Rolled Steel Strip Research Articles

Determination of Particular Relative Reduction in Cold Rolling of Thin and Extra Thin Strips to Implement the Process with the Least Force

Introduction. It has been theoretically established and experimentally confirmed that the elastic deformations of rolls and strips in cold rolling have a significant and, in the case of thin rod rolling, a crucial effect on all process parameters.Problem Statement. The influence of the elastic-plastic interaction of a thin strip with rolls, the tension, the temperature and rate of deformation, and the strength of strip material shall be taken into account for developing a modern theory of longitudinal cold rolling.Purpose. Modeling and forecasting the parameters of cold rolling of thin and extra thin strips.Materials and Methods. To solve this problem, the conditions of rolling strips made of 08kp steel with different degrees of preliminary metal hardening, which reflected the features and regularities of hardening the strip material in the multicellular state line, have been modeled. The partial relative reductions vary within 0.02—0.35,with the initial data corresponding to the most characteristic conditions of cold rolling of thin and extra thin steel strips on operating mills taken.Results. The quantitative data on the influence of the strip thickness, at the entrance to the deformation zone, partial and preliminary relative reduction during cold rolling on the process conditions with the least force. For the first time, the conditions and range of partial relative reductions for the cold rolling process of thin and extra thin strips with the least force have been determined. It has been established that in the case of cold rolling of thin and extra thin strips made of unriveted and pre-hardened steel, varying partial relative reductions within the rangefrom 0.1 to 0.30—0.35 provides the realization of process with the least force.Conclusions. The implementation of cold rolling process with the least force is advantageous in terms of energy saving and manufacturability, as it allows reducing the specific consumption of electricity and expanding the range of cold rolling mills for smaller thicknesses of rolling strips and indicates the need to determine conditionsfor such a process.

Stochastic model describing evolution of microstructural parameters during hot rolling of steel plates and strips

Enhancing strength-ductility synergy of materials has been for decades an objective of research on structural metallic materials. It has been shown by many researchers that significant improvement of this synergy can be obtained by tailoring heterogeneous multiphase microstructures. Since large gradients of properties in these microstructures cause a decrease of the local fracture resistance, the objective of research is to obtain smoother gradients of properties by control of the manufacturing process. Advanced material models are needed to design such microstructures with smooth gradients. These models should supply information about distributions of various microstructural features, instead of their average values. Models based on stochastic internal variables meet this requirement. Our objective was to account for the random character of the recrystallization and to transfer this randomness into equations describing the evolution of dislocations and grain size during hot deformation and during interpass times. The idea of this stochastic model is described in the paper. Experiments composed of uniaxial compression tests were performed to supply data for the identification and verification of the model in the hot deformation and static recrystallization parts. Histograms of the grain size were measured after hot deformation and at different times after the end of deformation. Identification and validation of the model were performed. The validated model, which predicts evolution of heterogeneous multiphase microstructure, is the main output of our work. The model was implemented in the finite element program for hot rolling of plates and sheets and simulations of these processes were performed. The model’s capability to compare and evaluate various rolling strategies are demonstrated in the paper.

Modeling and Monitoring for Laminar Cooling Process of Hot Steel Strip Rolling with Time–Space Nature

The laminar cooling process is an important procedure in hot steel strip rolling. The spatial distribution and the drop curve of the strip temperature are crucial for the production and the quality of the steel strip. Traditionally, lumped parameter methods are often used for the modeling of the laminar cooling process, making it difficult to consider the impact of the variation of state variables and related parameters on the system, which seriously affect the stability of the steel strip quality. In this paper, a modeling and monitoring method with a time–space nature for the laminar cooling process is proposed to monitor the spatial variation of the strip temperature. Firstly, the finite-dimensional model is obtained by time–space separation to describe the temperature variation of the steel strip. Next, a global model is constructed by using the multi-modeling integration method. Then, a residual generator is designed to monitor the strip temperature where the statistics and the threshold are calculated. Finally, the superiority and reliability of the proposed method are verified by the actual-process data of the laminar cooling process for hot steel strip rolling, and different types of faults are detected successfully.

Strip Steel Surface Defects Classification Based on Generative Adversarial Network and Attention Mechanism

In a complex industrial environment, it is difficult to obtain hot rolled strip steel surface defect images. Moreover, there is a lack of effective identification methods. In response to this, this paper implements accurate classification of strip steel surface defects based on generative adversarial network and attention mechanism. Firstly, a novel WGAN model is proposed to generate new surface defect images from random noises. By expanding the number of samples from 1360 to 3773, the generated images can be further used for training classification algorithm. Secondly, a Multi-SE-ResNet34 model integrating attention mechanism is proposed to identify defects. The accuracy rate on the test set is 99.20%, which is 6.71%, 4.56%, 1.88%, 0.54% and 1.34% higher than AlexNet, VGG16, ShuffleNet v2 1×, ResNet34, and ResNet50, respectively. Finally, a visual comparison of the features extracted by different models using Grad-CAM reveals that the proposed model is more calibrated for feature extraction. Therefore, it can be concluded that the proposed methods provide a significant reference for data augmentation and classification of strip steel surface defects.

YOLOv5-based Defect Detection Model for Hot Rolled Strip Steel

In the defect detection of hot-rolled strip steel, there are often problems of too small target size and unclear features that lead to wrong detection and missed detection, for which a YOLOv5-based defect detection method for hot-rolled strip steel is proposed in this paper. Firstly, the overall architecture of the method is proposed, and then the algorithm implementation process is highlighted. Experimental analysis shows that the average detection accuracy using YOLOv5 is improved by 11.9% compared to YOLOv4 improved, with stronger generalization capability, faster detection speed, and lower error and miss detection rate.

Comparative evaluation of performance and scalability of convolutional neural network implementations on a multisystem HPC architecture

The convolutional neural network training algorithm has been implemented for a central processing unit based high performance multisystem architecture machine. The multisystem or the multicomputer is a parallel machine model which is essentially an abstraction of distributed memory parallel machines. In actual practice, this model corresponds to high performance computing clusters. The proposed implementation of the convolutional neural network training algorithm is based on modeling the convolutional neural network as a computational pipeline. The various functions or tasks of the convolutional neural network pipeline have been mapped onto the multiple nodes of a central processing unit based high performance computing cluster for task parallelism. The pipeline implementation provides a first level performance gain through pipeline parallelism. Further performance gains are obtained by distributing the convolutional neural network training onto the different nodes of the compute cluster. The two gains are multiplicative. In this work, the authors have carried out a comparative evaluation of the computational performance and scalability of this pipeline implementation of the convolutional neural network training with a distributed neural network software program which is based on conventional multi-model training and makes use of a centralized server. The dataset considered for this work is the North Eastern University’s hot rolled steel strip surface defects imaging dataset. In both the cases, the convolutional neural networks have been trained to classify the different defects on hot rolled steel strips on the basis of the input image. One hundred images corresponding to each class of defects have been used for the training in order to keep the training times manageable. The hyperparameters of both the convolutional neural networks were kept identical and the programs were run on the same computational cluster to enable fair comparison. Both the convolutional neural network implementations have been observed to train to nearly 80% training accuracy in 200 epochs. In effect, therefore, the comparison is on the time taken to complete the training epochs.

UDDEHOLM VANADIS 23 SUPERCLEAN

Abstract Uddeholm Vanadis 23 SuperClean is a chromium-molybdenum-tungsten-vanadium high speed tool steel that is produced by the powder metallurgy process. This steel is similar to Type M3, Class 2 high speed tool steel, and combines high compressive strength with good abrasive wear resistance. It is suitable for demanding cold work applications like blanking of harder materials like carbon steel or cold rolled strip steel and for cutting tools. This datasheet provides information on composition, physical properties, hardness, and elasticity. It also includes information on heat treating, machining, and surface treatment. Filing Code: TS-820. Producer or source: Uddeholms AB (a voestalpine company).

Study of the friction coefficient during the steady-state process of continuous hot rolling of steel strips in conditions of the 1950 industrial mill

This paper describes a method for determining the value of the friction coefficient in a steady-state hot rolling under industrial conditions of the casting and rolling complex of “Vyksa Steel Works” located in Vyksa. The algorithm implies the forward slip method for determining the friction coefficient using the Ekelund formula. Measurements of hot rolling technological parameters from sensors are used as input parameters. Mathematical formulas are implemented in the ibaAnalyzer software, with the help of calculations are made for several thousand strips and data on the friction coefficient are obtained under various conditions. The data processing results demonstrate a normal distribution of the friction coefficient. The highest value of the friction coefficient is typical for the first finisher stands F1-F2, and the lowest values are found for the thinner gauge products rolled at high speed. The influence of the working layer of the work rolls on the friction value is demonstrated. High-chromium cast iron rolls have lower values of the friction coefficients, in contrast to indefinite cast iron, which is used for the working layer of the last finisher stands of mill 1950. A detailed comparison of the results with the classical methods for determining the friction coefficient in a steady-state process has been carried out. The dependence of the ratio of the friction coefficient during initial entry to the steady-state friction coefficient was refined.

HD-lubricated high-speed small reduction rolling of hard steel strips with elastically deformable work rolls

This research investigates the effects of elastic work rolls on the hydrodynamic (HD)-lubricated high-speed rolling of hard steel strips with a small reduction. An innovative numerical method was established to couple the interactions across deformable rolls, pressurised lubricant and strips undergoing elastoplastic deformation. A dynamic explicit finite element analysis (FEA) was used to obtain the deformed solid bodies while a transient Reynolds equation was utilised to govern the hydrodynamic pressure. A non-linear lubricant shearing stress was integrated in obtaining the interface friction. The analysis demonstrates that the hydrodynamic pressure can be more affected by the yield strength of the metal strip and roll radius for a fixed rolling gap in lubricated strip rolling.

A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel

Hot-rolled strip steel is widely used in automotive manufacturing, chemical and home appliance industries, and its surface quality has a great impact on the quality of the final product. In the manufacturing process of strip steel, due to the rolling process and many other reasons, the surface of hot rolled strip steel will inevitably produce slag, scratches and other surface defects. These defects not only affect the quality of the product, but may even lead to broken strips in the subsequent process, seriously affecting the continuation of production. Therefore, it is important to study the surface defects of strip steel and identify the types of defects in strip steel. In this paper, a scheme based on ResNet50 with the addition of FcaNet and Convolutional Block Attention Module (CBAM) is proposed for strip defect classification and validated on the X-SDD strip defect dataset. Our solution achieves a classification accuracy of 94.11%, higher than more than a dozen other compared deep learning models. Moreover, to adress the problem of low accuracy of the algorithm in classifying individual defects, we use ensemble learning to optimize. By integrating the original solution with VGG16 and SqueezeNet, the recognition rate of oxide scale of plate system defects improved by 21.05 percentage points, and the overall defect classification accuracy improved to 94.85%.

Effect of ageing on the mechanical properties of cold formed S700 rectangular hollow

The aim of this work is to study the effect of ageing (250 °C for 1 h) on the mechanical properties of the cold formed S700 rectangular hollow section. The investigated hot rolled steel strip was produced thermomechanical rolling followed by direct quenching. The dimension of the investigated hollow section was 120 x 120 x 10 mm and the corner radii and the other tolerances compliant with EN 10219. Tensile properties and Charpy-V impact toughness were determined for the base material and flat and corner area of the hollow section. The results showed that the tensile strength in the corner was slightly higher in comparison with the flat side, revealing higher cold deformation rate in the corner. Ageing increased the strength level relatively higher than cold forming without losing any elongation properties. The impact energies were at the high level at −40°C and −60°C in cold formed and aged materials. Even at −80°C, the CV results were 118 J/cm2. It is also notable that no difference in CV values between the flat and the corner samples were observed. Thus, the results showed that the flat side specimens testing provides sufficient information of mechanical properties of the cold formed rectangular hollow sections and no need demanding corner sample testing when the structural hollow section is produced by using the thermomechanical controlled and direct-quenched base material. Furthermore, results showed that cold formed S700 is excellent for offshore steels, as steels are used even colder conditions as before.

Machine Learning Model for Thickness Evolution of Oxide Scale During Hot Strip Rolling of Steels

Machine Learning Model for Thickness Evolution of Oxide Scale During Hot Strip Rolling of Steels

Study on Indentation Response of Cold Rolled Strip Steel

Study on Indentation Response of Cold Rolled Strip Steel

Mathematical Modeling of the Cold Rolling Process and Heat Treatment for DC01 Steel

The paper presents the elaboration of a mathematical model of the cold strip rolling process combined with the recrystallization annealing after the rolling at LBR Liberty Galati.The elaborated mathematical model allows the prediction of the mechanical properties of cold rolled strips subsequently subjected to a heat treatment.The realization of this mathematical model was based on statistical measurements of the mechanical properties Rm, Rp0.2 (Rc) and A5 for the rolled steel strip DC01 from Liberty Steel Galati. To achieve this mathematical model, the active experiment method was used.With the help of this mathematical model, it is possible to optimize the rolling process by significant savings of time and materials in the process of testing the mechanical properties for cold rolled tape, but also by choosing the most appropriate process parameters.

Research on Surface Defect Detection Algorithm of Strip Steel Based on Improved YOLOV3

Aiming at the problems of slow detection speed and low detection accuracy in the surface defect detection of hot rolled strip steel, an improved YOLOV3 algorithm model was proposed in this paper. Firstly, the data sets is expanded by using the data enhancement algorithm to improve the robustness of the algorithm. Secondly, K-means++ with less randomness was used to perform clustering analysis on defect labels instead of K-means algorithm, and appropriate Anchor Boxes were selected as the initial candidate boxes for the improved YOLOV3 network. Finally, this study changed the detection network of YOLOV3, added a layer of prediction box and performed feature fusion to improve the detection ability of the network for small targets. The experimental results show that the improved YOLOV3 algorithm achieves 89.5% mean average precision on NEU-DET data set, which is 14.7% higher than the original YOLOV3 algorithm. Detection speed in 34 FPS at the same time, can meet the needs of industrial production.

Effect of Oxide Scale Microstructure on Atmospheric Corrosion Behavior of Hot Rolled Steel Strip

The atmospheric corrosion behavior of a hot-rolled strip with four types (I–IV) of oxide scale was investigated using the accelerated wet–dry cycle corrosion test. Corrosion resistance and porosity of oxide scale were studied by potentiometric polarization measurements. Characterization of samples after 80 cycles of the wet–dry corrosion test showed that scale comprised wüstite and magnetite had strongest corrosion resistance. Oxide scale composed of inner magnetite/iron (>70%) and an outer magnetite layer had the weakest corrosion resistance. The corrosion kinetics (weight gain) of each type of oxide scale followed an initial linear and then parabolic (at middle to late corrosion) relationship. This could be predicted by a simple kinetic model which showed good agreement with the experimental results. Analysis of the potentiometric polarization curves, obtained from oxide coated steel electrodes, revealed that the type I oxide scale had the highest porosity, and the corrosion mechanism resulted from the joint effects of electrochemical behavior and the porosity of the oxide scale. In the initial stage of corrosion, the corrosion product nucleated and an outer rust layer formed. As the thickness of outer rust layer increased, the corrosion product developed on the scale defects. An inner rust layer then formed in the localized pits as crack growth of the scale. This attacked the scale and expanded into the substrate during the later stage of corrosion. At this stage, the protective effect of the oxide scale was lost.

X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

Prediction of mechanical properties of hot-rolled strip steel based on PCA-GBDT method

Improving the quality of rolled steel products is the primary task of the entire steel industry. As the main step of rolled steel production, hot rolling has received extensive attention. As far as the hot rolling process is concerned, the chemical composition of steel and related process parameters are the most direct factors affecting the quality of hot rolled steel sheets. Based on Principal Component Analysis (PCA) and Gradient Lifting Decision Tree (GBDT) methods, this paper takes the tensile strength as the research object and constructs a prediction model for the mechanical properties of hot rolled strip steel. Through principal component analysis of characteristic data, 28 variables were reduced into 8 new indicators to be used for GBDT regression analysis. 2489 pieces of data were divided into a training set and a test set at a ratio of 7:3, to be used in the training set to build a regression model, and get a root mean square error of 16.7393. The data in the test set was used to predict the tensile strength value, with the root mean square error reaching 18.2650.

Improvement of Technology for Producing Rolled Sheet from Continuously Cast Steel Slabs. Part 2. Physical Modeling of Low-Carbon Steel Strip Rolling*

Features of metal structure formation in the finishing group of a continuous broad-strip mill are studied using a physical modeling method. Wedge-shaped samples of low carbon steel are rolled in a two-roll laboratory mill at 880–1070 °С in one and two passes. Results of modeling rolling conditions for low-carbon steel strips and subsequent cooling make it possible to establish the main laws governing metal structure formation. Industrial testing of the calculated rolling regimes and subsequent cooling of low-carbon steel strips in the 2000 NLMK mill confirm the possibility of improving finished rolled product mechanical properties.

Surface Chemistry-Based Surface Defects Situated on Steel Strips Edges

Two thirds of all examined defect cases present on rolled steel strips appeared to be chemical in nature. They are characterized by a modification in surface chemistry. Chemistry-based defects on the steel strips can vary in composition and generally consist of reaction products with the steel substrate. First big category of widely occurring chemistry-based defects is corrosion or oxidation, second contamination with alien matter and third defect category is related to carbon sediments. A number of different surface chemistry-based defects are related to annealing process. Common problem, that occurs in communication is, that identical defects are often indicated by different names and identical names are given for different defects. In the present study an overview including possible causes of three types of the continuous chemistry-based defects situated on the steel strip edges, that appeared to be the same at first glance, is presented: carbon edge deposit, low reflectivity band and annealed border.