Roll gap control method of calendering system

Abstract

The invention discloses a roll gap control method of a calendering system, which comprises the following steps: a roll gap control subsystem adjusts the roll gap distance between two film forming press rolls and / or two thinning press rolls according to a roll gap signal and a diaphragm thickness signal; a control center of the roll gap control subsystem calculates a first deviation ratio according to the actual thickness detected by the first thickness gauge assembly and the calibrated thickness at the position, if the first deviation ratio is within the range of + / -5%, data are stored in a storage library, control operation is not carried out, and if the first deviation ratio is within the range of + / -5% to + / -10%, data are stored in a storage library; if the deviation ratio I is within the range of + / -10%, the control center adopts a first control module to adjust the roll gap of the thinning component, and if the deviation ratio I is within the range of + / -10%, the control center adopts a second control module to adjust the roll gap of the film forming component. According to the invention, the roll gap can be accurately controlled, the control performance of the system is improved, the stability and the accuracy of the system are improved, and the production efficiency and the product quality of the calender are also improved.

Description

Technical Field

[0001] The invention relates to the technical field of calender control, in particular to a roll gap control method of a calender system. Background Art

[0002] Calenders are used in calendering, film stretching, rolling and other industries. In the control system of the calender, multiple structural units involve feedback control: roll gap adjustment unit, pole piece correction unit, pole piece tension control unit. The roll gap adjustment structure is used to adjust the gap between the main rollers to complete the adjustment of the rolling force and the thickness of the pole piece; the pole piece correction structure is used to adjust the pole piece deviation to avoid the pole piece deviation causing tower-shaped rolls and other phenomena that affect the quality of the pole piece; the pole piece tension control system is used to maintain the stability of the pole piece tension, ensure the straightness of the rolled pole piece, and avoid the pole piece caused by uneven extension. Waves and other phenomena.

[0003] The roll gap control performance of the existing calender control system is poor, which affects the control stability and accuracy of the system and thus affects the quality of the pole piece. Summary of the invention

[0004] In view of the shortcomings of the above-mentioned existing calender control system, the applicant provides a reasonable roll gap control method for a calender system to improve the stability and accuracy of roll gap adjustment and ensure the quality of pole pieces.

[0005] The technical solution adopted by the present invention is as follows: A roll gap control method for a calendering system, wherein a roll gap control subsystem adjusts the roll gap distance between two film-forming rollers and / or two thinning rollers according to a roll gap signal and a film thickness signal, and a control center of the roll gap control subsystem calculates a deviation rate one according to the actual thickness detected by a first thickness gauge component and a calibrated thickness therein; if the deviation rate one is within the range of ±5%, the data is stored in a storage library and no control operation is performed; if the deviation rate one is within the range of ±5% to ±10%, the control center uses a first control module to adjust the roll gap of the thinning component; if the deviation rate one exceeds the range of ±10%, the control center uses a second control module to adjust the roll gap of the film-forming component.

[0006] As a further improvement of the above technical solution: After the second control module adjusts the roller gap of the film forming component to a deviation rate within the range of ±10%, it switches to the first control module.

[0007] The control process of the first control module is to use the current time series thickness data of the first thickness gauge as a reference, retrieve the 10 time series thickness data before this timestamp and the current time series thickness data from the storage library to form a time series data set {T1, T2, ..., T 11}, the time series data set {T1, T2, …, T 11} Input the zebra optimized recurrent neural network algorithm and calculate the output parameter set {K P , T I , T D , tsd, Mpd, ess, e(t)} to PID. After receiving the parameter set, PID calculates e(t) to obtain the position adjustment instruction to the servo-hydraulic proportional valve of the second roll gap control actuator of the thinning component. The time taken for this process is in milliseconds. The servo-hydraulic proportional valve controls the second roll gap control actuator to extend or retract to adjust the thinning support roller and then adjust the roll gap of the thinning component.

[0008] After the first control module adjusts the roller gap of the thinned component, the deviation diaphragm is thinned after the thinned component is corrected. The control center calculates the deviation rate 2 between the actual thickness detected by the second thickness gauge and the rated thickness here. When the deviation rate 2 is less than ±6%, the roller gap is not corrected. When the deviation rate 2 is greater than ±6%, the roller gap of the thinned component is adjusted based on the thickness signal of the second thickness gauge until the deviation rate 2 is reduced to within ±6%.

[0009] When the deviation rate is greater than 6%, the time series thickness data of the first thickness gauge is shielded, and the current time series thickness data of the second thickness gauge is used as the benchmark. The 10 time series thickness data before this timestamp and the current time series thickness data are retrieved from the storage library to form a time series data set {T1', T2', ..., T 11 '}, and then the time series data set {T1', T2', ..., T 11 '} Input the zebra optimized recurrent neural network algorithm and calculate the output parameter set {K P , T I , T D , tsd, Mpd, ess, e(t)} to PID. After receiving the parameter set, PID calculates e(t) to obtain a position adjustment instruction to the servo-hydraulic proportional valve of the second roll gap control actuator of the thinning component. The time used for this process is in the millisecond level. The servo-hydraulic proportional valve controls the extension or retraction of the second roll gap control actuator to adjust the roll gap of the thinning component until the deviation rate is reduced to within 6%. After the adjustment action is completed, the roll gap is detected by the second roll gap detection sensor and fed back to the PID. Thereafter, the shielding of the first thickness gauge component is unlocked, and the adjustment is performed based on the timing thickness signal of the first thickness gauge component.

[0010] When the deviation rate is less than ±6%, the time series thickness data is stored in the repository.

[0011] While the first control module adjusts the roll gap of the thinned component, the zebra optimized recurrent neural network algorithm (RNN-ZOA) that controls the roll gap of the thinned component is secondary optimized.

[0012] The control process of the second control module is to use the current time series thickness data of the first thickness gauge as a reference, retrieve the 10 time series thickness data before this timestamp and the current time series thickness data from the storage library to form a time series data set {T1, T2, ..., T 11}, the time series data set {T1, T2, …, T 11} Input the zebra optimized recurrent neural network algorithm and calculate the output parameter set {K P , T I , T D , tsd, Mpd, ess, e(t)} to PID. After receiving the parameter set, PID calculates e(t) to obtain the position adjustment command to the servo-hydraulic proportional valve of the first roller gap control actuator of the film forming component. This process takes milliseconds. The servo-hydraulic proportional valve sends a position command to control the first roller gap control actuator to extend or retract to adjust the film forming support roller and then adjust the roller gap of the film forming component until the thickness deviation rate of the film produced by the film forming component is within plus or minus 10%.

[0013] During the time controlled and adjusted by the second control module, the second thickness gauge monitors and records the time series data in real time, and removes the unqualified film within this period after the winding is completed.

[0014] During the roll gap adjustment process, the first roll gap detection sensor synchronously monitors the actual roll gap and feeds back the signal to the control center. All collected data are time series data.

[0015] The beneficial effects of the present invention are as follows: The press of the present invention can accurately control the roll gap, and when faced with nonlinear, time-varying or multi-variable systems, it can also accurately process complex dynamic performance and multi-variable coupling, with better adaptability and robustness, thereby improving the control performance of the system, improving the stability and accuracy of the system, and also improving the production efficiency and product quality of the calender.

[0016] The present invention performs fitting calculation or analytical calculation on time series data. Time series data refers to time series data, which is a data column recorded in chronological order. Time series data mainly focuses on the trend of a time period (a period of time). Compared with calculating data at a specific time point, calculating time series data can more accurately reflect the periodic or quasi-periodic changes of data, and make more accurate and real-time monitoring and prediction based on the periodicity or quasi-periodicity. The calculated control quantity is more precise and can better meet actual requirements, the analysis time is shorter, and the feedback is faster. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of the calendering system of the present invention.

[0018] Figure 2It is a schematic structural diagram of the micro-tension control unit of the present invention.

[0019] Figure 3 It is a structural schematic diagram of the swing roller seat.

[0020] Figure 4 This is the control flow chart of the roll gap control subsystem.

[0021] Figure 5 This is the control flow chart of the belt speed control subsystem.

[0022] Figure 6 This is the control flow chart of the micro-tension control subsystem.

[0023] Figure 7 This is the control flow chart of the deviation correction subsystem.

[0024] Figure 8 Optimization process for the Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) model.

[0025] Fig. 9 This is the neural network control flow chart of each subsystem control center.

[0026] Fig.10 This is a flow chart from neural network to PID controller and then to actuator.

[0027] In the figure: 10. Micro tension control unit; 1. Swing roller; 2. Swing arm; 3. Swing roller seat; 310. Top screw hole; 4. Mandrel; 5. Bearing with seat; 6. Swing roller motor; 7. Angle sensor; 8. Tension sensor; 100, film forming component; 11, film forming support roller; 12, film forming pressure roller; 13, first roller gap control actuator; 14, first roller gap detection sensor; 15, first bearing clearance eliminating mechanism; 16, first guide roller; 200, thinning component; 21, thinning support roller; 22, thinning pressure roller; 23, first tension cutoff device; 24, floating roller; 25, second bearing clearance elimination mechanism; 26, second guide roller; 27, first thickness gauge; 28, second roller gap control actuator; 29, second roller gap detection sensor; 300, winding component; 31, winding shaft assembly; 32, second tension isolation device; 33, second thickness gauge; 34, ultrasonic sensor; 35, deviation correction actuator; 36, third guide roller; 400. Diaphragm. DETAILED DESCRIPTION

[0028] The specific implementation of the present invention is described below in conjunction with the accompanying drawings.

[0029] like Figure 1As shown, the calendering system of the present invention includes a film forming component 100, a thinning component 200 and a winding component 300 which are sequentially arranged along the production direction of the film 400.

[0030] The film forming component 100 includes a pair of film forming support rollers 11 and film forming pressure rollers 12. The film forming pressure rollers 12 are located inside the film forming support rollers 11. The film forming pressure rollers 12 and the film forming support rollers 11 are eccentrically arranged. The film forming pressure rollers 12 are connected to a servo motor and driven by a servo motor (not shown in the figure). The film forming pressure rollers 12 can be adjusted up and down compared to the film forming support rollers 11. A first roll gap control actuator 13 is arranged on the outside of one of the film forming support rollers 11. The film forming support rollers 11, the film forming pressure rollers 12, and the first roll gap control actuator 13 are arranged in the horizontal direction. The first roll gap control actuator 13 controls the displacement of the film forming support rollers 11 by controlling the valve group to adjust the roll gap width between the film forming pressure rollers 12. A first roll gap detection sensor 14 is arranged on the outside of the opposing pressure point of the two film forming pressure rollers 12 for real-time detection of the roll gap between the two film forming pressure rollers 12. The first bearing clearance eliminating mechanism 15 is arranged outside the two film forming support rollers 11, which is used to eliminate the bearing clearance of the film forming support roller 11 and the film forming pressure roller 12, so as to avoid the bearing clearance affecting the roller gap adjustment and ensure the adjustment accuracy of the first roller gap control actuator 13. The film forming component 100 is also provided with a plurality of first guide rollers 16 for guiding the film sheet 400.

[0031] The thinning component 200 includes a pair of thinning support rollers 21 and thinning pressure rollers 22. The thinning pressure rollers 22 are located inside the thinning support rollers 21 and can be adjusted left and right relative to the thinning support rollers 21. The thinning pressure rollers 22 are connected to the servo motor and driven by the servo motor (not shown in the figure). A second roll gap control actuator 28 is arranged on the outside of one of the thinning support rollers 21. The thinning support rollers 21, the thinning pressure rollers 22 and the second roll gap control actuator 28 are arranged in the vertical direction. The second roll gap control actuator 28 controls the displacement of the thinning support rollers 21 through the control valve group to adjust the roll gap width between the thinning pressure rollers 22. A second roll gap detection sensor 29 is arranged on the outside of the pressing part of the two thinning pressure rollers 22 for real-time detection of the roll gap between the two thinning pressure rollers 22. A first tension isolating device 23 is provided on the material-incoming side of the thinning roller 22, which is used to isolate the tension of the film 400 to avoid the tension of the film-forming component 100 and the thinning component 200 from affecting each other. A floating roller 24 is provided on the material-outgoing side of the thinning roller 22, and the floating roller 24 tensions the film 400 by gravity. The floating roller 24 can be weighted according to the tension requirement of the film 400. A second bearing clearance elimination mechanism 25 is provided on the outer side of the two thinning support rollers 21, which is used to eliminate the bearing clearance between the thinning support roller 21 and the thinning roller 22 to ensure the accuracy of roller gap adjustment. A first thickness gauge 27 is provided on the material-incoming side of the first tension isolating device 23, and the first thickness gauge 27 is used to detect the thickness of the film 400 being formed. The thinning component 200 is also provided with a plurality of second guide rollers 26 for guiding the film 400.

[0032] The winding component 300 includes a winding shaft assembly 31, and the winding shaft of the winding shaft assembly 31 is connected to a servo motor and driven by the servo motor (not shown in the figure). A second tension isolation device 32, a second thickness gauge 33, and an ultrasonic sensor 34 are sequentially arranged on the material-incoming side of the winding shaft assembly 31 along the conveying direction of the film 400. The second tension isolation device 32 is used to isolate the tension of the film 400 to prevent the tension of the thinning component 200 and the winding component 300 from affecting each other. The second thickness gauge 33 is used to detect the thickness of the film 400. The ultrasonic sensor 34 is used to detect the position of the film 400. A deviation correction actuator 35 is arranged on the frame of the winding shaft assembly 31, which is used to move the frame for deviation correction to ensure that the film 400 is accurately centered when winding. The winding component 300 is also provided with a plurality of third guide rollers 36 for guiding the film 400.

[0033] The film forming part 100, the thinning part 200 and the winding part 300 are respectively provided with a micro tension control unit 10. The micro tension control unit 10 of the film forming part 100 is arranged on the discharge side of the film forming roller 12; the micro tension control unit 10 of the thinning part 200 is arranged between the thinning roller 22 and the first tension isolation device 23, and between the thinning roller 22 and the floating roller 24; the micro tension control unit 10 of the winding part 300 is arranged between the winding shaft assembly 31 and the second thickness gauge 33.

[0034] like Figure 2 As shown, the micro tension control unit 10 includes a tension swing roller assembly and at least one tension sensor 8. The tension sensor 8 is located at the discharge side of the tension swing roller assembly and is used to detect the tension of the film 400. Figure 1 As shown, in this embodiment, the film forming part 100 and the winding part 300 are respectively provided with a tension sensor 8; the thinning part 200 is provided with two tension sensors 8, and the two tension sensors 8 are respectively arranged on the feeding side and the discharging side of the thinning roller 22. Figure 2 As shown, the tension swing roller assembly includes two swing rollers 1 that are symmetrical on both sides. The two ends of the swing rollers 1 are connected to the swing arm 2 through the swing roller seat 3. The swing arm 2 is provided with a core shaft 4. The core shaft 4 on one side is connected to the swing roller motor 6 through the seat bearing 5. The core shaft 4 on the same side as the swing roller motor 6 is provided with an angle sensor 7. The angle sensor 7 is used to detect the rotation angle of the swing roller 1. The swing roller motor 6 adopts a servo motor. Figure 1 As shown, top screw holes 310 are provided on opposite sides of the swing roller seat 3, and the central axes of the top screw holes 310 on both sides pass through the center of the swing roller seat 3 and are located on the same plane. The swing roller seats 3 at both ends of the swing roller 1 are staggered 90° around the axial direction, and the central axes of the top screw holes 310 of the swing roller seats 3 at both ends are perpendicular to each other. The swing roller seat 3 at one end can be adjusted from two directions through its two top screw holes 310, and the swing roller seat 3 at the other end can be adjusted from the other two directions through its two top screw holes 310. Therefore, the positions of the two ends of the swing roller 1 can be adjusted in four directions in total. Without disassembling the machine, each swing roller 1 can be fine-tuned in its position in four directions to ensure the symmetry of the swing roller 1 and its parallelism with each other, and to ensure the accuracy of the tension adjustment.

[0035] The present invention uses a calendering control system to perform real-time monitoring and real-time parameter adjustment to ensure the stability and accuracy of the calendering process. The calendering control system includes a roll gap control subsystem, a belt speed control subsystem, a micro-tension control subsystem and a deviation correction subsystem.

[0036] The roll gap control subsystem is used to control the adjustment of the roll gaps of the film forming roller 12 of the film forming part 100 and the thinning roller 22 of the thinning part 200. The roll gap control actuators of the film forming part 100 and the thinning part 200 are electrically connected to the roll gap control subsystem with the roll gap detection sensor, the first thickness gauge 27 of the thinning part 200 and the second thickness gauge 33 of the winding part 300. The roll gap control actuators adjust the roll gaps of the film forming roller 12 and the thinning roller 22 according to the roll gap information detected by the roll gap detection sensor and the thickness information of the film 400 detected by the first thickness gauge 27 and the second thickness gauge 33.

[0037] The detailed steps are as follows: During the operation of the equipment, the first thickness gauge component 27 detects the thickness of the diaphragm 400 in real time and sends it to the control center. The control center calculates the deviation rate 1 between the actual thickness and the calibrated thickness. The calculation formula of the deviation rate 1 is:

[0038] Where a is the nominal thickness and x is the actual thickness If the deviation rate is within the normal fluctuation range (plus or minus 5%), the data is stored in the repository and no control operation is performed.

[0039] If the deviation rate is within the rated range (plus or minus 5% to plus or minus 10%), the control center will adopt the first control module. First, based on the current time series thickness data of the first thickness gauge 27, the 10 time series thickness data before this timestamp and the current time series thickness data are retrieved from the storage library to form a time series data set {T1, T2, …, T 11}, and then the time series data set {T1,T2,…,T 11} Input the zebra optimized recurrent neural network algorithm (RNN-ZOA) and calculate the output parameter set {K P ,T I ,T D ,tsd,Mpd,ess,e(t)} to PID. After receiving the parameter set, PID calculates e(t) to obtain a position adjustment instruction to the servo-hydraulic proportional valve of the second roll gap control actuator 28 of the thinning component 200. This process takes milliseconds. The servo-hydraulic proportional valve controls the second roll gap control actuator 28 to extend or retract to adjust the thinning support roller 21 and then adjust the roll gap of the thinning component 200.

[0040] The deviation film is thinned after being corrected by the thinning component 200, and the second thickness gauge 33 transmits the thickness of the monitored finished film to the control center, and the control center calculates the deviation rate of the actual thickness of the finished film and the rated thickness.

[0041] When the deviation rate is less than plus or minus 6%, the roll gap is not corrected and the time series thickness data is stored in the storage library; When the deviation rate is greater than 6%, the time series thickness data of the first thickness gauge 27 is shielded, and the current time series thickness data of the second thickness gauge 33 is used as a reference. The 10 time series thickness data before this timestamp and the current time series thickness data are retrieved from the storage library to form a time series data set {T1', T2', ..., T 11 '}, and then the time series data set {T1',T2',…,T 11 '} Input the zebra optimized recurrent neural network algorithm (RNN-ZOA) and calculate the output parameter set {K P ,T I ,T D ,tsd,Mpd,ess,e(t)} to PID. After receiving the parameter set, PID calculates e(t) to obtain a position adjustment instruction to the servo hydraulic proportional valve of the second roll gap control actuator 28 of the thinning component 200. The time taken for this process is in the millisecond level. The servo hydraulic proportional valve controls the extension or retraction of the second roll gap control actuator 28 to adjust the roll gap of the thinning component 200 until the deviation rate is reduced to within 6%. After the adjustment action is completed, the second roll gap detection sensor 29 detects the roll gap and feeds it back to PID. Thereafter, the second roll gap detection sensor 29 is released to control the roll gap of the thinning component 200. The shielding of the first thickness gauge component 27 is adjusted based on the timing thickness signal of the first thickness gauge component 27. At the same time, the zebra optimization recurrent neural network algorithm (RNN-ZOA) that controls the roller gap of the thinning component 200 is optimized twice to ensure that the thickness deviation rate of the finished film is within the set range. After the roller gap adjustment of the thinning component 200 is completed, the second thickness gauge 33 continuously monitors the thickness of the finished film 400 and sends it to the control center. If the thickness returns to the fluctuation range, the control is completed and the second roller gap control actuator 28 no longer operates.

[0042] If the deviation rate exceeds the rated range (plus or minus 10%), it means that the second roll gap control actuator 28 needs to be adjusted or cannot be adjusted. Then the control center will adopt the second control module. First, based on the current sequence thickness data of the first thickness gauge 27, the 10 time series thickness data before this timestamp and the current time series thickness data are retrieved from the storage library to form a time series data set {T1, T2, …, T 11}, and then the time series data set {T1,T2,…,T 11} Input the zebra optimized recurrent neural network algorithm (RNN-ZOA) and calculate the output parameter set {K P ,T I ,T D,tsd,Mpd,ess,e(t)} to PID. After receiving the parameter set, PID calculates e(t) to obtain the position adjustment instruction to the servo-hydraulic proportional valve of the first roller gap control actuator 13 of the film forming component 100. The time used for this process is in the millisecond level. The servo-hydraulic proportional valve sends a position instruction to control the first roller gap control actuator 13 to extend or retract to adjust the film forming support roller 11 and then adjust the roller gap of the film forming component 100 until the thickness deviation rate of the film 400 produced by the film forming component 100 is within plus or minus 10%. Then it switches to the first control module. During this period of time, the second thickness gauge 33 monitors and records the time series data in real time, and removes the unqualified film 400 during this period of time after the winding is completed.

[0043] During the roll gap adjustment process, the first roll gap detection sensor 14 synchronously monitors the actual roll gap and feeds back the signal to the control center to ensure the completion of the roll gap adjustment instruction. All collected data are time series data.

[0044] The general PID control process is as follows: the first thickness gauge component 27 monitors the thickness of the film 400 and sends the signal to the PID; after the PID takes the average of the data set for this period of time, if the average and the set value exceed the fluctuation range, the output signal is adjusted to the roller gap between the film pressing rollers 12. The traditional PID control method is to measure before adjustment, so it is inevitable that a section of the film 400 with unqualified thickness will be produced, so that the overall thickness uniformity of the film 400 will be poor, and the control method based on the zebra optimization recurrent neural network algorithm (RNN-ZOA) can effectively reduce the length of the film 400 with unqualified thickness, so that the overall thickness uniformity of the film 400 is higher.

[0045] like Figure 4 As shown, the control method of the roll gap control subsystem is as follows: 1) the first roll gap detection sensor 14 and the second roll gap detection sensor 29 transmit the real-time detected roll gap signal, and the first thickness gauge 27 and the second thickness gauge 33 transmit the real-time monitored thickness signal of the diaphragm 400 to the roll gap control center; 2) the roll gap control center performs fitting calculation on the roll gap signal and the thickness signal based on the zebra optimized recurrent neural network algorithm (RNN-ZOA); 3) the roll gap control center sends the time series data obtained after the fitting calculation to the first roll gap control actuator 13 and the second roll gap control actuator 28; 4) the first roll gap control actuator 13 controls the hydraulic system to adjust the displacement of the film forming support roller 11 and the thinning support roller 21 through the control valve group according to the instruction, thereby adjusting the displacement of the film forming pressure roller 12 and the thinning pressure roller 22, and then adjusting the roll gap distance between the two film forming pressure rollers 12 and the two thinning pressure rollers 22, so as to accurately adjust the thickness of the diaphragm 400.

[0046] The belt speed control subsystem is used to control the conveying speed of the film 400. The servo motors connected to the film forming roller 12, the thinning roller 22, the winding shaft assembly 31, the first tension isolation device 23, and the second tension isolation device 32 are electrically connected to the belt speed control subsystem. The belt speed control subsystem uses the speed of the servo motor of one of the rollers (here, the thinning roller 22) as a reference to adjust the speeds of the servo motors of the other rollers and the winding shaft and the swing roller motor 6.

[0047] The detailed steps are as follows: The servo motors connected to the film-forming roller 12, the thinning roller 22, the winding shaft assembly 31, the first tension isolation device 23, and the second tension isolation device 32 transmit the timing data-speed signal to the control center. The control center uses the speed of the servo motor of the thinning roller 22 as a reference, calculates the speeds of the remaining servo motors and the reference speed, and calculates the speed deviation rate of each servo motor. When the deviation rate is within the normal range, the control center does not issue instructions to other servo motors based on the zebra optimized recurrent neural network algorithm (RNN-ZOA). When the deviation rate exceeds the normal range, instructions are issued to the servo motors with the exceeded deviation rate to adjust their speeds.

[0048] When the material or process changes and the belt speed needs to be changed, the control center will issue instructions (including acceleration, final speed and other parameters) to the servo motors connected to the film forming roller 12, the thinning roller 22, the winding shaft assembly 31, the first tension barrier 23, and the second tension barrier 32 based on the zebra optimized recurrent neural network algorithm (RNN-ZOA) to increase or decrease their speeds to meet the speed change requirements. After adjusting the speed, the servo motor speed of the thinning roller 22 is also used as the reference to monitor the speeds of the remaining servo motors to ensure that the deviation rate between the speeds of each servo motor and the speed of the servo motor of the thinning roller 22 is within the normal range.

[0049] The Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) embedded PID is more accurate in output parameters compared to traditional PID control.

[0050] like Figure 5As shown, the control method of the belt speed control subsystem is as follows: 1) each servo motor feeds back the speed signal to the servo driver; 2) the servo driver transmits the received signal to the belt speed control center; 3) the belt speed control center uses the speed of the servo motor of one of the pressure rollers (reference roller) in the film forming part 100 or the thinning part 200 as a reference based on the zebra optimized recurrent neural network algorithm (RNN-ZOA) to analyze and calculate other servo motors; 4) the belt speed control center sends the instructions obtained after analysis and calculation to the servo driver; 5) the servo driver adjusts the speed of the servo motor according to the instruction to stabilize the servo motor at the set speed, thereby avoiding problems such as belt breakage or insufficient compaction density caused by excessive speed difference between the servo motors.

[0051] The micro-tension control subsystem is used to control the tension of the diaphragm 400 to prevent the diaphragm 400 from breaking, wrinkling, etc. The swing roller motor 6, angle sensor 7, and tension sensor 8 of the micro-tension control unit 10 are electrically connected to the micro-tension control subsystem. The swing roller motor 6 adjusts the micro-tension of the diaphragm 400 according to the rotation angle information detected by the angle sensor 7 and the tension information detected by the tension sensor 8.

[0052] The detailed steps are as follows: Here, the micro-tension control subsystem at the film forming part 100 is taken as an example, and the logic and steps of the other micro-tension control subsystems are consistent. Before the equipment is operated, the micro-tension control unit 10 is placed horizontally, the swing arm 2 is horizontal, and the swing roller 1 is at the same height, and then the swing roller motor 6 is initialized and adjusted to the torque mode to output the rated torque. When the equipment is running, the tension sensor 8 will monitor the tension of the diaphragm 400 in real time and transmit this time series data to the micro-tension control subsystem, collect its time series data tension and input it into the control center. If the tension time series data at a certain time exceeds the fluctuation range, the control center will retrieve the time series tension of the previous period of time, and calculate and fit this group of time series data sets through the zebra optimized recurrent neural network algorithm (RNN-ZOA) to predict its tension trend. If this trend is within the fluctuation range, the control center will not correct it; if this trend is outside the fluctuation range or it is predicted that it will exceed the fluctuation range in the future, the correction value is calculated based on this AI algorithm and an instruction is issued to the swing roller motor 6 to adjust this section of tension by changing the output torque of the swing roller motor 6. When the output torque adjustment of the swing roller motor 6 is completed, the tension sensor 8 continues to monitor the diaphragm tension. If the tension is within the normal fluctuation range at this time, the control is completed.

[0053] During the tension fluctuation correction process, the rotation angle of the swing roller motor 6 will change, which will cause the swing arm 2 to break the overall horizontal dynamic balance state. This overall horizontal dynamic balance has a significant impact on the control of micro-tension and the smooth operation of the diaphragm 400 in the tension section. Therefore, the present invention also corrects the position of the swing arm 2 to ensure the overall horizontal dynamic balance state of the swing arm 2. During the tension fluctuation correction process, the angle signal of the swing roller motor 6 will be synchronously sent to the control center. Based on the zebra optimized recurrent neural network algorithm (RNN-ZOA), the control center will jointly construct a function with the angle information of the swing roller motor 6 and the speed signal of the servo motor of the first tension isolation device 23 in advance, and output it to the servo motor of the first tension isolation device 23 after calculation through the correlation function, so that it adjusts the speed to adjust the position of the swing arm 2 and the angle of the swing roller motor 6; as the quasi-static increase of the speed of the servo motor of the first tension isolation device 23, the roller speed of the first tension isolation device 23 will be faster than the roller speed of the film forming roller 12, and a speed difference will gradually occur between the two, and the diaphragm 400 will be gradually tightened, which will make the swing arm 2 gradually tend to be horizontal, so as to adjust the angle deflection of the swing roller motor 6. When this adjustment process is completed, the angle information of the swing roller motor 6 is still continuously sent to the control center, and the control center judges this data. If it is out of the normal fluctuation range, the control is completed.

[0054] The traditional PID cannot construct an effective function when adjusting the overall horizontal dynamic balance of the swing arm 2. It is worse than the zebra optimized recurrent neural network algorithm (RNN-ZOA) in terms of variability and adaptability to data changes. The zebra optimized recurrent neural network algorithm (RNN-ZOA) can output parameters more accurately and provide predictions on this basis.

[0055] like Figure 6 As shown, the control method of the micro-tension control subsystem is as follows: 1) the angle sensor 7 transmits the rotation angle signal of the swing roller 1 detected in real time, and the tension sensor 8 transmits the tension signal of the diaphragm 400 detected in real time to the micro-tension control center; 2) the micro-tension control center analyzes and calculates the rotation angle signal and the tension signal based on the zebra optimization recurrent neural network algorithm (RNN-ZOA); 3) the micro-tension control center sends the instructions obtained by the analysis and calculation to the swing roller motor 6; 4) the swing roller motor 6 adjusts the rotation angle and speed of the swing roller 1 according to the specified adjustment, thereby adjusting the tension of the diaphragm 400, and the tension adjustment is more accurate. In addition to real-time monitoring of the tension of the diaphragm 400 through the tension sensor 8, the micro-tension control subsystem also detects the angle of the swing roller 1 in real time through the angle sensor 7. The angle sensor 7 can provide real-time feedback on the actual rotation angle of the swing roller 1 during tension adjustment, providing more accurate parameters for control, making the tension adjustment more stable and accurate.

[0056] The deviation correction subsystem is used to adjust the position of the winding shaft assembly 31 to ensure that the film 400 is accurately centered when winding. The ultrasonic sensor 34 of the winding component 300 is electrically connected to the deviation correction subsystem with the deviation correction actuator 35. The deviation correction actuator 35 adjusts the position of the winding shaft assembly 31 to correct the deviation according to the position information detected by the ultrasonic sensor 34.

[0057] The detailed steps are as follows: When the equipment is running, the ultrasonic sensor 34 continuously monitors the opposite edge of the diaphragm 400. Due to the influence of factors such as tension and friction during the operation of the diaphragm 400, the relative position deviation between the diaphragm 400 and the winding shaft assembly 31 at different times will cause uneven curling. Therefore, the ultrasonic sensor 34 monitors the opposite edge of the diaphragm and converts it into a position offset, and transmits the offset signal to the control center at the same time; the control center makes a real-time judgment on the offset. If it does not exceed the fluctuation range, the control center does not issue an instruction. If a certain time series offset exceeds the fluctuation range, the control center immediately uses this time series offset as a reference, retrieves part of the time series data before this time period, and calculates it through the Zebra Optimization Recurrent Neural Network Algorithm (RNN-ZOA) fitting calculation to predict its future trend. On this basis, the motor rotation angle of the correction actuator 35 is calculated, and the angle signal is transmitted to the servo motor of the correction actuator 35. The servo motor rotates the corresponding angle to drive the motion rod of the correction actuator 35 to extend and retract, thereby pushing the winding shaft assembly 31 to move, ensuring the uniformity of the curling of the diaphragm 400 after winding. When this step is completed, the ultrasonic sensor 34 continues to monitor and send signals. If the deviation is within the normal fluctuation range at the end, the servo motor angle of the deviation correction actuator 35 no longer changes, and the deviation correction control is completed.

[0058] like Figure 7 As shown, the control method of the correction subsystem is as follows: 1) The ultrasonic sensor 34 transmits the real-time detected position signal of the diaphragm 400 to the correction control center; 2) The correction control center analyzes and calculates the position signal based on the zebra optimized recurrent neural network algorithm (RNN-ZOA); 3) The correction control center sends the instructions obtained by the analysis and calculation to the correction actuator 35; 4) The correction actuator 35 controls the overall movement of the frame of the winding shaft assembly 31 according to the instructions, adjusts the position of the winding shaft assembly 31 to achieve correction, and ensures that the winding is centered.

[0059] The control centers of the roller gap control subsystem, belt speed control subsystem, micro-tension control subsystem and deviation correction subsystem all use the Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) to perform fitting calculation or analytical calculation on the time series data, and predict reasonable control quantities based on the Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA). The control quantities correspond to the displacement of the film forming support roller 11 and the thinning support roller 21 in the roller gap control subsystem, the speed of the servo motor in the belt speed control subsystem, the rotation angle and speed of the swing roller motor 6 in the micro-tension control subsystem, and the movement of the frame of the winding shaft assembly 31 in the deviation correction subsystem. The neural network structure includes an input layer, a hidden layer and an output layer, and the neural network also has a suitable activation function. The Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) performs fitting calculations or analytical calculations on time series data. Time series data refers to time series data, which is a data column recorded in chronological order. Time series data mainly focuses on the trend of a time period (a period of time). Compared with calculating data at a specific time point, calculating time series data can more accurately reflect the periodic or quasi-periodic changes of data, and make more accurate and real-time monitoring and prediction based on periodicity or quasi-periodicity. The calculated control quantity is more precise and can better meet actual requirements, with shorter analysis time and faster feedback.

[0060] The calendering control system of the present invention (using the zebra optimized recurrent neural network algorithm (RNN-ZOA)) is embedded and deployed in the PID controller as a control module. The control quantity obtained by the zebra optimized recurrent neural network algorithm (RNN-ZOA) is used as the input vector of the PID controller. The output quantity calculated by the PID controller is output to the corresponding actuator to adjust the corresponding components. The calendering control system of the present invention is embedded and deployed in the PID controller as a control module, and the zebra optimized recurrent neural network algorithm (RNN-ZOA) is centralized in the equipment and system to make it more intelligent and autonomous. Compared with the traditional centralized computing model, the embedded algorithm model embeds the AI ​​algorithm capability into the device itself, so that it can process data and make decisions locally without relying on cloud servers. It can achieve faster and more real-time data analysis and response, reduce dependence on the network, improve system efficiency and performance, and can quickly understand and respond to user needs, automatically adapt to environmental changes, and provide smarter and more personalized services.

[0061] In the present invention, a discrete time system is used in the PID controller. The discrete time system can suppress noise and interference through digital filtering and other technologies. Compared with the continuous time system, it is more robust and suitable for real-time control applications. Complex control strategies can be implemented through easy algorithms. In the present invention, PID uses the Euler transform discretization control algorithm: where P is the proportional band. The smaller P is, the larger the error signal (e(t)) is amplified. PThe larger the value, the greater the set value. I represents the integral part of the control algorithm. This part is to eliminate the "residual error". Without this part, the set value and the process value may be different even in the stable state. Finally, D represents the differential part of the algorithm. This part has two goals. It makes the system more flexible to sudden changes or disturbances in the set value, but most importantly, it helps to stabilize the system.

[0062] The transfer function of PID is:

[0063] Where s is the Laplace variable (s=α+jω), K p is the amplification factor, T I is the integration time, T D is the derivative time, {K p , T I , T D} is the parameter set of PID, which depends on the physical properties of the process.

[0064] The adjustment function of PID is:

[0065] The integral of e(t) can be approximated as a rectangular sum, that is

[0066] Among them, e'(t) can be approximated as a straight line, that is,

[0067] Therefore, when using the Euler transform discrete control algorithm, the PID gets the following expression:

[0068] like Fig.10 As shown, the present invention introduces the zebra algorithm to optimize the neural network model based on the Euler change discrete control algorithm, and improves the PID input parameter set {K p , T I , T D}, and at the same time, the three basic characteristics of the dynamic response of neural network training {maximum overshoot (Mp), settling time (ts), steady-state error (ess)} are introduced to improve the accuracy.

[0069] Before being embedded in the control system, the Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) needs to be trained to learn the mapping function X→Y, where X is the input vector and Y is the output vector. The recurrent neural network model uses historical data for expert training, and the historical data includes the roller gap width, diaphragm thickness, motor speed, diaphragm tension, swing roller angle, position and other key timing parameter data required by each sub-control system. The information collected by the sensor is preprocessed before being input into the recurrent neural network model. The preprocessing includes noise removal, data normalization and other operations, which is conducive to improving the training effect of the neural network model. The recurrent neural network model can also be updated and adjusted regularly, and retrained when the working conditions change to adapt to changes in the working environment and the evolution of the system. The recurrent neural network model can also store and self-learn multiple processes to obtain flexible self-regulation functions. During the training process of the recurrent neural network model, the zebra optimization algorithm is used to continuously adjust the weights and biases of the neural network to minimize the error value between the predicted output and the actual output, and improve the accuracy and stability of the predicted output.

[0070] Figure 8 The figure shows the optimization process of the Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) model. In the problem of optimizing the RNN model, there are five variables in the parameter matrix that need to be optimized: the number of network layers, the number of neurons, the learning rate, the epoch, and the batch size. Each variable needs to have an upper and lower limit.

[0071] The objective function to be minimized is:

[0072] t is the training time of the neural network, and loss is the loss value of the neural network. m and n represent weights, which are user-defined parameters, allowing users to selectively focus on optimization goals.

[0073] After the optimization starts, the data set needs to be divided into training set and test set.

[0074] Next, we build the RNN model. The variables that need to be optimized are not set yet and are pending.

[0075] Randomly initialize n sets of hyperparameters (n is a positive integer). These n sets of hyperparameters are called a population. Substitute the population into the RNN model and train the model. After the training, the objective function value can be obtained.

[0076] Next, the hyperparameters are optimized and updated according to the zebra optimization algorithm. The optimization goal is to minimize the objective function, and the parameter matrix to be optimized is abstracted as the location of the zebra.

[0077] In the zebra optimization algorithm, the first stage is the foraging stage. The individual that forages the most in the population (that is, the set of hyperparameters that minimize the objective function) is the pioneer zebra, and other individuals will move toward the location of the pioneer zebra. The mathematical model is as follows:

[0078]

[0079] Where r is a random number between [0,1] and I is a random value belonging to the set {1,2}.

[0080] The second stage is the predator encounter stage. In this stage, the individual will choose to disperse or gather according to the strength of the predator (determined by the random seed). Dispersion or gathering will update the position of the individual. If the new position of the individual makes the objective function smaller, the position of the individual will be updated. The mathematical model is as follows:

[0081]

[0082] Where t is the number of iterations, T is the maximum number of iterations, R is a constant of 0.01, Ps is the probability of switching between the two strategies. Its value is a random number between [0,1], and AZ is the state of the attacked zebra.

[0083] The optimization algorithm process is a loop iteration, and it is necessary to set an iteration end condition for the algorithm. The iteration end condition can be: reaching the upper limit of the loop iteration number; the objective function value obtained by the optimal parameters is less than the ideal value; the optimal parameters have not been updated after m rounds of iterations... one or more combinations of the above conditions.

[0084] After the iteration, find a set of parameters that minimizes the objective function. This set of parameters is the optimal solution. The RNN model corresponding to the optimal solution is the optimal RNN model. The accuracy of the model on the test set can be obtained by bringing the test set into the optimal RNN model.

[0085] like Fig. 9 As shown, the model selected by the Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) model is optimized and embedded in the control system. The specific control method of the control center of the system is as follows: 1) The Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) starts running; 2) Initialization of the actuators of each control subsystem: the first roll gap control actuator 13 and the second roll gap control actuator 28 of the roll gap control subsystem are initialized to the initial positions; the servo driver of the belt speed control subsystem is initialized to a constant speed; the swing roller motor 6 of the micro-tension control subsystem is initialized to a constant torque; the correction actuator 35 of the correction subsystem is initialized to the initial position; the initial positions of each actuator are preset during installation; 3) The Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) model predicts the initial control amount of the executive component according to the difference between the initialization actual parameter value and the set parameter value of the executive component of each control subsystem; 4) The actual parameter values ​​detected in real time by the detection components of each control subsystem are input into the Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) model as lightweight input vectors; For the roll gap control subsystem, the input vector X1=[W1_roll gap, W2_roll gap, T1_film, T2_film, timestamp] (W1 is the roll gap width between the two film forming rollers 12, W2 is the roll gap width between the two thinning rollers 22, T1 is the thickness of the film 400 before calendering (data detected by the first thickness gauge 27), T2 is the thickness of the film 400 after calendering (data detected by the second thickness gauge 33), and timestamp is the time sequence); For the belt speed control subsystem, the input vector X2=[n1_motor] (n1 is the speed of each servo motor); For the micro-tension control subsystem, the input vector X3=[θ_actual, θ_set, v, a] (where θ_actual is the actual angle of the swing roller 1, θ_set is the set angle of the swing roller 1, v is the speed of the swing roller motor 6, and a is the acceleration of the swing roller motor 6); For the deviation correction subsystem, the input vector X4=[ΔL] (ΔL is the displacement of the diaphragm 400); 5) The Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) model optimizes the data and calculates the predicted real-time control quantity, which is used as a lightweight output vector; For the roll gap control subsystem, the output vector Y1=[P] (P is the pressure execution instruction of the oil cylinder of the first roll gap control actuator 13 and the second roll gap control actuator 28); For the belt speed control subsystem, the output vector Y2=[n_motor] (n is the speed execution instruction of the motor of the reference roller); For the micro-tension control subsystem, the output vector Y3 = [y_1, y_2, ...] (where y_1 is the speed execution instruction of the swing roller motor 6, and y_2 is the acceleration execution instruction of the swing roller motor 6); For the deviation correction subsystem, the output vector Y4=[α] (the position execution instruction of the actuator of the deviation correction actuator 35); 6) The lightweight output vector of the Zebra Optimized Recurrent Neural Network Algorithm (RNN-ZOA) model is input into the PID controller, and the PID controller adjusts the output in real time according to the output vector (time series prediction control quantity) of the neural network model; The adjustment function of the PID controller is: u'(t) = u(t) + f(Y) (where u(t) is usually a function based on the error e(t), including proportional, integral and differential terms. The error e(t) refers to the difference between the actual parameter value and the set parameter value; f(Y) is an empirical adjustment function).

[0086] 7) Determine whether the signal is in a stable state based on the values ​​detected by the detection components of each control subsystem: if it is stable, execute the next step to complete the control; if it is unstable, return to the starting point and restart the operation until the signal is stable.

[0087] Each control subsystem of the calendering system of the present invention introduces a zebra optimized recurrent neural network algorithm (RNN-ZOA) in combination with a PID controller. The PID controller automatically adjusts the output in real time according to the real-time predicted control quantity of the zebra optimized recurrent neural network algorithm (RNN-ZOA), and can accurately control the roll gap, belt speed, micro-tension and deviation correction. When facing nonlinear, time-varying or multi-variable systems, complex dynamic performance and multi-variable coupling can also be accurately processed, with better adaptability and robustness, thereby improving the control performance of the system, the stability and accuracy of the system, and the production efficiency and product quality of the calender.

[0088] The above description is an explanation of the present invention, not a limitation of the present invention. The present invention may be modified in any form without violating the spirit of the present invention.

Claims

1. A roll gap control method for a calendering system, characterized in that: The roller gap control subsystem adjusts the roller gap distance between the two film forming rollers (12) and / or the two thinning rollers (22) according to the roller gap signal and the membrane (400) thickness signal. The control center of the roller gap control subsystem calculates the deviation rate one based on the actual thickness detected by the first thickness gauge component (27) and the calibrated thickness here. If the deviation rate one is within the range of ±5%, the data is stored in the storage library and no control operation is performed. If the deviation rate one is within the range of ±5% to ±10%, the control center uses the first control module to adjust the roller gap of the thinning component (200). If the deviation rate one exceeds the range of ±10%, the control center uses the second control module to adjust the roller gap of the film forming component (100).

2. The roll gap control method of the calendering system according to claim 1, characterized in that: After the second control module adjusts the roller gap of the film forming component (100) to a deviation rate within the range of ±10%, it switches to the first control module.

3. The roll gap control method of the calendering system according to claim 1, characterized in that: The control process of the first control module is to use the current time series thickness data of the first thickness gauge (27) as a reference, retrieve the 10 time series thickness data before the timestamp from the storage library and the current time series thickness data to form a time series data set {T1, T2, ..., T 11 }, the time series data set {T1,T2,…,T 11 } Input the zebra optimized recurrent neural network algorithm and calculate the output parameter set {K P ,T I ,T D ,tsd,Mpd,ess,e(t)} to PID. After receiving the parameter set, PID calculates e(t) to obtain a position adjustment instruction to the servo hydraulic proportional valve of the second roll gap control actuator (28) of the thinning component (200). The time used for this process is in the millisecond level. The servo hydraulic proportional valve controls the second roll gap control actuator (28) to extend or retract to adjust the thinning support roller (21) and further adjust the roll gap of the thinning component (200).

4. The roll gap control method of a calendering system according to claim 1, characterized in that: After the first control module adjusts the roller gap of the thinning component (200), the deviation film is thinned after being corrected by the thinning component (200), and the control center calculates the deviation rate 2 between the actual thickness detected by the second thickness gauge (33) and the rated thickness here. When the deviation rate 2 is less than ±6%, the roller gap is not corrected. When the deviation rate 2 is greater than ±6%, the roller gap of the thinning component (200) is adjusted based on the thickness signal of the second thickness gauge (33) until the deviation rate 2 is reduced to within ±6%.

5. The roll gap control method of a calendering system according to claim 3, characterized in that: When the deviation rate 2 is greater than 6%, the time series thickness data of the first thickness gauge (27) is shielded, and the current time series thickness data of the second thickness gauge (33) is used as a reference, and the 10 time series thickness data before the timestamp and the current time series thickness data are retrieved from the storage library to form a time series data set {T1', T2', ..., T 11 '}, and then the time series data set {T1',T2',…,T 11 '} Input the zebra optimized recurrent neural network algorithm and calculate the output parameter set {K P ,T I ,T D ,tsd,Mpd,ess,e(t)} to PID. After receiving the parameter set, PID calculates e(t) to obtain a position adjustment instruction to the servo hydraulic proportional valve of the second roll gap control actuator (28) of the thinning component (200). The time used for this process is in the millisecond level. The servo hydraulic proportional valve controls the extension or retraction of the second roll gap control actuator (28) to adjust the roll gap of the thinning component (200) until the deviation rate is reduced to within 6%. After the adjustment action is completed, the roll gap is detected by the second roll gap detection sensor (29) and fed back to the PID. Thereafter, the shielding of the first thickness gauge component (27) is unlocked, and the adjustment is performed based on the timing thickness signal of the first thickness gauge component (27).

6. The roll gap control method of a calendering system according to claim 3, characterized in that: When the deviation rate is less than ±6%, the time series thickness data is stored in the repository.

7. The roll gap control method of a calendering system according to claim 3, characterized in that: While the first control module adjusts the roller gap of the thinning component (200), a zebra optimization recurrent neural network algorithm (RNN-ZOA) for controlling the roller gap of the thinning component (200) is secondary optimized.

8. The roll gap control method of a calendering system according to claim 1, characterized in that: The control process of the second control module is to use the current time series thickness data of the first thickness gauge (27) as a reference, retrieve the 10 time series thickness data before the timestamp from the storage library and the current time series thickness data to form a time series data set {T1, T2, ..., T 11 }, the time series data set {T1,T2,…,T 11 } Input the zebra optimized recurrent neural network algorithm and calculate the output parameter set {K P ,T I ,T D ,tsd,Mpd,ess,e(t)} to PID. After receiving the parameter set, PID calculates e(t) to obtain a position adjustment command to the servo hydraulic proportional valve of the first roller gap control actuator (13) of the film forming component (100). The time used for this process is in the millisecond level. The servo hydraulic proportional valve sends a position command to control the first roller gap control actuator (13) to extend or retract to adjust the film forming support roller (11) and then adjust the roller gap of the film forming component (100) until the thickness deviation rate of the film (400) produced by the film forming component (100) is within plus or minus 10%.

9. The roll gap control method of a calendering system according to claim 1, characterized in that: During the time controlled and adjusted by the second control module, the second thickness gauge (33) monitors and records the time series data in real time, and after the winding is completed, unqualified film sheets (400) within this period are removed.

10. The roll gap control method of a calendering system according to claim 1, characterized in that: During the roll gap adjustment process, the first roll gap detection sensor (14) synchronously monitors the actual roll gap and feeds back the signal to the control center, and all collected data are time series data.

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