Intelligent punching pressure adjusting system for arc-shaped aluminum veneer

Abstract

The invention relates to the technical field of intelligent sensor control, in particular to an intelligent adjusting system for punching pressure of an arc-shaped aluminum veneer. The system comprises a sensor data acquisition module for acquiring each piece of monitoring data at each position in the punching process of the aluminum veneer; the sensor data processing module is used for acquiring confidence values of various monitoring data at each position and change response coefficients of the monitoring data at each position, and calculating the change response coefficients of the monitoring data according to the average level of the change response coefficients of the monitoring data in the data set of each position and the fluctuation condition of the confidence values of the monitoring data; adjusting coefficients of stable deviation change responses of all the positions are obtained, so that control parameters in a PI controller are adjusted when punching pressure adjustment is conducted on all the positions; and the punching pressure adjusting module is used for adjusting punching pressure through the adjusted PI controller. The adjusting precision of the punching pressure in the aluminum veneer punching process is improved.

Description

Technical Field

[0001] This application relates to the technical field of intelligent sensor control, and specifically relates to an intelligent punching pressure adjustment system for curved aluminum veneers. Background Art

[0002] Curved aluminum veneers are widely used in the field of modern architectural decoration. In the processing process of curved aluminum veneers, the punching process is a very crucial technology. In the punching process, the control of punching pressure plays a core role. Reasonable punching pressure is the key factor to ensure punching quality and extend the service life of molds and equipment. If the pressure is too small, the aluminum plate may not be pierced smoothly, resulting in quality problems such as uneven hole walls and excessive burrs; if the pressure is too large, the aluminum plate is prone to deformation, and in more serious cases, the mold and equipment will be damaged.

[0003] At present, intelligent sensors can collect various key data during the processing of curved aluminum veneers in real time and accurately, including information such as pressure, temperature, and aluminum plate deformation at different positions. By installing intelligent sensors on processing equipment, it is possible to comprehensively perceive different working conditions involved in the processing sequence and processing positions at different positions, providing rich and accurate data support for more precise control of punching pressure in the future. However, there are many problems in the punching pressure control link of curved aluminum veneers. At present, most enterprises still use the method of fixed pressure adjustment parameters for punching processing. Since the traditional punching pressure control method does not consider the influence of the processing sequence and processing positions under different working conditions in the actual processing process, without effective monitoring, the monitoring and control deviation of punching pressure in the actual processing process is relatively large. This not only makes the processing quality unstable and the scrap rate high, but also seriously restricts the improvement of production efficiency. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an intelligent punching pressure adjustment system for curved aluminum veneers, and the specific technical solutions adopted are as follows:

[0005] This application proposes an intelligent punching pressure adjustment system for curved aluminum veneers, and the system includes:

[0006] A sensor data acquisition module for obtaining each monitoring data at each position during the punching process of the aluminum veneer;

[0007] A sensor data processing module for obtaining the confidence values of various monitoring data at each position by the correlation relationship of each monitoring data at each position relative to other monitoring data, and combining the data deviation degree of each monitoring data between each position and multiple positions before it, and clustering and dividing the confidence degrees of all monitoring data at each position on each aluminum veneer in the same batch to obtain the data set at each position;

[0008] Analyze the differences between the monitored data at each position and the set target values, obtain the change response coefficients of the monitored data at each position, and based on the average level of the change response coefficients of the monitored data in the data set at each position and the fluctuation of the confidence values of the monitored data, obtain the adjustment coefficients for the change response of the stable deviation at each position. Adjust the control parameters in the PI controller for punching pressure adjustment at each position according to the adjustment coefficients and in combination with the initial control parameters at each position;

[0009] The punching pressure adjustment module is used to adjust the punching pressure through the adjusted PI controller.

[0010] Preferably, the monitored data includes: the pressure at each punching position on the aluminum single plate during the punching process of the aluminum single plate, the displacement at each punching position on the aluminum single plate, and the temperature of the mold.

[0011] Preferably, the calculation method for the confidence value of various monitored data at each position is:

[0012] a w,x =b w,x ×c w,x where a w,x represents the confidence value of the x-th type of monitored data at the w-th position, b w,x represents the average value of the cross-correlation coefficients of the x-th type of monitored data at the w-th position with all other monitored data with respect to the monitored data sequence, and c w,x represents the average value of the DTW distances between the monitored data sequences of the x-th type of monitored data at the w-th position and all its comparison positions.

[0013] Preferably, for each monitored data, sort the monitored data collected at each position in chronological order to form a monitored data sequence, and use a preset number of positions before the processing order of each position as the comparison positions for each position.

[0014] Preferably, the method for obtaining the data set at each position is:

[0015] Form a confidence value vector for each position in each aluminum single plate in the same batch by using the confidence values of all monitored data at each position, perform clustering division on the confidence value vectors at the same position of all aluminum single plates in the same batch, and use the clustering cluster with the largest number of divided vectors as the data set at each position.

[0016] Preferably, the method for obtaining the change response coefficients of the monitored data at each position is:

[0017] Calculate the difference between each monitored data at each position and its set target value respectively, and use the ratio of the difference to the set target value as the change response coefficient of each monitored data at each position.

[0018] Preferably, the calculation method of the adjustment coefficient for the stable deviation change response at each position is as follows:

[0019]

[0020] In the formula, s w represents the adjustment coefficient for the stable deviation change response at the w-th position; μ v represents the mean value of all change response coefficients corresponding to the v-th type of monitoring data in the data set at the w-th position; h v represents the normalized result of the variance of all confidence values of the v-th type of monitoring data in the data set at the w-th position.

[0021] Preferably, the control parameters in the PI controller include a proportional coefficient and an integral coefficient.

[0022] Preferably, when adjusting the punching pressure at each position, the control parameters in the PI controller are adjusted, including:

[0023] Obtain the initial proportional coefficient and integral coefficient for all punching positions of the aluminum single board through the decay curve method, and arrange them in the processing order of the positions to form the initial control parameter vectors p0 and i0 of the proportional coefficient and integral coefficient respectively. Then, form the adjustment vector S for the adjustment coefficients corresponding to all punching positions of the aluminum single board in the processing order of the positions. Combine the initial control parameter vectors of the proportional coefficient and integral coefficient and the adjustment vector to adjust the control parameters of the PI controller during punching pressure adjustment.

[0024] Preferably, the specific adjustment method for the proportional coefficient and integral coefficient of the PI controller during punching pressure adjustment is as follows:

[0025] p = p0 + p0 × S, i = i0 + i0 × S. In the formula, p and i are the control parameter vectors corresponding to the adjusted proportional coefficient and integral coefficient respectively.

[0026] The present application has the following beneficial effects:

[0027] In view of the fact that traditional pressure control does not analyze the influence of the processing sequence and processing position under different working conditions in the actual processing process, resulting in large deviations in the monitoring and control of the punching pressure in the actual processing process, and making the processing quality unstable. This application collects real-time monitoring data of punching processing at different positions during the processing of arc-shaped aluminum single plates, compares and analyzes the differences in punching states under different working conditions due to the processing sequence and processing position at different positions, extracts the characteristics of the stable deviation change response of punching processing at the same position for the same batch by comprehensively comparing the monitoring parameters of punching processing at each position in the aluminum single plate with the processing states of its surrounding positions, and then optimizes and adjusts the control of different positions according to the actual processing working condition characteristics of the aluminum single plate. The beneficial effect is that compared with the traditional control of the same pressure adjustment parameters for different positions of aluminum single plates under a fixed pressure, this application accurately adjusts the punching pressure adjustment parameters for different positions by combining the processing sequence and different working condition characteristics at different positions, improving the stability of the punching pressure adjustment for aluminum single plates. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0029] Figure 1 It is a block diagram of an intelligent punching pressure adjustment system for an arc-shaped aluminum single plate provided by the present application;

[0030] Figure 2 It is a flow schematic diagram for adjusting the control parameters in the PI controller when adjusting the punching pressure at each position provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manner, structure, characteristics and effects of an intelligent punching pressure adjustment system for an arc-shaped aluminum single plate proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0033] The following specifically describes the specific solution of an intelligent punching pressure adjustment system for arc-shaped aluminum single plates provided by this application in conjunction with the accompanying drawings.

[0034] Please refer to Figure 1 , which shows a block diagram of an intelligent punching pressure adjustment system for arc-shaped aluminum single plates provided by an embodiment of this application. The system includes:

[0035] A sensor data acquisition module for obtaining various monitoring data at each position during the punching process of the aluminum single plate.

[0036] In this embodiment, the monitoring data includes: the pressure at the positions of each punching hole on the aluminum single plate during the punching process of the aluminum single plate, the displacement at the positions of each punching hole on the aluminum single plate, and the temperature of the mold. Specifically, a pressure sensor is installed on the punch of the punching device to monitor the pressure value exerted on the arc-shaped aluminum single plate by the punch during the punching process in real time. In this embodiment, a high-precision strain-type pressure sensor is selected for the pressure sensor, which can accurately capture the subtle changes in pressure. A displacement sensor is installed on the aluminum plate fixing device. In this embodiment, a laser displacement sensor is used to monitor the deformation amount of the aluminum plate during the punching process. By arranging sensors at multiple key positions of the aluminum plate, comprehensive information on the deformation of the aluminum plate can be obtained. A temperature sensor is installed on the mold. In this embodiment, a thermocouple temperature sensor is used to monitor the temperature change of the mold during continuous punching. Because the increase in the mold temperature will affect its hardness and dimensional accuracy, and thus affect the punching pressure and quality.

[0037] It should be noted that in this embodiment, preferably, each sensor collects data in real time at a sampling frequency of 1 kHz, and transmits the collected analog signal to a data acquisition card so that the analog signal is converted into a digital signal and transmitted to the data processing module through a USB interface.

[0038] A sensor data processing module for obtaining the confidence values of various monitoring data at each position through the correlation relationship of each monitoring data at each position relative to other monitoring data, and combining the data deviation degree of each monitoring data between each position and its previous multiple positions, clustering and dividing the confidence levels of all monitoring data at each position on each aluminum single plate under the same batch to obtain the data set at each position; analyzing the difference between each monitoring data at each position and the set target value to obtain the change response coefficient of each monitoring data at each position, and based on the average level of the change response coefficients of each monitoring data in the data set at each position and the fluctuation of the confidence values of each monitoring data, obtaining the adjustment coefficient of the stable deviation change response at each position, and adjusting the control parameters in the PI controller for punching pressure adjustment at each position according to the adjustment coefficient and combining the initial control parameters at each position.

[0039] Due to the possible influence of interference factors such as equipment vibration, electromagnetic interference, and environmental temperature changes during the actual processing, noise is contained in the data collected by the sensor. Therefore, the data processing unit performs filtering processing on the received data. Specifically, in this embodiment, the Kalman filtering algorithm is used to filter and denoise the data collected by the sensor, removing the noise and interference signals in the collected data, and improving the accuracy and reliability of data analysis. Further, the collected data is normalized to convert data of different types and ranges into the same numerical range, facilitating subsequent analysis and comparison.

[0040] It is considered that in the actual processing process, the aluminum single plate is punched according to the set punching sequence. However, at different positions, due to different punching conditions around during processing, the pressure, temperature, and displacement will deviate to varying degrees during the punching process. Therefore, in order to perform stable processing under different working conditions, the differences in the parameter changes monitored between different positions during the aluminum single plate processing are analyzed. Furthermore, based on the analysis results, the stability change differences during the processing of the same position in the same batch are compared, and the punching pressure at different positions is optimized and controlled through the comparison results. The specific analysis and processing process is as follows:

[0041] (1) For each position during the aluminum single plate punching process, each monitored data at each position is sorted in chronological order to form each monitored data sequence. The monitored data includes pressure, temperature, and displacement data. Analyze the linear correlation degree between different monitored data. Specifically, in this embodiment, the cross-correlation coefficient between each monitored data and other monitored data with respect to the monitored data sequence is calculated. The cross-correlation coefficient accurately reflects the tightness of the linear correlation between different monitored data during the punching process at each position. For example, if the cross-correlation coefficient between the pressure data sequence and the displacement data sequence is relatively high, it indicates that during the punching process at this position, there is a strong linear correlation between the pressure change and the displacement change, that is, under the influence of different working conditions during the actual punching process at the current position, the relative relationship between the pressure and displacement during the punching process has not deviated.

[0042] To further accurately evaluate the stability of the punching process at each position, for each type of monitored data at each position, calculate the average value of the cross-correlation coefficients between each type of monitored data and all other monitored data. In this embodiment, for the convenience of expression, this average value is used as the first eigenvalue for the stable deviation analysis of each type of monitored data at each position during the punching process. The larger the first eigenvalue, the greater the possibility that the punching state will differ under the influence of stability changes during the actual punching process. Due to various interference factors under different working conditions, these interferences exhibit non-linear change characteristics, which will cause deviations in the relative relationship between different monitored data, thereby triggering differences in the punching state.

[0043] In addition, the stability deviation in the punching process of aluminum veneers is closely related to the working conditions of the surrounding processing. In actual processing, the processing positions and sequences of aluminum veneers are predetermined. Based on the above characteristics, for the position corresponding to the current punching, a preset number of positions before its processing sequence are obtained as the comparison positions of the current position. In this embodiment, the preset number is 5, and in the actual application scenario, the implementer sets it by himself. Furthermore, the DTW distances between the current position and each of its comparison positions for the monitoring data sequences of each type of monitoring data are calculated respectively, and the mean value of the DTW distances between the current position and all its comparison positions for each type of monitoring data is obtained. In this embodiment, for the convenience of description, the mean value of the DTW distances is used as the second eigenvalue of the position deviation correlation influence of each type of monitoring data at the current position. The larger this second eigenvalue is, the greater the difference in the punching state between the current position and the previous processing positions, that is, the greater the difference in the punching state caused by the influence of the surrounding positions during the actual processing process.

[0044] Taking into account the stable deviation characteristics of each type of monitoring data at each position and the punching state difference characteristics caused by the mutual influence between different positions, the confidence value of the stable deviation analysis of each type of monitoring data at each position during the punching process is analyzed. The confidence value measures the significance of the stable deviation correlation response characteristics of different positions of the corresponding monitoring data when a punching deviation occurs at the current position during the punching process. The specific calculation formula is: a w,x =b w,x ×c w,x , where a w,x represents the confidence value of the x-th type of monitoring data at the w-th position, b w,x represents the average value of the cross-correlation coefficients of the x-th type of monitoring data at the w-th position and all other monitoring data with respect to the monitoring data sequence, and c w,x represents the mean value of the DTW distances between the w-th position and all its comparison positions for the monitoring data sequence of the x-th type of monitoring data.

[0045] By means of the above calculations, the stability and the degree of influence of each type of monitoring data at each position during the punching process are analyzed, and then the subsequent processing process is optimized.

[0046] (2) Based on the linear correlation characteristics of each position in the punching process of aluminum single plates as described above, and the associated influence characteristics of the stable deviation between different processing sequences and processing positions, obtain the confidence value of the stable deviation analysis of each type of monitoring data in the actual punching process of each position. Further, considering that in the actual processing process, the processing requirements for aluminum single plates of the same batch are the same, that is, the set punching sequence and position are the same. Therefore, in the actual processing process, to optimize the control of the punching processing stability of the same position in the production process, analyze the stable deviation caused by the influence of different punching state deviations for the same position of aluminum single plates of the same batch in the processing process. Based on the analysis results, comprehensively analyze the stable deviation of the actual processing of different positions of aluminum single plates of the same batch.

[0047] Specifically, in this embodiment, form the confidence value vector of each position in each aluminum single plate of the same batch with the confidence values of all monitoring data at each position. Take the confidence value vectors corresponding to the same position in all aluminum single plates of the same batch as the input. In this embodiment, the agglomerative hierarchical clustering algorithm is used for division. The purpose is to reduce the large error in the stable deviation analysis caused by data anomalies in the same batch. Take the clustering cluster with the largest number of vectors after division as the data set corresponding to the same position. According to the above process of this embodiment, the data set corresponding to each position can be obtained.

[0048] Further, in this embodiment, for each position corresponding to each punching, calculate the difference between each type of monitoring data collected at each position and the set target value during its punching process, and take the ratio of the difference to the set target value as the change response coefficient of each type of monitoring data at each position. Further, combine the change response coefficients of each monitoring data at each position in the data set and the sensitivity characteristics of different monitoring data at the same position in the data set for the stable deviation analysis, and calculate the adjustment coefficient s of the stable deviation change response characteristics for each position in the aluminum single plate processing process. The specific calculation formula is as follows:

[0049]

[0050] In the formula, s w represents the adjustment coefficient of the stable deviation change response of the w-th position; μ v represents the mean value of all change response coefficients corresponding to the v-th type of monitoring data in the data set of the w-th position; h v represents the normalized result of the variance of all confidence values of the v-th type of monitoring data in the data set of the w-th position. In this embodiment, the softmax function is used for normalization processing. The specific normalization calculation process is prior art and will not be elaborated in this embodiment.

[0051] It should be understood that the larger the calculated adjustment coefficient is, the greater the possibility that the current position is affected by the deviation of the punching state caused by different working conditions in the processing sequence and the current processing position. It is necessary to timely adjust the response parameters in the actual punching process to improve the stability of punching processing.

[0052] (3) Based on the above analysis results, compared with the traditional unified processing control of aluminum single plates using a feedback controller, this application considers that the punching state of each position in the actual punching process may be affected by the punching states of other positions, resulting in a stable deviation in the actual punching process. Therefore, for the data in the same batch processing process, a comprehensive comparative analysis is carried out on the change response characteristics of the stable deviations of different positions to obtain the adjustment coefficient of the stable deviation change response corresponding to each position, and then the control parameters of different positions in the actual processing process are adaptively adjusted.

[0053] Specifically, in this application, the punching pressure during the processing is controlled by a PI controller, and the initial control parameters of all punching positions of the aluminum single plate are determined by the decay curve method. The control parameters include a proportional coefficient and an integral coefficient. The proportional coefficient and the integral coefficient are respectively arranged in the processing sequence of the positions to form the initial control parameter vectors p0 and i0 of the proportional coefficient and the integral coefficient; through the above analysis, if the adjustment coefficient is larger, it means that the influence of different positions being affected by different working conditions and having differences in punching states during the processing is greater. At this time, the proportional and integral coefficients need to be increased correspondingly to improve the response speed and reduce the steady-state error; therefore, the vector formed by arranging the adjustment coefficients corresponding to all punching positions of the aluminum single plate in the processing sequence of the corresponding positions is used as the adjustment vector S, and the control parameters for pressure adjustment of each punching corresponding position are adjusted based on the adjustment vector: p = p0 + p0×S, i = i0 + i0×S, where p and i are the control parameter vectors corresponding to the adjusted proportional coefficient and integral coefficient respectively. The elements in the control parameter vector of the adjusted proportional coefficient are the adjusted proportional coefficients corresponding to each position, and the elements in the control parameter vector of the integral coefficient are the adjusted integral coefficients corresponding to each position; it should be noted that in order to reduce the imbalance of system control, in this embodiment, all control parameters are adjusted in equal proportion.

[0054] Specifically, the flow chart of adjusting the control parameters in the PI controller for punching pressure adjustment at each position is as Figure 2 shown.

[0055] So far, according to the above process of this embodiment, the adjusted control parameters of the PI controller for pressure adjustment corresponding to all punching positions can be obtained.

[0056] The punching pressure adjustment module is used to adjust the punching pressure through the adjusted PI controller.

[0057] Through the above-mentioned sensor data processing module in this embodiment, the control parameters of the PI controller can be adjusted. According to the adjusted control parameters, the PI controller is used to intelligently adjust the punching pressure at each punching position. Among them, the specific control process of the PI controller is the prior art well-known to those skilled in the art. In this embodiment, preferably, the PI controller specifically uses an electro-hydraulic proportional control system to intelligently adjust the punching pressure. The electro-hydraulic proportional control system consists of an electro-hydraulic proportional valve, a hydraulic cylinder, and a controller. The electro-hydraulic proportional valve controls the pressure of the punch according to the adjusted control parameters output by the data processing module. The PI controller receives the target value of the pressure adjustment, compares it with the actual pressure value of the current punch, obtains the pressure deviation, and according to the pressure deviation, through the PI control algorithm and the optimized adjusted control parameters, outputs a control signal to the electro-hydraulic proportional valve to adjust the flow rate and pressure of the hydraulic oil, so that the punch pressure gradually approaches the target value. During the adjustment process, the punch pressure is monitored in real time and feedback adjustment is continuously performed to ensure the accuracy and stability of the pressure adjustment.

[0058] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0060] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent punching pressure adjustment system for arc-shaped aluminum single plates, characterized in that, The system includes: A sensor data acquisition module for acquiring various monitoring data at each position during the punching process of the aluminum veneer. A sensor data processing module for obtaining the confidence values of various monitoring data at each position by the correlation relationship of the monitoring data at each position relative to other monitoring data, and combining the data deviation degree of the monitoring data between each position and multiple previous positions, and performing clustering division on the confidence levels of all monitoring data at each position on each aluminum veneer under the same batch to obtain the data set at each position. Analyze the difference between the monitoring data at each position and the set target value to obtain the change response coefficient of the monitoring data at each position. According to the average level of the change response coefficients of the monitoring data in the data set at each position and the fluctuation of the confidence values of the monitoring data, obtain the adjustment coefficient of the stable deviation change response at each position. According to the adjustment coefficient and combined with the initial control parameters at each position, adjust the control parameters in the PI controller when adjusting the punching pressure at each position. A punching pressure adjustment module for adjusting the punching pressure through the adjusted PI controller.

2. The intelligent punching pressure adjustment system for an arc-shaped aluminum single panel according to claim 1, wherein, The monitoring data includes: the pressure at the position of each punching on the aluminum veneer, the displacement at the position of each punching on the aluminum veneer, and the temperature of the mold during the punching process of the aluminum veneer.

3. The intelligent punching pressure adjustment system for an arc-shaped aluminum single panel according to claim 1, characterized in that, The calculation method of the confidence value of various monitoring data at each position is: a w,x = b w,x × c w,x , where a w,x represents the confidence value of the x-th type of monitoring data at the w-th position, b w,x represents the average of the cross-correlation coefficients of the x-th type of monitoring data at the w-th position with all other monitoring data with respect to the monitoring data sequence, and c w,x represents the mean of the DTW distances between the monitoring data sequences of the x-th type of monitoring data at the w-th position and all its comparison positions.

4. The intelligent punching pressure adjustment system for an arc-shaped aluminum single panel according to claim 3, characterized in that, For each monitoring data, sort the monitoring data collected at each position in chronological order to form a monitoring data sequence, and use a preset number of positions before the processing order of each position as the comparison positions of each position.

5. The intelligent punching pressure adjustment system for an arc-shaped aluminum single panel as described in claim 1, wherein The method for obtaining the data set at each position is: Form a confidence value vector of each position in each aluminum veneer by the confidence values of all monitoring data at each position in each aluminum veneer under the same batch. Perform clustering division on the confidence value vectors at the same position of all aluminum veneers in the same batch, and use the clustering cluster with the largest number of divided vectors as the data set at each position.

6. The intelligent punching pressure adjustment system for an arc-shaped aluminum single panel as claimed in claim 1, wherein, The method for obtaining the change response coefficient of the monitoring data at each position is: Calculate the difference between the monitoring data at each position and the set target value respectively, and use the ratio of the difference to the set target value as the change response coefficient of the monitoring data at each position.

7. The intelligent punching pressure adjustment system for an arc-shaped aluminum single panel as described in claim 1, characterized in that, The calculation method of the adjustment coefficient of the stable deviation change response at each position is: where s w represents the adjustment coefficient of the stable deviation change response at the w-th position; μ v represents the mean of all change response coefficients corresponding to the v-th type of monitoring data in the data set at the w-th position; h v represents the normalized result of the variance of all confidence values of the v-th type of monitoring data in the data set at the w-th position.

8. The intelligent punching pressure adjustment system for an arc-shaped aluminum single panel according to claim 1, wherein, The control parameters in the PI controller include a proportional coefficient and an integral coefficient.

9. The intelligent punching pressure adjustment system for an arc-shaped aluminum single panel according to claim 1, characterized in that, The adjustment of the control parameters in the PI controller when adjusting the punching pressure at each position includes: Obtain the initial proportional coefficient and integral coefficient of all punching positions of the aluminum veneer through the attenuation curve method, and arrange them in the processing order of the positions to form the initial control parameter vectors p0 and i0 of the proportional coefficient and the integral coefficient respectively. And form an adjustment vector S of the adjustment coefficients corresponding to all punching positions of the aluminum veneer in the processing order of the positions. Combine the initial control parameter vectors of the proportional coefficient and the integral coefficient and the adjustment vector to adjust the control parameters of the PI controller when adjusting the punching pressure.

10. An intelligent punching pressure adjustment system for an arc-shaped aluminum single panel as described in claim 9, characterized in that, The specific adjustment method for adjusting the proportional coefficient and integral coefficient of the PI controller when adjusting the punching pressure is: p = p0 + p0 × S, i = i0 + i0 × S, where p and i are the control parameter vectors corresponding to the adjusted proportionality coefficient and integral coefficient, respectively.

Images