Cold rolling mill thickness control using the cascade-correlation neural network

The improvements in thickness accuracy of a steel strip produced by a tandem cold-roIling mill are of substantial interest to the steel industry. In this paper, we designed a direct model-reference adaptive control (MRAC)  scheme that exploits the natural level of excitation existing in the closed-loop with a dynamically constructed cascade-correlation neural network (CCNN) as a controller for cold roIling mill thickness control. Simulation results show that the combination of a such a direct MRAC scheme and the dynamically constructed CCNN significantly improves the thickness accuracy in the presence of disturbances and noise in comparison with to the conventional PID controllers.

Modeling and control of flatness in cold rolling mill using fuzzy petri nets

Today, having a good flatness control in steel industry is essential to ensure an overall product quality, productivity and successful processing. Flatness error, given as difference between measured strip flatness and target curve, can be minimized by modifying roll gap with various control functions. In most practical systems, knowing the definition of the model in order to have an acceptable control is essential. In this paper, a fuzzy Petri net method for modeling and control of flatness in cold rolling mill is developed. The method combines the concepts of Petri net and fuzzy control theories. It focuses on the fuzzy decision making problems of the fuzzy rule tree structures. The method is able to detect and recover possible errors that can occur in the fuzzy rule of the knowledge-based system. The method is implemented and simulated. The results show that its error is less than that of a PI conventional controller.

A neural network-based preset generation tool for a steel tandem cold mill

This paper traces the development of a software tool, based oil a combination of artificial neural networks (ANN) and a few process equations. aiming to serve as a backup operation instrument in the reference generation for real-time controllers of a steel tandem cold mill By emulating the mathematical model responsible for generating presets under normal operational conditions, the system works as ail option to maintain plant operation in the event of a failure in the processing unit that executes the mathematical model. The system, built from the production data collected over six years of plant operation, steered to the replacement of the former backup operation mode (based oil a lookup table). which degraded both product quality and plant productivity. The study showed that ANN are appropriated tools for the intended purpose and that by this instrument it is possible to achieve nearly the totality of the presets needed by this land of process. The text characterizes the problem, relates the investigated options to solve it. justifies the choice of the ANN approach, describes the methodology and system implementation and, finally, shows and discusses the attained results. (C) 2009 Elsevier Ltd. All rights reserved

Texture and microstructure evolution during cold rolling of Cu-Fe laminates prepared by accumulative roll bonding

Multi-layered materials have been made from Cu-Fe with approximately equal volume fractions using the Accumulated Roll Bonding (ARB) technique with less than 1 μm thickness of the individual layers. The so-obtained multi-layers have been subjected to deformation by cold rolling to 25, 50, 75, 87 and 93% reduction in thickness. A detailed characterization has been carried out using X-ray diffraction (line profile analysis and texture measurement) and electron (scanning and transmission) microscopy. It has been found that Fe layers are disintegrated whereas Cu retains its continuity within a layer. Microstructural Characterization from X-Ray Line profile Analysis (XRDLPA) through Variance Method reveals that large amount of strain is initially carried by Cu layers during deformation. In the Cu-Fe layer, the texture is comparatively weaker in Cu layer and strong in Fe layers. Brass Component increases up to 75% reduction and then decreases, while the ratio of Cu/S and Bs/S remains almost constant through out the deformation. After 50% reduction, dynamic recovery is predominant as indicated by the increase in the amount of low angle grain boundaries and decrease in dislocation density. The presence of R component indicates continuous dynamic recovery and recrystallization (CDRR) at the advanced stage of deformation.

Effect of Cold Rolling Ratio and Heating Rate of Batch Annealing on Textures of Micro-Carbon Steel Sheet

在四辊冷轧试验机和Gleeble-1500试验机上进行了热轧微碳钢板的冷轧和退火试验。用D/max-RC衍射仪测量了试样的,/”层织构,并用Roe软件进行了ODF分析。研究表明,所研究的热轧微碳深冲板压下率约为75%,退火升温速度为20-40℃/h时,试样为{111}织构特征；压下率较大(80%)时,退火织构为较弱的{111}组分。无论{111}织构还是非{111}织构都是在形核阶段开始形成,在晶粒长大优先长大,受到定向形核和选择生长双重机制的作用。

Analysis of asymmetrical cold rolling with varying coefficients of friction

In this research, asymmetrical cold rolling was produced by the difference in the coefficient of friction between rolls and sheets rather than the difference of roll radius or rotation speeds. The influence of friction coefficient ratio on the cross shear deformation, rolling pressure and torque was investigated using slab analysis. The results showed that the shear deformation zone length increased with the increase of the friction coefficient ratio. The rolling force decreased only under the condition that the friction coefficient ratio increased while the sum of the friction coefficients was held constant. As the reduction per pass was increased, the shear deformation zone length increased and the rolling force also increased. An increase of the front tension resulted in a decrease of the shear deformation zone length. An increase of back tension, however, led to an increase of the shear deformation zone length. The reduction of rolling torque for the work roll with higher surface roughness was greater than that for the work roll with lower surface roughness. (C) 2002 Elsevier Science B.V. All rights reserved.

Design, implementation and testing of an intelligent knowledge-based system for the supervisory control of a hot rolling mill

This paper describes the design, implementation and testing of an intelligent knowledge-based supervisory control (IKBSC) system for a hot rolling mill process. A novel architecture is used to integrate an expert system with an existing supervisory control system and a new optimization methodology for scheduling the soaking pits in which the material is heated prior to rolling. The resulting IKBSC system was applied to an aluminium hot rolling mill process to improve the shape quality of low-gauge plate and to optimise the use of the soaking pits to reduce energy consumption. The results from the trials demonstrate the advantages to be gained from the IKBSC system that integrates knowledge contained within data, plant and human resources with existing model-based systems. (c) 2005 Elsevier Ltd. All rights reserved.