Steel Strip Research Articles

Selection of Various Indications of Tempering Lubricant Concentration Reuse in the Wet Rolling Process

ABSTRACTDiscarded tempering lubricants retained significant reuse potential, making their recycling a vital step in reducing resource wastage and wastewater treatment costs in the strip steel industry. Hence, developing an accurate, rapid evaluation indicator for recycled fluid concentration was essential for facilitating this process. Research showed that among common evaluation indicators for metal fluids, three—electrical conductivity, refractive index and total base number (TBN)—due to their high linear correlation with tempering lubricant concentration (R2 > 0.995), could be utilised to monitor the dynamic changes in the concentration of tempering lubricants. Subsequent experiments on reused tempering lubricants revealed that electrical conductivity, significantly altered by iron powder (7%–24% variance), and refractive index, impacted by hydraulic oil (3% deviation), highlighted contaminant challenges; yet, filtration effectively mitigated iron powder's effect on TBN. Finally, A 17‐day reused tempering lubricants simulation demonstrated consistent effectiveness of the three indicators in monitoring the need to update tempering lubricant concentration. However, in terms of sensitivity, precision, and particularly stability and relative mean deviation, the TBN concentration evaluation indicator outperformed, with TBN (3.38%) < Refractive Index (7.92%) < Electrical Conductivity (11.05%). This indicates the TBN method's superior stability over conductivity and refractive index methods, with its accuracy deviation below 2%, making it a stable, simple and reliable metric worthy of broader adoption.

Lightweight-detection: The strip steel surface defect identification based on improved YOLOv5d

The surface defect of strip steel seriously affects the product quality, the current identification methods have the inherent defects of low detection efficiency and poor detection accuracy, and it is important an improved an algorithm based on the modified YOLOv5 framework to enhance the defect detection effectiveness and accuracy. To create a more lightweight network model, the C3 module and part of the convolutional structure in the original YOLOv5 network were replaced with the GhostBottleneck structure. Additionally, the Coordinate Attention (CA) mechanism was incorporated into the Backbone section to compensate for the original model's lack of attention mechanisms. To address the angular mismatch between the actual frame and the predicted bounding box, a limitation overlooked by the CIOU loss function was further refined. Experimental results demonstrate that the enhanced YOLOv5s-CSG model outperformed the original YOLOv5s by 7.3 %, achieving a significant 37.9 % reduction in model size and an mAP value of 77.5 %. Furthermore, the detection precision and speed were notably higher compared to several widely-used algorithms. The proposed YOLOv5s-CSG model is well-suited for deployment on mobile devices and shows great potential for rapidly and accurately detecting both the type and location of strip surface defects, surpassing the performance of existing methods.

Surface defect detection of strip steel based on GT-CutMix augmentation algorithm and improved DSSD model

Abstract Nowadays, defect detection technology based on deep learning continuously increases the surface quality requirements of hot-rolled strip steel. However, due to limitations in industrial production, defect datasets often suffer from insufficient training samples and imbalanced categories. This paper proposes effective solutions, namely the GT-CutMix offline data augmentation algorithm and lightweight small sample defect detection models. The GT-CutMix augmentation algorithm significantly improves defect utilization by accurately sampling defect locations and integrating them into the original data set. We design the S-DSSD defect detection model by constructing a lightweight SI-MobileNet to replace the ResNet101 backbone of the Deconvolutional Single Shot Detector (DSSD) network. This can reduce the resource parameters and consumption. At the same time, it can speed up training and inference. To further improve the detection accuracy, we integrate the Pyramid Split Attention (PSA) attention mechanism into the prediction module of DSSD and construct the SA-DSSD model. Under the GT-CutMix augmentation algorithm, the mAP of S-DSSD and SA-DSSD models on X-SDD dataset are 76.83% and 78.63%, respectively. Meanwhile, the corresponding detection speeds are 45 FPS and 40 FPS, respectively. In addition, on the NEU-DET cross-dataset experiment, the mAP of the SA-DSSD model reaches 74.88%. Our methods are highly effective and generalized for small sample defect detection, which can provide selective solutions for specific needs such as high speed and precision in different industrial production scenarios.

Strip Steel Defect Prediction Based on Improved Immune Particle Swarm Optimisation–Improved Synthetic Minority Oversampling Technique–Stacking

Strip Steel Defect Prediction Based on Improved Immune Particle Swarm Optimisation–Improved Synthetic Minority Oversampling Technique–Stacking

A novel slit damper configuration to enhance ductility and seismic behavior of concentrically braced frames

Steel slit dampers are the specific type of metallic yielding dampers used for the structural passive control. They are constructed by introducing a series of openings or slits in a steel plate to control the seismic behavior of structures. These dampers absorb energy through shear yielding, flexural yielding, or a combination of both. In this paper, a new form of steel slit damper is introduced and examined. The novel damper, called the Zipper-shaped Slit Damper (ZSD), is composed of two nested boxes connected through high-strength bolts. There are several steel strips in an external box and L-shaped profiles to prevent out-of-plane buckling, maximize flexural capacity, and ensure optimal lateral force distribution. Additionally, L-shaped profiles are welded to the ends of the steel strips. To assess the structural capacity and seismic behavior, ZSD is modeled in three dimensions using ABAQUS finite element software. The model is first validated against experimental results and then subjected to cyclic nonlinear static analysis, comprising 15 models with different width-to-thickness and height-to-width ratios of steel strips. The failure index (FI), based on equivalent plastic strain, is employed to predict the moment of failure. The results obtained from the FI were analyzed, and the analysis was limited until the moment of the first failure. On average, the novel damper demonstrated improved durability with an increase in the height-to-width ratio, resulting in enhanced ductility up to 8.75. Additionally, within each width -to-thickness ratio group, a 0.5 unit increase in this ratio corresponded to a reduction of approximately 10 % in energy dissipation efficiency. The equivalent viscous damping in the models averaged 42 %. The analysis indicated that all models exhibited stable hysteresis cycles and had high ductility and energy dissipation. Also, considering the obtained results, it can be concluded that the ZSD, due to its replaceable components, ease of construction, and easy installation, can be utilized in concentrically braced frames. Mechanical models, as well as stiffness and force relationships for design and implementation in braced frames, are presented. The findings provide valuable insights for the utilization of this innovative damper in seismic-resistant structural systems.

Development of the theory of joint plastic deformation of different metals during steel strip rolling

Development of the theory of joint plastic deformation of different metals during steel strip rolling

Hot Deformation Behavior of Free-Al 2.43 wt.% Si Electrical Steel Strip Produced by Twin-Roll Strip Casting and Its Effect on Microstructure and Texture.

Twin-roll strip casting (TRSC) technology has unique advantages in the production of non-oriented electrical steel. However, the hot deformation behavior of high-grade electrical steel produced by TRSC has hardly been reported. This work systematically studied the hot deformation behavior of free-Al 2.43 wt.% Si electrical steel strip produced by twin-roll strip casting. During the simulated hot rolling test, deformation reduction was set as 30%, and the ranges of deformation temperature and strain rate were 750~950 °C and 0.01~5 s-1, respectively. The obtained true stress-strain curves show that the peak true stress decreased with an increase in the deformation temperature and with a decrease in the strain rate. Then, the effect of hot deformation parameters on microstructure and texture was analyzed using optical microstructure observation, X-ray diffraction, and electron backscattered diffraction examination. In addition, based on the obtained true stress-strain curves of the strip cast during hot deformation, the constitutive equation for the studied silicon steel strip was established, from which it can be found that the deformation activation energy of the studied steel strip is 83.367 kJ/mol. Finally, the kinetics model of dynamic recrystallization for predicting the recrystallization volume percent was established and was verified by a hot rolling experiment conducted on a rolling mill.

Design and Simulation of Torque Control for Hot Rolling Steel Mill

Lateral movement of a strip in the hot rolling process is an important unsolved technical problem. To minimize the strip thickness variations, the state space model formulation is usually used. It results in reducing productivity and, in the extreme case, strip tearing during tailing out at the finishing mill. This movement is caused by various asymmetric factors such as mechanical asymmetry of the mill and different temperatures along the strip width direction. Furthermore, the difference in the temperature and the thickness of steel strip resulting in deformation of the steel, which affects the bending torque of the steel strip. This study aims to understand the influences of these various factors on lateral movement. In order to reduce the lateral movement and the disturbance terms, that act on uncertain inter-connected systems, a new control approach, based on a combination of Integral Sliding Mode Control (ISMC) and Linear Matrix Inequalities (LMI) technique, is proposed. Simulation results of hot rolling system with three interconnected systems are presented to validate the new controller performance.

Experimental Measurement of Emissivity of Polished Steel Strips from a Continuous Annealing Line.

The long-term use of steel strip in various industries makes it an important semi-finished product, which makes it necessary to improve its chemical composition and mechanical properties, reduce its thickness and weight, expand the range of new types of steel strip and increase its production. This entails a large number of technological operations dependent on precise temperature measurement and control. In some industrial plants, the steel strip is in continuous motion, which makes the use of contact measuring devices impossible. When using non-contact measuring devices such as pyrometers or thermal imaging cameras, the emissivity of the materials being measured is a problematic parameter, as setting an incorrect emissivity value to the measuring device results in inaccurate temperature readings. The essence of this research was to establish a measurement method and to perform experimental measurements of the emissivity of a polished steel strip used in a continuous annealing line, the subsequent processing of the data from these measurements and their evaluation. The emissivity measurements were carried out for 5 types of steel strip of different parameters, while the measurement itself was carried out in the long wavelength range of 7.5-14 µm and at strip temperatures of 100-300 °C. Depending on the type of steel strip, the mean emissivity values ranged from 0.0835- to 0.1143. The emissivity of the steel strip increased with increasing strip temperature, and it was not a linear dependence. The emissivity values determined in this research could be applied to measuring equipment in actual production, which could improve the accuracy of temperature measurement in the heat treatment of polished steel strip. Thermal camera measurements in the long wavelength range, taking thermal images and their processing and determining the emissivity value of polished steel strips are the parts of this research that make it different from other already published research.

DFFNet: a lightweight approach for efficient feature-optimized fusion in steel strip surface defect detection

Steel surface defect detection is crucial in manufacturing, but achieving high accuracy and real-time performance with limited computing resources is challenging. To address this issue, this paper proposes DFFNet, a lightweight fusion network, for fast and accurate steel surface defect detection. Firstly, a lightweight backbone network called LDD is introduced, utilizing partial convolution to reduce computational complexity and extract spatial features efficiently. Then, PANet is enhanced using the Efficient Feature-Optimized Converged Network and a Feature Enhancement Aggregation Module (FEAM) to improve feature fusion. FEAM combines the Efficient Layer Aggregation Network and reparameterization techniques to extend the receptive field for defect perception, and reduce information loss for small defects. Finally, a WIOU loss function with a dynamic non-monotonic mechanism is designed to improve defect localization in complex scenes. Evaluation results on the NEU-DET dataset demonstrate that the proposed DFFNet achieves competitive accuracy with lower computational complexity, with a detection speed of 101 FPS, meeting real-time performance requirements in industrial settings. Furthermore, experimental results on the PASCAL VOC and MS COCO datasets demonstrate the strong generalization capability of DFFNet for object detection in diverse scenarios.

Rtsds:a real-time and efficient method for detecting surface defects in strip steel

Rtsds:a real-time and efficient method for detecting surface defects in strip steel

Study on ultra-low cycle fatigue of steel slit dampers

Steel slit dampers are typically made by cutting a number of slits in a steel plate. With the existence of the slits, the steel strips between them would yield under earthquake action and dissipate seismic energy through plastic deformation. Ultra-low cycle fatigue (ULCF) fracture of the steel strips will significantly reduce the energy dissipation capacity of the slit damper. In this research, ULCF tests were conducted on uniform, taped and hourglass-shaped steel strip (USS, TSS and HSS) specimens, which are fundamental elements of steel slit dampers. During the ULCF tests, symmetric cyclic loadings with constant amplitude were applied on the steel strip specimens. All USS, TSS and HSS specimens fractured due to ULCF but at different sections. The ULCF life and energy dissipation capacity of these specimens increase significantly in that order. Finite element (FE) analysis was carried out to simulate the ULCF tests on the steel strip specimens, based on which the ULCF fracture of these specimens was predicted with the cyclic void growth model (CVGM). The simulated hysteretic curves and the predicted ULCF lives agree well with the test results, validating the applicability of the FE model and the CVGM for the ULCF analysis of steel slit dampers. ULCF analysis was then conducted on USS, TSS and HSS specimens subjected to symmetric cyclic loadings with variable amplitudes and asymmetric cyclic loadings with constant amplitude to confirm the superior ULCF performance of the HSS specimen. Beneficial effects of additional axial compression and deviation of cyclic loadings from the damper plane on the ULCF of steel slit dampers were also investigated through numerical analysis.

Fire performance enhancement of cold-formed steel walls: experimental and numerical study

Cavity-insulated cold-formed steel (CFS) walls, with gypsum plasterboard (GP) on both sides, are widely used in the construction industry. However, their fire performance is compromised by the faster rise in the temperature of the hot flange (HF), which is influenced by cavity insulation and board joints. This study aims to optimize cavity insulation configuration to enhance fire performance. Two mid-scale, non-load-bearing fire experiments on CFS walls were conducted, employing double-layered GP boards and rock wool insulation, under ISO834 time-temperature fire conditions. Meanwhile, a finite element model of physical space fluid-heat coupling of furnace-CFS walls considering joints with time was developed to reveal joint mechanisms and configurations. The results showed that non-insulated stud CFS walls had slower HF temperature rise and similar insulation compared to insulated counterparts, delaying HF temperature reach to 537 °C by 14 min. The base-layer joints had the least adverse impact on the HF temperature rise than overlapped joints and face-layer joints. The HF temperatures for various joint configurations reached 537 °C at different times: 57 min for the face layer, 62 min for the base layer, and 49 min for overlapped joints. Considering the construction cost-effectiveness, it was found that adding steel strips (the same width as the flange) at the joint position could significantly decelerate the HF temperature rise. In 60 min, the HF temperature in the model with additional steel strips dropped by 94 °C lower than in the 10 mm overlapped joint model. Finally, based on finite element results, design equations were proposed for predicting HF temperature variation over time across joint types.

DEW-YOLO: An Efficient Algorithm for Steel Surface Defect Detection

To address the current steel surface defect detection algorithms in practical applications involving low detection accuracy, an efficient and highly accurate strip steel surface defect detection algorithm, DEW-YOLO, is proposed in this paper. Firstly, by combining the advantages of deformable convolutional networks (DCNs), this paper innovates the C2F module in YOLOv8 and proposes a C2f_DCN module that can flexibly sample features to enhance the abilities of learning and expressing defect features of different sizes and shapes. Secondly, the explicit visual center (EVC) is introduced into the backbone network, which enhances feature extraction capabilities and adaptability and enables the model to better adjust features at different levels and scales. Finally, the original loss function is replaced with the Wise-IoU (WIoU) loss function to accurately measure the similarity between the target frames and improve the defect detection performance of the model. The experimental results on the NEU-DET dataset demonstrate that the algorithms proposed in this paper achieved a mean average precision (mAP) of 80.3% in steel surface defect detection tasks, which was a 3.9% improvement over the original YOLOv8 model. The model’s inference speed reached 91 frames per second (FPS). DEW-YOLO effectively enhances the accuracy of steel defect detection and better satisfies industrial inspection requirements.

Local strain heterogeneity and damage mechanisms in zirconia particle-reinforced TRIP steel MMCs: in situ tensile testing with digital image processing

In this work, the microstructural deformation and damage mechanisms of TRIP steel metal matrix composites (MMCs) reinforced with Magnesia Partially Stabilized Zirconia (Mg-PSZ) particles are investigated by employing in situ tensile testing within a scanning electron microscope chamber, complemented by digital image correlation and advanced image processing techniques. The study is carried out on samples with varied volume fractions (0%, 10%, and 20%) of zirconia particles and damage mechanisms in different samples under specified loading conditions. Through both qualitative and quantitative assessments of deformation, damage, and clustering, the investigation provides a comprehensive understanding of the distribution and damage initiation. The study findings reveal that, generally, the steel matrix exhibits high toughness, with minimal occurrences of microcracking at high strains that cause significant damage. In samples with increasing particle content, delamination at the matrix–particle interface and cracking of Mg-PSZ particles were found to be critical contributors to material failure and were quantitatively analyzed using computational analyses conducted with MATLAB. The work highlights the initiation and evolution of each damage mechanism in zirconia particle-reinforced TRIP steel MMCs to facilitate scientists and engineers in improving manufacturing and application decisions in industries such as automotive, aerospace, and heavy machinery, which demand materials with exceptional toughness and durability.Graphical

Lightweight strip steel defect detection algorithm based on improved YOLOv7

The precise identification of surface imperfections in steel strips is crucial for ensuring steel product quality. To address the challenges posed by the substantial model size and computational complexity in current algorithms for detecting surface defects in steel strips, this paper introduces SS-YOLO (YOLOv7 for Steel Strip), an enhanced lightweight YOLOv7 model. This method replaces the CBS module in the backbone network with a lightweight MobileNetv3 network, reducing the model size and accelerating the inference time. The D-SimSPPF module, which integrates depth separable convolution and a parameter-free attention mechanism, was specifically designed to replace the original SPPCSPC module within the YOLOv7 network, expanding the receptive field and reducing the number of network parameters. The parameter-free attention mechanism SimAM is incorporated into both the neck network and the prediction output section, enhancing the ability of the model to extract essential features of strip surface defects and improving detection accuracy. The experimental results on the NEU-DET dataset show that SS-YOLO achieves a 97% mAP50 accuracy, which is a 4.5% improvement over that of YOLOv7. Additionally, there was a 79.3% reduction in FLOPs(G) and a 20.7% decrease in params. Thus, SS-YOLO demonstrates an effective balance between detection accuracy and speed while maintaining a lightweight profile.

A 3D pilot simulation of masonry walls retrofitted with steel strips using a novel meso-scale damage-plasticity interface model

A novel interface model is introduced to faithfully and effectively simulate masonry walls retrofitted with steel strips (MWSS) under monotonic/cyclic lateral loads at the meso-scale level, where bricks and mortar joints are explicitly represented by 3D continuum elements and interface elements, respectively. The interface model’s formulation, rooted in a damage-plasticity framework, is presented herein, which allows for an accurate depiction of the complex nonlinear behaviors of masonry joints under tension, shear and compression. Subsequently, the model is calibrated and verified against existing experimental data from large-scale masonry specimens subjected to monotonic/cyclic lateral loading. The resulting push-over/hysteretic curves, along with the failure modes of the specimens, show relatively good agreement with their experimental counterparts. Leveraging this validated modelling approach, an extensive parametric study is undertaken to assess the impact of various factors on the in-plane behavior of MWSS. It turns out that the boundary conditions dictate the overall response of MWSS, whereas the material properties of the masonry have relatively minor effects. Given these insights, an improved truss-based analytical model is suggested to predict the lateral strength of MWSS, addressing the limitations of existing design procedures, particularly in scenarios where MWSS’s top edge is confined by high pre-compression.

Effect of quenching temperature on the austenite stability and mechanical properties of high-strength air-cooled TRIP steel prepared with hot-rolled C–Si–Mn sheets

This paper systematically studies the effect of quenching temperature on the microstructure evolution and the relationship between the microstructure and mechanical properties of air-cooled TRIP steel prepared with C–Si–Mn hot-rolled sheet. The results indicate that when the quenching temperature is lower than (200 °C) or close to (250 °C) the Ms temperature, the martensite transformation mainly occurs during quenching or at early stage of air cooling. When the quenching temperature reaches 300 °C, lathy bainite begins to form. As the quenching temperature increases, the morphology of bainite is transformed from lathy to granular. Bainite transformation during air cooling can promote the C enrichment in austenite, thus improving the thermal stability of the austenite. However, martensite transformation also occurs during subsequent air cooling, and the start temperature of martensite transformation first decreases and then increases with the increase of quenching temperature from 300 °C to 450 °C. The yield and tensile strength of TRIP specimens first decrease and then increase, while the retained austenite content, elongation and product of strength and elongation (PSE) first increase and then decrease. When the quenching temperature is 400 °C, the best elongation and PSE are obtained. The best elongation of 400TRIP specimen is mainly attributed to its highest content and suitable mechanical stability of retained austenite, which is favorable for the expansion of the TRIP effect range. The TRIP specimens prepared with hot-rolled sheet all exhibit ultra-high tensile strength (over 1050 MPa), which is related to the ultrafine ferrite grains, martensite transformation during air cooling and TRIP effect of retained austenite.

YOLOv8n-GAM: an improved surface defect detection network for hot-rolled strip steel

Production defects caused by irresistible factors such as process design problems or differences in steel properties in strip production affect the economic benefits of the enterprise and threaten production safety. Traditional defect detection methods are difficult to achieve real-time and high-precision detection, so developing surface defect detection methods based on deep learning is of great significance for strip production. In order to effectively improve the accuracy of the deep learning model in detecting surface defects on hot-rolled strip, in this work we propose a real-time detection model for surface defects on strip steel based on the YOLOv8n model. Firstly, the newly convolutional layer Con5v is designed to replace the original convolutional layer in the neck, and an attention mechanism is added in front of each Con5v to improve the algorithm’s ability to extract small target information. Secondly, an additional set of upsampled feature extraction units is added to the neck in order to enhance the spatial information of the feature map. Subsequently, a set of feature fusion units is incorporated and the convolutional layers in it are improved to provide better feature maps. Thirdly, the number of decoupling detection heads is increased to receive more high-quality features. The final experimental results show that YOLOv8n-GAM (YOLOv8 Nano Model with Global Attention Mechanism) achieves 81.4mAP and 82.0FPS on the NEU-DET dataset and 71.2mAP and 55.0FPS on the GC10-DET dataset, which are 5.7% and 6.9% higher than those of YOLOv8n, respectively. The model proposed in this paper achieves a comprehensive performance improvement in strip steel.

Statistical Models for Predicting Wear and Friction Coefficient of Valve Tappet Using ANOVA

The focus of this study is to propose statistical models for predicting wear and coefficient of friction of the 16MnCr5 steel valve tappet using the analysis of variance (ANOVA). The 16MnCr5 steel valve tappet was fabricated from a steel strip and thermochemically treated by carburizing, quenching, and tempering. Under dry conditions, tribological tests were performed for 16MnCr5 steel valve tappet with a ball-on-plane configuration in a reciprocating slide using an Optimol SRV® v4 device. Subsequently, the results of the coefficient of friction (COF) and the wear rate were then analyzed using the ANOVA. Regression analysis was used to derive the predictive equations for both friction coefficient and wear rate. The applied load was found to be the most significant parameter affecting the COF and wear rate. The proposed statistical models has 88 - 92% percent reliability. These models can be beneficial for predicting the tribological operating conditions for the 16MnCr5 steel valve tappet to avoid premature failure within the tested load and temperature conditions.