Rolling Model Research Articles

COMPUTER SIMULATION OF PRELIMINARY HEAT TREATMENT AND RADIAL-SHEAR ROLLING OF BRASS

This paper presents the results of computer modeling of preliminary heat treatment and radial-shear rolling of L63 brass in the Deform program by cellular automata method. Quenching and annealing were simulated as pre-heat treatment modes. Radial-shear rolling consisted of three passes with a compression of 3 mm per pass. The variable parameters were the roll rotation speed (40 rpm, 70 rpm and 100 rpm) and the workpiece heating temperature before rolling (500°C and 700°C). It was found that the most effective mode of preliminary heat treatment of L63 brass is annealing at a temperature of 500°C. The most rational value of the heating temperature of the workpiece before rolling is 500°C, since in this case, at all selected speeds the recrystallization process does not start, which contributes to intensive grain grinding. An increase of roll rotation speed causes additional deformation heating, which contributes to recrystallization and a decrease of grain grinding intensity.

Quantile regression network-based cross-domain prediction model for rolling bearing remaining useful life

Transfer learning improves remaining useful life (RUL) prediction accuracy across domains by aligning data distributions for different operating conditions. However, the uncertainty caused by the complex working conditions and stochastic degradation process of rolling bearings is not considered, leading to poor credibility of the prediction results and affecting the development of the predictive maintenance strategy. In response to this problem, the paper proposes a deep subdomain adaptation time-quantile regression network (DSATQRN) model to compress rolling bearing uncertainty intervals of RUL across prediction. The model uses deep subdomain adaptation to align the feature distribution and introduces temporal correlation to construct a temporal quantile regression network to obtain interval prediction results. Finally, the uncertainty in the prediction results is quantified by kernel density estimation. The model was experimented with using the open XJTU-SY Bearing Datasets and IEEE PHM 2012 Challenge Datasets. It verifies the performance of the proposed model from three aspects: point prediction accuracy, interval prediction suitability, and probabilistic prediction overall performance. The experimental results show that the average interval coverage of the proposed model on the two datasets is 91.25% and 90.43%, and the average prediction interval width is 16.65% and 13.69%, respectively. It is demonstrated that the cross-domain prediction results of the DSATQRN possess high prediction accuracy and narrow prediction interval, and the model has good robustness and reliability.

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

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

Modular Finite Element Modeling of Heavy Plate Rolling Processes Using Customized Model Reduction Approaches

Heavy plates are indispensable semi-finished products. Quality is strongly linked with production, so the rolling process must be performed within well-defined narrow tolerances. To meet this challenge, adequate modeling has become a necessity. In contrast to continuous strip rolling, where the workpiece can be modeled as a semi-infinite strip and 2D modeling can be argued quite well, this strategy is insufficient for the comprehensive modeling of heavy plate rolling. The geometry of the heavy plate favors an inhomogeneous distribution of relevant state variables, such as temperature. In addition, if the process involves longitudinal and spreading passes, the required plate rotation spoils the assumption of a symmetric arrangement that might have been acceptable before rotation. Consequently, the derivation of suitably reduced models is not trivial, and modeling tailored to the specific objective of investigation is of utmost importance. Models intended to resolve the evolution of inhomogeneities in the field variables are demanding and computationally expensive. An effective modular modeling strategy was developed for such models to be used offline. Mutually complementing and interchangeable modules may constitute an efficient modeling strategy valid for the specific subject of interest. The presented approach reduces the enormous cost of complete 3D simulation as much as the model purpose allows for.

Introducing surface roughness in adhesion for stochastic and Rock'n’Roll models to describe particle resuspension in turbulent flows

Resuspension of colloidal particles plays an important role in various environmental and industrial implications and is particularly influenced by the interactions at the particle-surface interface. Here we explore the effect of surface roughness on resuspension of colloidal particles by incorporating a phenomenological adhesion model that includes surface roughness into a stochastic resuspension model, and into the kinetic, probability-based approach Rock'n'Roll model to provide a more realistic description. Within this framework, we investigate the influence of stochasticity on the resuspended fraction and explore its dependence on particle size and surface roughness. Our findings show that introducing roughness into a stochastic resuspension scheme results in an increase of the resuspended fraction by an order of magnitude. Additionally, our results underscore the contribution of stochasticity in the resuspension process, in which the noise term enables the inclusion of important but less frequent events that contribute to the resuspension probabilities. In all, the introduction of roughness into both approaches result with a significant improvement in the reconstruction of experimental data, where the stochastic description shows better agreement.

A feature vector with insensitivity to the position of the outer race defect and its application in rolling bearing fault diagnosis

The fault diagnosis of rolling bearings is very important in industrial applications, which can avoid accidents and reduce operation and maintenance costs. Although the position of the bearing outer race defect has a significant impact on rolling bearing vibration response, most existing intelligent bearing fault diagnosis methods do not take this into account. In this paper, we establish a dynamic model of rolling bearing to clarify the influence of the outer race defect position on the dynamic response, and propose a feature vector that is insensitive to the outer race defect positions. First, the vibration characteristics of the outer race faults with different defect positions are analyzed, and the impact is evaluated using six indicators. Second, three indicators of insensitivity to the bearing outer race defect positions are constructed as the feature vector for bearing fault diagnosis. Finally, a bearing fault diagnosis method considering the positions of outer race defect is proposed based on the constructed feature vector and K nearest neighbor classifier. The diagnosis results of three datasets formed by experimental signals show that the constructed feature vector can separate different bearing states. Compared with the existing two diagnosis methods, the proposed diagnosis method obtains higher recognition accuracy, in the case of different outer race defect positions of the training set and the testing set. The above research results are expected to provide a reference for rolling bearing fault diagnosis, especially when considering the influence of the outer race defect positions.

High-speed rolling bearing lubrication reliability analysis based on probability box model

An efficient and high-precision method is proposed for the analysis and evaluation of high-speed rolling bearing lubrication reliability based on a probability box (p-box) model. This method expands the application of mixed aleatory and epistemic uncertainties analysis within the realm of bearing lubrication reliability. Initially, the method establishes a reliability model for high-speed rolling bearing lubrication, taking into account the shear thermal effect through the analytical solution of a Γубин-type entrance zone. Subsequently, the uncertainty surrounding lubrication parameters under high-speed conditions is examined, with its mixed aleatory and epistemic uncertainties accurately depicted by using the p-box model. Furthermore, an effective and precise method for analyzing the reliability of rolling bearing lubrication is introduced based on the p-box model, in which the optimization model involved is efficiently solved using a decoupling method. Finally, lubrication reliability analysis and sensitivity analysis of parameter uncertainty levels are conducted for high-speed rolling bearings in this study. The research results demonstrate that the proposed method achieves higher accuracy and efficiency.

A novel prediction model to evaluate the dynamic interrelationship between gold and crude oil

AbstractGlobal events, such as the pandemic and European conflicts, have caused significant inflation and high volatility in gold and crude oil prices. This has garnered substantial international attention while banks, governments, and businesses are devoted to developing strategies to hedge against the potential impacts of economic uncertainties. Focused on addressing economic uncertainties, this study delves into the crucial role of the relationship between gold and crude oil in shaping global financial and economic dynamics. The primary objective of this study is to conduct a comprehensive analysis and construct a rolling SARIMAX model for predicting the rolling 12-months correlation of the Gold–WTI and Gold–Brent relationship. Monthly data on Gold spot prices, WTI futures, and Brent futures is collected from May 1983 to December 2022. Three sophisticated data analysis techniques, the Rolling Correlation method, the SARIMAX model, and the Rolling Model are integrated to develop the Rolling SARIMAX module. R-square values of this newly developed model achieved 89.8% and 88.4% for predicting the rolling correlation for Gold Spot Price-WTI Futures and Gold Spot Price-Brent Futures, respectively, while the mean absolute percentage error was 10.33% and 10.84%, respectively. The higher accuracy in correlation prediction between gold and crude oil prices can present critical insights for risk management, economic planning, strategic investment, economic cycles, and global economic outlook. This newly developed prediction model adeptly handles both linear and non-linear relationships while adapting to external variables in dynamic market scenarios. Its judicious balance between complexity and practicality positions it as a sophisticated analytical tool with real-world applicability, setting a new benchmark in financial market analysis.

Wear prediction model of hot rolling backup roll based on FEM + ML algorithm

Wear prediction model of hot rolling backup roll based on FEM + ML algorithm

Comparative analysis of FEM tire-soft snow interaction and theoretical model based on Bekker’s coefficients

Tire-deformable terrain interaction is a complex phenomenon that has proven difficult to characterize accurately and comprehensively using numerical models. While researchers have attempted to develop various numerical models to estimate tire behavior on deformable terrain, the most accurate models to date rely on empirical data and are only applicable to specific rolling conditions. In this research paper, a comparison between theoretical mechanics and a finite element method (FEM) model of a rigid tire rolling on a layer of soft snow is presented. The goal is to compare the data obtained from the FEM tire-soft snow interaction analysis with the data generated by a theoretical numerical model. The FEM snow model is developed using the Drucker-Prager cap material model while the tire is assumed rigid. The assumption of the rigid tire can provide reasonably accurate results in the case of the soft snow interaction model. The theoretical pressure-sinkage coefficients of the Bekker equation (kc, kϕ and n) for soft snow are calculated by simulating vertical penetration tests in the FEM snow model. The results of the pressure distribution and rolling resistance due to compaction obtained by the FEM and the theoretical model results are then compared.

A novel fault diagnosis model of rolling bearing under variable working conditions based on attention mechanism and domain adversarial neural network

A novel fault diagnosis model of rolling bearing under variable working conditions based on attention mechanism and domain adversarial neural network

Dynamic modeling and analysis of rolling bearing faults under time-varying excitations considering defect deformation

Fault mechanism analysis is one of the methods in fault diagnosis, and the dynamic modeling of rolling bearing faults plays a crucial role in studying fault mechanisms. Existing dynamic fault models only consider the impact of fault size and bearing speed on the impact force, providing an incomplete description of the impact force. In order to more accurately describe the dynamic fault model of impact forces, this paper focuses on the deep groove ball bearing with outer race faults. Factors such as defect deformation, speed, and fault size are considered, and an instantaneous impact force excitation function is proposed. Based on this proposed excitation function, a dynamic model for the outer race fault of deep groove ball bearings is established. Finally, through simulation and experimental comparison, the results indicate that the fault characteristic frequencies and their harmonics of the model in this paper are closer to the actual fault characteristic frequencies, reducing the error by 1 to 2 Hz. Therefore, the model proposed in this paper is more effective and accurate, providing a more precise rolling bearing fault model for the study of fault mechanisms.

Rolling force model of V-shaped variable thickness rolling based on energy approach

V-shaped Variable Thickness Rolling (V-VTR) serves as a specialized cold rolling method for manufacturing rolled profiled strips, particularly useful for quickly preparing rolled profiled strips with large thickness ratios. In the process of variable thickness rolling, metal predominantly flows in the lateral direction. However, there is a scarcity of theoretical models for calculating the rolling force of variable thickness rolling, leading to a void in theoretical guidance for both device design and product thickness control. To fill this gap, we introduce a novel mathematical model specifically designed to calculate the rolling force, considering the lateral flow of the metal. Initially, the deformation pattern is investigated and subsequently modeled. Then, the models for kinematically permissible velocity and strain fields are developed, and their validity is confirmed through both experimental data and simulation analysis. Incorporating factors like strain energy and frictional work, a formula for rolling force of variable thickness rolling is derived based on the upper bound theory. The efficacy of this newly proposed rolling force model is confirmed through experimental testing, which indicates that the formula offers a high degree of accuracy.

Effect of Nb-Ti Microalloyed Steel Precipitation Behavior on Hot Rolling Strip Shape and FEM Simulation.

Strip shape control is a hotspot and challenge in strip rolling, where the development trend of rolling technology is towards high strength, high toughness, and a large width-to-thickness ratio. The influence of material microstructure evolution on strip shape control is being increasingly emphasized. In this paper, a Nb-Ti microalloyed steel is taken as the research object. Thermodynamic and kinetic models focusing on the precipitation of the austenite phase are established to quantify the precipitation process. A coupled model of rolls and strips is built using ABAQUS 2022 software, where the precipitation strengthening model and high-temperature constitutive model are embedded into the finite element model (FEM) through subroutines. A two-dimensional alternating differential model is employed to acquire real-time temperature differences in the width direction of the strip. The effects of precipitation inclusion and exclusion on the strip crown under different operating conditions are compared and analyzed. The results indicate that as the temperature decreases, the strengthening effect increases, reaching around 40 MPa at temperatures above 1000 °C and 96.6 MPa at 800 °C. Furthermore, the inclusion of crown in the precipitation consideration is more sensitive to overall temperature changes, but as the strip width decreases, the sensitivity of crown to temperature decreases. The research findings of this paper provide guidance for improving strip shape control and reducing abnormalities during the rolling process.

A dynamic composite rolling model based on Lemaitre damage theory

Exploring the bonding mechanisms within bimetallic composite plates throughout the cold rolling process poses a considerable challenge, especially in simulating the bonding between different metals. A novel finite element model for rolling, grounded in Lemaitre continuum damage theory, was developed using copper/aluminium composite plates as a case study to characterize the dynamic bonding process in cold rolling precisely. The model replicates the dynamics of crack formation at the metal interface, the subsequent flow of the matrix metal through these fissures, and the ensuing contact. Considering the intricate stress conditions at the interface during rolling and the influence of compression on damage accumulation, the damage evolution constant in the original Lemaitre model has been revised. This modification aims to improve the model's performance in dominant shear forces and low-stress triaxiality scenarios. The damage constant has been transformed into a variable correlating with the Lode parameter and the degree of stress triaxiality to achieve this. The model parameters were calibrated using force-displacement data derived from shear and tensile experiments. The rolling model is ingeniously designed, employing hardened layer elements as barriers to segregate the fresh metal. Experimental rolling trials and corresponding simulation analyses were performed on copper/aluminium composite plates with varying initial thickness ratios, specifically 1:2, 1.5:1.5, and 2:1. The outcomes demonstrated a high degree of agreement between the experimental and simulated profiles, including the post-rolling thickness ratios, with a maximum discrepancy of 6.1 %. The finite element model effectively captured the deformation behaviour of the bimetallic material throughout the rolling process and elucidated the micromechanical processes underlying the interfacial bonding between the dissimilar metals.

A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption

A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption

Multi-time scale operation optimization for a near-zero energy community energy system combined with electricity-heat-hydrogen storage

Hybrid energy storage offers a new way to increase the renewable energy proportion (REP) of near-zero energy community energy system (NZECES). However, the optimization operation method of NZECES combining hybrid energy storage is not yet mature. Therefore, this paper proposes integrating electricity storage, heat storage and hydrogen storage into the NZECES, and conducting multi-time scale (MTS) operation optimization research. Firstly, a NZECES with electricity-heat-hydrogen storage is constructed. Secondly, a MTS optimization operation model of day-ahead scheduling, intra-day rolling, and real-time adjustment is established. Among them, the hourly scheduling scheme targeting the lowest daily operating cost is obtained through the optimization of the day-ahead stage. In the intra-day stage, the scheduling scheme with a time interval of 15 min is obtained by the rolling optimization. The operation scheme of electrical-related equipment with the minimum adjustment cost is obtained in the real-time stage. Finally, a NZEC with different building types is used as the energy supply object. The results show that compared to the traditional day-ahead scheduling scheme, the REP is increased by 3.6 % and the operation cost of the energy system is reduced by 5.3 % by using the MTS operation optimization method.

Forecasting China’s CO2 emissions and identifying key drivers: an application of the improved RFAGM model and LMDI decomposition methods

ABSTRACT China, the world’s largest CO2 emitter, has pledged to reduce its carbon intensity by 18% by 2025, which requires accurate forecasting of its emissions and their drivers. However, existing gray models have limitations in dealing with fluctuating data or long-time series data, and they often suffer from overfitting and poor generalization ability. Moreover, there is a lack of research and judgment on the future changes of emission drivers. To address these issues, this study proposes a fractional order gray adaptive rolling model (RFAGM(1,1)) that optimizes the background generation and incorporates the rolling mechanism. We apply RFAGM(1,1) to forecast China’s emissions, GDP, population, and consumption of raw coal, crude oil, and natural gas from 2020 to 2025. Our results show that RFAGM(1,1) achieves significantly higher accuracy than standard gray models, except for population. The projections indicate that China will meet the 18% carbon intensity reduction target by 2025. Furthermore, the LMDI decomposition reveals that economic growth and population changes have positive cumulative impacts on emission growth (245.68% and 11.95%, respectively), while energy intensity and structural changes have negative cumulative impacts (−151.60% and −6.02%, respectively). The improved forecast enables the evaluation of climate policies, while the factor analysis provides valuable insights for developing evidence-based strategies to achieve China’s carbon peaking and neutrality goals by 2030/2060.

Mathematical modeling and analysis of the tailor rolled blank manufacturing process

A tailor rolled blank (TRB) is a plate of continuously varying thickness with the advantages of lightweight, high strength, and good surface quality. However, it is required that the roll gap can be adjusted online, and the plate and roll produce large elastic deformation in the TRB manufacturing process. These characteristics make the modeling of the force and deformation parameters more difficult. A new mathematical model of roll separating force in the TRB manufacturing process is established taking into account the feature of deformation in this research. The elastic deformation of the strip and the flattening deformation of the roll are considered in order to improve the prediction accuracy of the results. The roll separating forces of the entrance elastic compression region and the exit elastic recovery region are computed using the generalized Hooke's law. A new velocity field is proposed based on the deformation characteristics of the plastic region and velocity boundary conditions, and the power functionals of the internal deformation, shear, friction, and tension are calculated. The analytical models of force and deformation parameters combined the elastic and plastic deformation regions are obtained through iterative operation, which satisfies the convergence condition of the roll separating force and roll flattening. The roll separating force data obtained by the model in this research are compared with the actual measured values in the laboratory, and the deviation value is small, which proves the computational precision of the present model. Additionally, the variations of the entrance velocity and flow volume per second during the TRB manufacturing process are shown, and the influences of the tension and friction factor on the neutral angle and roll separating force are illustrated.

Development and analysis of temperature rolling model during reversing mill rolling

It is shown that the process of the formation of the structure and mechanical properties of plates products is determined by the change of the temperature of the roll through the stages of the production process in the line of a reversing plate mill. An analytical temperature model is developed, that allows to estimate the distribution of surface and average mass temperature along the length and width of the roll for various stages of rolling with intermediate cooling, considering the deformation conditions for each pass. The temperature model is tested; as result, the proposed model can be used to calculate the distribution of the average mass temperature along the length and width of the roll at various stages of the production of plates on reversing mills.