Search Results (103)

The steel industry, as a large-scale equipment-intensive sector, emphasizes the importance of maintaining and managing equipment without failure. In line with the recent Fourth Industrial Revolution, there is a growing shift from preventive to predictive maintenance (PdM) strategies for cost-effective equipment management. This study aims to develop a PdM model for the Run-Out Table (ROT) equipment in hot rolling mills of steel plants, utilizing artificial intelligence (AI) technology, and to propose methods for contributing to energy efficiency through this model. Considering the operational data characteristics of the ROT equipment, an autoencoder (AE), capable of detecting anomalies using only normal data, was selected as the base model. Furthermore, Long Short-Term Memory (LSTM) networks were chosen to address the time-series nature of the data. By integrating the technical advantages of these two algorithms, a predictive maintenance model based on the LSTM-AE algorithm, named the Run-Out Table Predictive Maintenance Model (ROT-PMM), was developed. Additionally, the concept of an anomaly ratio was applied to identify equipment anomalies for each coil production. The performance evaluation of the ROT-PMM demonstrated an F1-score of 91%. This study differentiates itself by developing an optimized model that considers the specific environment and large-scale equipment operation of steel plants, and by enhancing its applicability through performance verification using actual failure data. Furthermore, it emphasizes the importance of PdM strategies in contributing to energy efficiency. It is expected that this research will contribute to increased energy efficiency and productivity in industrial settings, including the steel industry. 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 steel industry is a hard-to-abate sector; it involves many energy-intensive and complex processes. Continuous performance improvement is a fundamental requirement. Efficiency enhancement of the involved sub-processes can serve as the basis of an effective roadmap for the industry’s decarbonization. Efficiency and performance can be investigated in terms of whole plants, parts of a plant, individual machines, or individual devices; in addition, efficiency and performance can be associated with different topics, e.g., energy, CO₂ emissions, sustainability, and product quality. In this context, computer and automation engineering innovations could have a massive impact due to both their specificity and their potential to contaminate other crucial disciplines in the field. This review paper aims to research and provide an update on state-of-the-art innovations (e.g., emerging technologies and best practices) for performance improvement in the steel industry production chain, focusing on Industry 4.0, digitalization, data, and key performance indicators. In addition, emphasis is placed on the reheating furnaces employed in hot rolling mills, due to their significant role in decarbonization and the creation of sustainability pathways. Full article

The assembly clearance between the bearing housing and rolling mill stand affects the roll change and rolling stability. In order to improve the accuracy and real-time measurement of the bearing housing clearance of the rolling mill, four kinds of measuring methods were designed, namely the laser ranging method, external force measuring method, internal force measuring method, and eddy current ranging method, and the characteristics of the four measuring methods were introduced, respectively. The real-time measuring experiment of bearing housing clearance was carried out in a 100 mm two-high mill in laboratory and a 1580 mm four-high hot tandem mill in the Qian’an Iron and Steel Company. The results show that clearance measurement technology is helpful to improve the accuracy of real-time measurements and can provide guidance for the clearance control work. Finally, based on the real-time measurement technology of bearing housing clearance, the control strategy of bearing housing clearance was developed. This technology is of great significance to realize the fine management of rolling mill clearance and to improve the intelligence level of rolling mill systems. 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

In the present work, the microstructure and texture evolution of ferritic stainless steel during unidirectional cold rolling were investigated, and the Visco-Plastic Self-Consistent (VPSC) polycrystal model was used for the simulation of texture during cold rolling. Comparison of different interaction models was made to obtain a model that better reproduces the texture evolution of ferritic stainless steels. The as-received hot-rolled samples were unidirectionally cold rolled in a laboratory rolling mill, and the thickness was reduced by 30%, 60% and 80%. Electron backscatter diffraction (EBSD) was used to observe the microstructure evolution and texture evolution, and micro-hardness was used to evaluate the work hardening of the sample. The important feature of the microstructure was the presence of shear bands (SBs), the frequency of which increased with the increase in cold-rolling reduction and was found to be orientation dependent. We found that the geometrically necessary dislocation (GND) density increased with cold-rolling reduction in accord with Ashby’s theory of work hardening, and higher GND density accumulates near the grain boundary. The grain fragmentation, Goss texture distribution and orientation gradient were found to be orientation dependent. The cold-rolled texture was composed of strong α-fiber and weak γ-fiber. The relative plastic compliance of grain and the homogeneous effective medium (HEM) were explored. The tangent interaction model was found to match reasonably well with the experimental texture. This work has great significance for achieving online monitoring of the texture of ferritic stainless steel under different industrial production processes and enhancing the intelligence level of ferritic stainless steel production process. 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

In this paper, the feasibility of an innovative solid-state recycling process for aluminum alloy AA6063 chips through direct rolling is studied, with the aim of offering an environmentally sustainable alternative to conventional recycling processes. Aluminum chips, produced by milling an AA6063 billet without the use of lubricants, were first compacted using a hydraulic press with a 200 kN load and subsequently heat-treated at 570 °C for 6 h. The compacted chips were directly hot-rolled through several successive passes at 490 °C. The bulk material underwent the same rolling schedule to allow comparison of the samples and assess the process, in terms of mechanical properties and microstructure. All the rolled samples were tested by tensile and microhardness tests, whereas the microstructure was observed by an optical microscope and the EBSD-SEM technique. The fracture surface of all tested samples was analyzed by SEM. Recycled samples exhibited good mechanical properties, comparable to those of the bulk material. In particular, the bulk material showed an ultimate tensile strength of 218 MPa, in contrast to 177 MPa for the recycled chips, and comparable elongation at break. This study demonstrates that direct rolling of compacted aluminum chips is both technically feasible and has environmental benefits, offering a promising approach for sustainable aluminum recycling in industrial applications within a circular economy framework. 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

As the core equipment of metal shaping processing, the vibration problem of rolling mill seriously affects the product quality and production efficiency. To address the problem of the limited prediction of mill vibration due to small sample data in non-steady states, we propose to predict dynamic mechanical parameters (rolling force and rolling torque) based on the TCN-LSTM step strategy transfer model. The prediction accuracies of the TCN-LSTM step strategy transfer model under 1000 sets of training data reach 95.8% and 93.2%, respectively, and at the same time, it saves the time of training and regulation of the deep learning model and can better meet the needs of online prediction. The horizontal-torsion hot rolling vibration model is further established to quantitatively analyze the relationship between process parameters and mill vibration. The experimental results show that with the vibration suppression measures of 10% reduction of the rolling speed and 10% reduction of the entrance thickness, the horizontal vibration displacement amplitude is reduced from 0.687 × 10⁻⁴ m to 0.229 × 10⁻⁴ m, and the rotational displacement amplitude is reduced from 0.0273 m to 0.0117 m, which effectively suppresses the vibration of the mill and improves the stability of the rolling process. Full article

Heating steel charges is essential for proper charge formation. At the same time, it is a highly energy-intensive process. Limiting the scale formed is critical for reducing heat consumption in this process. This paper applies fuzzy logic to model heating and scale formation in industrial re-heating furnaces. Scale formation depends on the temperature of the initial charge, heating time, excess air coefficient value, and initial scale thickness. These parameters were determined based on experimental tests, which are also the inputs in the model of the analyzed process. The research was carried out in walking beam furnaces operating in hot rolling mill departments. To minimize the excess energy consumption for heating a steel charge in an industrial furnace before forming, a heating and scale formation (HSF) model was developed using the fuzzy logic-based approach. The developed model allows for the prediction of the outputs, i.e., the charge’s final surface temperature and the scale layer’s final thickness. The comparison between the measured and calculated results shows that the model’s accuracy is acceptable. Full article

The escalating demand for sustainable rubber products has spurred research into alternative reinforcing fillers, driven by concerns regarding the detrimental effects of using conventional fillers like carbon black and silica. In this investigation, nano-crystalline cellulose (NCC), derived from micro crystalline cellulose (MCC), sourced from sugarcane bagasse via acid hydrolysis, serves as a bio-filler to reinforce Nitrile Butadiene Rubber (NBR) matrices. NBR-NCC nano-composites were prepared using a two-roll mill, varying NCC from 1–5 parts per hundred rubber matrices, followed by hot press curing. NCC and NBR-NCC nano-composites were characterized using Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), curing characteristics, thermo-mechanical testing, thermal aging and motor oil resistance. Chemical interactions between the NCC and NBR matrix were verified with FTIR. The SEM images of the NCC showed a combination of rod-like and spherical morphologies and a homogenous dispersion of NCC in NBR-NCC nano-composites with some agglomeration, notably at higher percentages of NCC. It is shown that the cure time decreases with increasing NCC loading which mimics a shorter industrial production cycle. The results also showed an increase in tensile strength, hardness, oil resistance and a rise in degradation temperature when compared to NBR at approximately 34%, 36%, 38% and 32 °C, respectively, at 3 phr NCC loading. Furthermore, NBR-NCC nano-composites showed a lower decrease in mechanical properties after aging when compared to NBR. The findings of this research suggest that the NBR-NCC nano-composites may find applications in high oil resistance seals and rubber gloves where higher thermal stability is strictly required. Full article

In the process of hot rolling silicon steel, roll wear directly affect its shape. Accurate prediction of roll wear is an important condition for rolling qualified silicon steel strips. The traditional roll wear prediction model is established by the slicing method. The wear of F5–F7 work rolls used for finishing rolling silicon steel on a 2250 mm production line in a steel mill was predicted by this model. It was found that there was deviation between the predicted results and the actual wear, and the prediction accuracy of the model was insufficient. Therefore, the wear of the surfaces of the rolls used for rolling silicon steel on this production line was studied. Based on the analysis of the work roll wear’s form and the rolling parameters that affect the roll wear, the traditional roll wear prediction model was optimized by the genetic algorithm. Finally, the optimized model was verified, and the prediction accuracy of the wear prediction model improved. The accurate prediction results provide a basis for the formulation of a shape control strategy when rolling silicon steel on this production line. Full article

The finishing mill is a critical link in the hot rolling process, influencing the final product’s quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi-LSTM. Depthwise separable convolution and Bi-LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat-headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering. Full article

The impact of rolling mill vibration extends beyond product quality to equipment health, making vibration control crucial. This study addresses the issue of frequent abnormal vibration in hot strip finishing mills by employing a combination of theory, simulation, and experimentation to analyze the dynamic behavior of the mill and apply findings to on-site vibration suppression. Initially, a torsional-vertical-horizontal coupled dynamic model for the rolling mill has been developed, taking into account the rolling process. The accuracy of this model is established through both finite element simulations and actual experiments. Subsequently, the vibration characteristics of the rolling mill system are investigated under typical process parameters utilizing the established dynamic model. The results reveal that the vibration amplitude notably escalates with the increase of rolling reduction rate and rolling speed, and the difference in front and rear tension has little impact on the mill’s vibration. Furthermore, an increase in the temperature of the rolled piece reduces the overall vibration amplitude, and harder material results in greater overall mill vibration. Lastly, abnormal vibration in the F2 finishing mill at a hot rolling plant is effectively mitigated by reducing rolling reduction rate, which further validates the correctness of the findings. Full article