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Mathematical Model of Hot Continuous Rolling of Packaging Steel Strip
by：Hongmei
2021-03-15
Mathematical Model of Hot Continuous Rolling of Packaging Steel Strip
From the description of computer control functions in Chapter 1, the control functions of hot strip rolling can actually be divided into four categories:
(1) Setting of rolling mill and other equipment (including related functions for setting service), including process automation level data tracking, actual value collection, setting model, model self-learning and basic automation equipment Control (position, speed control, used to finalize the positioning of the set value).
(2) Rolling parts transportation, including basic automation level position tracking, logic inspection, sequence control and control of each roller table.
(3) Product quality control, which mainly includes thickness accuracy, shape accuracy, temperature (final rolling temperature and coiling temperature) accuracy and width accuracy.
(4) Other functions, including data communication, man-machine interface and control of auxiliary facilities (hydraulic station, lubrication station, quick roll change, etc.).
The main thing involved in mathematics modulus is the function of setting calculation and quality control. In order to realize the hot strip rolling mill setting and quality control, the following mathematical models will be involved:
(1) Temperature drop model. Temperature is the most important parameter for hot rolling. The temperature drop model is not only used to control the final rolling temperature and coiling temperature, but also is an important molding in the setting model. The temperature prediction has a direct effect on the rolling force prediction. Therefore, the rolling temperature of each rolling mill must be accurately predicted.
(2) Rolling force model. Due to the bouncing phenomenon during strip rolling, the rolling force variation will be the main factor affecting the thickness accuracy. The rolling force model is also the main factor of whether the rolling mill setting is correct (whether the belt can be threaded smoothly). The rolling force model includes the deformation resistance model, the stress state model of the deformation zone and so on.
(3) Front sliding model. The forward slip model is used to calculate the speed setting of each stand of the continuous rolling mill.
(4) Wide stretch model. The widening model is used for rough rolling setting (flat roll and vertical roll setting).
(5) The model of the rolling mill. The model of the rolling mill includes the view equation, the shape of the roll gap and so on. The latter will involve hot roll spinning and worn roll models.
(6) The lining and tension model (equation) between the racks.
The majority of models used for setting calculations are static models. Considering the large variable range of variables and the strong computing power of the minicomputers used in the dynamic stage of the process, they all adopt the panoramic form and directly use nonlinear algebraic equations to solve them.
For quality intake control, on the one hand, its variable range is small (adjusted around the set value), and the quality control is undertaken by the basic automation level controller. The control cycle of the controller is small (10-30ms) Therefore, the incremental form is adopted.
If Chapter 2 and Chapter 3 of this book focus on the theoretical basis of these models, the following quantities will focus on introducing these models from the perspective of application.
From the description of computer control functions in Chapter 1, the control functions of hot strip rolling can actually be divided into four categories:
(1) Setting of rolling mill and other equipment (including related functions for setting service), including process automation level data tracking, actual value collection, setting model, model self-learning and basic automation equipment Control (position, speed control, used to finalize the positioning of the set value).
(2) Rolling parts transportation, including basic automation level position tracking, logic inspection, sequence control and control of each roller table.
(3) Product quality control, which mainly includes thickness accuracy, shape accuracy, temperature (final rolling temperature and coiling temperature) accuracy and width accuracy.
(4) Other functions, including data communication, man-machine interface and control of auxiliary facilities (hydraulic station, lubrication station, quick roll change, etc.).
The main thing involved in mathematics modulus is the function of setting calculation and quality control. In order to realize the hot strip rolling mill setting and quality control, the following mathematical models will be involved:
(1) Temperature drop model. Temperature is the most important parameter for hot rolling. The temperature drop model is not only used to control the final rolling temperature and coiling temperature, but also is an important molding in the setting model. The temperature prediction has a direct effect on the rolling force prediction. Therefore, the rolling temperature of each rolling mill must be accurately predicted.
(2) Rolling force model. Due to the bouncing phenomenon during strip rolling, the rolling force variation will be the main factor affecting the thickness accuracy. The rolling force model is also the main factor of whether the rolling mill setting is correct (whether the belt can be threaded smoothly). The rolling force model includes the deformation resistance model, the stress state model of the deformation zone and so on.
(3) Front sliding model. The forward slip model is used to calculate the speed setting of each stand of the continuous rolling mill.
(4) Wide stretch model. The widening model is used for rough rolling setting (flat roll and vertical roll setting).
(5) The model of the rolling mill. The model of the rolling mill includes the view equation, the shape of the roll gap and so on. The latter will involve hot roll spinning and worn roll models.
(6) The lining and tension model (equation) between the racks.
The majority of models used for setting calculations are static models. Considering the large variable range of variables and the strong computing power of the minicomputers used in the dynamic stage of the process, they all adopt the panoramic form and directly use nonlinear algebraic equations to solve them.
For quality intake control, on the one hand, its variable range is small (adjusted around the set value), and the quality control is undertaken by the basic automation level controller. The control cycle of the controller is small (10-30ms) Therefore, the incremental form is adopted.
If Chapter 2 and Chapter 3 of this book focus on the theoretical basis of these models, the following quantities will focus on introducing these models from the perspective of application.
