Saw blade angle adjustment method and system based on CNC sawing machine

Abstract

The present invention relates to the technical field of numerical control sawing machine control, and more specifically, the present invention relates to a saw blade angle adjustment method and system based on a numerical control sawing machine, the method comprising: calculating the noise performance of each angle data in the acquired angle data sequence, selecting a reference data segment, calculating the weighted value of the first-order difference of adjacent angle data in the reference data segment, the weighted value is negatively correlated with the average value of the noise performance of the corresponding angle data, summing up all the weighted values ​​of the calculated first-order differences to obtain the historical data change rate, performing least square fitting on the first-order difference of adjacent angle data in the reference data segment to determine the predicted data change rate, updating the preset sliding surface gain parameter according to the historical data change rate and the predicted data change rate, so as to control the angle of the saw blade according to the updated sliding surface gain parameter. The present invention can improve the accuracy of the control of the angle of the saw blade, and can improve the working efficiency of the numerical control sawing machine.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical control sawing machine control, and more specifically, to a saw blade angle adjustment method and system based on a numerical control sawing machine. Background Art

[0002] As an advanced processing equipment, CNC sawing machine plays an important role in many industries such as wood processing and metal processing. CNC sawing machine has increasingly adopted advanced control technology and sensor technology to achieve a high degree of automation and intelligence, such as Figure 1 As shown, when the profile is processed by a CNC sawing machine, the profile can be placed on the feed rack from the right side, and then the profile is transported to the left side and processed by a saw blade. Therefore, in order to improve the accuracy of CNC sawing machine processing, it is very important to perform closed-loop control of the saw blade angle.

[0003] Sliding Mode Control (SMC) is a nonlinear control method with high robustness and fast response characteristics. When adjusting the angle of the saw blade of a CNC sawing machine, sliding mode control can achieve closed-loop control of the angle adjustment, ensuring that the angle of the saw blade can accurately reach the required tilt angle, thereby improving cutting accuracy.

[0004] However, since the saw blade in the CNC sawing machine will be affected by various factors during operation, there may be an error between the actual angle of the saw blade and the set angle, and the actual angle of the saw blade will be affected by various factors, resulting in diverse changes in the angle. When the traditional sliding mode control method is used to perform closed-loop control of the saw blade angle, it may not be able to adapt to the various dynamic changes encountered during the saw blade angle adjustment process, which will affect the accuracy of the control of the saw blade angle and reduce the working efficiency of the CNC sawing machine to a certain extent. Summary of the invention

[0005] In order to solve the problems of low accuracy in controlling the angle of a saw blade and low working efficiency of a numerically controlled sawing machine, the present invention provides a saw blade angle adjustment method and system based on a numerically controlled sawing machine.

[0006] According to a first aspect of the present invention, a saw blade angle adjustment method based on a numerically controlled sawing machine is provided, comprising:

[0007] Obtaining the angle data sequence of the saw blade during operation, and calculating the noise performance of each angle data in the angle data sequence;

[0008] The data segment consisting of the current angle data and the previously preset number of angle data is used as a reference data segment, and the weighted value of the first-order difference of adjacent angle data in the reference data segment is calculated, and the weighted value is negatively correlated with the average value of the noise performance of the corresponding adjacent angle data. The weighted values ​​of the first-order differences of all adjacent angle data in the reference data segment are summed to obtain the historical data change rate;

[0009] The first-order difference of adjacent angle data in the reference data segment is fitted by least squares to obtain a fitting curve, and the slope of the corresponding data point at the current moment in the fitting curve is used as the predicted data change rate;

[0010] The preset synovial surface gain parameter is updated by using the product of the historical data change rate and the normalized predicted data change rate. The updated synovial surface gain parameter is negatively correlated with the product, so as to control the angle of the saw blade according to the updated synovial surface gain parameter.

[0011] The present invention utilizes noise performance to determine the weighted value of the first-order difference of adjacent angle data in a selected reference data segment, which can reduce the influence of noise data, thereby ensuring the accuracy of the determined historical data change rate; and in combination with the historical data change rate and the predicted data change rate, the sliding membrane surface gain parameters when controlling the angle of the saw blade are adaptively adjusted, which can adapt to various dynamic changes of the saw blade angle during the working process, improve the accuracy of the control of the saw blade angle, and can improve the response speed of the sliding membrane control method, thereby improving the working efficiency of the CNC sawing machine to a certain extent.

[0012] Preferably, the updated synovial surface gain parameter satisfies the following relationship:

[0013] ;

[0014] In the formula, is the updated synovial surface gain parameter; is the preset synovial surface gain parameter; is the rate of change of historical data; To predict the data change rate, The natural constant An exponential function with base .

[0015] The updated determination formula of the synovial surface gain parameter provided by the present invention includes a normalization process for the predicted data change rate, limits the range of the predicted data change rate, and can reduce the difficulty of calculating the correction amplitude of the predicted synovial surface gain parameter.

[0016] Preferably, the weighted value of the first-order difference of adjacent angle data in the reference data segment satisfies the following relationship:

[0017] ;

[0018] In the formula, The reference data segment Angle data and The weighted value of the first-order difference of angle data; The reference data segment Angle data and The first-order difference of angle data; , They are respectively Angle data and Noise performance of angle data; The natural constant An exponential function with base ; is the normalization function.

[0019] Preferably, the method for obtaining noise performance includes:

[0020] Obtaining a preset local range of any angle data, and a preset working angle range of the saw blade;

[0021] When any angle data is within the working angle range, the mean of the sine values ​​of each angle data within the local range is taken as the first mean, the mean of the sine values ​​of all angle data except any angle data within the local range is taken as the second mean, and the absolute value of the difference between the first mean and the second mean is calculated to obtain the noise performance of any angle data;

[0022] When any angle data is outside the working angle range, the noise performance of any angle data is set to 1.

[0023] The present invention utilizes the local range of any angle data to remove the difference in the mean of the sine values ​​before and after the angle data to determine the noise performance of the angle data. The value of the noise performance can be limited to 0-1, which reduces the difficulty of calculating the noise performance. The outlier characteristic of noise data is utilized to determine the noise performance of each angle data, thereby ensuring the accuracy of the determined noise performance.

[0024] Preferably, the local range acquisition method includes:

[0025] Taking any angle data as the center, select a data segment with a length of a preset value to obtain the local range of any angle data.

[0026] Preferably, the method for obtaining the first-order difference of adjacent angle data in a reference data segment includes:

[0027] The difference between adjacent angle data in the reference data segment is calculated, and the absolute value of the difference is used as the first-order difference of the adjacent angle data.

[0028] The present invention can determine the change rate of angle data at different moments in a simple manner.

[0029] Preferably, controlling the angle of the saw blade according to the updated sliding surface gain parameter includes:

[0030] The updated sliding membrane surface gain parameter is used as the sliding membrane surface gain parameter for performing sliding membrane control on the angle of the saw blade at the next moment to control the angle of the saw blade.

[0031] The present invention can improve the accuracy of controlling the angle of the saw blade by adaptively adjusting the sliding film surface gain parameter when performing sliding film control on the angle of the saw blade.

[0032] According to the second aspect of the present invention, a saw blade angle adjustment system based on a CNC sawing machine is provided. The saw blade angle adjustment system based on the CNC sawing machine includes a memory and a processor. A computer program is stored in the memory, and the processor executes the computer program to implement the steps of the first aspect of the present invention.

[0033] The present invention has the following effects:

[0034] The present invention can reduce the influence of noise data in the angle data, ensure the accuracy of the determined historical data change rate, and adapt to various changes in the angle of the saw blade of the CNC sawing machine during the operation by adaptively adjusting the sliding membrane surface gain parameters when controlling the angle of the saw blade, thereby achieving accurate control of the angle of the saw blade; and by combining the predicted data change rate to determine the updated gain parameters, the response speed of the sliding membrane control of the saw blade angle can be improved, thereby improving the working efficiency of the CNC sawing machine to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0036] Figure 1 It is a schematic diagram of a CNC sawing machine in the application scenario of an embodiment of the present invention.

[0037] Figure 2 It is a schematic flow chart of the steps of a method for adjusting the angle of a saw blade based on a CNC sawing machine according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0039] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0040] Reference Figure 2 The saw blade angle adjustment method based on a CNC sawing machine includes steps S1 to S4, which are specifically as follows:

[0041] S1: Obtain the angle data sequence of the saw blade during operation, and calculate the noise performance of each angle data in the angle data sequence.

[0042] Specifically, a photoelectric encoder can be used to collect angle data of the saw blade of a CNC sawing machine during operation at a certain frequency, such as 10 Hz, within a certain period of time to obtain an angle data sequence for the period. This embodiment does not specifically limit the collection frequency and collection duration.

[0043] The angle data refers to the swing angle of the saw blade in the CNC sawing machine. It should be noted that the angle data of the saw blade of the CNC sawing machine in this embodiment ranges from 45° to 95°.

[0044] Noise performance refers to the data that measures the possibility that each angle data is noise data. For example, when the possibility that any angle data is noise data is greater, the noise performance of the angle data is greater. It should be noted that when using a photoelectric encoder to collect the angle data of the saw blade of a CNC sawing machine, dust or dirt may block the light, causing the light signal received by the photosensitive element to weaken or change, which may generate noise data. The existence of noise data will affect the control process of the saw blade angle. Therefore, it is necessary to determine the noise performance of each angle data in order to further process the angle data and reduce the impact of the noise data.

[0045] In an exemplary embodiment of the present invention, the determination of the noise performance of each angle data can be achieved by the following steps:

[0046] (1) Obtaining a preset local range of any angle data and a preset working angle range of the saw blade;

[0047] Optionally, the working angle range of the saw blade can be set to any angle between 45° and 95°. Of course, a suitable range can also be set according to specific circumstances. This embodiment does not specifically limit the working angle range of the saw blade.

[0048] In an exemplary embodiment of the present invention, the determination of the local range of any angle data can be achieved by the following steps:

[0049] Taking any angle data as the center, select a data segment with a length of a preset value to obtain the local range of any angle data.

[0050] Specifically, when determining the angle data sequence When the local range of angle data is The angle data is the center and the length is surrounded by The data segment of the angle data is used as the local range of the angle data. .

[0051] Optionally, when the number of angle data contained on one side of any angle data is less than 5, a data segment consisting of the angle data and 5 angle data on the other side of the angle data is used as the local range of the angle data.

[0052] (2) When any angle data is within the working angle range, the mean of the sine values ​​of each angle data within the local range is taken as the first mean, and the mean of the sine values ​​of all angle data within the local range except any angle data is taken as the second mean. The absolute value of the difference between the first mean and the second mean is calculated to obtain the noise performance of any angle data;

[0053] It should be noted that since the noise data is randomly generated data, that is, the noise data has the characteristic of outliers, this characteristic can be used to determine the noise performance of each angle data.

[0054] Optionally, the first mean is determined by the mean of the sine values ​​of each angle data within a local range of any angle data, and the second mean is determined by the sine values ​​of all angle data except the angle data within the local range. The value range of the absolute value of the difference between the first mean and the second mean can be limited, that is, the value range of the noise performance can be limited to 0-1, thereby reducing the calculation difficulty of the noise performance of each angle data to a certain extent.

[0055] (3) When any angle data is outside the working angle range, the noise performance of any angle data is set to 1.

[0056] When any angle data is greater than 95° or less than 45°, the angle data can be determined to be noise data. Therefore, the noise performance of the angle data can be set to 1.

[0057] S2: Take the data segment consisting of the current angle data and the previously preset number of angle data as the reference data segment, calculate the weighted value of the first-order difference of adjacent angle data in the reference data segment, the weighted value is negatively correlated with the average value of the noise performance of the corresponding adjacent angle data, and sum the weighted values ​​of the first-order differences of all adjacent angle data in the reference data segment to obtain the historical data change rate.

[0058] Optionally, the preset number can be set to the number of angle data collected within 2 seconds based on empirical values, so as to obtain a reference data segment. This embodiment does not specifically limit the number of angle data included in the reference data segment.

[0059] Among them, the first-order difference between adjacent angle data in the reference data segment can reflect the size of the data change rate. For example, when the determined first-order difference is large, it means that the data change rate at the corresponding moment is large. Due to the influence of noise data, the determined first-order difference between adjacent angle data in the reference data segment may not be able to accurately reflect the change of the angle data at the corresponding moment. Therefore, by calculating the weighted value of the first-order difference of adjacent angle data, the influence of noise data can be reduced.

[0060] In an exemplary embodiment of the present invention, the determination of the first-order difference of adjacent angle data in the reference data segment can be achieved by the following steps:

[0061] The difference between adjacent angle data in the reference data segment is calculated, and the absolute value of the difference is used as the first-order difference of the adjacent angle data.

[0062] Specifically, refer to the data section Angle data, and The first-order difference between the angle data ,in, , They are respectively Angle data and Angle data, is the absolute value symbol, and the first-order difference between all adjacent angle data in the reference data segment can be recorded as ,in, is the number of angle data in the reference data segment.

[0063] Further, after determining the first-order difference between adjacent angle data in the reference data segment, the weighted value of the determined first-order difference can be calculated. Specifically, the weighted value of the first-order difference between adjacent angle data in the reference data segment satisfies the following relationship:

[0064] ;

[0065] In the formula, The reference data segment Angle data and The weighted value of the first-order difference of angle data; The reference data segment Angle data and The first-order difference of angle data; , They are respectively Angle data and Noise performance of angle data; The natural constant An exponential function with base ; is the normalization function.

[0066] Optionally, a sum operation may be performed on weighted values ​​of first-order differences between all adjacent angle data in the reference data segment to obtain a data change rate of the reference data segment, and the data change rate may be used as a historical data change rate.

[0067] S3: Perform least square fitting on the first-order differences of adjacent angle data in the reference data segment to obtain a fitting curve, and use the slope of the corresponding data point at the current moment in the fitting curve as the predicted data change rate.

[0068] Among them, the first-order difference of adjacent angle data can reflect the rate of change of the angle data at the corresponding moment, and the slope of each data point in the fitting curve can reflect the changing trend of the rate of change of the angle data at the corresponding moment; therefore, the slope of the data point corresponding to the current moment in the fitting curve, that is, the predicted data change rate, can determine the changing trend of the rate of change of the angle data at the current moment. For example, when the slope of the data point corresponding to the current moment is small, that is, the predicted data change rate is small, it means that the change rate of change of the angle data at the current moment has a downward trend. Then, adjusting the sliding surface gain parameter according to the changing trend of the determined rate of change of the angle data can improve the response speed of the sliding film control of the angle of the saw blade, thereby improving the working efficiency of the CNC sawing machine.

[0069] Optionally, the first-order differences of adjacent angle data in the reference data segment can be The least squares fitting is performed to obtain the fitting values ​​of each first-order difference, thereby determining the corresponding fitting curve, and then the slope of the corresponding data point at the current moment in the fitting curve is used as the predicted data change rate.

[0070] S4: using the product of the historical data change rate and the normalized predicted data change rate to update the preset synovial surface gain parameter, the updated synovial surface gain parameter is negatively correlated with the product, so as to control the angle of the saw blade according to the updated synovial surface gain parameter.

[0071] Specifically, the updated synovial surface gain parameter satisfies the following relationship:

[0072] ;

[0073] In the formula, is the updated synovial surface gain parameter; is the preset synovial surface gain parameter. is the experience value, ; is the rate of change of historical data; To predict the data change rate, The natural constant An exponential function with base .

[0074] Optionally, other methods may be used to normalize the predicted data change rate according to specific circumstances, such as by The normalization function is used for normalization, etc. This embodiment does not specifically limit the method for normalizing the predicted data change rate.

[0075] Among them, when the product of the historical data change rate and the normalized predicted data change rate is small, a larger synovial surface gain parameter is required to improve the response speed and speed up the speed at which the controlled dynamic system reaches the synovial surface, thereby improving the dynamic performance and stability of the dynamic system. When the product is large, it means that the change of the angle data is relatively stable and does not require a faster response speed. Therefore, setting a larger synovial surface gain parameter can improve the stability of the controlled dynamic system.

[0076] It should be noted that this embodiment only corrects downward the synovial surface gain parameters set according to empirical values, and only improves the setting of the synovial surface gain parameters when the dynamic system is controlled by the synovial membrane. The setting and determination methods of other parameters in the control method are not improved.

[0077] In another embodiment, the updated synovial surface gain parameter may also be determined by other calculation methods, such as not reflecting the negative correlation in the form of an exponential function but reflecting the negative correlation by direct difference.

[0078] In an exemplary embodiment of the present invention, the angle of the saw blade can be controlled by the following steps:

[0079] The updated sliding membrane surface gain parameter is used as the sliding membrane surface gain parameter for performing sliding membrane control on the angle of the saw blade at the next moment to control the angle of the saw blade.

[0080] Specifically, the angle data of the saw blade of the CNC sawing machine during operation can be input into the improved sliding film control method in real time, so that the angle of the saw blade can be closed-loop controlled according to the sliding film surface gain parameters that are adaptively adjusted. The sliding film surface gain parameters that are adaptively adjusted when the sliding film control is performed on the angle of the saw blade can adapt to various changes in the angle of the saw blade of the CNC sawing machine during operation, thereby achieving accurate control of the angle of the saw blade; and the response speed of the sliding film control method can be improved, thereby improving the working efficiency of the CNC sawing machine to a certain extent.

[0081] The present invention also provides a saw blade angle adjustment system based on a CNC sawing machine. The system includes a memory and a processor, and a computer program is stored in the memory. The computer program integrates the functions of the saw blade angle adjustment method based on the CNC sawing machine. When the computer program is executed, the saw blade angle adjustment method based on the CNC sawing machine can improve the accuracy of controlling the angle of the saw blade and improve the working efficiency of the CNC sawing machine.

[0082] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0083] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A saw blade angle adjustment method based on a CNC sawing machine, characterized in that: include: Acquire an angle data sequence of the saw blade during operation, and calculate the noise performance of each angle data in the angle data sequence; A data segment consisting of the current angle data and a preset number of angle data is used as a reference data segment, and a weighted value of the first-order difference of adjacent angle data in the reference data segment is calculated, wherein the weighted value is negatively correlated with the average value of the noise performance of the corresponding adjacent angle data, and the weighted values ​​of the first-order differences of all adjacent angle data in the reference data segment are summed to obtain the historical data change rate; Performing least square fitting on the first-order differences of adjacent angle data in the reference data segment to obtain a fitting curve, and taking the slope of the corresponding data point at the current moment in the fitting curve as the predicted data change rate; The preset synovial surface gain parameter is updated by utilizing the product of the historical data change rate and the normalized predicted data change rate, and the updated synovial surface gain parameter is negatively correlated with the product, so as to control the angle of the saw blade according to the updated synovial surface gain parameter. The synovial surface gain parameter for performing synovial control on the angle of the saw blade through adaptive adjustment includes: using the updated synovial surface gain parameter as the synovial surface gain parameter for performing synovial control on the angle of the saw blade at the next moment, so as to control the angle of the saw blade.

2. The saw blade angle adjustment method based on a CNC sawing machine according to claim 1, characterized in that: The updated synovial surface gain parameter satisfies the following relationship: ； In the formula, is the updated synovial surface gain parameter; is the preset synovial surface gain parameter; is the rate of change of historical data; To predict the data change rate, The natural constant An exponential function with base .

3. The saw blade angle adjustment method based on a CNC sawing machine according to claim 1, characterized in that: The weighted value of the first-order difference of adjacent angle data in the reference data segment satisfies the following relationship: ； In the formula, The reference data segment Angle data and The weighted value of the first-order difference of angle data; The reference data segment Angle data and The first-order difference of angle data; , They are respectively Angle data and Noise performance of angle data; The natural constant An exponential function with base ; is the normalization function.

4. The saw blade angle adjustment method based on a CNC sawing machine according to claim 3, characterized in that: The method for obtaining the noise performance comprises: Acquiring a preset local range of any angle data, and a preset working angle range of the saw blade; When any angle data is within the working angle range, the mean of the sine values ​​of each angle data within the local range is taken as the first mean, the mean of the sine values ​​of all angle data within the local range except the any angle data is taken as the second mean, and the absolute value of the difference between the first mean and the second mean is calculated to obtain the noise performance of any angle data; When any of the angle data is outside the working angle range, the noise performance of any of the angle data is set to 1.

5. The saw blade angle adjustment method based on a CNC sawing machine according to claim 4, characterized in that: The method for obtaining the local range includes: Taking any angle data as the center, a data segment with a length equal to a preset value is selected to obtain a local range of the any angle data.

6. The saw blade angle adjustment method based on a CNC sawing machine according to claim 3, characterized in that: The method for obtaining the first-order difference of adjacent angle data in the reference data segment includes: The difference between adjacent angle data in the reference data segment is calculated, and the absolute value of the difference is used as the first-order difference of the adjacent angle data.

7. The saw blade angle adjustment system based on a CNC sawing machine is characterized in that: The saw blade angle adjustment system based on a numerically controlled sawing machine includes a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

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