Zhu et al., 2003 - Google Patents
A fuzzy algorithm for flatness control in hot strip millZhu et al., 2003
- Document ID
- 9798977266844294551
- Author
- Zhu H
- Jiang Z
- Tieu A
- Wang G
- Publication year
- Publication venue
- Journal of Materials Processing Technology
External Links
Snippet
Based on BP neural network, a flatness prediction model in hot strip mill was developed, in which the same location point data were adopted for training and testing to avoid the influence of time-delay. Two fuzzy flatness control algorithms in hot strip mill were …
- 230000001537 neural 0 abstract description 14
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhu et al. | A fuzzy algorithm for flatness control in hot strip mill | |
| Heidari et al. | Optimization of cold rolling process parameters in order to increasing rolling speed limited by chatter vibrations | |
| Janabi-Sharifi | A neuro-fuzzy system for looper tension control in rolling mills | |
| Hu et al. | Multi-parameter deep-perception and many-objective autonomous-control of rolling schedule on high speed cold tandem mill | |
| Wang et al. | Application of mind evolutionary algorithm and artificial neural networks for prediction of profile and flatness in hot strip rolling process | |
| Chen et al. | Prediction of tandem cold-rolled strip flatness based on Attention-LSTM model | |
| Larkiola et al. | Prediction of rolling force in cold rolling by using physical models and neural computing | |
| Barrios et al. | Neural, fuzzy and grey-box modelling for entry temperature prediction in a hot strip mill | |
| Wang et al. | Deep learning-based flatness prediction via multivariate industrial data for steel strip during tandem cold rolling | |
| Han et al. | Prediction and control of profile for silicon steel strip in the whole tandem cold rolling based on PSO-BP algorithm | |
| Li et al. | Modeling and validation of bending force for 6-high tandem cold rolling mill based on machine learning models | |
| Hameed et al. | Strip thickness control of cold rolling mill with roll eccentricity compensation by using fuzzy neural network | |
| Bouhouche et al. | Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process | |
| Li et al. | An industrial IoT-based deformation resistance prediction and thickness control method of cold-rolled strip in steel production systems | |
| Qazani et al. | Multiobjective optimization of roll-forming procedure using NSGA-II and type-2 fuzzy neural network | |
| Ding et al. | Deep stochastic configuration networks with different distributions for crown prediction of hot-rolled non-oriented silicon steel | |
| Bruni et al. | Modelling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques | |
| Wang et al. | A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness | |
| Jung et al. | Simulation of fuzzy shape control for cold-rolled strip with randomly irregular strip shape | |
| Rumyantsev et al. | Further developments in simulation of metal forming processes | |
| Jung et al. | Fuzzy-control simulation of cross-sectional shape in six-high cold-rolling mills | |
| Wang et al. | Edge drop control of cold rolled silicon steel strip based on model predictive control | |
| Song et al. | A digital twin model for automatic width control of hot rolling mill | |
| Park et al. | Width control systems with roll force automatic width control and finishing vertical mill automatic width control in hot strip mill | |
| Sun et al. | Industrial IoT–enabled real-time prediction of strip cross-section shape for hot-rolling steel |