Improved Threading of the Tandem Cold Mill Using Advanced Control Techniques with Virtual Rolling

The threading of the tandem cold metal rolling mill is a significant function in the control of this process. During the threading process it is important to reduce the excursions in the strip thicknesses and tensions to contribute to the stability of rolling, to avoid reducing strip tension so as to preclude the throwing of a loop of strip at the entry of a mill stand, and to successfully reduce the effects of any drop in the strip speed when the head of the strip enters a mill stand. Additionally, in most cases it is desired by the operational personnel to control the individual mill stand speeds such that, as the head of the strip moves between two adjacent stands, its speed between the two stands is held at the strip speed at the exit of the last stand just before acceleration to run speed. Moreover, it is desired that disturbances in the thickness and the resistance to deformation in the incoming strip are successfully handled by the controller. In this paper we propose a method of improved control for this process that considers all of these requirements. The effectiveness of this method is shown by simulation using data from operating mills. © 2019 IEEE

Use of Advanced Control Techniques with Virtual Rolling to Improve Threading of the Tandem Cold Mill

The threading of the tandem cold metal rolling mill is a significant function in the control of this process. During the threading process it is important to reduce the excursions in the strip thicknesses and tensions. Additionally, in most cases it is desired by the operational personnel to control the individual mill stand speeds such that, as the head of the strip moves between two adjacent stands, its speed between the two stands is held at the strip speed at the exit of the last stand just before acceleration to run speed. Moreover, it is desired that disturbances in the thickness and the resistance to deformation in the incoming strip are successfully handled by the controller. In this article, we propose a method of improved control for this process that considers all of these requirements. The effectiveness of this method is shown by simulation using data from operating mills. © 1972-2012 IEEE

Multi-scale deconvolution of sensor array signals

We present a novel solution to a 'hands-off' deconvolution problem in which the data to be deconvolved consist of sensor array measurements. The aim is to find the original source signal (wavelet) and signature of the medium (reflectivity sequence) from the available sensor measurements. Our model assumes that the data are generated as a convolution of an unknown wavelet with various time-scaled versions of an unknown reflectivity sequence. This type of data occurs in many array signal processing applications, including radar, sonar and seismic processing. Our approach relies on exploiting the redundancy in the measurements due to time-scaling which is introduced by the geometry and the sensor placement, and does not require knowledge of the wavelet or reflectivity sequence. Furthermore, we make no assumptions on the statistical properties of these signals. We formulate and solve the deconvolution problem as a quadratic minimization subject to a quadratic constraint. We also illustrate the performance of the technique using simulation examples. © 1997 Elsevier Science B. V

Multiscale deconvolution of sensor array signals via sum-of-cumulants

This correspondence presents a solution to a multiscale deconvolution problem using higher order spectra where the data to be deconvolved consist of noise-corrupted sensor array measurements. We assume that the data are generated as a convolution of an unknown wavelet with reflectivity sequences that are linearly time-scaled versions of an unknown reference reflectivity sequence. This type of data occurs in many signal processing applications, including sonar and seismic processing. Our approach relies on exploiting the redundancy In the measurements due to time scaling and does not require knowledge of the wavelet or the reflectivity sequences. We formulate and solve the deconvolution problem as a quadratic minimization subject to a quadratic constraint in the sum-of-cumulants (SOC) domain. The formulation using the SOC approach reduces the effect of additive Gaussian noise on the accuracy of the results when compared with the standard time-domain formulation. We demonstrate this improvement using a simulation example. © 1997 IEEE