Eccentricity and hardness control in cold rolling mills with a dynamically constructed neural controller

The main objective of a steel strip rolling process is to produce high quality steel at a desired thickness.  Thickness reduction is the result of the speed difference between the incoming and the outgoing steel strip and the application of the large normal forces via the backup and the work rolls.  Gauge control of a cold rolled steel strip is achieved using the gaugemeter principle that works adequately for the input gauge changes and the strip hardness changes.  However, the compensation of some factors is problematic, for example, eccentricity of the backup rolls.  This cyclic eccentricity effect causes a gauge deviation, but more importantly, a signal is passed to the gap position control so to increase the eccentricity deviation.  Consequently, the required high product tolerances are severely limited by the presence of the roll eccentricity effects.
In this paper a direct model reference adaptive control (MRAC) scheme with dynamically constructed neural controller was used.  The aim here is to find the simplest controller structure capable of achieving an optimal performance.  The stability of the adaptive neural control scheme (i.e. the requirement of persistency of excitation and bounded learning rates) is addressed by using as the inputs to the reference model the plant's state variables.  In such a case, excitation is due to actual plant signals (states) affected by plant disturbances and noise.  In addition, a reference model in the form of a filter with a desired transfer function using Modulus Optimum design was used to ensure variance in the desired dynamic characteristics of the system.  The gradually decreasing learning rate employed by the neural controller in this paper is aimed at eliminating controller instability resulting from over-aggressive control.  The moving target problem (i.e. the difficulty of global neural networks to perfrom several separate computational tasks in closed -loop control) is addressed by the localized architecture of the controller.  The above control scheme and learning algorithm offers a method for automatic discovery of an efficient controller.
The resulting neural controller produces an excellent disturbance rejection in both cases of eccentricity and hardness disturbances, reducing the gauge deviation due to eccentricity disturbance from 33.36% to 4.57% on average, and the gauge deviation due to hardness disturbance from 12.59% to 2.08%.

Short-range order kinetics in alpha-AgZn for various states of post-deformation defect annealing after cold-rolling

Kinetics of short-range ordering (SRO) in Ag with 21, 23 and 28 at% Zn is investigated by residual resistometry during isochronal and isothermal heat treatment for different states of post-deformation defect annealing after cold-rolling to about 30 and 60% thickness reduction. Resistivity changes due to pure ordering can be separated from the as-measured total resistivity change which includes defect annealing. Although the initial state of SRO of the as-rolled material can be estimated to be comparably low, for as-rolled and partially annealed states by appropriate thermal treatment evolution of SRO is achieved which corresponds quite well to that of recrystallized samples. It is observed, however, that quenched-in surplus vacancies contribute considerably to the ordering process for the recrystallized state and that this contribution is still increased by the grain growth during the final stage of annealing. It therefore turns out that SRO-kinetics under equilibrium vacancy conditions can be better observed in a state of post-deformation annealing, for which deformation induced point defects are annealed-out, but a relatively high dislocation density is still present to act as a vacancy sink. Copyright (C) 1996 Acta Metallurgica Inc.

Modelling the foil rolling process

A review of the main rolling models is conducted to assess their suitability for modelling the foil rolling process. Two such models are Fleck and Johnson's Hertzian model and Fleck, Johnson, Mear and Zhang's Influence Function model. Both of these models are approximated through the use of perturbation methods. Decrease in the computation time resulted when compared with the numerical solution. The Hertzian model was approximated using the ratio of the yield stress of the strip to the plane-strain Young's Modulus of the rolls as the small perturbation parameter. The Influence Function model approximation takes advantage of the solution of the well-known Aerofoil Integral Equation to gain an insight into how the choice of interior boundary points affects the stability of numerical solution of the model's equations. These approximations require less computation than their full models and, in the case of the Hertzian approximation, only introduces a small error in the predictions of roll force roll torque. Hence the Hertzian approximate method is suitable for on-line control. The predictions from the Influence Function approximation underestimates the predictions from the numerical results. Better approximation of the pressure in the plastic reduction regions is the main source of this error.

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.