A comb-tooth mortising machine control method and related equipment for wooden door processing

Abstract

The present invention provides a comb-tooth tenoning machine control method and related equipment for wooden door processing, and relates to the technical field of comb-tooth tenoning machine control. The method comprises the steps of: obtaining the real-time cutting force of the comb-tooth tenoning machine during the tenoning process; comparing the real-time cutting force with a preset cutting force threshold to obtain a cutting force difference; obtaining a feed speed adjustment amount according to the cutting force difference and a preset proportional control coefficient; obtaining a theoretical feed speed according to the feed speed adjustment amount; limiting the theoretical feed speed to between the minimum feed speed and the maximum feed speed allowed by the comb-tooth tenoning machine to obtain the actual feed speed; and controlling the comb-tooth tenoning machine to operate according to the actual feed speed. The comb-tooth tenoning machine control method for wooden door processing of the present invention solves the contradiction between the existing hardwood door panel tenoning processing quality and production capacity, minimizes the loss of hardwood materials caused by poor processing, and effectively improves the mortise and tenon processing quality.

Description

Technical Field

[0001] The present invention relates to the technical field of comb-tooth tenoning machine control, and in particular to a comb-tooth tenoning machine control method and related equipment for wooden door processing. Background Art

[0002] To meet the dual demands of structural strength and aesthetic appearance, high-end solid wood furniture manufacturers generally utilize sophisticated comb-tooth mortise and tenon jointing techniques. Among the various wood types, hardwoods, such as oak and cherry, are the preferred raw materials for high-end solid wood furniture due to their excellent physical properties and naturally beautiful grain. To significantly improve production efficiency, these factories are often equipped with high-speed comb-tooth mortising machines precisely controlled by PLCs (Programmable Logic Controllers).

[0003] However, high-speed cutting of hardwoods like oak and cherry, especially those with complex and varied grain patterns and natural knots, is prone to surface defects such as chipping and gnaws. In severe cases, these defects can even lead to unexpected breakage of tenons or partial collapse of groove walls. This not only directly wastes valuable hardwood resources and significantly impacts manufacturing costs, but also reduces the overall quality and quality of the finished furniture. While artificially reducing cutting speeds can mitigate these defects to a certain extent, this inevitably results in a significant reduction in production efficiency, making it difficult to meet the factory's ever-increasing order delivery needs.

[0004] Therefore, how to effectively solve the quality problems arising from the high-speed mortise and tenoning process of hardwood while ensuring the existing production capacity has become an important technical problem that needs to be overcome urgently in the current high-end solid wood furniture manufacturing industry. Summary of the Invention

[0005] The purpose of the present invention is to provide a comb-tooth mortising machine control method and related equipment for wooden door processing, which solves the contradiction between the existing hardwood door panel mortising processing quality and production capacity, minimizes the loss of hardwood materials caused by poor processing, and effectively improves the mortise and tenon processing quality.

[0006] In a first aspect, the present invention provides a method for controlling a comb-tooth mortising machine for wooden door processing, comprising the following steps:

[0007] S1. Obtaining the real-time cutting force of the comb tenoning machine during the tenoning process;

[0008] S2. comparing the real-time cutting force with a preset cutting force threshold to obtain a cutting force difference;

[0009] S3. Get the feed speed adjustment amount according to the cutting force difference and the preset proportional control coefficient;

[0010] S4. According to the feed speed adjustment amount, obtain the theoretical feed speed;

[0011] S5. The theoretical feed rate is limited to the minimum feed rate and the maximum feed rate allowed by the comb tenoning machine to obtain the actual feed rate;

[0012] S6. Control the comb tenoning machine to operate according to the actual feed speed.

[0013] The comb-tooth tenoning machine control method for wooden door processing provided by the present invention monitors the cutting force in real time and dynamically, and adaptively adjusts the feed speed in proportion to the cutting force, thereby effectively avoiding processing defects such as chipping, gnawing, tenon breakage, and groove wall collapse caused by sudden changes in cutting force when cutting hardwood knots, irregular textures and other complex areas at high speed, thereby greatly reducing the scrap rate of high-grade hardwood materials in the comb-tooth tenoning process, improving the utilization rate of wood, and directly reducing production and manufacturing costs.

[0014] Furthermore, the specific steps in step S3 include:

[0015] S31. By analyzing the tool speed, obtaining the fluctuation frequency of the tool speed;

[0016] S32. Determine whether the fluctuation frequency exceeds a preset frequency threshold, and when it is confirmed that it exceeds, perform filtering on the tool speed to obtain a filtered tool speed;

[0017] S33. Obtain a first adjustment amount according to the filtered tool speed;

[0018] S34. Obtaining a second adjustment amount according to the cutting force difference and the proportional control coefficient;

[0019] S35. Obtain the feed speed adjustment amount according to the first adjustment amount and the second adjustment amount.

[0020] Under the premise of ensuring the existing production capacity level, the impact of tool speed fluctuation on processing quality is effectively reduced, and the processing quality and stability of comb teeth mortise and tenon of wooden doors are improved.

[0021] Furthermore, the specific steps of performing filtering processing on the tool speed to obtain the filtered tool speed include:

[0022] S321. Collecting the tool speed signal and performing a fast Fourier transform to obtain a spectrum of the tool speed signal; the spectrum is a two-dimensional spectrum diagram represented in the form of a time-frequency diagram;

[0023] S322. Analyze the frequency spectrum to identify the dominant frequency component causing tool speed fluctuations. If the dominant frequency component falls within a preset hardwood material uneven frequency range, determine that the fluctuations are caused by hardwood material unevenness, and execute step S323. If the dominant frequency component falls within a preset tool wear frequency range, determine that the fluctuations are caused by tool wear, and execute step S324. Otherwise, determine that the fluctuations are caused by equipment vibration, and execute step S325.

[0024] S323. If it is determined that the fluctuation is caused by uneven hardwood material, filtering is performed on the tool speed using a sliding average filtering algorithm to obtain a filtered tool speed;

[0025] S324. If it is determined that the fluctuation is caused by tool wear, a Kalman filter algorithm is used to filter the tool speed to obtain a filtered tool speed;

[0026] S325. If it is determined that the fluctuation is caused by equipment vibration, a filtering algorithm based on wavelet transform is used to perform filtering processing on the tool rotation speed to obtain a filtered tool rotation speed.

[0027] Targeted filtering is achieved according to different fluctuation causes to improve the accuracy and reliability of the tool speed signal.

[0028] Furthermore, the specific steps of analyzing the frequency spectrum and identifying the dominant frequency component causing the tool speed fluctuation include:

[0029] A1. Using a morphological opening operation, the spectrum is eroded using a structuring element to eliminate noise points caused by hardwood dust in the spectrum, thereby obtaining an intermediate spectrum; the structuring element is a two-dimensional image;

[0030] A2. performing expansion processing on the intermediate spectrum to restore the original spectrum structure and obtain a dust suppression spectrum;

[0031] A3. Analyze the dust suppression spectrum and obtain a grayscale histogram of the spectrum;

[0032] A4. Using an adaptive threshold segmentation algorithm based on the otsu algorithm, the segmentation threshold is calculated according to the grayscale histogram of the spectrum;

[0033] A5. Using the segmentation threshold to extract the peak points in the spectrum;

[0034] A6. Identify the dominant frequency component causing the tool rotation speed fluctuation based on the frequency value of the peak point.

[0035] By combining the above-mentioned image processing and spectrum analysis steps, this solution can effectively reduce the interference of hardwood dust on spectrum analysis, improve the accuracy of identifying the dominant frequency components, and thus provide a more reliable basis for subsequent control methods.

[0036] Furthermore, the specific steps in step A1 include:

[0037] A11. Get the average size of noise points in the spectrum.

[0038] A12. Setting the average size to the size of the structural element;

[0039] A13. Perform a morphological opening operation on the spectrum using a structure element of a set size to obtain an intermediate spectrum;

[0040] A14. Calculate the number of noise points in the current intermediate spectrum and determine whether the number of noise points is greater than a preset threshold. If so, adjust and update the size of the structure element according to a preset step size and return to step A13 or output the current intermediate spectrum. If not, output the current intermediate spectrum.

[0041] Furthermore, the specific steps in step A14 include:

[0042] A141. Calculate the number of noise points in the current intermediate spectrum and determine whether the number of noise points is greater than a preset threshold. If the number of noise points is less than or equal to the threshold, output the current intermediate spectrum. If the number of noise points is greater than the threshold, perform the following steps:

[0043] A1411. Calculate the total area of ​​the noise points of the current intermediate spectrum, and determine whether the total area of ​​the noise points is within a preset noise area threshold range. If the total area of ​​the noise points is greater than the upper limit value of the noise area threshold range, reduce the size of the structural element according to the preset first step length, and return to execute step A13; if the total area of ​​the noise points is less than the lower limit value of the noise area threshold range, enlarge the size of the structural element according to the preset second step length, and return to execute step A13; if the total area of ​​the noise points is within the noise area threshold range, output the current intermediate spectrum.

[0044] Furthermore, the specific steps in step S33 include:

[0045] S33A1. After establishing a first relationship model between tool speed, hardwood material, and cutting force, calculate a first theoretical change in cutting force caused by a change in tool speed based on the filtered tool speed and the hardwood material being processed, based on the first relationship model; the first relationship model is used to characterize the degree of influence of tool speed changes on cutting force for different hardwood materials;

[0046] S33A2. Divide the first theoretical cutting force change by a preset first conversion coefficient to obtain a corresponding first feed speed adjustment amount as the first adjustment amount.

[0047] In a second aspect, the present invention provides a comb-tooth mortising machine control device for wooden door processing, comprising:

[0048] The first acquisition module is used to obtain the real-time cutting force of the comb tenoning machine during the tenoning process;

[0049] A comparison module is used to compare the real-time cutting force with a preset cutting force threshold to obtain a cutting force difference;

[0050] A second acquisition module is used to acquire a feed speed adjustment amount according to the cutting force difference and a preset proportional control coefficient;

[0051] A third acquisition module is used to obtain a theoretical feed speed according to the feed speed adjustment amount;

[0052] a fourth acquisition module, configured to limit the theoretical feed speed to between a minimum feed speed and a maximum feed speed allowed by the comb tenoning machine, to obtain an actual feed speed;

[0053] The output control module is used to control the comb tenoning machine to operate according to the actual feed speed.

[0054] The comb-tooth mortising machine control device for wooden door processing provided by the present invention can avoid the occurrence of processing defects such as chipping and gnawing, and effectively reduce the waste of hardwood resources while ensuring the processing quality.

[0055] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the comb mortising machine control method for wooden door processing provided in the first aspect are executed.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, runs the steps of the comb mortising machine control method for wooden door processing provided in the first aspect above.

[0057] From the above, it can be seen that the comb-tooth mortising machine control method for wooden door processing provided by the present invention is aimed at high-hardness woods such as oak and cherry wood, especially complex hardwood materials containing natural knots and irregular textures. Without making large-scale adjustments to the existing PLC control system framework and ensuring that production capacity is basically unaffected, it effectively overcomes a series of quality defects such as chipping, gnawing, and even tenon fracture and groove wall collapse that are very easy to occur during high-speed cutting processing, thereby minimizing the loss of hardwood materials caused by poor processing and effectively ensuring the stable improvement of the mortise and tenon processing quality.

[0058] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flow chart of a control method for a comb mortising machine for wooden door processing provided in an embodiment of the present invention.

[0060] Figure 2 A structural schematic diagram of a comb mortising machine control device for wooden door processing provided in an embodiment of the present invention.

[0061] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0062] Description of labels:

[0063] 100, first acquisition module; 200, comparison module; 300, second acquisition module; 400, third acquisition module; 500, fourth acquisition module; 600, output control module; 13, electronic device; 1301, processor; 1302, memory; 1303, communication bus. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0065] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0066] Reference Attachment Figure 1 The present invention provides a control method for a comb-tooth mortising machine for wooden door processing, comprising the steps of:

[0067] S1. Obtaining the real-time cutting force of the comb tenoning machine during the tenoning process;

[0068] S2. comparing the real-time cutting force with a preset cutting force threshold to obtain a cutting force difference;

[0069] S3. Obtaining a feed speed adjustment amount based on the cutting force difference and a preset proportional control coefficient;

[0070] S4. Obtaining the theoretical feed speed according to the feed speed adjustment amount;

[0071] S5. Limit the theoretical feed rate to the range between the minimum and maximum feed rates allowed by the comb tenoning machine to obtain the actual feed rate;

[0072] S6. Control the comb tenoning machine to run according to the actual feed speed.

[0073] In step S1, the real-time cutting force can be directly measured using a force sensor mounted on the tool shaft or workbench of the comb tenoning machine. The force sensor monitors the interaction force between the tool and the wood during machining in real time and converts the force signal into an electrical signal that is transmitted to the controller.

[0074] In step S2, the preset cutting force threshold is pre-set based on factors such as wood type, tool sharpness, and desired processing quality. The cutting force threshold represents a reference value of the cutting force under normal processing conditions.

[0075] In step S3, the proportional control coefficient is a key parameter used to adjust the feed rate. The proportional control coefficient determines the sensitivity of the cutting force differential to the feed rate adjustment. A larger proportional control coefficient results in a more pronounced feed rate adjustment; a smaller proportional control coefficient results in a more gradual feed rate adjustment.

[0076] In step S4 , the theoretical feed speed may be obtained by adding the feed speed adjustment amount to the current feed speed to obtain the adjusted theoretical feed speed.

[0077] In step S5, the minimum and maximum feed rates are inherent parameter limitations of the comb tenoning machine, ensuring that the equipment operates within a safe and efficient range. The actual feed rate is obtained by comparing the theoretical feed rate, the minimum feed rate, and the maximum feed rate, and taking the value that satisfies the constraints.

[0078] Specifically, for example, the theoretical feed rate is mapped to the interval between the minimum feed rate and the maximum feed rate through the following clamping function:

[0079] ;

[0080] in, is the actual feed speed, is the minimum feed speed, is the theoretical feed speed, is the maximum feed rate.

[0081] In step S6, the controller generates a control signal according to the actual feed speed, drives the feed mechanism of the comb-tooth tenoning machine, and makes the comb-tooth tenoning machine perform tenoning processing according to the actual feed speed.

[0082] Specifically, this technology aims to address the problem of surface defects such as chipping and gnaws, which are common in wooden door processing due to the natural knots in hardwood. In severe cases, these defects can even lead to unexpected breakage of the tenon teeth or partial collapse of the groove wall, resulting in a waste of hardwood resources. This technical solution addresses this issue through a closed-loop control method. First, the cutting force during the tenoning process is collected in real time, providing real-time data for subsequent feed rate adjustments. The real-time cutting force is then compared with a preset cutting force threshold to determine the difference in cutting force. This difference in cutting force reflects the degree of deviation between the current processing state and the ideal state. The feed rate adjustment is then calculated based on the cutting force difference and the proportional control coefficient to determine the specific value by which the feed rate should be adjusted. The theoretical feed rate is then calculated based on the feed rate adjustment. Taking into account the actual operating limitations of the comb-tooth tenoning machine, the theoretical feed rate is limited to between the minimum and maximum feed rates allowed by the machine, resulting in an actual, executable feed rate. Finally, the comb-tooth tenoning machine is controlled to operate at the actual feed rate. Therefore, when the real-time cutting force exceeds the preset threshold, it indicates that the cutting load is too large. The system will reduce the feed speed to reduce the cutting force and avoid the occurrence of processing defects such as chipping and gnawing. When the real-time cutting force is lower than the preset threshold, it indicates that the cutting load is small. The system can appropriately increase the feed speed to improve the processing efficiency while ensuring the processing quality.

[0083] In some specific embodiments, for the comb-tooth tenoning processing of hardwood doors, the preset cutting force threshold is set to 500N. During the tenoning machine processing, the cutting force detected in real time by the force sensor is 600N. First, the real-time cutting force of 600N is compared with the preset cutting force threshold of 500N to obtain a cutting force difference of +100N (indicating that the cutting load is too large). The proportional control coefficient is preset to 0.01mm / N. Then, based on the cutting force difference of 100N and the proportional control coefficient of 0.01mm / N, the feed speed adjustment of -1mm / min is calculated. Assuming that the current feed speed is 50mm / min, the feed speed adjustment is added to the current feed speed to obtain a theoretical feed speed of 49mm / min (reducing the cutting force by reducing the speed). The minimum feed speed allowed by the comb-tooth tenoning machine is set to 30mm / min, and the maximum feed speed is set to 80mm / min. The theoretical feed rate of 49 mm / min is then compared with the minimum feed rate of 30 mm / min and the maximum feed rate of 80 mm / min. Since 49 mm / min is between 30 mm / min and 80 mm / min, the actual feed rate is determined to be 49 mm / min. Finally, the comb tenoning machine is controlled to operate at the actual feed rate of 49 mm / min.

[0084] In some embodiments, the specific steps in step S3 include:

[0085] S31. By analyzing the tool speed, the fluctuation frequency of the tool speed is obtained;

[0086] S32 determines whether the fluctuation frequency exceeds a preset frequency threshold, and when it is confirmed that it exceeds, performs filtering on the tool speed to obtain a filtered tool speed;

[0087] S33. Obtain a first adjustment amount according to the filtered tool speed;

[0088] S34. Obtaining a second adjustment amount based on the cutting force difference and the proportional control coefficient;

[0089] S35. Obtain a feed speed adjustment amount according to the first adjustment amount and the second adjustment amount.

[0090] In step S31, the acquisition of the tool speed fluctuation frequency can be completed by a speed sensor installed on the tool shaft of the comb tenoning machine. The speed sensor collects the tool speed signal in real time, and the control system performs fast Fourier transform analysis on the speed signal to obtain a spectrum diagram of the tool speed. The frequency corresponding to the peak in the spectrum diagram is the fluctuation frequency of the tool speed.

[0091] In step S32, the preset frequency threshold is determined based on the tool speed fluctuation range during normal operation of the comb tenoning machine. When the analyzed fluctuation frequency exceeds this threshold, it indicates abnormal tool speed fluctuations and requires filtering. The purpose of filtering is to eliminate noise interference in the speed signal and more accurately reflect the actual tool speed status.

[0092] In step S33, the first adjustment is calculated based on the filtered tool speed. This calculation refers to a pre-established first relationship model that describes the impact of tool speed fluctuations on cutting force for different hardwood materials. This model predicts changes in cutting force caused by tool speed fluctuations and converts them into a first feed rate adjustment.

[0093] In step S34, the second adjustment value is obtained based on the cutting force difference and the proportional control coefficient, reflecting the adjustment of the feed rate by the cutting force feedback control. Obtaining the second adjustment value refers to following the basic cutting force feedback control principle and multiplying the difference between the real-time cutting force and the cutting force at the preset threshold by the proportional control coefficient to obtain another component for adjusting the feed rate. For example, according to the formula: ;in, is the second adjustment amount, is the proportional control coefficient, is the cutting force difference. For example, the cutting force difference is and proportional control coefficient , calculate the second adjustment amount .

[0094] In step S35 , the first adjustment amount and the second adjustment amount are comprehensively considered, for example, a weighted average method can be adopted to obtain the final feed speed adjustment amount, thereby achieving accurate adjustment of the feed speed.

[0095] Specifically, to address the problem of tool speed fluctuations during comb-tooth tenoning of wooden doors, which are susceptible to interference from various factors, this solution achieves adaptive feed speed adjustment by adding a monitoring and processing step for tool speed fluctuations. First, by analyzing the tool speed signal, the tool speed fluctuation frequency is determined, thereby determining the tool speed stability. When the fluctuation frequency exceeds a preset threshold, filtering is initiated to remove noise from the speed signal, ensuring the accuracy of subsequent control decisions. Then, based on the filtered tool speed and a pre-established first relationship model, a first adjustment caused by tool speed fluctuations is calculated. This adjustment is used to make a preliminary adjustment to the feed speed based on tool speed stability. Simultaneously, in conjunction with cutting force feedback control, a second adjustment is calculated based on the cutting force difference, further correcting the feed speed. Finally, the first and second adjustments are comprehensively considered to determine the final feed speed adjustment, which is used to control the operation of the comb-tooth tenoning machine, achieving precise adaptive feed speed control. This approach effectively reduces the impact of tool speed fluctuations on processing quality while maintaining existing production capacity, improving the processing quality and stability of comb-tooth tenoning of wooden doors.

[0096] In some specific embodiments, the speed sensor can be a high-precision photoelectric encoder installed at the rear end of the main shaft of the comb tenoning machine to monitor the spindle speed changes in real time. The preset frequency threshold can be set to 10 Hz. When the tool speed fluctuation frequency exceeds 10 Hz, it is determined to be an abnormal fluctuation and filtering is initiated. The filtering process can use a median filter algorithm, set the filter window size to 5, sort five consecutive speed sampling points, and take the median value as the filtered speed value. The first relationship model can be obtained by fitting experimental data. For example, for oak material, cutting force data at different tool speeds can be recorded through multiple experiments. Then, a data fitting method can be used to establish a functional relationship model between tool speed and cutting force. When calculating the first adjustment value, the theoretical cutting force change can be calculated using the first relationship model based on the filtered tool speed. This change is then divided by a preset conversion coefficient to obtain the corresponding first feed rate adjustment value. The first adjustment value and the second adjustment value can be combined using a linear weighting method. For example, the weight of the first adjustment value can be set to 0.3 and the weight of the second adjustment value can be set to 0.7. The weighted sum of the two is used to obtain the final feed rate adjustment value.

[0097] In some embodiments, the specific steps of performing filtering on the tool rotation speed to obtain the filtered tool rotation speed include:

[0098] S321. Collecting the tool speed signal and performing a fast Fourier transform to obtain a spectrum of the tool speed signal; the spectrum is a two-dimensional spectrum diagram represented in the form of a time-frequency diagram;

[0099] S322. Analyze the frequency spectrum to identify the dominant frequency component causing tool speed fluctuations. If the dominant frequency component falls within the preset hardwood material unevenness frequency range, the fluctuation is determined to be caused by hardwood material unevenness, and step S323 is executed. If the dominant frequency component falls within the preset tool wear frequency range, the fluctuation is determined to be caused by tool wear, and step S324 is executed. Otherwise, the fluctuation is determined to be caused by equipment vibration, and step S325 is executed.

[0100] S323. If it is determined that the fluctuation is caused by uneven hardwood material, the tool speed is filtered using a sliding average filter algorithm to obtain a filtered tool speed;

[0101] S324. If the fluctuation is determined to be caused by tool wear, a Kalman filter algorithm is used to filter the tool speed to obtain a filtered tool speed;

[0102] S325. If it is determined that the fluctuation is caused by equipment vibration, a filtering algorithm based on wavelet transform is used to perform filtering processing on the tool speed to obtain a filtered tool speed.

[0103] In step S321, the tool speed signal is collected by a sensor. Fast Fourier transform (FFT) converts the time-domain signal into a frequency-domain signal, thereby obtaining a spectrum. This spectrum is presented as a time-frequency graph, showing how the frequency components change over time and providing a data foundation for subsequent frequency analysis.

[0104] In step S322, the spectrum is analyzed to identify dominant frequency components—frequency regions where energy is concentrated. These dominant frequency components indicate the primary source of tool speed fluctuations. Preset frequency ranges for hardwood material inhomogeneity, tool wear, and equipment vibration are pre-calibrated. For example, the hardwood material inhomogeneity frequency range can be obtained through experimentation or empirical data, while the tool wear and equipment vibration frequency ranges can be determined through analysis of the equipment's own characteristics. By comparing the dominant frequency components with these pre-set frequency ranges, the specific cause of tool speed fluctuations can be determined.

[0105] In steps S323, S324, and S325, different filtering algorithms are used to address different causes of fluctuations. The sliding average filter smoothes random fluctuations caused by uneven hardwood material; the Kalman filter estimates and filters out regular fluctuations caused by tool wear; and the wavelet transform filter effectively filters out transient shock fluctuations caused by equipment vibration. The selection of different filtering algorithms is based on the characteristics of various fluctuation components and the filtering features of each filtering algorithm, enabling targeted filtering and improving filtering effectiveness.

[0106] Specifically, this embodiment first uses a speed sensor installed on the comb tenoning machine to collect tool speed signals in real time. The collected speed signal is input into a signal processing unit, where a fast Fourier transform is performed to convert the time-domain speed signal into a frequency-domain spectrum. The obtained spectrum is displayed on the display interface in the form of a two-dimensional time-frequency graph, with the horizontal axis representing time, the vertical axis representing frequency, and the color depth representing signal strength. Then, by analyzing the spectrum graph, the peak frequency components in the spectrum are identified. For example, if a clear peak appears in the frequency range of 20-30Hz in the spectrum graph, and this frequency range is preset to the frequency range of uneven hardwood material, it is determined that the current tool speed fluctuation is mainly caused by uneven hardwood material. Conversely, if the peak frequency falls within the tool wear frequency range of 80-100Hz, it is determined to be caused by tool wear. If the peak frequency is in a lower frequency band, such as 5-10Hz, and is consistent with the natural vibration frequency of the equipment, it is determined to be caused by equipment vibration. After determining the cause of the fluctuation, the system automatically selects the corresponding filtering algorithm. If the hardwood material is determined to be uneven, the sliding average filter algorithm is activated, with a sliding window size of 5, to smooth the subsequent tool speed signal. If tool wear is determined, the Kalman filter algorithm is activated, and the tool speed signal is optimally estimated and filtered based on the preset tool wear model and noise parameters. If equipment vibration is determined, the wavelet transform filter algorithm is activated, selecting the db4 wavelet basis, and performing multi-scale decomposition on the tool speed signal to filter out vibration frequency components. The filtered tool speed signal is then used in subsequent feed speed adjustment calculations, enabling targeted filtering based on the different causes of fluctuations, improving the accuracy and reliability of the tool speed signal.

[0107] In some specific embodiments, the preset hardwood material uneven frequency range is set to 15-35Hz, the tool wear frequency range is set to 75-125Hz, and the equipment vibration frequency range is set to 5-15Hz. During spectrum analysis, a peak detection algorithm is used to automatically extract the first three peak frequencies in the spectrum and sort them according to energy size. The peak frequency with the largest energy is taken as the dominant frequency component, and it is compared with the preset frequency range to determine the cause of the fluctuation. The sliding window size of the sliding average filtering algorithm can be adjusted according to the actual degree of hardwood material unevenness. For example, for hardwood with more serious material unevenness, the sliding window size can be appropriately increased to obtain a stronger smoothing effect. The tool wear model in the Kalman filter algorithm can be offline calibrated and online updated according to the actual wear of the tool to improve the estimation accuracy of the Kalman filter. The wavelet basis type and decomposition layer number of the wavelet transform filtering algorithm can be selected and adjusted according to the specific frequency characteristics of the equipment vibration to obtain the best vibration filtering effect.

[0108] In some embodiments, the specific steps of analyzing the frequency spectrum and identifying the dominant frequency component causing the tool rotation speed fluctuation include:

[0109] A1. Using a morphological opening operation, the spectrum is eroded using a structuring element to remove noise points caused by hardwood dust and obtain an intermediate spectrum. The structuring element is a two-dimensional image.

[0110] A2. Dilate the intermediate spectrum to restore its original structure and obtain the dust suppression spectrum.

[0111] A3. Analyze the dust suppression spectrum and obtain the grayscale histogram of the spectrum;

[0112] A4. Adopt the adaptive threshold segmentation algorithm based on the OTSU algorithm and calculate the segmentation threshold according to the grayscale histogram of the spectrum;

[0113] A5. Use the segmentation threshold to extract the peak points in the spectrum;

[0114] A6. Based on the frequency value of the peak point, identify the dominant frequency component that causes the tool speed fluctuation.

[0115] In practice, dust accumulation is common during wooden door processing. Hardwood dust, in particular, can easily cause excessive vibration in the tool, generating high-frequency harmonic components. These components appear as non-dominant frequency spikes in the spectrum. This embodiment proposes a technical solution to address the problem of hardwood dust causing noise interference in spectrum analysis, leading to inaccurate spectrum analysis results.

[0116] In step A1, the morphological opening operation erodes the spectrum using a structuring element, effectively removing the fine noise points caused by hardwood dust. The structuring element, the basis of the morphological operation, determines the effectiveness of noise removal through its size and shape.

[0117] In step A2, the dilation process is used to restore the spectrum structure information that may be lost after the erosion process, ensuring that the main features of the spectrum are retained.

[0118] In step A3, the acquisition of the grayscale histogram provides data support for the subsequent adaptive threshold segmentation. The grayscale histogram can reflect the grayscale distribution of the spectrum image.

[0119] In step A4, the Otsu algorithm is used to adaptively calculate the optimal segmentation threshold to achieve effective segmentation of the spectrum image.

[0120] In step A5, the segmentation threshold is used to extract peak points from the spectrum. These peak points represent the main frequency components in the spectrum.

[0121] In step A6, the dominant frequency component is finally identified based on the frequency value of the extracted peak point.

[0122] Therefore, through the above steps, the interference of hardwood dust on spectrum analysis can be effectively suppressed, and the accuracy of identifying the dominant frequency components can be improved, laying the foundation for subsequent targeted filtering processing based on different causes.

[0123] Specifically, this embodiment addresses this problem by incorporating image processing techniques into the spectrum analysis process. First, in step A1, the spectrum is treated as a two-dimensional image, and the erosion process within the morphological opening operation is used to eliminate noise points in the spectrum, which are believed to be caused by hardwood dust. A structuring element, such as a 3x3 square structure, is used to detect and eliminate noise points smaller than this size. Then, in step A2, a dilation process is performed to restore the true signal portion of the spectrum that may have been eliminated during the erosion process, thereby ensuring the integrity of the spectral information. Next, in step A3, a grayscale histogram of the dust suppression spectrum is calculated to describe the distribution of grayscale values ​​in the spectrum. In step A4, the Otsu algorithm analyzes the grayscale histogram and automatically calculates an optimal threshold that maximizes the between-class variance, thereby effectively segmenting the spectrum foreground and background. In step A5, using the segmentation threshold derived from the Otsu algorithm, peak points in the spectrum are extracted. These peak points correspond to the main frequency components in the spectrum. Finally, in step A6, based on the frequency values ​​of these peak points, the dominant frequency components that cause tool speed fluctuations are accurately identified. By combining the above-mentioned image processing and spectrum analysis steps, this solution can effectively reduce the interference of hardwood dust on spectrum analysis, improve the accuracy of identifying the dominant frequency components, and thus provide a more reliable basis for subsequent control methods.

[0124] In some specific embodiments, for the tool speed spectrum generated by a comb-tooth tenoning machine during the processing of hardwood doors, step A1 is first performed to perform a morphological corrosion operation on the spectrum using a 3x3 square structuring element to eliminate noise points in the spectrum with a size smaller than 3x3 pixels. These noise points mainly come from the interference of hardwood dust. After the operation, an intermediate spectrum is obtained. Subsequently, step A2 is performed to perform an expansion operation on the intermediate spectrum, also using a 3x3 square structuring element to restore the spectrum structure and obtain a dust suppression spectrum. Next, step A3 is performed to calculate the grayscale histogram of the dust suppression spectrum and analyze the grayscale distribution of the spectrum. Then, step A4 is performed to use the Otsu algorithm to calculate an adaptive segmentation threshold based on the grayscale histogram. For example, the calculated threshold is a grayscale value of 120. Thereafter, step A5 is performed to use the grayscale value of 120 as the threshold to extract pixel points in the spectrum with grayscale values ​​greater than 120. These pixel points are considered to be peak points of the spectrum. Finally, step A6 analyzes the frequencies of the extracted peak points. For example, if the frequencies corresponding to the peak points are concentrated around 50 Hz, the dominant frequency component causing tool speed fluctuations is determined to be 50 Hz. This specific step effectively suppresses the interference of hardwood dust on spectrum analysis, accurately identifies the dominant frequency component causing tool speed fluctuations, and provides accurate frequency information for subsequent filtering.

[0125] In some embodiments, the specific steps in step A1 include:

[0126] A11. Get the average size of noise points in the spectrum.

[0127] A12. Set the average size to the size of the structural element;

[0128] A13. Perform a morphological opening operation on the spectrum using a structuring element of a predetermined size to obtain an intermediate spectrum.

[0129] A14. Calculate the number of noise points in the current intermediate spectrum and determine whether the number of noise points is greater than a preset threshold. If so, adjust and update the size of the structuring element according to a preset step size and return to step A13 or output the current intermediate spectrum. If not, output the current intermediate spectrum.

[0130] This embodiment further aims to solve the problem of hardwood dust noise points in the spectrum graph affecting the identification of dominant frequencies. By obtaining the average size of the noise points in the spectrum graph and setting the average size as the size of the structural element, the initial setting of the structural element is achieved. Then, the spectrum is subjected to morphological opening operation using the structural element of the set size to obtain an intermediate spectrum. By calculating the number of noise points in the current intermediate spectrum and judging whether the number of noise points is greater than a preset number threshold, the evaluation of the noise elimination effect is achieved. If the number of noise points is still too many, the size of the structural element is adjusted and updated according to the preset step size, and the morphological opening operation is returned to perform iterative optimization. This method of iteratively adjusting the size of the structural element enables the morphological opening operation to more accurately adapt to the actual situation of the noise points in the spectrum, thereby more thoroughly eliminating noise interference, providing a more accurate data basis for subsequent spectrum analysis, and thereby improving the accuracy of identifying the dominant frequency components of tool speed fluctuations.

[0131] Specifically, to more effectively eliminate the problem of hardwood dust noise points in the spectrum graph affecting the identification of dominant frequencies, the spectrum image can be analyzed first. Image processing algorithms can then be used to automatically identify and measure the size of noise points in the spectrum graph, calculating their average size. For example, connected component analysis can be used to identify discrete noise regions in the spectrum graph and calculate the pixel area of ​​these regions to estimate the average size of the noise points. The calculated average size is then used as the size of the structuring element in the morphological opening operation. The structuring element can be a two-dimensional image with a square, circle, or other shape, and its size directly affects the effectiveness of the morphological operation. By setting the structuring element size to the average size of the noise points, the structuring element can better adapt to the shape and size of the noise points, resulting in more accurate noise removal during the morphological opening operation while preserving the valid information in the spectrum. After performing the morphological opening operation on the spectrum using the structuring element of the set size, an intermediate spectrum is obtained. The morphological opening operation effectively removes small objects and noise points from the image and smoothes image contours. To evaluate the effectiveness of noise removal, the number of noise points in the current intermediate spectrum needs to be counted. This number of noise points can be automatically counted using image analysis algorithms. The preset number threshold is a preset benchmark value for judging the noise elimination effect. If the calculated number of noise points is greater than the preset number threshold, it indicates that the current noise elimination effect is not good and the size of the structural element needs to be further adjusted. At this time, the size of the structural element is adjusted and updated according to the preset step size. The step size can be a preset fixed value, such as 1 pixel or 2 pixels. The adjustment method can be to increase or decrease the size of the structural element, and the specific adjustment direction can be selected according to the actual situation. After adjusting the size of the structural element, return to execute step A13, use the new structural element size to re-perform the morphological opening operation, and re-evaluate the noise elimination effect, and perform iterative optimization until the number of noise points in the intermediate spectrum is less than or equal to the preset number threshold, indicating that the noise elimination effect meets the requirements. At this time, the current intermediate spectrum is output to complete the noise elimination process.

[0132] In some specific embodiments, in order to obtain the average size of hardwood dust noise points in the spectrum graph, an edge detection algorithm in image processing, such as the Canny edge detection operator, can be used to perform edge detection on the spectrum graph to obtain the edge contours of the noise points in the spectrum graph. Then, by analyzing the shape and size of the edge contours, the average size of the noise points is calculated. For example, the pixel area surrounded by the edge contour of each noise point can be calculated and then divided by the perimeter of the edge contour to obtain the average radius of the noise point, and the average radius is used as the size of the noise point. As a preferred embodiment, the preset number threshold can be set to 50 noise points. The step size can be set to 2 pixels. The initial structural element size can be set to a square structural element of 3×3 pixels. In the process of iteratively adjusting the size of the structural element, the size of the structural element can be gradually increased, for example, the size of the structural element can be increased by 2 pixels each iteration until the number of noise points in the intermediate spectrum is less than or equal to 50.

[0133] In some embodiments, the specific steps in step A14 include:

[0134] A141. Calculate the number of noise points in the current intermediate spectrum and determine whether the number of noise points is greater than a preset threshold. If the number of noise points is less than or equal to the threshold, output the current intermediate spectrum. If the number of noise points is greater than the threshold, perform the following steps:

[0135] A1411. Calculate the total area of ​​the noise points in the current intermediate spectrum and determine whether the total area of ​​the noise points is within a preset noise area threshold range. If the total area of ​​the noise points is greater than the upper limit of the noise area threshold range, reduce the size of the structural element according to the preset first step length and return to step A13. If the total area of ​​the noise points is less than the lower limit of the noise area threshold range, enlarge the size of the structural element according to the preset second step length and return to step A13. If the total area of ​​the noise points is within the noise area threshold range, output the current intermediate spectrum.

[0136] In this embodiment, calculating the number of noise points in the current intermediate spectrum refers to identifying and counting the noise points in the intermediate spectrum using image analysis techniques, such as the connected component labeling algorithm, to obtain the total number of noise points. Specifically, this can be achieved by scanning the binarized spectrum image pixel by pixel, marking continuous noise pixel regions, and counting the number of marked regions. The number threshold refers to a pre-set standard value used to determine whether the number of noise points is excessive. Specifically, an appropriate numerical range, such as 100 or 200, can be determined through experimentation or experience based on the actual application scenario and spectrum quality requirements.

[0137] Determining whether the number of noise points is greater than a preset threshold value involves comparing the number of noise points calculated in step A1 with the preset threshold value to determine whether the current spectral noise has been effectively suppressed. Specifically, if the number of noise points exceeds the threshold value, it indicates that the spectral noise is still significant and further resizing of the structural elements is necessary to enhance the noise reduction effect. Conversely, if the number of noise points is less than or equal to the threshold value, the noise reduction effect has met the preset standard and the resizing process can be terminated.

[0138] Calculating the total area of ​​noise points in the current intermediate spectrum means accumulating the number of pixels contained in each noise point region in the intermediate spectrum to obtain the total area occupied by the noise points in the spectrum image. This can be achieved by calculating the sum of the pixels in the noise point region. For example, after marking the noise point region using the connected component marking algorithm, the number of pixels in each region is counted, and the number of pixels in all regions is added together to obtain the total area of ​​the noise points. The noise area threshold range refers to a pre-set numerical interval used to determine whether the total area of ​​the noise points is appropriate. Specifically, a reasonable area range, such as 500-1000 pixels, can be determined through experiments or experience based on factors such as the size of the spectrum image, the average size of the noise points, and the spectrum purity requirements.

[0139] Determining whether the total area of ​​the noise points falls within a preset noise area threshold range involves comparing the total area of ​​the noise points calculated in step A1411 with the preset noise area threshold range, thereby more precisely evaluating the effectiveness of spectrum noise removal. Specifically, if the total area of ​​the noise points is greater than the upper limit of the area threshold range, this indicates insufficient noise removal, and the structural element size needs to be reduced to enhance the erosion effect. If the total area of ​​the noise points is less than the lower limit of the area threshold range, this indicates excessive noise removal, potentially damaging effective spectral information, and the structural element size needs to be enlarged to mitigate the erosion effect. If the total area of ​​the noise points is within the area threshold range, this indicates satisfactory noise removal, and no structural element size adjustment is necessary.

[0140] Reducing the size of the structuring element according to a preset first step size means that when the total area of ​​the noise points is too large, the size of the structuring element needs to be reduced to enhance the erosion effect of the morphological opening operation. Specifically, the size of the structuring element can be reduced by a preset fixed value or ratio, for example, by 1 pixel or 10% of the current size. The first step size can be adjusted based on actual needs and experimental results.

[0141] Scaling the structuring element by a preset second step size means that when the total area of ​​the noise points is too small, the structuring element size needs to be increased to mitigate the erosive effect of the morphological opening operation and avoid excessive elimination of spectral information. Specifically, the structuring element size can be scaled by a preset fixed value or ratio, for example, by one pixel or by 10% of the current size. The second step size can be adjusted based on actual needs and experimental results.

[0142] Outputting the current intermediate spectrum means that when the number of noise points is less than or equal to the number threshold, or the total area of ​​the noise points is within the noise area threshold, the current spectrum noise is considered to have been effectively suppressed or eliminated. At this time, the current intermediate spectrum is output as the final dust suppression spectrum for subsequent spectrum analysis and dominant frequency component identification.

[0143] Specifically, in the control method for a comb-tooth tenoning machine used in wooden door processing, to more accurately eliminate hardwood dust noise from the tool speed spectrum and improve the accuracy of dominant frequency identification, after completing a morphological opening operation based on the initial structuring element size, the noise elimination effect is further evaluated and the structuring element size is adaptively adjusted. First, the number of noise points in the current intermediate spectrum is calculated and compared with a preset number threshold. If the number of noise points is already low, indicating that the noise has been initially suppressed, the current intermediate spectrum is directly output, completing the noise elimination process. Conversely, if the number of noise points is still excessive, the total area of ​​the noise points is further calculated and compared with a preset area threshold range. By introducing an area threshold range, the direction and magnitude of the adjustment of the structuring element size can be more finely controlled. When the total area of ​​the noise points is too large, it means that the noise is still severe, and the erosion effect needs to be enhanced by reducing the size of the structuring element to more thoroughly eliminate the noise. When the total area of ​​the noise points is too small, it indicates that excessive erosion may occur, and the erosion effect needs to be weakened by increasing the size of the structuring element to avoid damaging the effective information in the spectrum. When the total area of ​​the noise points is exactly within the appropriate area threshold range, it is considered that a good balance has been achieved between noise elimination and spectral information preservation, and the current intermediate spectrum is output. Therefore, through the dual threshold judgment mechanism of number and area, and the adaptive adjustment strategy of the structuring element size based on the area threshold range, more precise control of the morphological opening process can be achieved. While ensuring effective spectral noise removal, the loss of spectral information is minimized, thereby improving the accuracy and reliability of spectral analysis and laying the foundation for subsequent dominant frequency identification and tool speed filtering.

[0144] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0145] In step A1, the calculated number of noise points in the current intermediate spectrum is 150, and the preset number threshold is 200. Since 150 is less than 200, it is determined that the number of noise points is less than the number threshold, the current intermediate spectrum is output, and the noise elimination process ends.

[0146] In step A1, the calculated number of noise points in the current intermediate spectrum is 250, and the preset number threshold is 200. Since 250 is greater than 200, it is determined that the number of noise points is greater than the number threshold, and step A1411 is executed.

[0147] In step A1411, the calculated total area of ​​the noise points of the current intermediate spectrum is 1200 pixels, and the preset noise area threshold range is 500-1000 pixels. Since 1200 is greater than 1000, it is determined that the total area of ​​the noise points is greater than the upper limit value of the noise area threshold range, and the size of the structural element is reduced according to the preset first step length. Assuming that the first step length is 1 pixel, the size of the structural element is reduced by 1 pixel unit based on the original size.

[0148] In step A1411, the calculated total area of ​​the noise points of the current intermediate spectrum is 400 pixels, and the preset noise area threshold range is 500-1000 pixels. Since 400 is less than 500, it is determined that the total area of ​​the noise points is less than the lower limit of the noise area threshold range, and the size of the structural element is enlarged according to the preset second step size. Assuming that the second step size is 1 pixel, the size of the structural element is enlarged by 1 pixel unit based on the original size.

[0149] In step A1411, the calculated total area of ​​the noise points of the current intermediate spectrum is 700 pixels, and the preset noise area threshold range is 500-1000 pixels. Since 700 is within the range of 500-1000 pixels, it is determined that the total area of ​​the noise points is within the noise area threshold range, the current intermediate spectrum is output, and the noise elimination process ends.

[0150] In some embodiments, the specific steps in step S33 include:

[0151] S33A1. After establishing a first relationship model between tool speed, hardwood material, and cutting force, a first theoretical change in cutting force due to a change in tool speed is calculated based on the filtered tool speed and the hardwood material being processed, based on the first relationship model. The first relationship model is used to characterize the degree to which a change in tool speed affects the cutting force for different hardwood materials.

[0152] S33A2. Divide the first theoretical cutting force change by the preset first conversion coefficient to obtain the corresponding first feed speed adjustment amount as the first adjustment amount.

[0153] In this embodiment, a first relationship model is established to reflect the relationship between tool speed, hardwood material and cutting force. Specifically, this model can quantify the degree of influence of tool speed changes on cutting force under different hardwood materials. After the model is established, when the filtered tool speed and the currently processed hardwood material information are obtained, the system can calculate the theoretical cutting force change caused by the tool speed change based on this first relationship model. A first conversion coefficient is preset to convert the first theoretical cutting force change into a corresponding first feed speed adjustment amount. The first feed speed adjustment amount can be obtained by dividing the calculated first theoretical cutting force change by the first conversion coefficient. This adjustment amount is then used as the first adjustment amount in the subsequent feed speed adjustment calculation, thereby achieving precise adjustment of the feed speed based on the difference in hardwood materials.

[0154] Specifically, in actual applications, there may be a problem of inaccurate adjustment due to ignoring the differences in wood materials. In step S33A1, it is first necessary to determine the type of hardwood material currently being processed, such as oak or cherry. Then, a first relationship model needs to be established. As a preferred embodiment, this first relationship model can be in the form of a lookup table, wherein the lookup table records the cutting force values ​​of different hardwood materials and different tool speeds. For example, the lookup table may contain cutting force data of oak at speeds of 10,000 rpm and 12,000 rpm, as well as cutting force data of cherry at the same speed. After obtaining the filtered tool speed and the current hardwood material, the system can query in this lookup table, or obtain the first theoretical cutting force variation caused by the change in tool speed through model interpolation calculation. In step S33A2, the role of the first conversion coefficient is to convert the cutting force variation into the adjustment amplitude of the feed speed. The specific value of this coefficient can be calibrated according to the specific parameters of the comb tenoning machine, the tool characteristics and the desired control sensitivity. For example, if the first conversion coefficient is set to 0.1 N / (mm / min), it means that every 0.1N change in cutting force corresponds to a 1mm / min feed rate adjustment. By dividing the first theoretical cutting force change by the first conversion coefficient, the first feed rate adjustment can be obtained. This adjustment can more accurately reflect the demand for feed rate adjustment due to changes in tool speed under different hardwood materials, thereby improving control accuracy. Therefore, by considering the differences in hardwood materials, the feed rate adjustment can be made more refined, thereby effectively improving the processing quality of comb tooth tenoning for different hardwood materials while ensuring processing efficiency.

[0155] In some specific embodiments, the first relationship model can be a mathematical formula, such as a linear function, a polynomial function, or a neural network model, which describes the relationship between changes in tool speed and cutting force. The input parameters of the model include tool speed and hardwood material type (which can be digitally encoded), and the output parameter is cutting force. The training data of the model can be obtained through experimental measurement, that is, actual cutting force data is measured at different tool speeds and different hardwood materials, and the model parameters are trained (e.g., data fitting) based on this data. For example, cutting force data of oak and cherry wood at different speeds can be collected to establish a linear function model: and ;

[0156] in, is the first cutting force, is the tool speed, Represents the material type, which can be encoded as a numerical variable (such as oak is 2, cherry is 1), 、 are the preset model parameters, is the first theoretical cutting force variation, is the change in tool speed. Specifically, and The unit of is Newton (N). and The unit is N / rpm. and The unit is rpm. Is dimensionless.

[0157] In practical applications, when the filtered tool speed and the current hardwood material type are input, this model can calculate the corresponding cutting force and then calculate the first theoretical cutting force change.

[0158] In some embodiments, the specific steps in step S33 include:

[0159] S33B1 obtain the comb mortising machine in the mortising process mortising depth;

[0160] S33B2 according to the mortise depth from the preset mortise depth and tool speed relationship table to find the corresponding tool speed correction factor;

[0161] S33B3 according to the tool speed correction coefficient of the filtered tool speed correction;

[0162] S33B4. After establishing the second relationship model between tool speed and cutting force, the second theoretical cutting force change caused by the change in tool speed is calculated based on the second relationship model according to the corrected tool speed; the second relationship model is used to characterize the degree of influence of the change in tool speed on the cutting force;

[0163] S33A5. Divide the second theoretical cutting force change by the preset second conversion coefficient to obtain the corresponding second feed speed adjustment amount and use it as the first adjustment amount.

[0164] In step S33B1, the mortising depth is measured in real time by a depth sensor installed on the comb mortising machine. The depth sensor can be a non-contact laser displacement sensor installed near the tool to monitor the depth of the tool cutting the wood in real time.

[0165] In step S33B2, the preset relationship table between mortising depth and tool speed can be a data table stored in the controller, in which each row of data represents a mortising depth value and a tool speed correction coefficient corresponding to the depth value. The tool speed correction coefficient is pre-calibrated through experiments or simulations. The purpose is to enable the tool to cut at a more appropriate speed at different mortising depths to ensure processing quality.

[0166] In step S33B3, tool speed correction refers to multiplying the filtered tool speed by the tool speed correction coefficient to obtain a corrected tool speed. The corrected tool speed is more in line with the current tenoning depth processing requirements.

[0167] In step S33B4, the second relationship model can be a mathematical formula, such as a linear function, a polynomial function or a neural network model, which describes the relationship between the change in tool speed and the change in cutting force. The parameters of the model can be obtained by fitting experimental data.

[0168] In step S33A5, the second conversion coefficient is a preset constant, which is used to convert the theoretical cutting force change into the feed speed adjustment amount. The size of the second conversion coefficient will affect the sensitivity of the feed speed adjustment.

[0169] Specifically, when the comb-tooth tenoning machine is processing wooden doors, the current tenoning depth is first obtained in real time through the depth sensor. Thereby, the control system searches for the corresponding tool speed correction coefficient in the preset tenoning depth and tool speed relationship table based on the acquired tenoning depth. Furthermore, the tool speed correction coefficient found is used to correct the tool speed after filtering to obtain a tool speed that adapts to the current tenoning depth. Then, based on the pre-established second relationship model, the second theoretical cutting force change caused by the change in tool speed is calculated according to the corrected tool speed. Finally, the calculated second theoretical cutting force change is divided by the preset second conversion coefficient to obtain the second feed speed adjustment amount, and this adjustment amount is used as the first adjustment amount for subsequent feed speed adjustment. Through the above steps, the tool speed can be dynamically adjusted according to the tenoning depth, thereby more accurately controlling the feed speed and ensuring the processing quality.

[0170] In some embodiments, the relationship table between mortising depth and tool speed is configured as a two-dimensional array, with the first column storing the mortising depth value and the second column storing the corresponding tool speed correction coefficient. For example, when the mortising depth is 10 mm, the corresponding tool speed correction coefficient is 1.02; when the mortising depth is 20 mm, the corresponding tool speed correction coefficient is 1.05. The second relationship model is established as a linear function model: and ;

[0171] in, is the second cutting force, is the corrected tool speed ( ), is the tool speed correction coefficient, is the second theoretical cutting force variation, is a constant obtained through experimental calibration, is the corrected tool speed change ( ).

[0172] Specifically, and The unit of is Newton (N). and The unit is rpm. The unit is N / rpm.

[0173] The second conversion coefficient is set to 0.5, in N / (mm / min). When the calculated second theoretical cutting force change is 10N, the first adjustment is 10N / 0.5N / (mm / min) = 20mm / min. This means that the feed rate needs to be increased by 20mm / min. By adopting the above specific parameters and models, the feed rate can be precisely adjusted according to the tenoning depth and tool speed changes, thereby improving the processing performance of the comb tenoning machine.

[0174] Please refer to Figure 2 , Figure 2 In some embodiments of the present invention, a comb-tooth tenoning machine control device for wooden door processing is provided. The comb-tooth tenoning machine control device for wooden door processing is integrated into a back-end control device in the form of a computer program, and includes:

[0175] The first acquisition module 100 is used to obtain the real-time cutting force of the comb tenoning machine during the tenoning process;

[0176] A comparison module 200 is used to compare the real-time cutting force with a preset cutting force threshold to obtain a cutting force difference;

[0177] The second acquisition module 300 is used to obtain the feed speed adjustment amount according to the cutting force difference and a preset proportional control coefficient;

[0178] The third acquisition module 400 is used to obtain the theoretical feed speed according to the feed speed adjustment amount;

[0179] A fourth acquisition module 500 is used to limit the theoretical feed speed to between the minimum feed speed and the maximum feed speed allowed by the comb tenoning machine to obtain the actual feed speed;

[0180] The output control module 600 is used to control the comb tenoning machine to operate according to the actual feed speed.

[0181] In some embodiments, the second acquisition module 300 executes the following when acquiring the feed speed adjustment amount according to the cutting force difference and the preset proportional control coefficient:

[0182] S31. By analyzing the tool speed, the fluctuation frequency of the tool speed is obtained;

[0183] S32 determines whether the fluctuation frequency exceeds a preset frequency threshold, and when it is confirmed that it exceeds, performs filtering on the tool speed to obtain a filtered tool speed;

[0184] S33. Obtain a first adjustment amount according to the filtered tool speed;

[0185] S34. Obtaining a second adjustment amount based on the cutting force difference and the proportional control coefficient;

[0186] S35. Obtain a feed speed adjustment amount according to the first adjustment amount and the second adjustment amount.

[0187] In some embodiments, the second acquisition module 300 performs the following when filtering the tool rotation speed to obtain the filtered tool rotation speed:

[0188] S321. Collecting the tool speed signal and performing a fast Fourier transform to obtain a spectrum of the tool speed signal; the spectrum is a two-dimensional spectrum diagram represented in the form of a time-frequency diagram;

[0189] S322. Analyze the frequency spectrum to identify the dominant frequency component causing tool speed fluctuations. If the dominant frequency component falls within the preset hardwood material unevenness frequency range, the fluctuation is determined to be caused by hardwood material unevenness, and step S323 is executed. If the dominant frequency component falls within the preset tool wear frequency range, the fluctuation is determined to be caused by tool wear, and step S324 is executed. Otherwise, the fluctuation is determined to be caused by equipment vibration, and step S325 is executed.

[0190] S323. If it is determined that the fluctuation is caused by uneven hardwood material, the tool speed is filtered using a sliding average filter algorithm to obtain a filtered tool speed;

[0191] S324. If the fluctuation is determined to be caused by tool wear, a Kalman filter algorithm is used to filter the tool speed to obtain a filtered tool speed;

[0192] S325. If it is determined that the fluctuation is caused by equipment vibration, a filtering algorithm based on wavelet transform is used to perform filtering processing on the tool speed to obtain a filtered tool speed.

[0193] In some embodiments, the second acquisition module 300 performs the following when analyzing the frequency spectrum to identify the dominant frequency component causing the tool rotation speed fluctuation:

[0194] A1. Using a morphological opening operation, the spectrum is eroded using a structuring element to remove noise points caused by hardwood dust and obtain an intermediate spectrum. The structuring element is a two-dimensional image.

[0195] A2. Dilate the intermediate spectrum to restore its original structure and obtain the dust suppression spectrum.

[0196] A3. Analyze the dust suppression spectrum and obtain the grayscale histogram of the spectrum;

[0197] A4. Adopt the adaptive threshold segmentation algorithm based on the OTSU algorithm and calculate the segmentation threshold according to the grayscale histogram of the spectrum;

[0198] A5. Use the segmentation threshold to extract the peak points in the spectrum;

[0199] A6. Based on the frequency value of the peak point, identify the dominant frequency component that causes the tool speed fluctuation.

[0200] In some embodiments, the second acquisition module 300 performs the following when performing erosion processing on the spectrum using a morphological opening operation and a structural element to eliminate noise points caused by hardwood dust in the spectrum and obtain an intermediate spectrum:

[0201] A11. Get the average size of noise points in the spectrum.

[0202] A12. Set the average size to the size of the structural element;

[0203] A13. Perform a morphological opening operation on the spectrum using a structuring element of a predetermined size to obtain an intermediate spectrum.

[0204] A14. Calculate the number of noise points in the current intermediate spectrum and determine whether the number of noise points is greater than a preset threshold. If so, adjust and update the size of the structuring element according to a preset step size and return to step A13 or output the current intermediate spectrum. If not, output the current intermediate spectrum.

[0205] In some embodiments, the second acquisition module 300 performs the following when calculating the number of noise points in the current intermediate spectrum and determining whether the number of noise points is greater than a preset threshold:

[0206] A141. Calculate the number of noise points in the current intermediate spectrum and determine whether the number of noise points is greater than a preset threshold. If the number of noise points is less than or equal to the threshold, output the current intermediate spectrum. If the number of noise points is greater than the threshold, perform the following steps:

[0207] A1411. Calculate the total area of ​​the noise points in the current intermediate spectrum and determine whether the total area of ​​the noise points is within a preset noise area threshold range. If the total area of ​​the noise points is greater than the upper limit of the noise area threshold range, reduce the size of the structural element according to the preset first step length and return to step A13. If the total area of ​​the noise points is less than the lower limit of the noise area threshold range, enlarge the size of the structural element according to the preset second step length and return to step A13. If the total area of ​​the noise points is within the noise area threshold range, output the current intermediate spectrum.

[0208] In some embodiments, the second acquisition module 300 executes the following when acquiring the first adjustment value according to the filtered tool rotation speed:

[0209] S33A1. After establishing a first relationship model between tool speed, hardwood material, and cutting force, a first theoretical change in cutting force due to a change in tool speed is calculated based on the filtered tool speed and the hardwood material being processed, based on the first relationship model. The first relationship model is used to characterize the degree to which a change in tool speed affects the cutting force for different hardwood materials.

[0210] S33A2. Divide the first theoretical cutting force change by the preset first conversion coefficient to obtain the corresponding first feed speed adjustment amount as the first adjustment amount.

[0211] In some embodiments, the second acquisition module 300 executes the following when acquiring the first adjustment value according to the filtered tool rotation speed:

[0212] S33B1 obtain the comb mortising machine in the mortising process mortising depth;

[0213] S33B2 according to the mortise depth from the preset mortise depth and tool speed relationship table to find the corresponding tool speed correction factor;

[0214] S33B3 according to the tool speed correction coefficient of the filtered tool speed correction;

[0215] S33B4. After establishing the second relationship model between tool speed and cutting force, the second theoretical cutting force change caused by the change in tool speed is calculated based on the second relationship model according to the corrected tool speed; the second relationship model is used to characterize the degree of influence of the change in tool speed on the cutting force;

[0216] S33A5. Divide the second theoretical cutting force change by the preset second conversion coefficient to obtain the corresponding second feed speed adjustment amount and use it as the first adjustment amount.

[0217] Please refer to Figure 3 , Figure 3A structural schematic diagram of an electronic device provided in an embodiment of the present invention, the present invention provides an electronic device 13, including: a processor 1301 and a memory 1302, the processor 1301 and the memory 1302 are interconnected and communicate with each other through a communication bus 1303 and / or other forms of connection mechanisms (not marked), the memory 1302 stores computer-readable instructions executable by the processor 1301, and when the electronic device is running, the processor 1301 executes the computer-readable instructions to execute the comb-tooth tenoning machine control method for wooden door processing in any optional implementation of the above-mentioned embodiment to achieve the following functions: obtaining the real-time cutting force of the comb-tooth tenoning machine during the tenoning process; comparing the real-time cutting force with a preset cutting force threshold to obtain a cutting force difference; obtaining a feed speed adjustment amount based on the cutting force difference and a preset proportional control coefficient; obtaining a theoretical feed speed based on the feed speed adjustment amount; limiting the theoretical feed speed between the minimum feed speed and the maximum feed speed allowed by the comb-tooth tenoning machine to obtain an actual feed speed; and controlling the comb-tooth tenoning machine to operate according to the actual feed speed.

[0218] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the comb teeth tenoning machine control method for wooden door processing in any optional implementation of the above embodiments is executed to achieve the following functions: obtaining the real-time cutting force of the comb teeth tenoning machine during the tenoning process; comparing the real-time cutting force with a preset cutting force threshold to obtain a cutting force difference; obtaining a feed speed adjustment amount based on the cutting force difference and a preset proportional control coefficient; obtaining a theoretical feed speed based on the feed speed adjustment amount; limiting the theoretical feed speed to between the minimum feed speed and the maximum feed speed allowed by the comb teeth tenoning machine to obtain the actual feed speed; and controlling the comb teeth tenoning machine to operate according to the actual feed speed.

[0219] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0220] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, the indirect coupling or communication connection of the device or unit may be electrical, mechanical or other forms.

[0221] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0222] Furthermore, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0223] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0224] The foregoing description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A control method for a comb-tooth mortising machine for wooden door processing, characterized in that: Including steps: S1. Obtaining the real-time cutting force of the comb tenoning machine during the tenoning process; S2. comparing the real-time cutting force with a preset cutting force threshold to obtain a cutting force difference; S3. Get the feed speed adjustment amount according to the cutting force difference and the preset proportional control coefficient; S4. According to the feed speed adjustment amount, obtain the theoretical feed speed; S5. The theoretical feed rate is limited to the minimum feed rate and the maximum feed rate allowed by the comb tenoning machine to obtain the actual feed rate; S6. Control the comb tenoning machine to run at the actual feed speed; The specific steps in step S3 include: S31. By analyzing the tool speed, obtaining the fluctuation frequency of the tool speed; S32. Determine whether the fluctuation frequency exceeds a preset frequency threshold, and when it is confirmed that it exceeds, perform filtering on the tool speed to obtain a filtered tool speed; S33. Obtain a first adjustment amount according to the filtered tool speed; S34. Obtaining a second adjustment amount according to the cutting force difference and the proportional control coefficient; S35. Obtaining the feed speed adjustment amount according to the first adjustment amount and the second adjustment amount; The specific steps of performing filtering processing on the tool speed to obtain the filtered tool speed include: S321. Collecting the tool speed signal and performing a fast Fourier transform to obtain a spectrum of the tool speed signal; the spectrum is a two-dimensional spectrum diagram represented in the form of a time-frequency diagram; S322. Analyze the frequency spectrum to identify the dominant frequency component causing tool speed fluctuations. If the dominant frequency component falls within a preset hardwood material uneven frequency range, determine that the fluctuations are caused by hardwood material unevenness, and execute step S323. If the dominant frequency component falls within a preset tool wear frequency range, determine that the fluctuations are caused by tool wear, and execute step S324. Otherwise, determine that the fluctuations are caused by equipment vibration, and execute step S325. S323. If it is determined that the fluctuation is caused by uneven hardwood material, filtering is performed on the tool speed using a sliding average filtering algorithm to obtain a filtered tool speed; S324. If it is determined that the fluctuation is caused by tool wear, a Kalman filter algorithm is used to filter the tool speed to obtain a filtered tool speed; S325. If it is determined that the fluctuation is caused by equipment vibration, a filtering algorithm based on wavelet transform is used to perform filtering processing on the tool rotation speed to obtain a filtered tool rotation speed.

2. The comb mortising machine control method for wooden door processing according to claim 1 is characterized in that: The specific steps of analyzing the frequency spectrum and identifying the dominant frequency component causing the tool speed fluctuation include: A1. Using a morphological opening operation, the spectrum is eroded using a structuring element to eliminate noise points caused by hardwood dust in the spectrum, thereby obtaining an intermediate spectrum; the structuring element is a two-dimensional image; A2. performing expansion processing on the intermediate spectrum to restore the original spectrum structure and obtain a dust suppression spectrum; A3. Analyze the dust suppression spectrum and obtain a grayscale histogram of the spectrum; A4. Using an adaptive threshold segmentation algorithm based on the otsu algorithm, the segmentation threshold is calculated according to the grayscale histogram of the spectrum; A5. Using the segmentation threshold to extract the peak points in the spectrum; A6. Identify the dominant frequency component causing the tool rotation speed fluctuation based on the frequency value of the peak point.

3. The control method of the comb-tooth mortising machine for wooden door processing according to claim 2 is characterized in that: The specific steps in step A1 include: A11. Get the average size of noise points in the spectrum. A12. Setting the average size to the size of the structural element; A13. Perform a morphological opening operation on the spectrum using a structure element of a set size to obtain an intermediate spectrum; A14. Calculate the number of noise points in the current intermediate spectrum and determine whether the number of noise points is greater than a preset threshold. If so, adjust and update the size of the structure element according to a preset step size and return to step A13 or output the current intermediate spectrum. If not, output the current intermediate spectrum.

4. The control method for a comb-tooth mortising machine for wooden door processing according to claim 3, characterized in that: The specific steps in step A14 include: A141. Calculate the number of noise points in the current intermediate spectrum and determine whether the number of noise points is greater than a preset threshold. If the number of noise points is less than or equal to the threshold, output the current intermediate spectrum. If the number of noise points is greater than the threshold, perform the following steps: A1411. Calculate the total area of ​​the noise points of the current intermediate spectrum, and determine whether the total area of ​​the noise points is within a preset noise area threshold range. If the total area of ​​the noise points is greater than the upper limit value of the noise area threshold range, reduce the size of the structural element according to the preset first step length, and return to execute step A13; if the total area of ​​the noise points is less than the lower limit value of the noise area threshold range, enlarge the size of the structural element according to the preset second step length, and return to execute step A13; if the total area of ​​the noise points is within the noise area threshold range, output the current intermediate spectrum.

5. The control method of the comb-tooth mortising machine for wooden door processing according to claim 1, characterized in that: The specific steps in step S33 include: S33A1. After establishing a first relationship model between tool speed, hardwood material, and cutting force, calculate a first theoretical change in cutting force caused by a change in tool speed based on the filtered tool speed and the hardwood material being processed, based on the first relationship model; the first relationship model is used to characterize the degree of influence of tool speed changes on cutting force for different hardwood materials; S33A2. Divide the first theoretical cutting force change by a preset first conversion coefficient to obtain a corresponding first feed speed adjustment amount as the first adjustment amount.

6. A comb-tooth mortising machine control device for wooden door processing, characterized in that: include: The first acquisition module is used to obtain the real-time cutting force of the comb tenoning machine during the tenoning process; A comparison module is used to compare the real-time cutting force with a preset cutting force threshold to obtain a cutting force difference; A second acquisition module is used to acquire a feed speed adjustment amount according to the cutting force difference and a preset proportional control coefficient; A third acquisition module is used to obtain a theoretical feed speed according to the feed speed adjustment amount; a fourth acquisition module, configured to limit the theoretical feed speed to between a minimum feed speed and a maximum feed speed allowed by the comb tenoning machine, to obtain an actual feed speed; An output control module, used for controlling the comb tenoning machine to operate according to the actual feed speed; The second acquisition module is executed when it is used to obtain the feed speed adjustment amount according to the cutting force difference and the preset proportional control coefficient: S31. Obtaining the fluctuation frequency of the tool speed by analyzing the tool speed; S32. Determine whether the fluctuation frequency exceeds a preset frequency threshold, and when it is confirmed that it exceeds, perform filtering on the tool speed to obtain a filtered tool speed; S33. Obtain a first adjustment amount according to the filtered tool speed; S34. Obtaining a second adjustment amount based on the cutting force difference and the proportional control coefficient; S35. Obtaining a feed speed adjustment amount based on the first adjustment amount and the second adjustment amount; The second acquisition module is used to perform filtering on the tool speed and execute the following when obtaining the filtered tool speed: S321. Collecting the tool speed signal and performing a fast Fourier transform to obtain the spectrum of the tool speed signal; the spectrum is a two-dimensional spectrum diagram represented in the form of a time-frequency diagram; S322. Analyze the frequency spectrum to identify the dominant frequency component causing tool speed fluctuations. If the dominant frequency component falls within the preset hardwood material uneven frequency range, it is determined that the fluctuation is caused by hardwood material unevenness, and step S323 is executed. If the dominant frequency component falls within the preset tool wear frequency range, it is determined that the fluctuation is caused by tool wear, and step S324 is executed. Otherwise, it is determined that the fluctuation is caused by equipment vibration, and step S325 is executed. S323. If it is determined that the fluctuation is caused by the unevenness of the hardwood material, a filtering process is performed on the tool speed based on a sliding average filtering algorithm to obtain a filtered tool speed; S324. If it is determined that the fluctuation is caused by tool wear, a Kalman filter algorithm is used to filter the tool speed to obtain a filtered tool speed; S325. If it is determined that the fluctuation is caused by equipment vibration, a filtering algorithm based on wavelet transform is used to perform filtering processing on the tool speed to obtain a filtered tool speed.

7. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the comb mortising machine control method for wooden door processing as described in any one of claims 1 to 5 are executed.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the comb mortising machine control method for wooden door processing as described in any one of claims 1 to 5 are executed.

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