Long- and Short-Term Self-Learning Models of Rolling Force in Rolling Process Without Gaugemeter of Plate

2009 ◽  
Vol 16 (1) ◽  
pp. 27-31 ◽  
Author(s):  
Fu-wen Zhu ◽  
Qing-liang Zeng ◽  
Xian-lei Hu ◽  
Xi-an Li ◽  
Xiang-hua Liu
2010 ◽  
Vol 97-101 ◽  
pp. 3091-3096 ◽  
Author(s):  
Jun Wang ◽  
Chun Li Jia ◽  
Zhong Zhao ◽  
Zhi Jie Jiao ◽  
Jian Ping Wang

Rolling force model is the core of all the mathematical models of plate for rolling process, but the accuracy of traditional rolling force model is not high enough in application, so in this study the rolling force model of plate is researched and improved. The effects of different physical conditions on resistance of deformation are decoupled, and the formula acquired is practical. While the composition, Nb is used to calculate residual strain. At the same time, the self-learning method, which is based on the thickness layer is applied. The on-line application results show that the predictive error between force model calculated and measured can be controlled at less than 9% and 80% of the passes can be controlled within 5%.


2020 ◽  
Vol 14 ◽  
Author(s):  
Xiao-bin Fan ◽  
Hao Li ◽  
Yu Jiang ◽  
Bing-xu Fan ◽  
Liang-jing Li

Background: Rolling mill vibration mechanism is very complex, and people haven't found a satisfactory vibration control method. Rolling interface is one of the vibration sources of the rolling mill system, and its friction and lubrication state has a great impact on the vibration of the rolling mill system. It is necessary to establish an accurate friction model for unsteady lubrication process of roll gap and a nonlinear vibration dynamic model for rolling process. In addition, it is necessary to obtain more direct and real rolling mill vibration characteristics from the measured vibration signals, and then study the vibration suppression method and design the vibration suppression device. Methods: This paper summarizes the friction lubrication characteristics of rolling interface and its influence on rolling mill vibration, as well as the dynamic friction model of rolling interface, the tribological model of unsteady lubrication process of roll gap, the non-linear vibration dynamic model of rolling process, the random and non-stationary dynamic behavior of rolling mill vibration, etc. At the same time, the research status of rolling mill vibration testing technology and vibration suppression methods were summarized. Time-frequency analysis of non-stationary vibration signals was reviewed, such as wavelet transform, Wigner-Ville distribution, empirical mode decomposition, blind source signal extraction, rolling vibration suppression equipment development. Results: The lubrication interface of the roller gap under vibration state presents unsteady dynamic characteristics. The signals generated by the vibration must be analyzed in time and frequency simultaneously. In the aspect of vibration suppression of rolling mill, the calculation of inherent characteristics should be carried out in the design of rolling mill to avoid dynamic defects such as resonance. When designing or upgrading the mill structure, it is necessary to optimize the structure of the work roll bending and roll shifting system, such as designing and developing the automatic adjustment mechanism of the gap between the roller bearing seat and the mill stand, adding floating support device to the drum shaped toothed joint shaft, etc. In terms of rolling technology, rolling vibration can be restrained by improving roll lubrication, reasonably distributing rolling force of each rolling mill, reducing rolling force of vibration prone rolling mill, increasing entrance temperature, reducing rolling inlet tension, reducing strip outlet temperature and reasonably arranging roll diameter. The coupling vibration can also be suppressed by optimizing the hydraulic servo system and the frequency conversion control of the motor. Conclusion: Under the vibration state, the lubrication interface of roll gap presents unsteady dynamic characteristics. The signal generated by vibration must be analyzed by time-frequency distribution. In the aspect of vibration suppression of rolling mill, the calculation of inherent characteristics should be carried out in the design of rolling mill to avoid dynamic defects such as resonance. It is necessary to optimize the structure of work roll bending and roll shifting system when designing or reforming the mill structure. In rolling process, rolling vibration can be restrained by improving roll lubrication, reasonably distributing rolling force of each rolling mill, increasing billet temperature, reasonably arranging roll diameter and reducing rolling inlet tension. Through the optimization of the hydraulic servo system and the frequency conversion control of the motor, the coupling vibration can be suppressed. The paper has important reference significance for vibration suppression of continuous rolling mill and efficient production of high quality strip products.


2010 ◽  
Vol 139-141 ◽  
pp. 1889-1893 ◽  
Author(s):  
Peng Fei Wang ◽  
Dian Hua Zhang ◽  
Xu Li ◽  
Jia Wei Liu

In order to improve the flatness of cold rolled strips, strategies of closed loop feedback flatness control and rolling force feed forward control were established respectively, based on actuator efficiency factors. As the basis of flatness control system, efficiencies of flatness actuators provide a quantitative description to the law of flatness control. For the purpose of obtaining accurate efficiency factors matrixes of actuators, a self-learning model of actuator efficiency factors was established. The precision of actuator efficiency factors could be improved continuously by correlative measurement flatness data inputs. Meanwhile, the self-learning model of actuator efficiency factors permits the application of this flatness control for all possible types of actuators and every stand type. The developed flatness control system has been applied to a 1250mm single stand 6-H reversible UCM cold mill. Applications show that the flatness control system based on actuator efficiency factors is capable to obtain good flatness.


2021 ◽  
Vol 11 (9) ◽  
pp. 4266
Author(s):  
Md. Shahriare Satu ◽  
Koushik Chandra Howlader ◽  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
Sheikh Mohammad Shariful Islam ◽  
...  

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3678
Author(s):  
Dongwon Lee ◽  
Minji Choi ◽  
Joohyun Lee

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.


Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3020
Author(s):  
Anam-Nawaz Khan ◽  
Naeem Iqbal ◽  
Atif Rizwan ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.


2021 ◽  
Author(s):  
Navid Korhani ◽  
Babak Taati ◽  
Andrea Iaboni ◽  
Andrea Sabo ◽  
Sina Mehdizadeh ◽  
...  

Data consists of baseline clinical assessments of gait, mobility, and fall risk at the time of admission of 54 adults with dementia. Furthermore, it includes the participants' daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system.


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