Representation and control of the cold rolling process through artificial neural networks via sensitivity factors
Representation and control of the cold rolling process through artificial neural networks via sensitivity factors
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DOI：
10.1016/j.jmatprotec.2007.06.063
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发表时间：
2008-02
影响因子：
6.3
通讯作者：
Luis E. Zárate;F. R. Bittencout
中科院分区：
文献类型：
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作者：
Luis E. Zárate;F. R. Bittencout
轧制过程的数学建模涉及几个参数，这些参数可能导致难以解析解的非线性方程。这就是亚历山大模型的情况[亚历山大，论轧制理论，R. Soc. Lond. A 326（1972）535-563]，被认为是滚动理论中最完整的理论之一。该模型需要大量的计算时间，这阻碍了其在在线控制和监督系统中的应用。因此，仍然需要新的和有效的形式来表示这种过程。唯一的要求是，新的表示纳入过程的定性行为，并且它们可以用于控制系统的设计。本文介绍了用亚历山大模型得到的数据训练的神经网络来表示冷轧过程。两个神经网络的训练表示轧制过程和操作。对于他们来说，他们的行为的定量和定性方面的验证，通过模拟和通过敏感性方程。这些方程是基于通过区分先前训练的神经网络获得的灵敏度因子;并且对于不同的操作点，可以以较低的计算时间获得不同的方程。另一方面，在轧制系统的控制器设计的资本问题之一是难以测量最终的厚度没有时间延迟。该时间延迟是输出厚度传感器的位置的结果，该输出厚度传感器总是放置在辊缝区域之前的一定距离处。基于灵敏度因子的表示具有将由控制策略使用的预测特性。该预测模型允许克服存在于这样的过程中的时间延迟，并且可以消除通常基于X射线的厚度传感器。该模型作为通过软件实现的虚拟传感器工作。此外，本文还提出了一种方法来确定适当的调整厚度控制考虑三个可能的控制参数：辊缝，前，后张力。该方法认为，作为最佳的控制动作，需要最小的调整。仿真结果表明，所提出的技术的可行性和应用程序的一个例子，单机架轧机进行了讨论。
The mathematical modeling of the rolling process involves several parameters that may lead to non-linear equations of difficult analytical solution. Such is the case of Alexander's model [Alexander, On the theory of rolling, Proc. R. Soc. Lond. A 326 (1972) 535–563], considered one of the most complete in the rolling theory. This model requires significant computational time, which prevents its application in on-line control and supervision systems. For this reason new and efficient forms to represent this kind of process are still necessary. The only requirement is that the new representations incorporate the qualitative behavior of the process, and that they can be used in the control system design. In this paper, the representation of the cold rolling process through Neural Networks, trained with data obtained by Alexander's model, is presented. Two neural networks are trained to represent the rolling process and operation. For them, the quantitative and qualitative aspects of their behaviors are verified through simulation and via sensitivity equations. These equations are based on sensitivity factors obtained by differentiating the previously trained neural networks; and for different operation points, different equations can be obtained with low computational time. On the other hand, one of the capital issues in the controller design for rolling systems is the difficulty to measure the final thickness without time delays. The time delay is a consequence of the location of the output thickness sensor that is always placed to a certain distance ahead of the roll-gap region. The representation based in sensitivity factors has predictive characteristics that will be used by the control strategy. This predictive model permits to overcome the time delay that exists in such processes and can eliminate the thickness sensor, usually based on X-ray. This model works as a virtual sensor implemented via software. Besides, this paper presents a method to determinate the appropriate adjustment for thickness control considering three possible control parameters: roll gap, front and back tensions. The method considers, as the best control action, the one that demands the smallest adjustment. Simulation results show the viability of the proposed techniques and an example of the application to a single stand rolling mill is discussed.