Cutting speed control method and system for an aluminum sheet cutting device

Abstract

The present application relates to the technical field of aluminum plate cutting, and particularly relates to a cutting speed control method and system of an aluminum plate cutting device. The method comprises the following steps: collecting spindle vibration acceleration signals during cutting, and obtaining a signal frame sequence through detrending and windowing and framing processing. The sequence is subjected to wavelet packet decomposition to extract sub-band energy features, and fluctuation energy entropy is calculated to evaluate the cutting chaos degree. The entropy value is compared with a threshold value, a nonlinear adaptive speed control strategy including risk-avoiding diving, steady-state observation and S-shaped recovery after abnormal removal is executed according to the result, and a target feed speed is calculated. The speed is converted into an instruction and sent to a feed shaft servo driver to adjust the motor speed, and the steps are cycled to form a closed-loop control. The scheme of the present application can ensure that the strategy acts on the equipment in real time, ensure continuous and stable cutting, and realize efficient and safe aluminum plate cutting.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aluminum plate cutting. More particularly, the present application relates to a cutting speed control method and system for an aluminum plate cutting device. BACKGROUND

[0002] In the aluminum profile extrusion production process, the fixed-length cutting of aluminum plates is a primary and critical process. Currently, full-automatic disc saw cutting machines are mainly used in industrial sites for operation. However, in actual production, there are often defects such as uneven material quality, inclusion of hard particles, or coarse grains inside the aluminum plate, which bring great challenges to the stability of the cutting process.

[0003] The existing cutting control technology mainly has the problem of rigid strategy. Most devices adopt a constant feed speed mode. When the saw blade cuts into the hard point inside the aluminum plate, if the original speed is maintained, the cutting resistance will instantaneously increase dramatically, which can easily cause the saw blade to collapse, the tool bit to break, or even cause the aluminum plate end face to appear burn or burr, seriously affecting the yield and the service life of the saw blade. Although some high-end devices attempt to use the current signal of the main shaft motor for feedback control, the current signal reflects the average change of the mechanical load, has significant hysteresis, and is easily disturbed by fluctuations in the factory power grid. Often, when the current signal triggers protection, the saw blade has already suffered irreversible physical damage.

[0004] In addition, the threshold judgment based solely on the vibration amplitude or current size is prone to misjudgment. For example, normal cutting under a large feed amount can also produce a larger vibration amplitude, which is difficult to distinguish from abnormal vibration when encountering a hard point in terms of "energy size", leading to frequent false actions of the system and reducing the processing efficiency. Therefore, there is an urgent need for a cutting speed control method that can sensitively capture small mutations in the cutting state and respond quickly and accurately. SUMMARY

[0005] The purpose of the present application is to provide a cutting speed control method and system for an aluminum plate cutting device to solve the technical problems of the prior art, such as the saw blade being easily damaged, the service life of the saw blade being shortened, the current feedback being lagging, and the high misjudgment rate of amplitude judgment leading to reduced processing efficiency due to the rigid control strategy. To this end, the present application provides solutions in the following two aspects.

[0006] In a first aspect, the application provides a cutting speed control method of an aluminum plate cutting device, comprising the following steps: collecting spindle vibration acceleration signals in real time during the cutting process, and performing detrending and windowing and framing processing on the collected original time domain signals to obtain a preprocessed signal frame sequence; performing wavelet packet decomposition on the preprocessed signal frame sequence, extracting the energy features of each sub-band, and calculating the fluctuation energy entropy based on the energy proportion of each sub-band to evaluate the chaos degree of the cutting process; comparing the real-time calculated fluctuation energy entropy with a preset threshold, and executing a nonlinear adaptive speed control strategy according to the comparison result to calculate the target feed speed at the current time; the control strategy includes a risk avoidance diving stage when the entropy value exceeds the standard, a steady state observation stage when the speed is low, and an S-shaped adaptive recovery stage after the abnormality is removed; the calculated target feed speed is converted into a control instruction and sent to the feed shaft servo driver to adjust the motor speed in real time, and the above steps are continuously cycled to form a closed loop control, thereby realizing the cutting speed control of the aluminum plate cutting device.

[0007] In this way, the application no longer simply relies on the amplitude of the signal, but starts from the "chaos degree" of the signal, uses the fluctuation energy entropy as a characteristic index, can sensitively capture the slight mutations of the cutting state, effectively distinguishes the vibration caused by normal large cutting amount from the vibration caused by abnormal hard points, and reduces the misjudgment rate.

[0008] Preferably, the real-time collection of spindle vibration acceleration signals during the cutting process comprises: setting the sampling frequency to 20 kHz or higher to capture the high-frequency impact characteristics when the saw teeth cut into the material.

[0009] In this way, through high sampling rate and windowing processing, the influence of environmental noise and direct current component can be eliminated, the frequency spectrum leakage is reduced, and the quality of the original signal is ensured, providing a reliable data foundation for subsequent feature extraction.

[0010] Preferably, the wavelet packet decomposition of the preprocessed signal frame sequence to extract the energy features of each sub-band comprises: using wavelet packet transform to perform layer decomposition on the signal frame to obtain a plurality of sub-bands. The signal energy of each sub-band is obtained by calculating the sum of the square of the wavelet packet reconstruction coefficient amplitudes of all discrete sampling points in the sub-band.

[0011] In this way, through wavelet packet decomposition, the vibration signal is finely decomposed into different frequency bands, which can more comprehensively reflect the energy distribution of the signal at different frequencies, and provide fine data support for entropy calculation.

[0012] ​Preferably, the fluctuation energy entropy is calculated based on the energy proportion of each sub-band, comprising: normalizing the energy probability distribution before calculating the fluctuation energy entropy, specifically, calculating the cumulative sum of the signal energy of all sub-bands as the total energy of the frame signal, and dividing the signal energy of each sub-band by the total energy to obtain the energy proportion of each sub-band; when the total energy is less than a preset minimum energy threshold, it is determined that the system is in an idle state, and the entropy value calculation is skipped.

[0013] In this way, through the normalization processing, the influence of the overall vibration intensity is eliminated, so that the feature index focuses on the energy distribution structure, and the robustness of the index under different working conditions is improved.

[0014] Preferably, the fluctuation energy entropy satisfies the expression: ; in the expression, is the fluctuation energy entropy; denotes the natural logarithm; is the energy proportion of the i-th sub-band; is the energy proportion of the i-th sub-band; is the total number of sub-bands.

[0015] In this way, the fluctuation energy entropy as the core feature index directly reflects the order degree of the system through its numerical value, and can accurately identify the energy distribution disorder state caused by encountering hard points, thereby providing accurate basis for control decision.

[0016] Preferably, the signal energy of the i-th sub-band satisfies the relationship: ; in the expression, denotes the signal energy of the i-th sub-band, which is used to represent the vibration intensity in the frequency range; denotes the total number of discrete sampling points in the signal frame; denotes the amplitude of the i-th point in the reconstruction coefficient sequence of the i-th frequency band after wavelet packet decomposition. Preferably, during the risk-avoiding diving stage when the entropy value exceeds the standard, the target feed speed satisfies:

[0017] ; in the expression,

[0018] is the target feed speed during the risk-avoiding diving stage when the entropy value exceeds the standard; is a preset normal process cutting speed; is a diving sensitivity coefficient; is a preset threshold value; is the fluctuation energy entropy.

[0019] ​​​​Thus, by the exponential decay model, the feed speed can be quickly reduced within the millisecond level time when the abnormality is detected, greatly reducing the cutting load of the sawtooth, and effectively preventing the tooth collapse and tool breakage accidents.

[0020] Preferably, the control strategy of the steady-state observation stage during low-speed maintenance is: after the speed is reduced, if the fluctuation energy entropy does not recover to the system preset reference entropy value, the allowable minimum safe speed is maintained, and a low-frequency disturbance signal with an amplitude of 5% of the allowable minimum safe speed is superimposed.

[0021] Thus, by low-speed maintenance and superimposing a small disturbance, it is helpful to assist the saw blade to safely pass through the hard point area under low load state, and avoid equipment damage caused by static friction or jamming.

[0022] Preferably, the control strategy of the S-shaped adaptive recovery stage after the abnormality is removed is:

[0023] When it is monitored that the fluctuation energy entropy falls back and stabilizes in the safe range, and the duration exceeds the preset time threshold, the target feed speed satisfies: ; in the formula, is the target feed speed after the abnormal condition is removed; is the current system time; is the allowable minimum safe speed; is the preset normal process cutting speed; is the starting time of triggering the recovery mode; is the recovery rate factor, is the time offset constant.

[0024] Thus, the speed is smoothly raised by the variant of the Sigmoid function, realizing "soft take-off", effectively inhibiting the mechanical impact caused by speed mutation, and ensuring the flatness and smoothness of the aluminum plate cutting surface.

[0025] In a second aspect, a cutting speed control system of an aluminum plate cutting device includes:

[0026] a processor;

[0027] a memory storing computer instructions for cutting speed control of the aluminum plate cutting device, when the computer instructions are run by the processor, the system executes the above-mentioned cutting speed control method of the aluminum plate cutting device.

[0028] The present application has the beneficial effects that: the present application introduces fluctuation energy entropy as a characteristic index, and combines a nonlinear control strategy of "risk avoidance diving, steady-state observation, and S-shaped recovery", to realize adaptive intelligent control of the aluminum plate cutting process. The method has extremely high abnormal perception sensitivity and can react before a large amplitude vibration; through an exponential risk avoidance diving strategy, equipment damage is effectively prevented, and the saw blade life is extended; through an S-shaped adaptive recovery strategy, smooth transition of the processing process is realized, the processing quality and efficiency are guaranteed, manual shutdown intervention is not required, and the continuity of production is maintained. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 A step flowchart of the cutting speed control method of the aluminum plate cutting device in the embodiment is schematically shown;

[0030] Figure 2 A cutting seam width / vibration suppression effect comparison graph of the prior art and the embodiment of the present application is schematically shown;

[0031] Figure 3 An adaptive speed control response curve based on fluctuation energy entropy of the embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0033] As shown in Figure 1 The cutting speed control method of the aluminum plate cutting device in the embodiment includes the following steps:

[0034] Step S1, real-time acquisition of spindle vibration acceleration signals during cutting, and de-trending and windowing and framing processing of the acquired original time domain signals to obtain a pre-processed signal frame sequence.

[0035] In some examples, a high-frequency piezoelectric acceleration sensor is installed at the spindle bearing seat position of the disc saw, and real-time acquisition of spindle vibration acceleration signals during cutting is performed.

[0036] Specifically, the high-frequency piezoelectric acceleration sensor can be used to acquire the spindle vibration acceleration signals during cutting in real time according to a fixed sampling frequency. For example, the spindle vibration acceleration signals of the disc saw are continuously acquired at a sampling frequency of 20 kHz during cutting, so as to obtain the original time domain signals. When the set sampling frequency is high, the high-frequency impact characteristics when the saw teeth cut into the material can be captured.

[0037] Subsequently, the original time-domain signal collected is subjected to detrending processing, and a sliding window mechanism is used for frame division, so as to eliminate the influence of environmental noise and direct current components. Specifically, each frame of signal collected is subjected to Hanning window processing, so as to reduce spectral leakage, and a sequence of preprocessed signal frames is obtained.

[0038] For example, the sampling frequency is set to 20 kHz, the sliding window size is set to 1024 points, and the overlap rate is set to 50%. For the continuous vibration acceleration signal collected, every time 512 new data points are collected, a new 1024-point signal frame is formed in combination with the previous 512 data points. Then, the frame signal is subtracted by the mean value or subjected to polynomial fitting to remove the trend item, and then multiplied by the Hanning window function, so as to obtain a sequence of preprocessed signal frames.

[0039] The Hanning window function is as follows:

[0040]

[0041] wherein, is the length of the window, is the current sampling point, is the weighting coefficient of the current sampling point calculated through the Hanning window.

[0042] In this way, through high-frequency sampling and professional signal preprocessing, environmental interference can be effectively filtered out, and a pure signal containing the essential characteristics of the cutting state is obtained, so as to ensure subsequent accurate analysis.

[0043] In step S2, the sequence of preprocessed signal frames is subjected to wavelet packet decomposition, the energy features of each sub-band are extracted, and the fluctuation energy entropy is calculated based on the energy proportion of each sub-band, so as to evaluate the degree of chaos of the cutting process.

[0044] In an optional embodiment, the Daubechies series wavelet is selected as the base function, the sequence of preprocessed signal frames is subjected to layer decomposition, and independent sub-bands are obtained. Among them, .

[0045] Then, the signal energy of each sub-band is calculated. Among them, the signal energy of the th sub-band satisfies the relationship:

[0046]

[0047] In the formula, represents the signal energy of the th sub-band, which is used to represent the vibration intensity in the frequency range; represents the total number of discrete sampling points in the signal frame; represents the amplitude of the point in the sequence of the reconstructed coefficients of the first frequency band after wavelet packet decomposition.

[0048] In order to eliminate the influence of the overall vibration strength, the energy needs to be normalized.

[0049] Specifically, first, the total energy of the frame signal is calculated. The total energy of the frame signal satisfies the expression:

[0050]

[0051] In the formula, represents the total energy of the frame signal; represents the signal energy of the first sub-band; represents the signal energy of the first sub-band; represents the total number of sub-bands in the frame signal.

[0052] If the total energy of the frame signal is less than a preset minimum energy threshold, it is directly determined that the system is in an idle state; otherwise, the energy proportion of each sub-band is calculated. For example, the minimum energy threshold is .

[0053] The energy proportion of the first sub-band satisfies the expression: . .

[0054] Then, the fluctuation energy entropy of the current time is calculated, and the fluctuation energy entropy satisfies the expression:

[0055]

[0056] In the formula, is the fluctuation energy entropy; represents the natural logarithm; is the total number of sub-bands.

[0057] The greater the fluctuation energy entropy, the more uniform the energy distribution in each frequency band, and the more chaotic the system; the smaller the fluctuation energy entropy, the more energy concentrated in a specific frequency band, and the more orderly the system. Specifically, if the cutting is smooth, the energy is concentrated in a small number of frequency bands, and the fluctuation energy entropy will be very low; if a hard point is encountered, the vibration is chaotic and the energy is dispersed, and the fluctuation energy entropy will increase significantly.

[0058] In this way, by calculating the fluctuation energy entropy, the complex vibration spectrum characteristics are converted into a single numerical index, which is extremely sensitive to the order degree of the system and can effectively distinguish between normal large cutting amount ordered vibration and abnormal working condition disordered vibration, solving the misjudgment problem of traditional amplitude judgment.

[0059] ​​Step S3, compare the real-time calculated fluctuation energy entropy with the preset threshold value, execute a nonlinear adaptive speed control strategy according to the comparison result, and calculate the target feeding speed at the current time; the control strategy includes a risk-avoiding diving stage when the entropy value exceeds the standard, a steady-state observation stage when the speed is low, and an S-shaped adaptive recovery stage after the anomaly is removed.

[0060] Specifically, the control strategy includes:

[0061] (1) the risk-avoiding diving stage when the entropy value exceeds the standard;

[0062] When the real-time calculated fluctuation energy entropy is greater than the preset threshold value, it is determined that an anomaly occurs. At this time, the controller outputs a sharply reduced speed instruction; wherein,

[0063]

[0064] In the formula, is the target feeding speed in the risk-avoiding diving stage; is the preset normal process cutting speed; is the diving sensitivity coefficient; is the preset threshold value. The preset normal process cutting speed is 120 mm / s; the diving sensitivity coefficient is 5.0; is the fluctuation energy entropy.

[0065] In addition, in the risk-avoiding diving stage, if the calculated target feeding speed is less than the allowed minimum safe speed, the target feeding speed output by the controller is the allowed minimum safe speed.

[0066] For example, the allowed minimum safe speed of the circular saw is 40 mm / s, and when the calculated target feeding speed value is 9.84 mm / s, the calculated target feeding speed is less than the allowed minimum safe speed, at this time, the system finally outputs the cutting speed as the allowed minimum safe speed, that is, 40 mm / s, so that the visible speed is pulled to the minimum safe value in an instant.

[0067] (2) the steady-state observation stage when the speed is low;

[0068] When the speed is reduced, if the fluctuation energy entropy has not recovered to the system preset baseline entropy value, the allowed minimum safe speed is maintained, and a small low-frequency disturbance signal is superimposed to assist the saw blade to pass through the hard point area under low load. The amplitude of the low-frequency disturbance can be 5% of the allowed minimum safe speed.

[0069] (3) the S-shaped adaptive recovery stage after the anomaly is removed;

[0070] When the fluctuation energy entropy is monitored to fall back and stabilize in the safe range, and the duration exceeds the preset time threshold, it is determined that the abnormal working condition has been removed. Wherein, the above-mentioned safe range is: ; The system is preset with a reference entropy value; The safety threshold is set.

[0071] At this time, in order to avoid the sudden increase in speed to stimulate the system shock again, the speed is controlled by a variant of the Sigmoid function to rise smoothly along the S-shaped curve. The Sigmoid function is:

[0072]

[0073] In the formula, The target feed speed after the abnormal working condition is removed; The current system time; The minimum safe speed allowed; The preset normal process cutting speed; The starting time of triggering the recovery mode; The recovery rate factor is determined by the dynamic response performance of the machine tool, which is used to control the slope of the rising section of the S-shaped curve, for example The constant is 2.0; The time offset constant, .

[0074] In the above formula, when The exponential part is If are positive numbers, the exponential is a large positive number, the denominator is large, and the fraction value is small, which is close to the minimum safe speed allowed . With the passage of time , The exponential term gradually decreases to a negative large number, so that the denominator in the above formula tends to 1, and the whole tends to the preset normal process cutting speed .

[0075] In this way, through this phased nonlinear control strategy, not only can the equipment be quickly "braked" to protect the equipment when danger occurs, but also the production can be smoothly "accelerated" to restore production after the danger is removed, realizing intelligent adaptive adjustment throughout the process.

[0076] Step S4, the calculated target feed speed is converted into a control command and sent to the feed shaft servo driver, the motor speed is adjusted in real time, and the above steps are continuously cycled to form a closed loop control, thereby realizing the cutting speed control of the aluminum plate cutting device.

[0077] ​Specifically, the target feed speed calculated in step S3 is converted into an analog voltage signal by a digital-to-analog converter or sent to the servo driver of the feed axis via an EtherCAT bus to adjust the motor speed in real time.

[0078] The system continuously executes steps S1 to S4 in a loop, forming a closed-loop control.

[0079] In this way, through closed-loop feedback execution, the control strategy is ensured to act on the physical equipment in real time, guaranteeing the continuity and stability of the cutting process, and ultimately achieving efficient and safe aluminum plate cutting.

[0080] like Figure 2 and Figure 3 As shown, Figure 2 The diagram schematically illustrates a comparison of the kerf width / vibration suppression effect between the prior art and an embodiment of the present invention; Figure 3 The diagram illustrates the adaptive velocity control response curve based on wave energy entropy according to an embodiment of the present invention.

[0081] according to Figure 2 As can be seen, in existing technologies, after encountering a hard point at t=5 seconds, the cutting vibration begins to diverge due to the lack of adaptive adjustment capability of the system. The amplitude continuously increases in a funnel shape, leading to severe deviations in the kerf width and making the workpiece highly susceptible to damage. In this invention, although a hard point is encountered at t=5 seconds, generating an instantaneous oscillation pulse, the controller detects a sudden increase in the wave energy entropy around t=5.5 seconds and immediately triggers an active deceleration and vibration suppression strategy. The oscillation waveform exhibits a "pulse envelope" shape and is rapidly suppressed and attenuated to near the baseline within approximately 1.5 seconds, effectively preventing resonance divergence and protecting the stability of the machining process.

[0082] according to Figure 3 It can be seen that in the first 0-5 seconds, the characteristic index "cutting kerf energy entropy" is in a low and stable state; at 5 seconds, due to the encounter with a hard point, the "cutting kerf energy entropy" rises sharply; then in the period of 5.5-8 seconds, with the intervention of the control strategy, the system state gradually stabilizes and the entropy value drops; in the 5-5.5 second interval, in response to the surge in the wave energy entropy value, the speed performs "risk avoidance diving", rapidly decreasing from 120mm / s, and the speed decrease is strictly limited to the minimum threshold of 40mm / s, ensuring that the cutting process does not stop or reverse; at 8 seconds, when the wave energy entropy value completely recovers to a stable state, the speed curve shows a standard S-shaped (Sigmoid) rebound, smoothly accelerating and finally stabilizing at the target speed, reflecting the flexible control characteristics of adaptive recovery.

[0083] The application further provides a cutting speed control system of an aluminum plate cutting device. The system comprises a processor and a memory, and the memory stores computer program instructions which, when executed by the processor, implement the cutting speed control method of the aluminum plate cutting device according to the application.

[0084] The system further comprises a communication bus and a communication interface and other components well known to those skilled in the art, the settings and functions of which are known in the art, and thus will not be described here.

[0085] In the present application, the aforementioned memory can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, device or apparatus. For example, the computer readable storage medium can be any appropriate magnetic storage medium or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random-Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, a module or both. Any such computer storage medium can be part of a device or accessible or connectable to a device. Any application or module described in the present application can be implemented by computer readable / executable instructions stored or otherwise held by such computer readable medium.

[0086] In the description of the present application, the meaning of "a plurality of" is at least two, such as two, three or more, etc., unless otherwise explicitly specified.

[0087] Although the present application has shown and described the preferred embodiments of the application, it will be apparent to those skilled in the art that many modifications, changes and substitutions can be made thereto without departing from the spirit and scope of the present application.

Claims

1. A method for controlling the cutting speed of an aluminum plate cutting device, characterized in that, Includes the following steps: During the cutting process, the spindle vibration acceleration signal is acquired in real time, and the acquired raw time domain signal is detrended and windowed and framed to obtain a preprocessed signal frame sequence. Wavelet packet decomposition is performed on the preprocessed signal frame sequence to extract the energy features of each sub-band, and the wave energy entropy is calculated based on the energy proportion of each sub-band to assess the disorder of the segmentation process. The real-time calculated fluctuation energy entropy is compared with a preset threshold. Based on the comparison result, a nonlinear adaptive velocity control strategy is executed to calculate the target feed velocity at the current moment. The control strategy includes a hazard-avoidance descent phase when the entropy value exceeds the limit, a steady-state observation phase when maintaining low speed, and an S-shaped adaptive recovery phase after the anomaly is resolved. In the hazard-avoidance descent phase when the entropy value exceeds the limit, the following conditions are met: ； The target feed rate during the descent phase for risk avoidance when the entropy value exceeds the limit; This is the preset normal process cutting speed; This is the diving sensitivity coefficient; The preset threshold; The entropy of wave energy; The control strategy for the steady-state observation phase during low-speed maintenance is as follows: If the fluctuation energy entropy does not recover to the system's preset reference entropy value after the speed decreases, the minimum allowable safe speed will be maintained, and a low-frequency disturbance signal with an amplitude of 5% of the minimum allowable safe speed will be superimposed. The control strategy for the S-shaped adaptive recovery phase after the anomaly is resolved is as follows: When the fluctuating energy entropy is detected to have fallen back and stabilized within a safe range for a period exceeding a preset time threshold, the target feed rate satisfies the following: The target feed rate after the abnormal working condition is resolved; The current system time; The minimum safe speed allowed; This is the preset normal process cutting speed; The starting point for triggering recovery mode; For the recovery rate factor, This is the time offset constant; The calculated target feed speed is converted into control commands and sent to the feed axis servo driver. The motor speed is adjusted in real time, and the above steps are continuously repeated to form a closed-loop control, thereby realizing the cutting speed control of the aluminum plate cutting device.

2. The cutting speed control method of the aluminum plate cutting device according to claim 1, characterized in that, The real-time acquisition of spindle vibration acceleration signals during the cutting process includes setting the sampling frequency to above 20kHz to capture the high-frequency impact characteristics when the saw teeth cut into the material.

3. The cutting speed control method of the aluminum plate cutting device according to claim 1, characterized in that, The step of performing wavelet packet decomposition on the preprocessed signal frame sequence to extract the energy features of each sub-band includes: performing wavelet packet transform on the signal frame... Layer decomposition, to obtain Each sub-band is represented by a sub-band; the signal energy of the sub-band is obtained by calculating the sum of the squares of the wavelet packet reconstruction coefficient amplitudes at all discrete sampling points within that sub-band.

4. The cutting speed control method of the aluminum plate cutting device according to claim 3, characterized in that, The calculation of fluctuation energy entropy based on the energy proportion of each sub-band includes: Before calculating the wave energy entropy, the energy probability distribution is normalized. Specifically, the sum of the energy of all sub-band signals is calculated as the total energy of the frame signal, and the energy of each sub-band signal is divided by the total energy to obtain the energy ratio of each sub-band. When the total energy is less than a preset minimum energy threshold, the system is determined to be in an idle state, and the entropy calculation is skipped.

5. The cutting speed control method of the aluminum plate cutting device according to claim 4, characterized in that, Wave energy entropy satisfies the expression: ； In the formula, The entropy of wave energy; Represents the natural logarithm; For the first Energy percentage of each sub-band; This represents the total number of sub-bands.

6. The cutting speed control method of the aluminum plate cutting device according to claim 5, characterized in that, No. The signal energy of each sub-band satisfies the following relationship: ； In the formula, Indicates the first The signal energy of each sub-band is used to characterize the vibration intensity within the frequency range of that sub-band; This indicates the total number of discrete sampling points within the signal frame; This indicates that after wavelet packet decomposition, the th The first frequency band reconstruction coefficient sequence The amplitude of each point.

7. A cutting speed control system for an aluminum plate cutting device, characterized in that, include: processor; A memory storing computer instructions for controlling the cutting speed of an aluminum plate cutting apparatus, wherein when the computer instructions are executed by the processor, the system performs the cutting speed control method of the aluminum plate cutting apparatus according to any one of claims 1-6.

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