Siemens Aktiengesellschaft

Thomas Poppe

Otto Gramckow

Thomas Martinetz

Einar Broese

Guenter Soergel

Albrecht Sieber

Gunter Sorgel

Thomas Peuker

Bjoern Feldkeller

In a method for determining the rolling force in a roll stand for rolling metallic material which is to be rolled, the rolling force is determined by means of at least one neural network. According to the invention, the neural network is trained using, in particular measured, values for the rolling force under different operating conditions with a view to improving the determination of the rolling force. The neural network is advantageously trained using values for the rolling force and values for the different operating conditions for roll stands of different rolling trains.

Method and device for determining the rolling force in a roll stand

Regulation or control of a fusion process in a three-phase current arc

In the control of a material-processing process in a regulated system, a preliminary adjustment of the system takes place at the beginning of each process cycle as a function of a precalculated process parameter. A material characteristic which is relevant for the process and which in turn is dependent on state variables (such as the composition of the material and its temperature), is included in an advance calculation of the process parameter. The relationship between the state variables and the material property is modelled in a neural network which forms a prediction value for the material property on its output side. As a function of the deviation between the prediction value and an actual value for the material property which is determined based on measuring the process parameter during the process cycle, an adaptive change of the network parameters takes place in the sense of reducing this deviation.

Method and control device for controlling a material-processing process

In rolling a metal strip in a roughing line and a finishing line, the rolling process in the roughing line is adjusted as a function of a predicted value for the change in width of the metal strip in the finishing line such that the metal strip has a given specified finished strip width on leaving the finishing line. In order to permit a reliable prediction of the change in width despite the lack of accurate information regarding the dependence of the change in width on influencing parameters that affect the process, this dependence is simulated in a neural network whose network parameters are adapted after each passage of a metal strip through the finishing line as a function of the influencing parameters measured or calculated during the passage and the measured actual change in width.

Method and apparatus for rolling a metal strip

For the control of a process in a controlled system, a presetting of the system takes place at the start of each process sequence as a function of a precalculated process parameter which exhibits a system-induced dependence on faulty input variables. In this case, the description of the dependence takes place by a model of the process which is adapted during the course of the process. To prevent dependence on the creation of models, which as a rule are imprecise, the input variables are fed before the start of the process to a neural network having variable network parameters for the precalculation of the process parameter based on measurements of the input variables and of the process parameter. These variables are recalculated during the course of the process and utilized for the adaptation of the network parameters.

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Munchen, Germany

Thomas Poppe