Search Results (156)

The surface topography of the rolls used in skin-pass cold rolling determines the surface finish of rolled sheets. In this sense, work rolls can be intentionally textured to produce certain topographical features on the final sheet surface. The maskless electrochemical texturing method (MECT) is a potential candidate for industrial-scale application due to its reduced texturing cost and time when compared to traditional texturing methods. However, there are few studies in the literature that address the MECT method applied to the topography control of cold rolling work rolls. The present work aims to analyze the viability of surface texturing via MECT of work rolls used in skin-pass cold rolling. In this study, we first investigated how texturing occurs for tool steel using flat textured samples to facilitate the understanding of the dissolution mechanisms involved. In this case, a specially designed texturing chamber was built to texture flat samples extracted from an actual work roll. The results indicated that the anodic dissolution involved in tool steel texturing occurs preferentially in the metallic matrix around the primary carbides. Then, we textured a work roll used in pilot-scale rolling tests, which required the development of a special prototype to texture cylindrical surfaces. After texturing, the texture transfer from the work roll to the sheets was investigated. Rolling tests showed that the work roll surface textured with a dimple pattern generated a pillar-shaped texture pattern on the sheet surface, possibly due to a reverse extrusion mechanism. Full article

This study investigated the application of neural network techniques to predict the mechanical properties of low-carbon hot-rolled steel strips using industrial data. A feedforward neural network (FFNN) model was developed to predict the yield strength (YS), ultimate tensile strength (UTS), and elongation (%EL) based on the chemical composition and processing parameters. For the low-carbon hot-rolled steel strip (C: 0.02–0.06%, Mn: 0.17–0.38%), 435 datasets were utilized with 17 input parameters, including 15 composition elements, finish rolling temperature (FRT), and coil target temperature (CTT). The model was trained using 335 datasets and tested using 100 randomly selected datasets. The optimum network architecture consisted of two hidden layers with 34 neurons each, achieving a mean squared error of 0.014 after 200,000 iterations. The model predictions showed excellent agreement with the actual values, with mean percentage errors of 4.44%, 3.54%, and 4.84% for the YS, UTS, and %EL, respectively. The study further examined the influence of FRT and CTT on mechanical properties, demonstrating that FRT has more complex effects on mechanical properties than CTT. The model successfully predicted property variations with different processing parameters, thereby providing a valuable tool for alloy design and process optimization in steel manufacturing. Full article

Summer turnips (Brassica rapa L.) have become one of the main supplementary crops in ruminant livestock systems in southern Chile because of accelerated forage growth as well as greater forage yield and nutritive value in the dry season. This study investigated the effects of forage turnip supplementation on the physicochemical and sensory quality of beef from steers. Twenty-seven Holstein–Friesian steers were allocated to one of three dietary treatments: pasture plus concentrate (Control), 50% turnip with a basal diet of pasture hay and rolled corn (T50), and 70% turnip with the same basal diet (T70). Carcass yields and physicochemical and sensory beef attributes, including fatty acid composition of intramuscular fat (IMF) in lean tissue, were measured. Compared to the Control diet, finishing steers on 50% or 70% turnips increased meat redness (a* > 25.9 vs. 22.9 in Control), IMF (1.79% in T50 vs. 1.12% in Control), polyunsaturated fatty acids (PUFAs) (especially n-3), cholesterol, and specific minerals (sodium, manganese and iron); this resulted in a reduction in the subcutaneous fat thickness (0.29 cm in T50, 0.25 cm in T70 vs. 0.51 cm in Control) and shear force of cooked meat (p < 0.05). However, no differences were found between diets in beef juiciness, flavor, and tenderness assessed by trained panelists (p > 0.05). Increasing forage turnip inclusion to 70% resulted in similar beef quality to the 50% inclusion level. Foraged turnips present a promising strategy for producing high-quality beef during periods of limited pasture growth. Full article

Roll wear significantly affects production efficiency and product quality in hot-rolled strip steel manufacturing by reducing roll lifespan and impeding the control of strip shape. This study addresses these challenges through a comprehensive analysis of the roll wear mechanism and the integration of an elastic deformation model. We propose an optimized wear prediction model for work and backup rolls in a hot continuous rolling finishing mill, dynamically accounting for variations in strip specifications and cumulative wear effects. A three-dimensional elastic–plastic thermo-mechanical coupled finite element model was established using MARC 2020 software, with experimental calibration of wear coefficients under specific production conditions. The developed dynamic simulation software achieved high-precision wear prediction, validated by field measurements. The optimized model reduced prediction deviations for work and backup rolls to 0.012 and 0.004, respectively, improving accuracy by 5.3% and 3.25% for uniform and mixed strip specifications. This research provides a robust theoretical framework and practical tool for precision roll wear management in industrial hot rolling processes. Full article

The final rolling temperature has a significant impact on the grain recrystallization and mechanical properties of rolled materials and is a key factor in the rolling process. With the development of the aerospace industry, higher requirements have been put forward for the quality of TiAl alloys. The suitable rolling temperature range of TiAl alloys is high and narrow, making it difficult to accurately control the final rolling temperature in real-time under the influence of environmental heat transfer and rolling heat. Finite element analysis can simulate the temperature fields, but takes a long time and is not suitable for online monitoring. Neural networks have the characteristic of fast response speeds and can be used for online control and rolling plan optimization. This article proposes a BP neural network prediction model (GABP) based on a genetic algorithm to predict the final rolling temperature. In order to determine the input parameters of the neural network, MATLAB was used to analyze the effects of various factors on the final rolling temperature. The prediction error of GABP is mainly concentrated at 0–1 °C. Compared with fuzzy neural networks (FNN), GABP has a higher prediction accuracy and can effectively predict the final rolling temperature of a TiAl alloy. Full article

The looper-tension control is a crucial aspect of a hot strip finishing mill. It involves a highly nonlinear system with strong states coupling and uncertainty, and the performance directly impacts the thickness deviation, which is the most critical product index. From the system dynamics, it is known that tension is highly sensitive to the strip velocity variation, which is typically unmeasurable. Instead, it needs to be calculated through work roll speed and strip slip which contains uncertainties, negatively affecting tension control performance. First, a feedback linearization-based proportional–integral (PI) controller design approach is proposed for the hot rolling looper-tension system. Second, to reduce the impact of speed uncertainties and enhance thickness response, an adaptive PI controller is introduced. Validation was conducted by numerical simulations; the result indicates that an adaptive PI controller reduces the magnitude of thickness variation and shortens the duration of its impact, verifying the consistency between theoretical derivation. The proposed control method effectively addresses the impact of uncertainties encountered in real-world applications. Additionally, it simplifies control parameter adjustment in practical use, reduces testing time, and improves product quality. Full article

The rolling system for stainless steel, particularly in the production of diamond plates, represents a complex industrial control scenario. The process requires precise load distribution to effectively manage pattern height, due to the high strength, hardness, and required dimensional accuracy of the material. This paper addresses the limitations of offline methods, which include heavy reliance on initial conditions, intricate parameter settings, susceptibility to local optima, and suboptimal performance under stringent constraints. A Multi-Objective Adaptive Rolling Iteration method that incorporates local constraints (MOARI-LC) is proposed. The MOARI-LC method simplifies the complex multi-dimensional nonlinear constrained optimization problem of load distribution, into a one-dimensional multi-stage optimization problem without explicit constraints. This simplification is achieved through a single variable cycle iteration involving reduction rate and rolling equipment selection. The rolling results of HBD-SUS304 show that the pattern height to thickness ratio obtained by MOARI-LC is 0.20–0.22, which is within a specific range of dimensional accuracy. It outperforms the other two existing methods, FCRA-NC and DCRA-GC, with results of 0.19~0.24 and 0.15~0.25, respectively. MOARI-LC has increased the qualification rate of test products by more than 25%, and it has also been applied to the other six industrial production experiments. The results show that MOARI-LC can control the absolute value of the rolling force prediction error of the downstream stands of the hot strip finishing rolls within 5%, and the absolute value of the finished stand within 3%. These results validate the scalability and accuracy of MOARI-LC. Full article

Industrialization trial production of high permeability (Hi-B) steel was carried out by “one cold rolled + decarburization and nitridation technologies”. The finished product reached the level of 23Q100 with an average grain size of 5.47 cm, magnetic flux density B₈ of 1.902T, and the iron loss P_1.7/50 of 0.975 W/Kg. The evolution law of the microstructure and texture under different processes was analyzed with the help of OM, EBSD, and XRD. The results showed that the microstructure of the hot rolled plate was equiaxed crystals in the surface layer, a mixture of recrystallization grains and banded fiber in the quarter of the thickness layer, and banded fiber in the center layer. The texture gradient of the hot rolled plate from the surface layer to the center layer was {112}<111> + {110}<114> → {441}<014> → {001}~{111}<110>. The texture of the normalized plate was in major {110}<113> in the surface layer, diffuse α-fiber texture and {441}<014> in the quarter of the thickness layer, and sharp α texture {001}~{111}<110> in the center layer. The texture of the cold-rolled sheet was concentrated in {001}~{332}<110>. The average grain size of the decarburizing and nitriding sheet was 26.4 μm, and the texture of the first recrystallization is sharp α*-fiber and weak {111}<112>. The finished product has a sharp single Goss texture. For Hi-B steel, the Goss secondary nucleus originated from the surface layer to 1/4 layer of the hot rolled plate and reached the highest content of 11.5% in the quarter of the thickness. The content of the Goss texture decreased with the subsequent normalization and cold rolling, then the Goss grains nucleated again during the decarburization annealing and high temperature annealing processes. Full article

In order to improve the magnetic properties of non-oriented silicon steel, the effects of different normalizing temperatures on the microstructure, texture, and magnetic properties of 3.0%Si 0.8%Al non-oriented silicon steel were studied by OM, EBSD, and a magnetic measuring instrument. The results show that the microstructure of the hot-rolled plate is obviously different along the thickness direction. Strong Goss texture and {001} ~ {112} texture are the main textures in the hot-rolled plate. After normalizing at 900 °C, 940 °C, and 980 °C and annealing at 940 °C, respectively, the average grain size of the normalized plates and the annealed sheets increases with the increase in the normalizing temperature, and the texture types of the normalized plates basically inherit that of the hot-rolled plates. With the increase in normalizing temperature, the intensity of the γ-fiber texture decreases, and the main texture types in the finished plates are {100} <012> texture and {111} <112> texture. The area fraction of {100} <012> texture in the finished sheet normalized at 980 °C and annealed is the largest, which is 20.3%, and the area fraction of {114} <481> texture is larger, which is 15.2%. The magnetic induction B₅₀ of the finished sheets increases gradually with the increase in the normalizing temperature, from 1.662 T to 1.720 T; the low-frequency iron loss P_1.5/50 decreased slightly from 2.46 W·kg⁻¹ to 2.30 W·kg⁻¹. The high-frequency iron loss P_1.0/400 decreased significantly from 17.40 W·kg⁻¹ to 15.75 W·kg⁻¹. The results of the microstructure, texture, and magnetic properties show that the best normalizing temperature in this experiment is 980 °C. Full article

Medium manganese steels provide a good combination of tensile strength and ductility due to their multiphase microstructure produced during the multi-step heat treatment process. This study primarily focused on testing and analyzing the tensile properties of 0.17C-5Mn-0.76Al-0.9Si-Nb medium manganese quenching and partitioning (QP) steel using both the experimental and finite element method (FEM) in the multilinear isotropic hardening material model. The 7 mm and 12 mm thick plates exhibited a similar microstructure of tempered primary martensite, lath-type retained austenite, and secondary martensite. The experiments measured tensile strengths of 1400 MPa for 12 mm round specimens and 1325 MPa for 7 mm flat specimens, with total elongations of 15% for round specimens and 11% for flat specimens. The results indicated that the sample’s geometry has some effect on the UTS and ductility of the studied medium-Mn QP steel. However, the more important is the complex relationship between the plate thickness and yield stress and ductility, which are affected by finishing hot rolling conditions. The FEM results showed that the von Mises stresses for flat and round specimens were 1496 MPa and 1514 MPa, respectively, and were consistent with the calculated true stresses of experimental results. This shows that numerical modeling, specifically a multilinear isotropic hardening material model, properly describes the material properties beyond the yield stress and accurately predicts the plastic deformation of the investigated multiphase QP steel. Full article

The interfacial bonding properties of stainless steel clad (SSC) rebars determine whether they can be widely used. In the industrial production of SSC rebars, the process of intermediate and finish rolling of the microstructure evolution, element diffusion behavior, and interfacial bonding properties of bimetallic interfaces are investigated. In this paper, 316L seamless stainless steel (SS) tube and HRB400E carbon steel (CS) bar were prepared by a vacuum oxidation-free composite round billet, and the industrial emergency stopping of SSC rebars’ hot rolling was carried out. The metallographic results showed that the thicknesses of the carburized austenite zone (CAZ) varied greatly (832–238 μm) and showed a parabolic downward trend, while the thicknesses of the decarburized ferrite zone (DFZ) varied little (85–99 μm). The elemental line scans showed that Fe and Cr had the same parabolic downward trend. The intermediate-rolling had a great influence on element diffusion, and, in S6–9, the diffusion distance of Fe and Cr decreased significantly. The diffusion distances of the elements in the intermediate-rolling back stage and finishing-rolling front stage (S9–12) were basically balanced. The elemental diffusion distances and interfacial bonding strength were not consistent. Among them, the shear strength (τ) of S13 was 410.7 MPa. Compared with ordinary rebars, the yield strength (Re) and tensile strength (Rm) of finished SSC rebars were increased by 7.05% (30.9 MPa) and 7.10% (43.0 MPa), respectively. The tensile properties exceed those of mixture effects. The paper provides a theoretical basis for the improvement of the interfacial bonding strength and optimization of the rolling process system for the industrial production of SSC rebars. Full article

This study investigates the grain morphology, microstructure, magnetic properties and shape memory properties of an Fe_41.265Ni_28.2Co₁₇Al₁₁Ta_2.5B_0.04 (at%) high-entropy alloy (HEA) cold-rolled to 98%. The EBSD results show that the texture intensities of the samples annealed at 1300 °C for 0.5 or 1 h are 2.45 and 2.82, respectively. This indicates that both samples were formed without any strong texture. The grain morphology results show that the grain size increased from 356.8 to 504.6 μm when the annealing time was increased from 0.5 to 1 h. The large grain size improved the recoverable strain due to a reduction in the grain constraint. As a result, annealing was carried out at 1300 °C/1 h for the remainder of the study. The hardness decreased at 24 h, then increased again at 48 h; this phenomenon was related to the austenite finish temperature. Thermo-magnetic analysis revealed that the austenite finish temperature increased when the samples were aged at 600 °C for between 12 and 24 h. When the aging time was prolonged to 48 h, the austenite finish temperature value decreased. X-ray diffraction (XRD) demonstrated that the peak of the precipitates emerged and intensified when the aging time was increased from 12 to 24 h at 600 °C. From the three-point bending shape memory test, the samples aged at 600 °C for 12 and 24 h had maximum recoverable strains of 2% and 3.6%, respectively. The stress–temperature slopes of the austenite finish temperature were 10.3 MPa/°C for 12 h and 6 MPa/°C for 24 h, respectively. Higher slope values correspond to lower recoverable strains. Full article

The Cutting Stock Problem (CSP) is an optimization challenge that involves dividing large objects into smaller components while considering various managerial objectives. The problem’s complexity can differ based on factors such as object dimensionality, the number of cutting stages required, and any technological constraints. The demand for coils of varying sizes and quantities necessitates intermediate splitting and slitting stages to produce the finished rolls. Additionally, relationships between orders are affected by dimensional variations at each stage of processing. This specific variant of the problem is known as the One-and-a-Half Dimensional Two-Stage Cutting Stock Problem (1.5-D TSCSP). To address the 1.5-D TSCSP, two algorithmic approaches were developed: the Generate-and-Solve (G&S) method and a hybrid Row-and-Column Generation (R&CG) approach. Both aim to minimize total trim loss while navigating the complexities of the problem. Inspired by existing problems in the literature for simpler versions of the problem, a set of randomly generated test cases was prepared, as detailed in this paper. An implementation of the two approaches was used to obtain solutions for the generated test campaign. The simpler G&S approach demonstrated superior performance in solving smaller instances of the problem, while the R&CG approach exhibited greater efficiency and provided superior solutions for larger instances. Full article

The presence of island grains in the initial finished sheets of grain-oriented electrical steel is inevitable in the preparation of ultra-thin strips. Owing to their distinctive shape and size effects, their deformation behavior during rolling differs from that of grain-oriented electrical steels of conventional thickness. This study focuses on the orientation evolution and deformation heterogeneity of island grains during rolling. Four types of island grains with orientations of {210}<001>, {110}<112>, {114}<481>, and {100}<021> were selected and modeled within the Goss-oriented matrix using full-field crystal plasticity finite element (CPFEM) simulation under plane strain compression. The results are then compared with corresponding experimental measurements. The results reveal that orientation rotation and grain fragmentation vary among the island grains of different orientations, with the first two orientations exhibiting more significant deformation heterogeneity compared to the latter two. Additionally, the orientations of the island grains significantly affect the distribution of residual Goss orientations within the surrounding matrix. Pancake-like island grains exhibit a higher degree of orientation scatter and greater deformation heterogeneity in the central layer compared to their spherical counterparts. The initial {210}<001> island grains can form a cube orientation, which can be optimized by subsequent process control to enhance magnetic properties. Full article

After rolling, TP2 copper tubes exhibit defects such as sawtooth marks, cracks, and uneven wall thickness after joint drawing, which severely affects the quality of the finished copper tubes. To study the effect of drawing process parameters on wall thickness uniformity, an ultrasonic detection platform for measuring the wall thickness of rolled copper tubes was constructed to verify the accuracy of the experimental equipment. Using the detected data, a finite element model of drawn copper tubes was established, and numerical simulation studies were conducted to analyze the influence of parameters such as outer die taper angle, drawing speed, and friction coefficient on drawing force, maximum temperature, average wall thickness, and wall thickness uniformity. To address the problem of the large number of finite element model meshes and low solution efficiency, the wall thickness uniformity was predicted using a radial basis function (RBF) neural network, and parameter optimization was performed using the particle swarm optimization (PSO) algorithm. The research results show that the RBF neural network can accurately predict wall thickness uniformity, and using the PSO optimization algorithm, the best parameter combination can reduce the wall thickness uniformity after drawing in finite element simulation. Full article