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.

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.

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%.

Deformation behaviour of ultrafine grained steel produced by cold rolling of martensite

An ultrafine grained Nb microalloyed steel was produced by cold rolling of martensite followed by annealing heat treatments at different times to study its effect on the microstructure and mechanical behaviour of the ultrafine grained steel. High strength was achieved by this thermomechanical processing due to the formation of cell and subgrain dislocation substructure; however annealing reduced both strength and elongation.

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.