Intelligent numerical control plate cutting system for furniture production

Abstract

The invention relates to the technical field of cutting adjustment control systems, in particular to an intelligent numerical control plate cutting system for furniture production. The system comprises a data monitoring module for monitoring the cutting force and the vibration strength in the plate cutting process; the wear analysis module is used for acquiring vibration wear anomalies at all collection moments in the cutting process and calculating collaborative wear degrees at all collection moments in the cutting process; the expectation adjusting module is used for obtaining the cutting interference degree in the cutting process, calculating a process control coefficient and obtaining the expectation cutting speed of each collection moment in the plate cutting process; and the process control module is used for adjusting the rotating speed of the motor by utilizing a PID (Proportion Integration Differentiation) controller according to the actual cutting speed and the expected cutting speed in the plate cutting process so as to control the cutting speed in the cutting process. The intelligent numerical control cutting machining precision of the plate is improved.

Description

Technical Field

[0001] The present application relates to the technical field of cutting adjustment control systems, and in particular to an intelligent CNC cutting system for panels used in furniture production. Background Art

[0002] A large amount of board materials are often required in the furniture manufacturing process, and the board materials often need to be quickly cut with the help of cutting equipment during processing. By introducing intelligent CNC technology, precise control of the board cutting process can be achieved, thereby achieving high-efficiency and high-precision furniture production and manufacturing.

[0003] Most existing technologies use CNC panel saws to perform high-speed cutting of furniture panels. This involves analyzing the wear of the saw by monitoring cutting parameters in real time during the cutting process. This wear is then used to control and adjust the cutting speed of the CNC panel saw, thereby preventing the continued increase in saw wear. However, due to the complex effects of saw wear during the cutting process, existing technologies do not fully consider the impact of saw wear on cutting accuracy when controlling the cutting speed. This results in an inability to accurately control the cutting speed, which in turn affects the accuracy of the furniture panel cutting process. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an intelligent CNC cutting system for panels used in furniture production. The technical solutions adopted are as follows:

[0005] This application proposes an intelligent CNC cutting system for panels used in furniture production, the system comprising:

[0006] Data monitoring module: used to monitor cutting force and vibration intensity during the sheet cutting process;

[0007] Wear analysis module: This module performs frequency domain analysis on the vibration intensity within a preset time period before each acquisition moment. The vibration wear anomaly at each acquisition moment during the cutting process is obtained through the changes in the frequency domain amplitude and the degree of difference. The cutting force within a preset time period before each acquisition moment is decomposed in time series. The difference between the cutting forces within the trend sequence after time series decomposition, combined with the vibration wear anomaly, is used to obtain the coordinated wear degree at each acquisition moment during the cutting process.

[0008] Expected Adjustment Module: Detrending analysis is performed on the cutting force, vibration intensity, and coordinated wear. The correlation between the coordinated wear and the cutting force and vibration intensity after detrending analysis, as well as the randomness of the changes in the cutting force and vibration intensity after detrending analysis, are used to obtain the cutting interference at each acquisition moment in the cutting process. Based on the changing characteristics of the cutting interference, the process control coefficient at each acquisition moment is obtained. Combined with the actual cutting speed, the expected cutting speed at each acquisition moment in the plate cutting process is obtained.

[0009] Process control module: According to the actual cutting speed and expected cutting speed during the sheet cutting process, the PID controller is used to adjust the motor speed to control the cutting speed during the cutting process.

[0010] Preferably, all vibration intensities and cutting forces within a preset time period before each acquisition moment are normalized and arranged in time sequence to obtain a vibration intensity sequence and a cutting force sequence at each acquisition moment.

[0011] Preferably, the calculation process of the vibration wear abnormality at each collection moment during the cutting process is:

[0012]

[0013] Where G t is the vibration wear abnormality at the tth acquisition moment, PE t is the permutation entropy of the frequency domain amplitude sequence corresponding to the t-th acquisition moment, M is the number of elements in the frequency domain amplitude sequence corresponding to the t-th acquisition moment, f t,i and f t,i-1 They are respectively the i-th and i-1-th elements in the frequency domain amplitude sequence corresponding to the t-th acquisition moment.

[0014] Preferably, the vibration intensity sequence at each acquisition moment is transformed in the frequency domain, and the amplitudes of all frequencies after the frequency domain transformation of the vibration intensity sequence are arranged from small to large in frequency to form a frequency domain amplitude sequence at each acquisition moment.

[0015] Preferably, the calculation process of the coordinated wear degree at each collection moment during the cutting process is:

[0016]

[0017] Where R t is the degree of collaborative wear at the tth acquisition moment, N is the number of elements in the trend sequence at the tth acquisition moment, exp() is an exponential function with a natural constant as the base, d t,j and d t,j-1 They are respectively the jth and j-1th elements in the trend sequence at the tth acquisition moment, wherein the cutting force sequence at each acquisition moment is decomposed into time series to obtain the trend sequence at each acquisition moment.

[0018] Preferably, the calculation process of the cutting interference degree at each acquisition moment in the cutting process is:

[0019] H t =Rs t ×σz t +Rv t ×σc t Where, H t is the cutting interference degree at the tth acquisition moment, Rs t and Rv t are the first correlation and the second correlation at the t-th acquisition moment, respectively. The first correlation and the second correlation are obtained by detrending the correlation between the collaborative wear degree and the cutting force and vibration intensity, σz t is the information entropy of the first-order difference sequence of the cutting force fluctuation sequence at the t-th acquisition moment, σc t is the information entropy of the first-order difference sequence of the vibration intensity fluctuation sequence at the t-th acquisition moment, wherein the cutting force fluctuation sequence and vibration intensity fluctuation sequence at each acquisition moment are obtained by performing detrending analysis on the cutting force sequence and vibration intensity sequence respectively.

[0020] Preferably, the process of obtaining the first correlation and the second correlation further includes:

[0021] All collaborative wear degrees within the preset time period before each collection moment are arranged in chronological order to form a collaborative wear degree sequence at each collection moment. The absolute values ​​of the correlation degrees between the collaborative wear fluctuation sequence and the cutting force fluctuation sequence and the vibration intensity fluctuation sequence are calculated respectively as the first correlation and second correlation of each collection moment.

[0022] Preferably, the calculation process of the process control coefficient at each acquisition moment is:

[0023] Where W is the process control coefficient at the current acquisition moment, α is the proportional adjustment factor, arctan() is the inverse tangent function, m is the number of elements in the cutting interference sequence at the current acquisition moment, and H ... s and H s-1 They are respectively the sth and s-1th elements in the cutting interference degree sequence at the current acquisition moment.

[0024] Preferably, all cutting interference levels within a preset time period before each acquisition moment are arranged in time sequence to obtain a cutting interference level sequence at each acquisition moment.

[0025] Preferably, the calculation process of the expected cutting speed at each acquisition moment during the sheet cutting process is as follows:

[0026] EV = (1 + W) × V; where EV is the expected cutting speed at the current acquisition time, V is the actual cutting speed at the current acquisition time, and W is the process control coefficient at the current acquisition time.

[0027] This application has the following beneficial effects:

[0028] This application considers that existing technologies for controlling cutting speed fail to fully analyze the impact of panel saw wear on cutting accuracy, resulting in an inability to accurately control cutting speed during the cutting process, thereby affecting the precision of furniture panel cutting. Therefore, this application uses frequency domain analysis to measure the abnormal characteristics of vibration wear on panel saws during panel cutting. Combined with the trend changes in cutting force on the panel saw, this application measures the coordinated wear characteristics between different influencing factors during the panel cutting process. This more clearly demonstrates the wear characteristics of the panel saw during the panel cutting process, facilitating more accurate control of the cutting speed during the subsequent cutting process.

[0029] Furthermore, the present application analyzes the interference effect of the coordinated wear characteristics on the panel cutting saw on the plate cutting accuracy, and uses correlation analysis to accurately measure the degree of interference caused by the coordinated wear characteristics when cutting the plate, which is used to control and adjust the cutting speed during the subsequent cutting process, thereby avoiding further aggravation of the wear of the panel cutting saw and reducing the impact of the panel cutting saw wear on the cutting accuracy;

[0030] At the same time, this application fully considers the influence of panel saw wear on cutting accuracy, and calculates the expected cutting speed during the panel cutting process in real time according to the changing characteristics of wear interference during panel cutting. It also uses a PID controller to accurately control the cutting speed during the cutting process, thereby improving the accuracy of cutting furniture panels. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 A block diagram of an intelligent CNC cutting system for panel materials used in furniture production, provided in one embodiment of the present application;

[0033] Figure 2 A schematic diagram of the cutting speed control process during the plate cutting process provided in one embodiment of the present application. DETAILED DESCRIPTION

[0034] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of an intelligent CNC cutting system for panel materials used in furniture production proposed in this application. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0035] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0036] The following describes in detail a specific solution of an intelligent CNC cutting system for panels used in furniture production provided by this application in conjunction with the accompanying drawings.

[0037] See also Figure 1 , which shows a block diagram of an intelligent CNC cutting system for panel production for furniture production provided by one embodiment of the present application, the system comprising:

[0038] Data monitoring module: used to monitor cutting force and vibration intensity during the sheet metal cutting process.

[0039] When using CNC panel saw equipment to cut furniture panels, in order to achieve precise control of the panel cutting process, it is necessary to use sensor technology to monitor the processing parameters in real time during the cutting process, and promptly discover potential problems with the cutting speed, so as to more accurately control the cutting speed during the cutting process.

[0040] The data monitoring module of the CNC cutting system is used to collect the processing parameters during the cutting process in real time. The sensors of the data monitoring module include force sensors and vibration sensors. The force sensor is installed at the connection between the panel saw and the main shaft, and the vibration sensor is installed on the main shaft of the panel saw. The sensors are fixed by bolts to collect the cutting force and vibration intensity in real time during the cutting process. In this embodiment, the collection frequency of the cutting force and vibration intensity is 100 Hz. The implementer can adaptively set the collection frequency according to actual conditions.

[0041] To improve the accuracy of subsequent sheet metal cutting process control, it is necessary to eliminate the dimension differences between different processing parameters. All cutting force and vibration intensity data collected within a preset time period before each collection moment are normalized. These normalized data are then arranged in chronological order to obtain the cutting force and vibration intensity sequences for each collection moment. In this embodiment, the preset time period is 1 second; in actual application scenarios, the implementer can set it at will.

[0042] Wear analysis module: Perform frequency domain analysis on the vibration intensity within the preset time period before each collection moment. Obtain the vibration wear anomaly at each collection moment in the cutting process through the change in frequency domain amplitude and the degree of difference. Perform time series decomposition on the cutting force within the preset time period before each collection moment. Combined with the difference between the cutting forces in the trend sequence after time series decomposition and the vibration wear anomaly, obtain the coordinated wear degree at each collection moment in the cutting process.

[0043] The factors that influence saw wear during sheet metal cutting are often complex. Tool wear not only affects cutting accuracy but can even lead to saw breakage in severe cases. Therefore, it's crucial to analyze the complex impact of saw wear on cutting accuracy and accurately control the cutting process to ensure high quality.

[0044] In order to analyze the vibration state of the panel saw during the board cutting process, the vibration intensity sequence at each acquisition moment is used as the input of the Fourier transform. The Fourier transform can be a fast Fourier transform or a discrete Fourier transform. This embodiment uses the discrete Fourier transform to obtain the amplitude of all frequencies in the vibration intensity sequence. The discrete Fourier transform is a well-known technology and will not be described in detail.

[0045] Furthermore, the amplitudes of all frequencies in the vibration intensity sequence at each acquisition moment are arranged in order of frequency from small to large, and recorded as the frequency domain amplitude sequence at each acquisition moment, reflecting the variation characteristics of the vibration amplitude of the panel cutting saw with frequency during the plate cutting process. If the difference between adjacent amplitudes in the frequency domain amplitude sequence as the frequency changes is greater, and the degree of chaos of the amplitude variation in the frequency domain amplitude sequence is higher, the abnormal variation of the vibration amplitude of the panel cutting saw with frequency is more significant, indicating that the panel cutting saw will have abnormal characteristic phenomena of vibration wear at this time, and it is more necessary to accurately control the cutting speed of the plate cutting process to avoid adverse effects on the accuracy of the cutting process.

[0046] Through the above analysis, the vibration wear anomaly degree at each sampling moment during the sheet cutting process is calculated:

[0047]

[0048] Where G t is the vibration wear abnormality at the tth acquisition moment, PE t is the permutation entropy of the frequency domain amplitude sequence corresponding to the t-th acquisition moment, M is the number of elements in the frequency domain amplitude sequence corresponding to the t-th acquisition moment, f t,i and f t,i-1 They are respectively the i-th and i-1-th elements in the frequency domain amplitude sequence corresponding to the t-th acquisition moment.

[0049] It should be noted that the calculation of permutation entropy is a well-known technology and will not be described in detail.

[0050] Among them, it can be understood that the vibration wear abnormality reflects the size of the wear characteristics on the panel saw caused by the abnormal spindle vibration in the process control of CNC cutting. The larger the vibration wear characteristics reflected on the panel saw, the more unstable the contact state between the panel saw and the panel is at this time, and the more accurate process control of the cutting speed of the panel cutting is needed, thereby improving the accuracy of cutting processing of furniture panels.

[0051] Generally speaking, if the abnormal characteristics of vibration wear on the panel saw are higher, and at the same time the upward trend of cutting force on the panel saw is stronger, the more it can reflect the coordinated wear characteristics between different influencing factors on the panel saw, and at this time it is more necessary to accurately control the process of panel cutting.

[0052] Therefore, in order to more accurately measure the collaborative wear characteristics between different influencing factors, the cutting force sequence at each acquisition moment is used as the input of STL time series decomposition, and the trend sequence at each acquisition moment is obtained by using STL time series decomposition. STL time series decomposition is a well-known technology and will not be elaborated on in detail.

[0053] Through the above analysis, the collaborative wear degree at each acquisition moment in the sheet cutting process is calculated:

[0054]

[0055] Where R t is the degree of collaborative wear at the tth acquisition moment, N is the number of elements in the trend sequence at the tth acquisition moment, exp() is an exponential function with a natural constant as the base, d t,j and d t,j-1 They are respectively the j-th and j-1-th elements in the trend sequence at the t-th collection moment.

[0056] Among them, the degree of coordinated wear reflects the coordinated wear characteristics between different influencing factors on the panel saw. If the coordinated wear characteristics are more significant at this time, it is more likely to interfere with the cutting accuracy of furniture boards. At this time, it is necessary to timely control and adjust the cutting speed during the cutting process to avoid affecting the accuracy of furniture board cutting processing.

[0057] Expected adjustment module: Detrending analysis is performed on the cutting force, vibration intensity and coordinated wear. The correlation between the coordinated wear and the cutting force and vibration intensity after detrending analysis, as well as the randomness of the changes in the cutting force and vibration intensity after detrending analysis, are used to obtain the cutting interference degree at each collection moment in the cutting process. Then, based on the changing characteristics of the cutting interference degree, the process control coefficient at each collection moment is obtained, and then combined with the actual cutting speed, the expected cutting speed at each collection moment in the plate cutting process is obtained.

[0058] During the panel cutting process, the stronger the synergy between the various factors affecting saw wear, the more likely it is to affect the stability of the CNC saw, and thus the quality of the panel cutting process. Therefore, it is necessary to fully consider the complex impact of saw wear on cutting accuracy and improve the accuracy of cutting speed control during the cutting process to ensure the quality of the panel cutting process.

[0059] Furthermore, the coordinated wear degrees of all acquisition moments within one second before each acquisition moment are arranged in chronological order to obtain a coordinated wear degree sequence for each acquisition moment, reflecting the change of coordinated wear characteristics over time, which facilitates the subsequent analysis of the complex impact on the plate cutting accuracy.

[0060] In order to accurately analyze the interference effect of collaborative wear characteristics on cutting accuracy, the collaborative wear degree sequence, cutting force sequence, and vibration intensity sequence at each acquisition moment are respectively used as inputs of the DFA detrended fluctuation analysis algorithm. The DFA detrended fluctuation analysis algorithm is used to obtain the corresponding sequences of the collaborative wear degree sequence, cutting force sequence, and vibration intensity sequence after detrended analysis. In this embodiment, for the convenience of understanding and expression, they are respectively recorded as collaborative wear fluctuation sequence, cutting force fluctuation sequence, and vibration intensity fluctuation sequence. The DFA detrended fluctuation analysis algorithm is a well-known technology and will not be elaborated on.

[0061] Furthermore, the absolute values ​​of the correlation between the collaborative wear fluctuation sequence and the cutting force fluctuation sequence and the vibration intensity fluctuation sequence are calculated respectively, and recorded as the first correlation and the second correlation at each acquisition moment, respectively. The correlation degree can be measured by the Pearson correlation coefficient, mutual information or covariance. In this embodiment, the covariance is used to measure the correlation degree.

[0062] Among them, the greater the first correlation and the second correlation, the greater the impact of the coordinated wear characteristics on the panel cutting saw on the stability of the CNC panel cutting saw, causing the cutting force and vibration intensity in the CNC panel cutting saw to show non-stationary irregular changes. At this time, the interference effect of the coordinated wear characteristics on the plate cutting accuracy during the cutting process can be more prominent.

[0063] Based on the above analysis, the cutting interference degree at each acquisition moment during the sheet cutting process is calculated. In this embodiment, the specific calculation formula is:

[0064] H t =Rs t ×σz t +Rv t ×σc t ;

[0065] Where H t is the cutting interference degree at the tth acquisition moment, Rs t and Rv t are the first correlation and the second correlation at the t-th acquisition moment, σz t is the information entropy of the first-order difference sequence of the cutting force fluctuation sequence at the t-th acquisition moment, σc t is the information entropy of the first-order difference sequence of the vibration intensity fluctuation sequence at the t-th acquisition moment. The calculation of information entropy is a well-known technology and will not be described in detail.

[0066] The cutting interference degree reflects the degree of interference caused by the coordinated wear characteristics of the panel saw when cutting the plate. The higher the degree of interference caused by the coordinated wear characteristics when cutting the plate, the greater the impact of the panel saw wear on the cutting accuracy. At this time, the cutting speed of the CNC panel saw should be reduced to avoid further aggravation of the panel saw wear and reduce the impact of panel saw wear on cutting accuracy.

[0067] Generally speaking, the more significant the upward trend of the cutting interference feature on the panel saw in the short period of time before the current collection moment, the more likely it is that the board cutting will be continuously affected by the interference of the coordinated wear feature. At this time, the cutting speed of the CNC panel saw should be appropriately reduced to reduce the interference effect of the coordinated wear feature on the panel saw on the board cutting; conversely, if the downward trend of the cutting interference feature on the panel saw in the short period of time before the current collection moment is smaller, it means that the board cutting is less affected by the interference of the coordinated wear feature. At this time, the cutting speed of the CNC panel saw should be appropriately increased to improve the efficiency of cutting furniture boards.

[0068] Therefore, in this embodiment, the cutting interference degrees of all acquisition moments within one second before the current acquisition moment are arranged in time sequence to obtain the cutting interference degree sequence of the current acquisition moment, which reflects the changes in the cutting interference characteristics on the panel saw in the short period before the current acquisition moment. Based on the changes in the elements within the cutting interference degree, the process control coefficient of each acquisition moment in the cutting process is calculated. Preferably, in this embodiment, the calculation formula is:

[0069]

[0070] Where W is the process control coefficient at the current acquisition moment, α is the proportional adjustment factor, which is used to control the range of the process control coefficient to avoid adjusting the cutting speed too large or too small in the future. In this embodiment, the value is 0.2, arctan() is the inverse tangent function, m is the number of elements in the cutting interference degree sequence at the current acquisition moment, and H is the proportional adjustment factor used to control the range of the process control coefficient to avoid adjusting the cutting speed too large or too small in the future. s and H s-1 They are respectively the sth and s-1th elements in the cutting interference degree sequence at the current acquisition moment.

[0071] Furthermore, the expected cutting speed at the current acquisition moment is obtained according to the process control coefficient and the actual cutting speed at the current acquisition moment. The specific calculation method in this embodiment is:

[0072] EV = (1 + W) × V; where EV is the expected cutting speed at the current acquisition time, V is the actual cutting speed at the current acquisition time, and W is the process control coefficient at the current acquisition time.

[0073] It can be understood that by setting the process control coefficient of panel cutting through the changes in the cutting interference characteristics on the panel saw in a short period of time before the current acquisition moment, and adjusting the actual cutting speed up and down through the process control coefficient, it is possible to avoid the cutting speed being too high or too low during the panel cutting process, thereby improving the quality and efficiency of cutting furniture panels.

[0074] Process control module: According to the actual cutting speed and expected cutting speed during the sheet cutting process, the PID controller is used to adjust the motor speed to control the cutting speed during the cutting process.

[0075] Furthermore, a PID controller is used to accurately control the cutting speed during the cutting process. The actual cutting speed and the expected adjustment speed during the plate cutting process are monitored and calculated in real time, and the actual cutting speed and the expected adjustment speed at the current acquisition moment are input into the PID controller. The PID controller outputs a control signal through the error between the actual cutting speed and the expected adjustment speed. The control signal acts on the frequency converter in the CNC panel saw equipment. The frequency converter adjusts the speed of the motor through the output signal of the controller, thereby changing the cutting speed of the panel saw, and realizing accurate process control of the cutting speed during the cutting process, thereby avoiding aggravating the wear of the panel saw during the plate cutting process and reducing the influence of panel saw wear on the cutting accuracy.

[0076] Specifically, in this embodiment, a flow chart of cutting speed control during the cutting process of the board material for furniture production is shown as follows: Figure 2 shown.

[0077] It should be noted that the order of the embodiments of the present application is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. An intelligent CNC cutting system for panels used in furniture production, characterized in that: The system comprises: Data monitoring module: used to monitor cutting force and vibration intensity during the sheet cutting process; Wear analysis module: This module performs frequency domain analysis on the vibration intensity within a preset time period before each acquisition moment. The vibration wear anomaly at each acquisition moment during the cutting process is obtained through the changes in the frequency domain amplitude and the degree of difference. The cutting force within a preset time period before each acquisition moment is decomposed in time series. The difference between the cutting forces within the trend sequence after time series decomposition, combined with the vibration wear anomaly, is used to obtain the coordinated wear degree at each acquisition moment during the cutting process. Expected Adjustment Module: Detrending analysis is performed on the cutting force, vibration intensity, and coordinated wear. The correlation between the coordinated wear and the cutting force and vibration intensity after detrending analysis, as well as the randomness of the changes in the cutting force and vibration intensity after detrending analysis, are used to obtain the cutting interference at each acquisition moment in the cutting process. Based on the changing characteristics of the cutting interference, the process control coefficient at each acquisition moment is obtained. Combined with the actual cutting speed, the expected cutting speed at each acquisition moment in the plate cutting process is obtained. Process control module: According to the actual cutting speed and expected cutting speed during the sheet cutting process, the PID controller is used to adjust the motor speed to control the cutting speed during the cutting process.

2. The intelligent CNC cutting system for sheet materials used in furniture production according to claim 1, characterized in that: The normalized data of all vibration intensities and cutting forces within a preset time period before each acquisition moment are arranged in time sequence to obtain the vibration intensity sequence and cutting force sequence at each acquisition moment.

3. The intelligent CNC cutting system for sheet materials used in furniture production according to claim 2, characterized in that: The calculation process of the vibration wear abnormality at each sampling moment during the cutting process is as follows: Where G t is the vibration wear abnormality at the tth acquisition moment, PE t is the permutation entropy of the frequency domain amplitude sequence corresponding to the t-th acquisition moment, M is the number of elements in the frequency domain amplitude sequence corresponding to the t-th acquisition moment, f t,i and f t,i-1 They are respectively the i-th and i-1-th elements in the frequency domain amplitude sequence corresponding to the t-th acquisition moment.

4. The intelligent CNC cutting system for panel production for furniture production according to claim 3, characterized in that: The vibration intensity sequence at each acquisition moment is transformed into a frequency domain, and the amplitudes of all frequencies after the frequency domain transformation of the vibration intensity sequence are arranged from small to large in order to form a frequency domain amplitude sequence at each acquisition moment.

5. The intelligent CNC cutting system for panels used in furniture production according to claim 2, characterized in that: The calculation process of the coordinated wear degree at each collection moment in the cutting process is as follows: Where R t is the degree of collaborative wear at the tth acquisition moment, N is the number of elements in the trend sequence at the tth acquisition moment, exp() is an exponential function with a natural constant as the base, d t,j and d t,j-1 They are respectively the jth and j-1th elements in the trend sequence at the tth acquisition moment, wherein the cutting force sequence at each acquisition moment is decomposed into time series to obtain the trend sequence at each acquisition moment.

6. The intelligent CNC cutting system for panel materials used in furniture production according to claim 2, characterized in that: The calculation process of the cutting interference degree at each acquisition moment in the cutting process is as follows: H t =Rs t ×σz t +Rv t ×σc t Where, H t is the cutting interference degree at the tth acquisition moment, Rs t and Rv t are the first correlation and the second correlation at the t-th acquisition moment, respectively. The first correlation and the second correlation are obtained by detrending the correlation between the collaborative wear degree and the cutting force and vibration intensity, σz t is the information entropy of the first-order difference sequence of the cutting force fluctuation sequence at the t-th acquisition moment, σc t is the information entropy of the first-order difference sequence of the vibration intensity fluctuation sequence at the t-th acquisition moment, wherein the cutting force fluctuation sequence and vibration intensity fluctuation sequence at each acquisition moment are obtained by performing detrending analysis on the cutting force sequence and vibration intensity sequence respectively.

7. The intelligent CNC cutting system for panel materials used in furniture production according to claim 6, characterized in that: The process of obtaining the first correlation and the second correlation further includes: All collaborative wear degrees within a preset time period before each collection moment are arranged in chronological order to form a collaborative wear degree sequence at each collection moment. The absolute values ​​of the correlation degrees between the collaborative wear fluctuation sequence and the cutting force fluctuation sequence and the vibration intensity fluctuation sequence are calculated respectively as the first correlation and second correlation of each collection moment.

8. The intelligent CNC cutting system for panel materials used in furniture production according to claim 1, characterized in that: The calculation process of the process control coefficient at each acquisition moment is: Where W is the process control coefficient at the current acquisition moment, α is the proportional adjustment factor, arctan() is the inverse tangent function, m is the number of elements in the cutting interference sequence at the current acquisition moment, and H ... s and H s-1 They are respectively the sth and s-1th elements in the cutting interference degree sequence at the current acquisition moment.

9. The intelligent CNC cutting system for panel production for furniture production according to claim 8, characterized in that: All cutting interference levels within a preset time period before each acquisition moment are arranged in time sequence to obtain a cutting interference level sequence at each acquisition moment.

10. The intelligent CNC cutting system for panel materials used in furniture production according to claim 1, characterized in that: The calculation process of the expected cutting speed at each acquisition moment during the sheet cutting process is as follows: EV = (1 + W) × V; where EV is the expected cutting speed at the current acquisition time, V is the actual cutting speed at the current acquisition time, and W is the process control coefficient at the current acquisition time.

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