Cold-rolled Steel Strip Research Articles

Optimization of Continuous Steel Annealing Operations Using Model Predictive Control at Tata Steel, India

In steel manufacturing, continuous annealing is a crucial heat treatment applied to cold-rolled steel strips to achieve a prescribed temperature, which will ensure quality in terms of mechanical properties. However, controlling this process is challenging because of slow furnace temperature dynamics, significant time delays, frequent changes in the steel mass flow rate, target annealing temperature changes induced by steel grade transitions, and multivariable interactions within the furnace zones. To address these challenges, Tata Steel, India, with consultancy support from the Indian Institute of Technology Bombay, developed a novel model predictive control (MPC) technology-based solution for a continuous annealing furnace, which produces automotive grade steels. The dynamic model was developed using data from both perturbation trials and scraping historical processes. We then converted the model to a discrete-time state-space form and used it to formulate an optimal control problem over a moving time window. The solution generates optimal furnace setpoints by solving this finite-horizon optimal control problem each minute, ensuring smooth temperature transitions during steel grade changes while avoiding operational constraint violations. Tata Steel successfully implemented an MPC-based real-time supervisory optimal control solution, which became fully operational in January 2023. The implementation of the solution has led to a significant improvement in the proportion of annealed products meeting the premium quality band (±5°C of the target temperature), increasing from 30% (manually operated) to 50% (MPC operated), thereby ensuring better uniformity of properties. Furthermore, an 8% reduction in products outside the widest band (±15°C) has prevented the reprocessing of 13,000 tons of material from one line alone, annually. We have seen a consistent 8% reduction in specific fuel consumption per ton of steel. When considering Tata Steel’s current installations and those under commissioning, these improvements translate to savings of US$2.5 million and a reduction of 10,000 tons of CO2 emissions annually.

Flatness jump phenomenon in cold-rolled steel strip with high temperature: Root cause and solution

The steel strip produced in the 20-high cold rolling mill has the characteristic of high temperature in a certain factory, and a rare strip flatness phenomenon occurs in the fourth pass, different from the common flatness defects, the flatness value of each position in the strip width shows an irregular and drastic fluctuation state along with time, and it is called the flatness jump phenomenon, which seriously affects the stability of the rolling process and the production efficiency. Aiming at this problem, a method combining the rolling experiment with the numerical simulation is used to study its generation mechanism and seek for effective control technique. Through the thermal imaging measurement and the analysis of the production data, the temperature fluctuation in the fourth pass is the main reason for the flatness jump, which is related to the nucleate boiling state when the emulsion impacts on the high-temperature strip surface in the cooling process. Based on the rolling control experiments of different cooling factors, the critical temperature range for the occurrence of flatness jump is determined, which is used for the improvement of cooling technique subsequently, and the range of emulsion exit flow is optimized to 500 L/min ∼ 2050 L/min. The rolling experiment is carried out in the industrial field, it is found that the production process is stable and the flatness jump does not appear, with the small value basically within the range of ± 10 IU, and the scheme is planned for long-term application after a period of optimization, which could ultimately enhance the stability of the rolling process and the performance of finished product. Further, the paradigm and information provided in this work would be beneficial for understanding the formation mechanism of the similar complicated flatness phenomenon in cold rolling and optimizing the industrial applications of cooling technology.

An efficient detector for detecting surface defects on cold-rolled steel strips

Surface-defect inspection is vital in cold-rolled steel-strip manufacturing, given the complexities of production environments and the high speeds involved. Further, the defects on cold-rolled steel strips are often characterized by their small size, diversity of types, and similarities among different types, posing significant challenges in balancing detection accuracy and efficiency. To address the challenges, we designed a detector based on You Only Look Once version 5 (YOLOv5) to achieve precise detection of surface defects on cold-rolled steel strips. First, a dataset containing seven types of defects was curated, named the Cold-Rolled Steel Defect Dataset (CR7-DET). Next, a feature-extraction network based on residual-like connections within a single residual block (Res2net) was developed to enhance the model’s feature-extraction capability, alongside introducing a multi-head attention module to focus on key information features. To reduce the information loss during feature fusion, we established an adaptive feature-fusion Path Aggregation Network (aff-PAN), which was optimized by designing a lightweight adaptive down-sampling module (LAD) to increase the sensory-field implementation of feature fusion. The ghost convolution effectively reduced the number of parameters and increased the speed without affecting the model’s performance. Finally, experiments were conducted on our CR7-DET and a public dataset (GC10-DET). With a reduced parameter count of 6.85 million, our model achieved a mean average precision(mAP) of 87.6% on CR7-DET and 79.7% on GC10-DET. The experimental results demonstrated that our model achieved a balance between detection accuracy and inference efficiency. The model has the potential to reduce scrap rates caused by defects and improve the overall surface quality of cold-rolled steel strips.

Prediction and control of profile for silicon steel strip in the whole tandem cold rolling based on PSO-BP algorithm

In the rolling process of silicon steel strip of the five-stand UCMW tandem cold rolling line, the quality of finished profile is the result of cooperative control of five stands, the exit profiles of the first four stands jointly decide the exit profile of the fifth stand, so it is necessary to accurately control the strip profile in the rolling process. However, the shapemeter is only typically equipped at the exit of fifth stand, making it impossible to obtain the strip profile of the first four stands. A profile prediction model for the whole tandem cold rolling based on PSO-BP algorithm is established, compared with the finite element method, this model can satisfy the demand for fast prediction of strip profile and quickly analyzing the profile change characteristics for five stands according to the adjustment of profile control parameters in the industrial field. Firstly, a 3D elastic-plastic finite element model of rolls and strip for five stands is built, and a large number of profile calculation data are as the training sample sets to build the PSO-BP model. Through the transfer calculation of strip profile between adjacent stands, the PSO-BP model of five stands is established, and the validity is verified by comparing with the actual measured data. Secondly, to improve the quality of product profile, focusing on the unreasonable setting of production parameters, many improvement schemes of WRB and WRS for five stands are designed, and the PSO-BP model is used to quickly calculate the strip profile, then comparing the result of each scheme, the optimum improvement scheme is selected, which is furtherly carried out rolling experiments in the industrial field, and the quality of strip profile is well improved. At present, the new scheme has been successfully applied to industrial production.

Effect of Preheating on the Mechanical Workability Improvement of High-Strength Electrical Steels during Tandem Cold Rolling

Cold-rolled silicon steel strip products are widely used as the main soft magnetic components in cores of electrical motors, generators, and transformers. In the case of rotating electrical machines, the so-called non-oriented electro-technical steels are normally applied. They are characterized by a similar behaviour to the induced magnetic field found in all sheet plane directions. The kind of soft magnetic alloys that defined herein not only possess an isotropy of electromagnetic properties, but also high mechanical strength; such alloys are called high-strength electro-technical (HSET) steels. These commercially produced HSET steels contain a high silicon content in the range of 3–4 wt.%. However, if the silicon content exceeds 3%, the machinability of Fe–Si alloys is dramatically reduced and they become much more brittle as a consequence. According to this, regular hot band brittle damage occurs during cold deformation at a high-speed tandem rolling mill. In accordance with these reasons, the production of thin high-strength silicon steel grades using the traditional methods of cold rolling deformation is extremely problematic and it is characterised by a high degree of steel sheet mechanical damage. In this scientific work, the effect of preheating hot-rolled strips on their mechanical workability improvement during tandem cold rolling was investigated. The results of this study indicate that the cold rolling of hot bands at elevated temperatures increases their resistance to brittle failure and mechanical plasticity. Moreover, the mathematical simulation clearly demonstrates that residual stress is distributed relatively homogeneously across the thickness of samples, which were cold rolled at 100 °C in contrast to the same ones deformed at room temperature.

Real-Time Detection of Nickel Plated Punched Steel Strip Parameters Based on Improved Circle Fitting Algorithm

Nickel-plated punched steel strip is a product obtained by punching holes on the surface of cold-rolled white sheet steel strip and then electrochemical nickel plating. It is necessary to make accurate and fast detection of punching circle parameters, since it is of crucial importance to ensuring the quality of nickel-plated punched steel strips. Accordingly, in this article, an improved circle fitting algorithm of nickel-plated punched steel strip is proposed. Firstly, the least squares fitting is performed to obtain the circle center and radius dataset by iterative algorithm with different values for the initial point positions and intervals. Then, the mean shift algorithm is used to optimize the results after iteration, and the segmented fitted circle centers are all concentrated around the true circle center to obtain the best radius and center coordinates. Finally, comparison experiments with different numbers of circular holes and verification experiments with nickel-plated punched steel strips are carried out. As the results show, the algorithm proposed in this article is more robust than the least squares algorithm in detecting multiple circles and has better real-time performance than the Hough transform. Therefore, it can meet the industrial production needs with high accuracy and real-time requirements, such as nickel-plated punched steel strips.

Optimization of Continuous Casting for Preventing Surface Peeling Defects on Titanium-Containing Ferrite Stainless Steel.

The surfaces of cold-rolled titanium-containing ferrite stainless steel (TCFSS) strips produced from scrap are prone to severe peeling owing to cracking near slab inclusions during hot rolling. In this study, the Taguchi method was used to prevent peeling defects and clogging of the submerged entrance nozzle, and the optimal casting parameters, such as the degree of casting overheating, casting speed, stirring time, and inclination, were determined. The results showed that increasing the degree of casting overheating and decreasing the casting speed prevented clogging and effectively mitigated peeling defects. Sample A3B1C3D2 had the optimal parameters to reduce the clog thickness to less than 1.5 mm, i.e., a degree of overheating of 60 °C, a casting speed of 0.80 m/min, a stirring time of 12.0 s, and an inclination angle of 6.0°. Sample A3B1C1D3 had the optimal parameters to prevent peeling defects, i.e., a degree of overheating of 60 °C, a casting speed of 0.80 m/min, a stirring time of 10.0 s, and an inclination angle of 6.2°. When casting using these optimal parameters, no peeling defects were observed on the surfaces of the TCFSS strips. The TCFSS strips produced using the optimized parameters exhibited the required mechanical properties and satisfied the design criteria. The parameters included a tensile strength of ≥415 MPa, a yield strength of ≥205 MPa, an elongation of ≥22%, and a hardness of ≤89 HRB.

In-process detection of miniature size holes in cold-rolled steel strips

In-process detection of miniature size holes in cold-rolled steel strips

Quality reliability analysis of flash welding joint of cold-rolled 50JW800 silicon steel strip

In this study, the quality reliability of the flash butt welding (FBW) joint of a 50JW800 silicon steel strip is analyzed through metallographic observation, cupping tests, and tensile tests. It is found that the recrystallization in some areas of the joint is insufficient, as is the mechanically forced connection, owing to the plastic deformation and uneven heat distribution of the joint during the process of FBW. Insufficient recrystallization is the root cause of strip breakage during cold rolling. The quality reliability of the 50JW800 silicon steel strip FBW joint is also analyzed using eddy current nondestructive testing technology. It is found that the eddy current testing signal has a good response to different structural characteristics of the joint. Therefore, eddy current test technology can be used to ensure the quality reliability of silicon steel strip FBW joints in actual production.

Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies.

Multiple micro-magnetic non-destructive testing (NDT) technologies are suitable candidates for predicting the mechanical properties of cold-rolled steel strips. In this work, based on magnetic domain dynamics behavior and magnetization theory, the correlation between electromagnetic characteristics extracted by multiple micro-magnetic NDT technologies and the influence factors was investigated. It was found that temperature and tension can subsequently affect the electromagnetic parameters by altering the domain structure and domain walls’ motion properties. Pearson’s correlation coefficients were employed to reflect the dependence of micromagnetic characteristics on influencing factors. The lift-off was determined as the largest influence factor among influence factors. A pseudo-static detection was reached by polynomial fitting, which could eliminate the influence of lift-off on the detection results. The number of training models was optimized, and the detection accuracy was improved via the improved Generalized Regression Neural Network (GRNN) model, based on the Gaussian Mixture Clustering (GMC) algorithm.

Modeling and exploiting the strip tension influence on surface imprinting during temper rolling of cold-rolled steel

To produce cold-rolled steel strips with specific mechanical properties and surface roughness typically temper rolling is adopted. In most cases, a uniform roughness pattern on the strip surface is mandatory. Due to the wear of the textured work rolls, their surface roughness (Ra) continuously reduces during the process, which should be accounted for process control. However, conventional temper rolling systems fail to guarantee a uniform surface roughness. In this work, the influence of strip tension on the imprinting of surface roughness during temper rolling is analyzed based on a multi-scale FE modeling concept to explore new ways for surface roughness control. This is done in simulation where, a macroscopic rolling model incorporating strip tension is coupled to a mesoscopic imprinting model and both models are validated using copper rolling trials. The influence of different thickness reductions, strip tensions and incoming strip's surface roughness on imprinting is modeled and compared. The numerical results reveal that a higher strip tension decreases the roughness transfer, which presents potential to control the roughness transfer ratio without changing other process parameters like the prescribed thickness reduction in the future.

Advances of numerical simulation research on the emulsion flow field of wet skin-pass mill

Anti-rust agents are used as emulsion liquids to spray the roll gaps for wet skin-pass of cold-rolled steel strips. The wet-skin strip steel has a good shape, fewer surface defects, good anti-rust effect, higher strength, low roller consumption, and high work efficiency. Some research advances on the numerical simulation of the emulsion flow field in the wet skin-pass mill are discussed, including simplified two-dimensional nozzle models such as straight or convergent, high-pressure jets and conical converging nozzles to provide a theoretical reference for the numerical simulation of the emulsion flow field in the wet skin-pass mill.

Automated Visual Defect Classification for Flat Steel Surface: A Survey

For a typical surface automated visual inspection (AVI) instrument of planar materials, defect classification is an indispensable part after defect detection, which acts as a crucial precondition for achieving the online quality inspection of end products. In the industrial environment of manufacturing flat steels, this task is awfully difficult due to diverse defect appearances, ambiguous intraclass, and interclass distances. This article attempts to present a focused but systematic review of the traditional and emerging automated computer-vision-based defect classification methods by investigating approximately 140 studies on three specific flat steel products of con-casting slabs, hot-rolled steel strips, and cold-rolled steel strips. According to the natural image processing procedure of defect recognition, the diverse approaches are grouped into five successive parts: image acquisition, image preprocessing, feature extraction, feature selection, and defect classifier. Recent literature has been reviewed from an industrial goal-oriented perspective to provide some guidelines for future studies and recommend suitable methods for boosting the surface quality inspection level of AVI instruments.

Improved mechanical properties of V-microalloyed dual phase steel by enhancing martensite deformability

A good combination of ultimate tensile strength (UTS) up to 1365 MPa and total strain to failure (StF) to 15.5 % has been achieved due to deformable martensite in the invented vanadium-microalloyed dual-phase (DP) steel, which was manufactured by two-stage annealing of cold rolled steel strip. The employed extensive characterizations revealed that the ductile martensitic phase in this DP steel differentiated from ordinarily low-carbon martensitic lath in both morphology and lattice structure. Complex coherent orientation relationships between ferrite, reverse austenite, martensitic phase and vanadium carbide (VC) do exist, leading to a new martensitic transformation mechanism and resultant dual-phase microstructure. Besides, a detailed characterization including essential phase transformation analysis in combination with in situ TEM observation, shows that, all the essential processing including recrystallization, reverse austenitic and martensitic transformation, in debt to the particular effects of VC, can be recognized as phase transformations with higher thermodynamic driving force and higher kinetic energy barrier as compared to previously common processing, which actually changes the microstructure and, indirectly leads to higher strength and higher ductility. This synergy of thermodynamics and kinetics can be generalized to improve mechanical properties of present steels.

Multi-faceted modelling for strip breakage in cold rolling using machine learning

In the cold rolling process of steel strip products, strip breakage is an undesired production failure which can lead to yield loss, reduced work speed and equipment damage. To perform a root cause analysis, conventional physics-based approaches which focus on mechanical and metallurgical principles have been applied in a retrospective manner. With the advancement of data acquisition technologies, numerous process monitoring data is collected by various sensors deployed along this process; however, conventional approaches cannot take advantage of these data. In this paper, a machine learning-based approach is proposed to characterise and model strip breakage in a predictive manner. First, to match the temporal characteristic of strip breakage which occurs instantaneously, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from three facets – physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using recurrent neural networks (RNNs), which are specialised in discovering underlying patterns embedded in time-series data. An experimental study using real-world data collected from a cold-rolled electrical steel strip manufacturer revealed the effectiveness of the proposed approach.

Investigation and Optimization of Load Distribution for Tandem Cold Steel Strip Rolling Process

In order to improve the cold rolled steel strip flatness, the load distribution of the tandem cold rolling process is subject to investigation and optimization. The strip deformation resistance model is corrected by an artificial neural network that is trained with the actual measured data of 4500 strip coils. Based on the model, a flatness prediction model of strip steel is established in a five-stand tandem cold rolling mill, and the precision of the flatness prediction model is verified by rolling experiment data. To analyze the effect of load distribution on flatness, the flatness of stand 4 is calculated to be 7.4 IU, 10.6 IU, and 16.8 IU under three typical load distribution modes. A genetic algorithm based on the excellent flatness is proposed to optimize the load distribution further. In the genetic algorithm, the classification of flatness of stand 4 calculated by the developed flatness prediction model is taken as the fitness function, with the optimal reduction of 28.6%, 34.6%, 27.3%, and 18.6% proposed for stands 1, 2, 3, and 4, respectively. The optimal solution is applied to a 1740 mm tandem cold rolling mill, which reduce the flatness classification from 10.8 IU to 3.2 IU for a 1-mm thick steel strip.

Influence of Finish Rolling Temperature on Microstructure and Mechanical Properties of a 19Cr1.5Mo0.5 W Ferritic Stainless Steel

A 444-type heat-resistant ferritic stainless steel containing 0.05 wt% Ce (rare earth element) and 2 wt% (Mo + W) was adopted as an experimental material to study the effect of finish rolling temperature on microstructure and texture evolution as well as on mechanical properties and formability. The rolling processes contain hot rolling at two different finish rolling temperatures (860 °C and 770 °C) and annealing, cold rolling and subsequent annealing. It was found that the microstructures after hot rolling and annealing could be refined by lowering finish rolling temperature. The resultant microstructures after cold rolling and annealing were hereditarily refined. Lowering finish rolling temperature can weaken α-fiber texture in hot-rolled or cold-rolled ferritic stainless steel strip, while γ-fiber texture in the final product was homogeneously strengthened. Additionally, enhanced mechanical property and formability in terms of strength and average plastic strain ratio could be obtained via decreasing finish rolling temperature.

Research of material of damaged poly-V pulleys for increasing the service life of promising automotive generators

The material of the deteriorated poly-V pulleys made of cold-rolled steel strip 08kp and 08ps was studied using electron microscopy with an X-ray microanalyzer and the causes of their fatigue failure were determined. The main reasons for their destruction can be heterogeneous structure and properties and non-optimal mode of annealing and, as a result, the low hardness on the transitional zone of the pulley, through which they are damaged in service. So, to avoid pieces deterioration, cold-rolled tape made of 035Yu steel with a homogeneous structure and properties was developed. The study also proposes a new mode of its recrystallization annealing, which increases the hardness at the transition zone of the poly-V pulley and the service life of promising automotive generators.

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This article attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs and hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: statistical, spectral, model-based, and machine learning. These works are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

Industrial X-ray Fluorescence Analyzer for Real-Time Thickness Measurements of Aluminium Coatings on Rolled Steel

Aluminium coatings that are formed by physical vapour deposition (PVD) on rolled steel products are more resistant to atmospheric and seawater corrosion than zinc coatings. We developed a coating thickness analyzer (CTA) with an X-ray fluorescence (XRF) measuring head, that is integrated into the PVD pilot line. In this study, to conduct measurements of elements with atomic numbers less than 20 while avoiding the problem of registration of light elements, the measuring head was integrated into a process vacuum chamber to maintain a vacuum during the measurements. To validate the proposed tool, cold-rolled steel strips of different grades are used as substrates, and aluminium was deposited on the surface via PVD in thicknesses ranging from 1 to 20 g/m2. The thin-film thickness measurements during a pre-acceptance test were found to have a relative accuracy of less than 5% and a relative precision of less than 1% – 2%. The proposed CTA can be readily integrated in the factory’s automatic process control system and the real-time measurements in operating and calibration modes, and the status of all spectrometric equipment (X-ray tube, detector etc.) can be transmitted to the upper-level computer. Thus, the process engineer can properly control the deposition process.