Search Results (99)

This research introduces a new model to predict the roll force during hot rolling of steel, based on a statistical analysis of approximately 38,980 sets of measurements in a commercial mill with five finishing stands. The study includes ten different steel grades and features models of both single grades and the entire dataset. Three models are developed and compared: a temperature-dependent strain rate model (M1), a strain rate model (M2), and a simplified strain rate model (M3). The decrease in temperature with roll stand has a strong cross-correlation with compensating decreases in strain and contact length by roll stand, such that both the temperature and strain terms are statistically insignificant. The final model (M3)—$F\left[\mathrm{N}\right]=113.1·\dot{ϵ}{\left[{\mathrm{s}}^{-1}\right]}^{0.3141}·w\left[\mathrm{mm}\right]·\ell \left[\mathrm{mm}\right]$—relates force (F) to strain rate ($\dot{ϵ}$), width (w), and contact length (ℓ) and achieves an $R^2$ fit of 0.946 over all 10 steel grades. Although the single-grade models show slightly higher accuracy, the final model retains robust predictive capability with only two fitting parameters. This model enables fast and easy estimation of roll force for commercial hot rolling of low-carbon, medium-carbon, and high-strength–low-alloy steels. Full article

This study offers a comprehensive study of power quality in industrial rolling mill grids, focusing on total harmonic distortion (THD) and its forecasting under different operational conditions. The research begins with a measurement-based evaluation of load variations and the effects of reactive power compensation using capacitor banks. To improve these results, forecasting algorithms were developed utilizing modern methods based on data capable of recognizing both short-term and long-term dependencies within the THD signal. The models were evaluated using three forecasting strategies: classical prediction on test data, autoregressive one-step forecasting, and direct multi-step forecasting. This was done using well-known error and correlation indices like RMSE, MAE, sMAPE, the coefficient of determination (R²), and the Pearson correlation coefficient (ρ). The results indicate that models incorporating both local feature extraction and temporal dynamics provide the most accurate forecasts. In particular, the hybrid convolutional-recurrent structure achieved the best overall performance, with R² = 0.923 and ρ = 0.961 in classical prediction, and it was the only approach to maintain a positive R² (0.285) in multi-step forecasting. These results demonstrate the usefulness of modern predictive modeling for Total Harmonic Distortion (THD) in industrial grids, combining conventional measurement-based techniques by offering relevant observations for power quality monitoring and control. Full article

This study investigates additive-free cold calendering of ELT-derived rubber powders across three particle size fractions (<0.5 mm, 0.5–0.71 mm, and 0.71–0.90 mm) using a two-roll mill without external heating or virgin polymers, aiming to obtain a cohesive material. Results demonstrate particle size effects on material properties. The finest fraction exhibited the highest crosslink density (5.30 × 10⁻⁴ mol·cm⁻³), approximately 18% greater than coarser fractions, correlating with superior hardness (≈65 ShA) and elastic modulus (≈7.5 MPa). Tensile properties ranged from 1.6–1.8 MPa stress and 60–75% elongation at break, positioning calendered sheets between low-temperature compression-molded GTR and high-pressure sintered materials reported in the literature. The cold calendering process achieves competitive mechanical performance with reduced energy consumption, simplified processing, and complete retention of recycled content. These findings support the development of regulation-compliant ELT recycling technologies, with potential applications in nonstructural construction panels, vibration-damping components, and protective barriers, advancing circular economy objectives while addressing emerging microplastic concerns. Full article

The aim of this study, as part of a collaboration between a malt house, a brewery, and a university, was to optimize the beer production process while simultaneously maintaining or even improving the quality of the beer and creating conditions for the optimization of the malting of barley grain. The Hurbanovo malt house provided 100 t of a specially prepared batch of malt for use in industrial-scale beer production at the Żywiec brewery (which produces 4.7 million hl annually). The malt, produced from barley variety Overture, was characterized by a higher extract and protein content and increased enzymatic activity. The test malt also demonstrated favorable properties such as higher friability, lower viscosity, and a two-fold shorter saccharification time. Four HGB worts were produced during production tests. Each brew used 21.5 tons of malt, yielding an average 1020 hl of wort, with an extract content of 15.5°Blg. The malt was milled in a two-roll wet mill with a capacity of 40 t per hour. Mash filtration took place in lauter tuns with a diameter of 12.4 m each. The produced worts were transferred into a fermentation tank with a capacity of 5500 hl, and then fermentation, maturation, and lagering processes were carried out. The tested batch of malt was examined in detail and compared with a standard malt blend from three different suppliers. The tests showed an increase in extract efficiency in the process, with a simultaneous reduction in extract losses (1.2%pt.). The filterability of the mash improved compared to the standard blend, and an improvement in wort quality was observed as a result of lower turbidity (by approximately 34%). The data obtained indicate an improvement in the process with the use of the specially prepared batch of malt. Full article

The milling process is a critical technological step that regulates wheat flour characteristics and ultimately determines end-product quality. This study systematically evaluated the effects of three key milling parameter adjustments in a laboratory-scale roller mill—double sifting (2S), double break milling (2BM), and increased roll gap (IRG)—on the physicochemical properties of wheat flour and the quality of microwave freeze-dried non-fried instant noodles. The results demonstrated that milling processes significantly influenced the particle size and composition of flour. The 2BM-IRG process increased the volume mean diameter of flour to 86.38 μm, while significantly improving flour extraction rate (69.80%), protein content (10.98%), and ash content (0.54%). In contrast, the 2S process significantly reduced the volume mean diameter (65.27 μm). These changes in flour properties directly affected noodle quality—noodles made from 2BM-IRG flour exhibited the highest rehydration ratio but also the greatest cooking loss, along with the lowest expected glycaemic index (eGI); noodles produced from 2S flour showed the highest hardness, while the 2BM process endowed noodles with superior elasticity. A correlation analysis revealed that the digestibility characteristics of noodles (eGI) were predominantly and significantly influenced by flour protein and ash content (p < 0.01), while also being significantly affected by particle size (p < 0.05). The study confirmed distinct quality trade-offs between different milling strategies. Therefore, by optimizing combinations of break milling and sifting processes, it is possible to develop specialized flour tailored for specific quality requirements. Full article

Effective real-time control systems require fast and accurate models. The roll bending models presented in this paper are proposed for a real-time control system for the design of the rolling schedule. The roll bending, with other factors, defines the shape of the roll surface, its convexity, and finally the shape of the final product of the flat rolling, its convexity, and its flatness. This paper presents accurate finite element (FE) models for a four-high mill. The models serve to obtain accurate solutions to the problem of roll bending, taking into account the rolling force, width of the rolling sheet (strip), initial shape of the roll surface, and the anti-bending force. The results of the FE simulation are used to train three models developed on the basis of the neural network (NN) for the solution of one direct and two inverse tasks. The pre-trained NN model gives accurate results and is faster than the FE model (FEM). The calculation time on a personal computer for one case of 3D FEM is 1 to 2 min, for 2D FEM it is 1 s, and for NN it is less than 1 ms. The results can be immediately used by other models of the real-time control system. A novelty of the research presented in the paper is the creation of complex applications of the FE method and an NN as a reduced-order model (ROM) for prediction of roll bending and calculation of sheet (strip) convexity, rolling, and anti-bending forces to obtain the required convexity. Full article

Modern requirements for aluminum alloys used in mechanical engineering and aviation include increased strength characteristics and refined microstructure. One of the promising methods for improving the properties of aluminum alloys is rolling on a three-high skew rolling mill, which provides intense plastic deformation and a fine-grained structure. This study describes the results of numerical modeling of the rolling process of aluminum alloy 6082 rods in a three-high skew-type mill. Numerical modeling of alloy 6082 was conducted using the ForgeNxT 2.1 software designed to simulate metal-forming processes, including rolling. The rheological behavior of the material under study was investigated by compression tests using a Gleeble 3800 plastometer (“DSI”, Austin, TX, USA), which enabled the determination of the main parameters of material flow under specified conditions. The process of rolling bars of alloy 6082 on a three-high skew mill was numerically analyzed in the temperature range of 350–400 °C. This allowed for the study of the distribution of stresses, temperatures, and strain rates from the rolling mode. A physical experiment was conducted to validate the results of numerical modeling. The obtained results enabled the identification of rolling modes that promote microstructure refinement and enhance the mechanical properties of the alloy. 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

This study explores the preparation of lithium iron phosphate (LFP) electrodes for lithium-ion batteries (LIBs), focusing on electrode loadings, dispersion techniques, and drying methods. Using a three-roll mill for LFP slurry dispersion, good electrochemical properties were achieved with loadings of 5–8 mg·cm⁻² (0.8–1.2 mAh·cm⁻² areal capacity). Adding polyvinylidene fluoride (PVDF) during the final milling stage reduced performance due to premature solidification in-between rolls. Vacuum-free drying improved ionic conductivity, stability against lithium metal, and discharge capacity, whereas vacuum-dried samples exhibited higher initial resistance and lower capacity retention. These findings highlight critical parameters for enhancing LFP electrode performance, paving the way for high-performance, and sustainable energy-storage solutions. Full article

Sunflower meal (SFM), a byproduct of sunflower oil extraction, is a relatively inexpensive protein source with high potential for feed formulations. Dry fractionation methodologies are emerging as ‘green’ and affordable technologies with the potential to additionally enhance the nutritional quality of plant-based raw materials for animal feed, including sunflower meal. Following the optimization of a dry fractionation process in a previous study of the authors, this research aims to validate the defined parameters through the processing of three sunflower meals (SFM1, SFM2, and SFM3) with different characteristics. The dry fractionation process includes two-stage grinding using hammer mill and roll mill and fractionation of sunflower meal by sieving. The process successfully increased the protein content of sunflower meals in the range of 17.5% to 31.2%, reaching levels high enough to be categorized as “high protein” sunflower meals of first quality (42% as is). Exceptionally high fraction yields (76.5–78.9%) were obtained for all three sunflower meals. The lowest energy consumption was recorded for SFM1 (8.44 Wh/kg), while slightly higher values were observed during the processing of SFM2 and SFM3 (9.30 and 9.93 Wh/kg, respectively). Relative amino acid enrichments ranging from 13.35% to 26.64% were achieved, with lysine enrichment ranging from 18.9% to 36% and methionine from 30.6% to 44.1%. Full article

Graph data and multi-sensor information fusion have been integrated into the abnormal vibration type classification and the identification of the rolling mill for extracting spatial–temporal and robust features. However, most of the existing deep learning (DL) based methods exploit only single sensor information and Euclidean space data, which results in incomplete information contained in the features extracted by in-depth networks. To solve this issue, a multi-sensor information fusion with multi-scale adaptive graph convolutional networks (M²AGCNs) framework is proposed to model graph data and multi-sensor information fusion in a unified in-depth network and then to achieve abnormal vibration diagnosis. First, convolutional neural networks (CNNs) were adopted for the deeper features of multi-sensor signals. And then, the extracted features were fed into the proposed feature-driven adaptive graph generation network to build graphs to extract spatial–temporal correlation between multi-sensor data. After that, the multi-scale graph convolutional networks (MSGCNs) were employed to aggregate and enrich several different receptive information to further improve valuable features. Finally, the extracted multi-sensor features were integrated into a unified network to achieve the abnormal vibration type classification and identification of the rolling mill. Meanwhile, we performed horizontal, vertical, and coupled abnormal vibration experiments, and then three different types of studies were conducted to illustrate the superiority and usefulness of this method in the paper and the feasibility of rolling mill abnormal vibration diagnosis. It can be seen from the results that the proposed M²AGCNs can be able to achieve valuable feature extraction effectively from multi-sensor information and to obtain more excellent behavior of the abnormal vibration diagnosis of the rolling mill in comparison with the mainstream methods. Full article

One of the long-standing challenges in the field of titanium matrix composites is achieving the synergistic optimization of high strength and excellent ductility. When pursuing high strength characteristics in materials, it is often difficult to consider their ductility. Therefore, this study prepared a Ti1400 alloy and in situ synthesized TiC-reinforced (TiC + Ti1400)/TC4 composites using low-energy ball milling and spark plasma sintering technology, followed by hot rolling, to obtain titanium matrix composites with excellent mechanical properties. The Ti1400 alloy bonded well with the matrix, forming uniformly distributed Ti1400 regions within the matrix, and TiC particles were discontinuously distributed around the TiC-lean regions, forming a three-dimensional network structure. The (TiC + Ti1400)/TC4 composites effectively enhanced their yield strength to 1524 MPa by using 3 wt.% of Ti1400 alloy while preserving an impressive elongation of 9%. When the Ti1400 alloy content reaches 20 wt.%, the overall mechanical properties of the composites decrease. A small amount of Ti1400 does not reduce the strength of the composite. On the contrary, it can undergo stress-induced phase transformation during plastic deformation, thereby coordinating deformation, which not only provides higher strain hardening and increases tensile strength but also benefits ductility. Full article

W-Mo-V high-speed steel (HSS) is a high-alloy high-carbon steel with a high content of carbon, tungsten, chromium, molybdenum, and vanadium components. This type of high-speed steel has excellent red hardness, wear resistance, and corrosion resistance. In this study, the alloying element ratios were adjusted based on commercial HSS powders. The resulting chemical composition (wt.%) is C 1.9%, W 5.5%, Mo 5.0%, V 5.5%, Cr 4.5%, Si 0.7%, Mn 0.55%, Nb 0.5%, B 0.2%, N 0.06%, and the rest is Fe. This design is distinguished by the inclusion of a high content of molybdenum, vanadium, and trace boron in high-speed steel. When compared to traditional tungsten-based high-speed steel rolls, the addition of these three types of elements effectively improves the wear resistance and red hardness of high-speed steel, thereby increasing the service life of high-speed steel mill-roll covers. JMatPro (version 7.0) simulation software was used to create the composition of W-Mo-V HSS. The phase composition diagrams at various temperatures were examined, as well as the contents of distinct phases within the organization at various temperatures. The influence of austenite content on the martensitic transformation temperature at different temperatures was estimated. The heat treatment parameters for W-Mo-V HSS were optimized. By studying the phase equilibrium of W-Mo-V high-speed steel at different temperatures and drawing CCT diagrams, the starting temperature for the transformation of pearlite to austenite (A_c1 = 796.91 °C) and the ending temperature for the complete dissolution of secondary carbides into austenite (A_ccm = 819.49 °C) during heating was determined. The changes in carbide content and grain size of W-Mo-V high-speed steel at different tempering temperatures were calculated using JMatPro software. Combined with analysis of A_c1 and A_ccm temperature points, it was found that the optimal annealing temperatures were 817–827 °C, quenching temperatures were 1150–1160 °C, and tempering temperatures were 550–610 °C. The scanning electron microscopy (SEM) examination of the samples obtained with the aforementioned heat treatment parameters revealed that the martensitic substrate and vanadium carbide grains were finely and evenly scattered, consistent with the simulation results. This suggests that the simulation is a useful reference for guiding actual production. Full article

This study investigated the moisture absorption and mechanical degradation of epoxy-based polymer systems with Mg-Al/NO₃ layered double hydroxide (LDH) nanoparticles content up to 5 wt%. Such systems are developed for multilayer corrosion protective coatings. A sorption model was developed to calculate the moisture concentration field in the multilayer structures using Fick’s law of diffusion. The finite-difference method was used for the numerical solution. Epoxy/LDH nanocomposites were prepared using various dispersion methods with solvents, wetting agents, and via a three-roll mill. Moisture absorption was measured under different environmental conditions, including temperatures up to 50 °C and salinity levels up to 26.3 wt% salt solution. The results showed that equilibrium moisture content increased by 50% in hot water, while it was reduced by up to two times in salt solution. The diffusion coefficient in hot water increased up to four times compared to room temperature. The numerical algorithm was validated against experimental data, accurately predicting moisture distribution over time in complex polymer systems. Mechanical tests revealed that the elastic modulus did not change after water exposure; however, the ultimate strength decreased by 10–15%, especially in specimens with 5 wt% LDH. Full article

This study addresses the usage of data from industrial plate mills to calculate the mean flow stress of different steel grades. Accurate flow stress values may optimize rolling technology, but the existing literature often provides coefficients like those in the Hensel–Spittel equation for a limited number of steel grades, whereas in modern production, the chemical composition may vary by thickness, customer requirements, and economic factors, making it necessary to conduct costly and labor-intensive laboratory studies. This research demonstrates that leveraging data from industrial rolling mills and employing machine learning (ML) methods can predict material rheological behavior without extensive laboratory research. Two modeling approaches are employed: Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures. The model comprising one GRU layer and two fully connected layers, each containing 32 neurons, yields the best performance, achieving a Root Mean Squared Error (RMSE) of 7.5 MPa for the predicted flow stress of three steel grades in the validation set. Full article