity （ RH ） of 95% at 50℃ and 70℃, respectively. The results show that the matrix structures, state, composition and thickness of mill scales vary in the strips. The rusting starting time of the specimens with scales is generally a bit longer than that of the specimens without scales, but their corrosion mass-gain is higher. For these two kinds of specimens ,their corrosion rate increases significantly with the increase of temperature. The rusting behavior of the 510L strips produced by various plants is different due to the variations of hot-rolling processes and designed chemical compositions. Various relevant aspects should be taken into account in the evaluation of the corrosion behavior of hot-rolled strips.

“Quartus scale” of hot-rolled strip steels and its formation mechanism

The types and growth of various oxide scales formed during the different phases of the production of hotrolled strip steel products are reviewed. Similarities and differences between the ＂tertiary scale＂ on the surface of carbon steels at high temperatures and the oxide scale on pure iron are compared. The micro-structural features of the ＂final oxide scale＂ on the surface of strip steels at room temperature as well as the relationship between these features and the position of the steel coil （plate） and the subsequent processes of recoiling, temper rolling and trimming, etc. are summarized. The actual oxide scales retained on the commercial hot-rolled strip steels at room temperature have been proposed to define as ＂ quartus scale＂ for the first time. The micro-structural development and phase transformation of the initial ＂tertiary scale＂ during and after cooling and coiling are described. The reasons for the ＂tertiary scale＂ on carbon steels differing from the oxide scale formed on pure iron, and the major influencing factors in the formation of various types of ＂quartus scales＂ are analyzed from both thermodynamic and dynamic viewpoints. The development mechanism of ＂ quartus scales＂ is discussed and the potential effects of the ＂ quartus scale＂ state （thickness, constitution, structure and defects）, on the rusting and pickling properties of commercial hot-rolled strip steel, as well as on the mechanical properties of oxide scales are analyzed.

Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems

Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.

Prediction of hot-rolled strip crown based on Boruta and extremely randomized trees algorithms

The quality of hot-rolled steel strip is directly affected by the strip crown.Traditional machine learning models have shown limitations in accurately predicting the strip crown,particularly when dealing with imbalanced data.This limitation results in poor production quality and efficiency,leading to increased production costs.Thus,a novel strip crown prediction model that uses the Boruta and extremely randomized trees(Boruta-ERT)algorithms to address this issue was proposed.To improve the accuracy of our model,we utilized the synthetic minority over-sampling technique to balance the imbalance data sets.The Boruta-ERT prediction model was then used to select features and predict the strip crown.With the 2160 mm hot rolling production lines of a steel plant serving as the research object,the experimental results showed that 97.01% of prediction data have an absolute error of less than 8 lm.This level of accuracy met the control requirements for strip crown and demonstrated significant benefits for the improvement in production quality of steel strip.

Prediction model for mechanical properties of hot-rolled strips by deep learning

The prediction of the mechanical properties of hot-rolled strips is a very complex,highly dimensional and nonlinear problem,and the published models might lack reliability,practicability and generalization.Thus,a new model was proposed for predicting the mechanical properties of hot-rolled strips by deep learning.First,the one-dimensional numerical data were transformed into two-dimensional data for expressing the complex interaction between the influencing factors.Subsequently,a new convolutional network was proposed to establish the prediction model of tensile strength of hot-rolled strips,and an improved inception module was introduced into this network to abstract features from different scales.Many comparative experiments were carried out to find the optimal network structure and its hyperparameters.Finally,the prediction experiments were carried out on different models to evaluate the performance of the new convolutional network,which includes the stepwise regression,ridge regression,support vector machine,random forest,shallow neural network,Bayesian neural network,deep feed-forward network and improved LeNet-5 convolutional neural network.The results show that the proposed convolutional network has better prediction accuracy of the mechanical properties of hot-rolled strips compared with other models.

Hot-rolled strips of up to 19 mm in thickness and their processing to helically welded large diameter pipes of grade X80

Hot-rolled wide strip for production of large diameter,heavy gauged(up to 19 mm) helical line pipe grade X80 was a priority development over the last three years.Microstructure,texture and mechanical properties of strips have been characterised.Also the welding conditions have been simulated.The favourable microstructure is achieved by the proper selection of an appropriate chemical composition of low carbon content and increased niobium micro alloying in combination with suitable strictly controlled hot-rolling parameters.The addition of niobium in combination with the adjustment of other alloying elements increases the recrystallisation stop temperature and thus makes it possible to apply a high temperature processing(HTP) concept.The homogeneous bainitic microstructure across the strip gauge is then formed during accelerated cooling on the run-out table of the hot-rolling mill.All results indicated excellent properties of these hot strips which make it suitable for spiral pipes of grade X80 for example 18.9mm×Φ1 220 mm at dimension.

A defect recognition model for cross-section profile of hot-rolled strip based on deep learning

The cross-section profile is a key signal for evaluating hot-rolled strip quality,and ignoring its defects can easily lead to a final failure.The characteristics of complex curve,significant irregular fluctuation and imperfect sample data make it a challenge of recognizing cross-section defects,and current industrial judgment methods rely excessively on human decision making.A novel stacked denoising autoencoders(SDAE)model optimized with support vector machine(SVM)theory was proposed for the recognition of cross-section defects.Firstly,interpolation filtering and principal component analysis were employed to linearly reduce the data dimensionality of the profile curve.Secondly,the deep learning algorithm SDAE was used layer by layer for greedy unsupervised feature learning,and its final layer of back-propagation neural network was replaced by SVM for supervised learning of the final features,and the final model SDAE_SVM was obtained by further optimizing the entire network parameters via error back-propagation.Finally,the curve mirroring and combination stitching methods were used as data augmentation for the training set,which dealt with the problem of sample imbalance in the original data set,and the accuracy of cross-section defect prediction was further improved.The approach was applied in a 1780-mm hot rolling line of a steel mill to achieve the automatic diagnosis and classification of defects in cross-section profile of hot-rolled strip,which helps to reduce flatness quality concerns in downstream processes.

Stream Surface Strip Element Method and Simulation of Three-Dimensional Deformation of Continuous Hot Rolled Strip

A new method,the stream surface strip element method,for simulating the three-dimensional deformation of plate and strip rolling process was proposed.The rolling deformation zone was divided into a number of stream surface(curved surface)strip elements along metal flow traces,and the stream surface strip elements were mapped into the corresponding plane strip elements for analysis and computation.The longitudinal distributions of the lateral displacement and the altitudinal displacement of metal were respectively constructed to be a quartic curve and a quadratic curve,of which the lateral distributions were expressed as the third-power spline function,and the altitudinal distributions were fitted in the quadratic curve.From the flow theory of plastic mechanics,the mathematical models of the three-dimensional deformations and stresses of the deformation zone were constructed.Compared with the streamline strip element method proposed by the first author of this paper,the stream surface strip element method takes into account the uneven distributions of stresses and deformations along altitudinal direction,and realizes the precise three-dimensional analysis and computation.The simulation example of continuous hot rolled strip indicates that the method and the model accord with facts and provide a new reliable engineering-computation method for the three-dimensional mechanics simulation of plate and strip rolling process.

Steckel mill as a green steel strip and plate production technology

The Steckel mill,a long established solution for the economical production of relatively small volumes of hot rolled strip,has been rejuvenated in recent years by a range of new applications,boosted by the need of improving the energetic efficiency of the rolling process.The traditional advantages of the Steckel mill in terms of flexibility and reduced capital and operational costs are now enhanced by technological developments that have significantly expanded its application range into the combined production of strip and plate and improved the product quality.The increased awareness of the necessity of a sustainable growth in the steel industry has stimulated the development of process solutions with an improved efficiency in the use of natural resources,lower carbon emissions and increased yield.Modern Steckel mills are an adequate response to the trend towards low energy strip and plate production,in particular in their plate-Steckel mill variant.Siemens VAI have played a key role in the innovation and transformation of the Steckel mill concept,with a number of recent installations,presented in this paper from the point of view of their contribution to the development of greener steel rolling technologies.

Making Use of the Eddy Currents Effect in the Examination of the Tribological Characteristics of Strip Rolling Mill Rolls

The key element in the proper performance of a rolling mill is the careful planning of the rolls operational conditions, since this factor constitutes the restricting element in the manufacturing process. In the article, a collection of operation and research steel strips hot-rolling mill information was presented, which was processed based on the advanced computer programmes for rolls grinders. The research outcomes were produced, presenting the application of eddy currents to detect materials flaws in metallurgical mill rolls.

Effects of Al contents on microstructure and properties of hot-dip Zn-Al alloy coatings on hydrogen reduced hot-rolled steel without acid pickling

A new hot-dip galvanizing method was employed on hot-rolled low carbon steel.The effects of Al contents on microstructure,micro-hardness and corrosion resistance of Zn-Al alloy coatings were systematically investigated.Phase composition,microstructure and element distribution in Zn-Al alloy coatings were analyzed using X-ray diffraction（XRD）and electron probe micro analysis（EPMA）,respectively.It is found that Al content（0.6-6.0 wt.%）in galvanizing zinc affects surface quality and adhesion between coatings and matrix in the newly developed method.In addition,with increasing Al content,micro-hardness significantly increased due to the increase in Zn-Al eutectoid phases.Potentiodynamic polarization and electrochemical impedance spectroscopy（EIS）also revealed that increase in Al plays a noticeable role in improving the corrosion resistance of Zn-Al alloy coatings.