“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.

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.

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.

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.

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.

Correlation of morphological anisotropy and Lankford value of deep drawing sheets hot-rolled by compact strip production

Cold-rolled steel sheets in automotive applications require an excellent deep draw ability, which is characterized by the Lankford value （r-value）. In this study, a correlation was identified between r-value and pancake-shaped grain flatness which is indicated as the ratio of grain diameter in the rolling direction （RD） and normal direction （ND） of sheets （dr/dn）. A mathematical model （ r = e^0.345（dn^1/2-dr^1/2） ） was developed to calculate r-value by the microstructure of steel sheets hot-rolled by compact strip production （CSP）. It is shown that the r-value is higher, if the microstructure of steel sheet is of pancake-shaped grains elongated in the rolling direction. The calculated r-value is confirmed to fit exactly to the measured one from the large-scale production.

Influence of oxide scales on the corrosion behaviors of B510L hot-rolled steel strips

The influence of oxide scales on the corrosion behaviors of B510 L hot-rolled steel strips was investigated in this study. Focused ion beams and scanning electron microscopy were used to observe the morphologies of oxide scales on the surface and cross sections of the hot-rolled steel. Raman spectroscopy and X-ray diffraction were used for the phase analysis of the oxide scales and corrosion products. The corrosion potential and impedance were measured by anodic polarization and electrochemical impedance spectroscopy. According to the results, oxide scales on the hot-rolled strips mainly comprise iron and iron oxides. The correlation between mass gain and test time follows a power exponential rule in the damp-heat test. The corrosion products are found to be mainly composed of γ-Fe OOH, Fe3O4, ？-Fe OOH, and γ-Fe2O3. The contents of the corrosion products are different on the surfaces of the steels with and without oxide scales. The steel with oxide scales is found to show a higher corrosion resistance and lower corrosion rate.

Concave Cooling Control Method for Hot-rolled Strip

Hot rolled strips usually have higher strength and lower plasticity at the ends, and the mechanical properties are distributed unevenly along the length direction. Such phenomena are caused by the different cooling rates between the end and the center. The ends of the coiled strip cool down faster than the center, inducing finer grains in the sections. Furthermore, the center of the coil is kept at high temperature for longer time, which affects the precipitation of the carbides and creates the different mechanical properties from the ends. In this paper, the temperature field of the strip during cooling was simulated to discover the characteristics of the temperature change and the effect on mechanical properties. Based on the analysis, a concept of concave cooling control was introduced and implemented in the production. Results indicated that applying the concave cooling control method could significantly improve the uniformity of the properties and promote the quality of the products.

Application of multi-scale feature extraction to surface defect classification of hot-rolled steels

Feature extraction is essential to the classification of surface defect images. The defects of hot-rolled steels distribute in different directions. Therefore, the methods of multi-scale geometric analysis （MGA） were employed to decompose the image into several directional subba^ds at several scales. Then, the statistical features of each subband were calculated to produce a high-dimensional feature vector, which was reduced to a lower-dimensional vector by graph embedding algorithms. Finally, support vector machine （SVM） was used for defect classification. The multi-scale feature extraction method was implemented via curvelet transform and kernel locality preserving projections （KLPP）. Experiment results show that the proposed method is effective for classifying the surface defects of hot-rolled steels and the total classification rate is up to 97.33%.

Microstructures and Mechanical Properties of Nb-Ti Bearing Hot-rolled TRIP Steels

Nb-Tihot-rolled TRIP-assisted steel with high plasticity and appropriate volume percentage of retained austenite based on fine ferrite grain have been developed in the experiment. The test results showed that niobium tend to exist in solution state in matrix with less precipitation, and niobium-titanium could be precipitated in form of （Nb, Ti）C or （Nb, Ti） （C, N）, which play an important role in increasing yield strength （from 495 MPa to 610 MPa）. Besides, the retained austenite had a positive effect on improving the plasticity by transformation into martensite during tensile deformation.

Research on Retained Austenite for Advanced Aluminum-containing Hot-rolled TRIP Steel

A new type of hot-rolled transformation induced plasticity （TRIP） steel with 2.3%AI was developed to replace conventional Si-bearing TRIP steel to improve surface quality of the steel sheet. The relationship between retained austenite volume fraction and hot-rolling processing was researched by Gleeble-2000 thermo-dynamic test for the Al-bearing steel. The experimental result showed that aluminum played an important role on retaining austenite and the volume fraction increased from 4.4% to 7.5% as coiling temperature increased from 350℃ to 450℃, while coiling temperature had a stronger effect on retaining austenite than finishing rolling temoerature.

Effect of Reduction on Bonding Interface of Hot-rolled Wear-resistant Steel BTW1/Q345R Cladding Plate

Wear-resistant cladding plates consisting of a substrate（Q345 R） and a clad layer（BTW1） were bonded through hot rolling at the temperature of 1 200 ℃ and a rolling speed of 0.5 m/s. The microhardness of the cladding plate was also tested after being heat treated. The microstructure evolution on the interface of BTW1/Q345 R sheets under various reduction rates was investigated with a scanning electron microscope（SEM） and EBSD. It is found that the micro-cracks and oxide films on the interface disappear when the reduction is 80%, whereas the maximum uniform diffusion distance reaches 10 μm. As a result, a wide range of metallurgical bonding layers forms, which indicates an improved combination between the BTW1 and the Q345 R. Additionally, it is discovered that the unbroken oxide films on the interface are composed of Mn, Si or Cr at the reductions of 50% and 65%. The SEM fractography of tensile specimen demonstrates that the BTW1 has significant dimple characteristics and possesses lower-sized dimples with the increment in reduction, suggesting that the toughness and bonding strength of the cladding plates would be improved by the increase of reduction. The results reveal that a high rolling reduction causes the interfacial oxide film broken and further forms a higher-sized composite metallurgical bonding interface. The peak microhardness is achieved near the interface.

Properties,Microstructures and Precipitate Morphology of Hot-rolled Interstital-Free(IF)Steel Sheets

In order to simplify production process and to decrease production cost of thicker cold-rolled iF steel sheets for deep drawing applications, a new hot-rolled IF steel sheet is developed through hot-rolling in or region. In this paper, properties, microstructures and precipitate morphology of hot-rolled iF steel sheets are described..

Rusting behavior and preventative measures of hot-rolled steel plates for automobile applications

As one of the important categories of hot-rolled products, hot-rolled steel plates for automobile applications generally undergo uniform corrosion or localized corrosion according to different environments of manufacturing, transportation and/or storage of the plates. General corrosion often takes place on the surface of a plate in the exterior part of a package, and only reduces the thickness of the plate and slightly increases the roughness of the surface; however, localized corrosion on the surface of a plate inside the package is likely to result in the formation of pit-like defects on the substrate of the plate, which cannot be removed thoroughly by normal acid pickling or sand blasting, and affects the application of the plate. This research report analyzes the phenomena and characteristics of the rusting behavior of hot- rolled steel plates for automobile applications, and the influencing factors are summaried. The corresponding preventative measures are proposed.

An Experimental Study on Constraint Cooling Process of Hot-rolled CoilS

In order to master mechanical property, surface quality and microstructure of constraint cooling (CC) coils undervarious water cooling parameters, more than 100 coils cooling experiments were done with real production process,of which is designed a cooling experimental instrument in the end. The experiments show that high initial coolingtemperature, discontinuous cooling style, and long cooling time can improve mechanical property of cooling coilsand shorten cooling time. The CC coils experiments cover the different steel grades, so that CC process effects onhot-rolled coils may be predicted and controlled actively.

二维矩形Strip Packing问题的算法研究与改进

二维矩形Strip Packing问题的约束条件及目标函数与基本型二维矩形Packing问题类似,都是在有限的矩形容器中,有效地摆放各个矩形块,以最大化容器利用率为目标。为了解决这一NP-hard问题,该文在邓见凯、王磊提出的拟人型全局优化算法的基础上进行了深入的算法研究与改进。针对Strip Packing问题特点,提出了QHG(Quasi-Human Group)算法,其核心改进涵盖了多个方面,包括扩充初始点集合、删除和替换评价标准以及扩大邻域空间搜索范围。和单个局部极小值点的迭代相比,对局部极小值点集合进行迭代所生成布局优度更高,跳坑策略用于跳出局部极小值点,将搜索引向有希望的区域,优美度枚举有望进一步提高布局优度。通过这些措施,QHG算法更好地模拟人类决策过程,提高了全局搜索的效率。为评估QHG算法性能,对8组标准问题实例(C组、N组、NT组、CX组、NP组、ZDF组、2sp组、bwmv组)进行了大量实验。实验结果表明,QHG算法生成的布局优度优于当前国际文献中的几种较先进算法,展现了其在Strip Packing问题上的卓越性能。

Patenting of hot-rolled high-carbon steel and its applications

The patenting process of three hot-rolled steels with carbon mass contents of 0.70%-0. 90% was studied. The effect of the quenching temperature on the cementite lamellar distance in the steel was evaluated on the basis of microstructural characterization and mechanical property tests. The patenting treatment of high-carbon hot- rolled strip and its application in springs were discussed.

Phase transformation behavior of a hot-rolled,titanium microalloyed steel during heating and continuous cooling

The critical transformation temperatures,A_(c1) and A_(c3),of a hot-rolled low-carbon titanium microalloyed steel were determined as a part of an examination of its phase-transformation behavior. Austenite decomposition during the continuous cooling of the titanium microalloyed steel was studied by heating it to 1 250 ℃,cooling it to 880 ℃,holding for 2 s,and then cooling it to room temperature at different cooling rates. The transformation kinetics( CCT curve) was characterized as well.