Method and device for evaluating pressing stability of cover plate

Abstract

The invention provides a stability evaluation method and device for cover plate hold-down, and relates to the technical field of cover plate hold-down, and the method comprises the steps: loading a calibration test workpiece to a target clamp, and fixing the calibration test workpiece through a cover plate; controlling the machine tool to start and execute machining; monitoring data are read in real time, and the contact two-dimensional scale force field is normalized into a time sequence grayscale image; establishing a feature recognition result for the time sequence grayscale image; synchronously acquiring acquisition information of a triaxial accelerometer arranged on a clamp, and taking a micro-vibration signal as a first verification signal; workpiece key point monitoring is carried out, and workpiece time sequence displacement is used as a second verification signal; and performing stability verification on the feature recognition result, and outputting a stability verification evaluation result. According to the method and the device, the technical targets of multi-dimensional fusion modeling and stability intelligent evaluation based on the workpiece structure features and the machining dynamic process can be realized, and the technical effects of improving the hold-down stability recognition precision, reducing the misjudgment rate and enhancing the adaptability of the system to the complex machining environment are achieved.

Description

Technical Field

[0001] This application relates to the technical field of cover plate pressing, and particularly to a method and device for evaluating the stability of cover plate pressing. Background Art

[0002] In the process of precision manufacturing and high-reliability assembly, the stable pressing state of workpieces during the processing or assembly stage is directly related to the quality and performance of the final product. Especially in the machining process of complex structural parts or non-standard customized parts, if there are phenomena such as uneven pressing, loosening, or instantaneous slippage in the pressing state, it is extremely easy to cause machining errors, surface defects, and even equipment damage. Therefore, effectively and real-time evaluating the stability of cover plate pressing has become an important link to ensure machining reliability.

[0003] Currently, in the machining of complex parts in the prior art, the structural characteristics of workpieces are closely related to the force distribution. Traditional pressing evaluation methods cannot accurately model the force changes in different geometric regions, and also lack the ability to construct differential thresholds starting from the workpiece body structure. At the same time, most methods fail to achieve multi-source information fusion and only rely on single-channel feedback signals, resulting in large fluctuations and high misjudgment rates in the stability determination results in a dynamic machining environment, and it is difficult to meet the requirements of high-precision manufacturing scenarios for robustness and real-time performance.

[0004] In summary, in the prior art, due to the lack of refined force modeling and differential determination mechanisms based on workpiece structural characteristics, it is impossible to accurately reflect the real force state when facing different geometric regions, further affecting the accuracy and reliability of the pressing stability evaluation, and thus it is difficult to meet the technical problems of real-time performance and robustness in high-precision machining scenarios. Summary of the Invention

[0005] The purpose of this application is to provide a method and device for evaluating the stability of cover plate pressing, so as to solve the technical problems in the prior art that due to the lack of refined force modeling and differential determination mechanisms based on workpiece structural characteristics, it is impossible to accurately reflect the real force state when facing different geometric regions, further affecting the accuracy and reliability of the pressing stability evaluation, and thus it is difficult to meet the requirements of real-time performance and robustness in high-precision machining scenarios.

[0006] In view of the above problems, this application provides a method and device for evaluating the stability of cover plate pressing.

[0007] In a first aspect, the present application provides a method for evaluating the stability of cover plate pressing, which is implemented by a device for evaluating the stability of cover plate pressing. The method includes: after setting a sensor array on the contact surface of a calibration test workpiece, loading the calibration test workpiece onto a target fixture and fixing it with a cover plate; after confirming that the cover plate assembly is completed, activating the sensor array and controlling the machine tool to start and perform machining; reading the monitoring data of the sensor array in real time, establishing a two-dimensional contact force field, and normalizing the two-dimensional contact force field into a time-sequence grayscale image; identifying features such as the offset of the center of the hot zone, the jump of the boundary pressing, the clustering of local maxima, the integrity of the gray-scale connected domain, and the imbalance of symmetry in the time-sequence grayscale image, and establishing a feature recognition result; synchronously obtaining the acquisition information of a three-axis accelerometer set on the fixture, establishing a micro-vibration signal, and using the micro-vibration signal as a first verification signal; monitoring key points of the workpiece with a laser displacement sensor, establishing a time-sequence displacement of the workpiece, and using the time-sequence displacement of the workpiece as a second verification signal; performing a stability verification on the feature recognition result through the first verification signal and the second verification signal, and outputting a stability verification evaluation result.

[0008] In a second aspect, the present application further provides a device for evaluating the stability of cover plate pressing, which is used to execute the method for evaluating the stability of cover plate pressing as described in the first aspect. The device includes: a fixing module, which is used to load the calibration test workpiece onto the target fixture and fix it with the cover plate after setting the sensor array on the contact surface of the calibration test workpiece; a machining module, which is used to activate the sensor array and control the machine tool to start and perform machining after confirming that the cover plate assembly is completed; a reading module, which is used to read the monitoring data of the sensor array in real time, establish a two-dimensional contact force field, and normalize the two-dimensional contact force field into a time-sequence grayscale image; a feature recognition module, which is used to identify features such as the offset of the center of the hot zone, the jump of the boundary pressing, the clustering of local maxima, the integrity of the gray-scale connected domain, and the imbalance of symmetry in the time-sequence grayscale image, and establish a feature recognition result; a signal establishment module, which is used to synchronously obtain the acquisition information of the three-axis accelerometer set on the fixture, establish a micro-vibration signal, and use the micro-vibration signal as a first verification signal; a monitoring module, which is used to monitor key points of the workpiece with a laser displacement sensor, establish a time-sequence displacement of the workpiece, and use the time-sequence displacement of the workpiece as a second verification signal; a verification module, which is used to perform a stability verification on the feature recognition result through the first verification signal and the second verification signal, and output a stability verification evaluation result.

[0009] The technical solution provided in the present application has at least the following technical effects or advantages: by achieving the technical goal of multi-dimensional fusion modeling and stability intelligent evaluation based on the workpiece structure characteristics and the machining dynamic process, the technical effects of improving the recognition accuracy of pressing stability, reducing the misjudgment rate, and enhancing the adaptability of the system to complex machining environments are achieved.

[0010] The above description is only an overview of the technical solution of the present application. In order to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically illustrates the specific embodiments of the present application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Brief Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0012] Figure 1 It is a schematic flowchart of the method for evaluating the stability of the cover plate pressing of the present application; Figure 2 It is a schematic structural diagram of the device for evaluating the stability of the cover plate pressing of the present application.

[0013] Description of the reference numerals: fixing module 11, processing module 12, reading module 13, feature recognition module 14, signal establishment module 15, monitoring module 16, verification module 17. Detailed Description of the Embodiments

[0014] By providing a method and device for evaluating the stability of the cover plate pressing, the present application solves the technical problem in the prior art that due to the lack of refined force modeling and differential judgment mechanism based on the workpiece structure characteristics, it is impossible to accurately reflect the real stress state when facing different geometric regions, further affecting the accuracy and reliability of the evaluation of the pressing stability, and thus it is difficult to meet the requirements of real-time and robustness in high-precision machining scenarios. The technical goal of realizing multi-dimensional fusion modeling and intelligent stability evaluation based on the workpiece structure characteristics and the machining dynamic process is achieved, and the technical effects of improving the recognition accuracy of the pressing stability, reducing the misjudgment rate, and enhancing the adaptability of the system to complex machining environments are achieved.

[0015] Next, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application. Additionally, it should be noted that for the sake of description, only the parts related to the present application rather than all are shown in the accompanying drawings.

[0016] Example 1. Please refer to the attached Figure 1 , the present application provides a method for evaluating the stability of cover plate pressing, which is applied to a device for evaluating the stability of cover plate pressing, and specifically includes the following steps: S1: After setting up a sensor array on the contact surface of the calibration test workpiece, load the calibration test workpiece into the target fixture and fix it with a cover plate.

[0017] Specifically, the purpose of setting up a sensor array on the contact surface of the calibration workpiece is to obtain distributed pressure information during the pressing process. The sensor array refers to multiple sensors arranged on the surface where the workpiece contacts the fixture according to a certain rule. Each sensor can independently collect the pressure data at its location, thereby forming a pressure distribution map on the entire surface. For example, if 20×10 pressure sensors are arranged on a steel plate with a length and width of 200 mm×100 mm, real-time pressure data of 100 measurement points can be obtained at an interval of 10 mm. The calibration test workpiece is a workpiece with a standard structure and known material properties, which is used to verify the accuracy and consistency of the system. Load the calibration test workpiece into the target fixture. The target fixture is a device used to position and fix the workpiece, and its structure is designed according to the geometric shape of the workpiece to ensure the stability of the workpiece during assembly and processing. After loading, fix it with a cover plate. The cover plate is a rigid structure, and its function is to apply a pre-pressure from top to bottom to press the workpiece tightly on the fixture to prevent the workpiece from shifting or vibrating during subsequent processing. In this way, a test environment including the complete pressing contact process can be established. The spatial pressure distribution information provided by the sensor array, combined with the overall pressing effect of the cover plate, enables data collection under simulated real processing conditions, laying a foundation for subsequent image conversion, feature recognition, and stability analysis.

[0018] S2: After confirming that the cover plate assembly is completed, activate the sensor array and control the machine tool to start and perform machining.

[0019] Specifically, after confirming the completion of the cover plate assembly, it means that the preparatory action of pressing the workpiece has ended, and the contact state between the workpiece and the fixture is stable. At this time, the entire structure is ready for data acquisition and processing. If the cover plate is not properly pressed, the data collected subsequently will be distorted due to workpiece displacement or loosening. Immediately afterwards, activating the sensor array refers to turning on multiple pressure sensors arranged on the contact surface of the workpiece, enabling them to start working and collect pressure data in real time. The sensors are strain-type or resistive pressure sensors, which can convert pressure changes into electrical signals, thereby recording the force conditions at each measurement point. Activating the sensors changes from the standby state to the working state, preparing to receive the dynamic pressure changes generated during the machining process, and ensuring that data is recorded synchronously from the moment the workpiece starts to be stressed.

[0020] Subsequently, controlling the machine tool to start means making the CNC machine tool start its machining task through the numerical control system or manual control. The machine tool is a device used for operations such as cutting, drilling, and milling of metals or other materials. Starting the machine tool represents the entire system entering the actual machining stage. During the operation of the CNC machine tool, various mechanical loads will be applied to the workpiece, such as cutting force, feed force, etc. The transmission of external forces will be fed back to the sensors through the fixture and the cover plate, thereby affecting the collected pressure data. Executing machining refers to the machine tool performing specific cutting or other operations on the workpiece according to a preset program. The force on the workpiece is constantly changing, and the sensor array can record the dynamic pressure distribution map formed by these changes in real time.

[0021] S3: Read the monitoring data of the sensor array in real time, establish a two-dimensional contact scalar force field, and normalize the two-dimensional contact scalar force field into a time-series grayscale image.

[0022] Specifically, reading the monitoring data of the sensor array in real time means continuously collecting data from multiple sensors arranged on the contact surface between the workpiece and the fixture during the machining process. Each sensor records the pressure value at a measurement point, and high-time-resolution mechanical information can be obtained through periodic sampling.

[0023] Subsequently, combine the pressure values of all measurement points to establish a two-dimensional contact scalar force field. Two-dimensional means the data is mapped onto a plane, corresponding to the length and width of the workpiece contact surface. The scalar force field means that each coordinate point corresponds to a pressure value, without including direction information, only representing the magnitude. By pairing the spatial coordinates of each sensor with its corresponding pressure value, a force field similar to a heat map can be generated on the plane.

[0024] Next, the two-dimensional scalar force field is normalized into a temporal grayscale image. Normalization means converting the original pressure values of different magnitudes to a standard range to represent the grayscale levels of the image pixels. The higher the pressure value, the larger the grayscale value, that is, the closer the image is to white; the lower the pressure value, the smaller the grayscale, and the closer the image is to black. The conversion is repeated at consecutive time points to form a sequence of images evolving over time, namely the temporal grayscale image, which can intuitively reflect the trend of pressure changing with time.

[0025] S4: Identify the characteristics of the center offset of the hot zone, the boundary holding jump characteristic, the local maximum clustering characteristic, the integrity characteristic of the grayscale connected domain, and the symmetry imbalance characteristic in the temporal grayscale image, and establish the characteristic recognition result.

[0026] Specifically, identify the regions with higher brightness in each frame of the image, which represent the parts where the pressure is concentrated or the holding is tight. By calculating the center of gravity position and comparing it with the ideal geometric center, the degree of deviation is obtained. The center offset of the hot zone reflects whether the applied pressure is uniform. If the center deviates greatly, it indicates that there is pressure skew or unilateral force application. Subsequently, identify the boundary holding jump characteristic by analyzing the grayscale change gradient in the edge region of the image. Within the set boundary region width range, extract the grayscale values of this region for each frame of the image, and calculate the change rate of the average grayscale value between adjacent regions. If a sudden change occurs, it indicates that the edge force is discontinuous or the pressure jumps. Next, identify the local maximum clustering characteristic, mainly detecting whether multiple independent high-grayscale points in the image are concentratedly distributed. The local maximum refers to the position where the pixel has the highest grayscale value in its surrounding neighborhood. If the maxima are highly concentrated, it indicates local overload at the pressure application points, which may cause local material deformation. Then, after binarizing the image, analyze whether the white region representing the presence of pressure is coherent to perform the integrity characteristic recognition of the grayscale connected domain and evaluate whether the overall force is continuous and complete. If there are many broken or fractured connected domains, it indicates that there are breakpoints on the holding surface or the holding area is interrupted. Finally, identify the symmetry imbalance characteristic. Flip each frame of the image horizontally and vertically respectively to obtain the mirror image, and calculate the absolute difference in grayscale at each pixel position between the mirror image and the original image. If the difference is large, it indicates that the grayscale distribution of the image is asymmetric left and right or up and down, that is, the holding is unbalanced.

[0027] S5: Synchronously obtain the acquisition information of the triaxial accelerometer set on the fixture, establish a micro-vibration signal, and use the micro-vibration signal as the first verification signal.

[0028] Specifically, during the machining process, the data output by the triaxial accelerometer mounted on the fixture is read simultaneously. The triaxial accelerometer is a sensor capable of detecting acceleration changes in three directions, namely the X-axis, Y-axis, and Z-axis, and is used to monitor the dynamic response or minute vibrations of the mechanical structure in three dimensions. The collected information refers to the acceleration values recorded by the sensor at each moment, which is used to capture high-frequency mechanical changes. Based on the collected data, a micro-vibration signal is established, and the variation law of the acceleration values over time is extracted, thereby forming a time-series signal representing the minute vibrations of the fixture during operation. The micro-vibration signal contains low-amplitude and high-frequency vibration changes, reflecting the dynamic stability during the machining process. For example, during the machining process, the fixture exhibits periodic vibrations with an amplitude of 0.02 meters per second squared and a frequency of 100 hertz in the Z-axis direction. The micro-vibration signal is used as a preliminary basis for determining the stability of the machining process to verify whether there are abnormalities in the clamping state or the force on the workpiece. If the intensity of the micro-vibration significantly increases during a certain period, it may indicate unstable clamping force or abnormal force transmission between the machine tool cutter and the workpiece.

[0029] S6: Use a laser displacement sensor to monitor key points of the workpiece, establish a time-series displacement of the workpiece, and use the time-series displacement of the workpiece as the second verification signal.

[0030] Specifically, the laser displacement sensor installed in the machining area is used to measure the position changes of some representative parts on the workpiece surface in real time during the machining process. The laser displacement sensor is a high-precision non-contact measurement device that can record the distance between the target surface and the sensor with a resolution of micrometers or even nanometers. The key points of the workpiece refer to the areas on the workpiece where the force or deformation is more obvious and where displacement changes are likely to occur, such as the edges, center hole positions, or structural mutation points, etc. Monitoring these positions can more directly reflect the actual state of force and stability. The time series formed by arranging the position data continuously measured by the laser displacement sensor in chronological order is used to establish the time-series displacement of the workpiece, which is used to describe the displacement trend of the key points during the entire machining process and reflects the minute movement changes of the workpiece caused by reasons such as machine tool movement, uneven force, or unstable clamping. Using the time-series displacement of the workpiece as the second verification signal, it serves as the second layer of reference basis for the analysis of the clamping stability during the machining process. By comparing the displacement data, it can be determined whether the workpiece has abnormal movement due to uneven pressure or unreasonable structural support.

[0031] S7: Perform stability verification on the feature recognition result through the first verification signal and the second verification signal, and output the stability verification evaluation result.

[0032] Specifically, two signal channels with different but complementary sources are utilized to detect the authenticity and consistency of the feature recognition results obtained from image analysis. The first verification signal refers to the micro-vibration signal collected by the triaxial accelerometer on the fixture, which can reflect the tiny mechanical vibrations generated due to uneven force or structural looseness during the machining or clamping process. The second verification signal is the displacement information of the key points of the workpiece measured by the laser displacement sensor, which is used to judge the deformation degree or displacement amplitude of the workpiece during the machining or clamping process. The feature recognition result is the discrimination information of the clamping or stress state obtained through the image processing process. By comparing the two signals with the image feature results, it can be determined whether the state of image recognition truly reflects the mechanical changes in the physical process, and the stability verification evaluation result is output to characterize whether the clamping state under the current working condition is reliable. If the first verification signal shows that the vibration is very small, and at the same time the second verification signal indicates that the displacement of the key points is also within the normal range, and the image features also show that the clamping force distribution is uniform, then the determination of "stable" can be output. On the contrary, if any one of them shows an abnormality, such as severe micro-vibration or sudden increase in displacement change, and there is a difference from the results such as image grayscale or symmetry, then it is determined as "unstable" or "needs adjustment".

[0033] Furthermore, this application also includes: obtaining the zero-point grayscale image corresponding to the timing zero point; calculating the grayscale mean value of the zero-point grayscale image, and using the grayscale mean value to perform traversal deviation comparison on the zero-point grayscale image to generate a zero-point pressure deviation map; performing feature extraction on the calibrated test workpiece, and establishing a zero-point feature recognition result by using the workpiece feature extraction result and the zero-point pressure deviation map.

[0034] Specifically, during the workpiece machining process, select a moment as the reference starting point, that is, the time point when the pressurization has not started or just finished clamping but has not started the machining action, and use the clamping grayscale image collected at this moment as the zero-point image. The grayscale image means that each pixel point expresses the stress intensity through the grayscale value, and the grayscale value usually varies between 0 and 255, and the higher the value, the greater the pressure.

[0035] Subsequently, calculate the grayscale mean value of the zero-point grayscale image, that is, average the grayscale values of all pixel points in the image to reflect the basic stress level of the entire image at the zero point. Then, traverse each pixel point in the image, compare its grayscale value with the grayscale mean value, and obtain the deviation value, that is, the difference between the actual grayscale of a certain pixel and the overall mean value. Finally, generate a new image called the zero-point pressure deviation map, which shows the areas with uneven stress distribution in the zero-point state. For example, if the pixels on the left side of the image are 20 lower than the average value and the right side is 15 higher, it means that there is a left-right imbalance in the clamping structure at the zero-point start.

[0036] Next, perform feature extraction on the calibrated test workpiece, that is, identify the structural features of the workpiece in terms of geometric structure, shape contour, symmetry, etc. The extracted information includes boundary coordinates, center point position, and key structure distribution. The workpiece features provide a basis for explaining the meaning of each region in the image. For example, whether a region with a concentrated gray-scale deviation exactly falls on the weak structure of the workpiece. Finally, combine and analyze these workpiece features with the zero-pressure deviation map to establish a set of zero-feature recognition results for judging whether there are structural abnormalities or uneven clamping in the initial state of the workpiece during the initial clamping. Table 1 shows the record of the most recent zero-clamping feature recognition.

[0037] Table 1: Record of the Most Recent Zero-Clamping Feature Recognition Pixel coordinates (X, Y) Zero gray value Average gray value Deviation value = gray value - average value Match the characteristic area of the workpiece Zero pressure deviation state (10,15) 123 130 -7 Yes Slightly on the low side (22,33) 142 130 +12 No Ignore (45,18) 129 130 -1 Yes Normal (50,50) 117 130 -13 Yes Significantly on the low side (64,72) 134 130 +4 Yes Normal (78,29) 111 130 -19 Yes Seriously on the low side Furthermore, this application also includes: performing control information interaction of the machine tool system to obtain machining control information; obtaining the tool feed corresponding to the tool path mapping based on the machining control information, compensating for the influence of the tool feed based on the workpiece feature extraction result and the tool path, and establishing the actual feed; using the actual feed to perform machining fitting on the calibrated test workpiece fixed by the pressing plate to generate a time-series compensation force field; normalizing the time-series compensation force field and then correcting the time-series gray-scale image, and establishing a feature recognition result based on the corrected time-series gray-scale image.

[0038] Specifically, real-time read machining parameters from the machine tool control unit through the numerical control system or industrial bus, such as machining instructions, start signals, spindle speed, feed rate, etc. The control information includes the movement path, speed, and feed amount of each tool cut that the tool should execute during machining. Based on the control information, the tool feed corresponding to the actual tool path can be further extracted. The tool path is the path that the tool moves in space, such as moving 50 millimeters along a straight line or cutting along an arc with a radius of 20 millimeters. The feed amount refers to the distance that the tool advances along a given direction each time it cuts, which may be a fixed value such as 0.1 millimeter each time. However, the structure of the workpiece is not always solid. For example, there are cavities or grooves in some positions. Even if the tool executes the feed action, no material is actually cut. Therefore, it is necessary to compensate for the influence on the original feed amount by combining the workpiece feature information extracted previously, and then calculate the actual feed, that is, the position and amplitude of the actual cutting force applied by the tool in the effective material area.

[0039] After obtaining the actual feed, the stress state of the calibrated test workpiece fixed by the pressing plate during machining can be further fitted to establish a compensation force field corresponding to the machining time series. The compensation force field is a two-dimensional distribution map that changes with time and reflects the actual pressing force generated in different regions under the action of the tool. The compensation force field takes into account the relationship between the machining path, feed amount, and workpiece structure, and can more realistically restore the force change during the pressing process.

[0040] Finally, the compensation force field is normalized and transformed into a form with a unified dimension and numerical range for comparison and correction with the original time-series grayscale image. The normalized compensation force field is superimposed or adjusted as a correction factor on the grayscale values of each pixel in the original grayscale image to generate a set of processed grayscale image sequences with actual influence correction. Using the corrected images for subsequent image feature recognition can improve the judgment accuracy and reduce misjudgments caused by differences in machining paths or material structures.

[0041] Furthermore, the present application further includes: establishing force-similar position points at different time nodes by using the true feed rate, cover plate structure, and workpiece feature extraction results; performing comparison and verification of the time-series grayscale images through the force-similar position points to establish additional verification features; and compensating the stability verification evaluation results according to the additional verification features.

[0042] Specifically, establishing force-similar position points at different time nodes by using the true feed rate, cover plate structure, and workpiece feature extraction results means that during the machining process, combining the actual feed depth and speed of the tool at a certain moment, i.e., the true feed rate, the geometric feature information of the workpiece, and the structural distribution of the clamping cover plate, comparing the machining states at different time points, and identifying position points with similar force conditions. The true feed rate refers to the actual material cutting depth considering the influence of air cutting, feed speed change, etc.; the cover plate structure refers to the physical structure shape, rigidity distribution, etc. of the clamping cover plate; and the workpiece feature extraction result refers to the surface, internal, or edge feature information of the workpiece obtained through image processing or modeling. Through joint analysis, position points with similar force states can be found at different time points although the machining times are different due to similar structures, feeds, and material distributions.

[0043] Compare the image grayscales of these similar force position points at different times to confirm whether the same or similar sensing responses are given under the same stress conditions. The time-series grayscale image reflects the time change of the pressure or force state monitored by the sensor. By comparing the change in the grayscale values of the force-similar points on the image, it can be determined whether the system is stable and consistent. The comparison result is used to establish additional verification features, that is, a class of additional information features, to test the consistency or repeatability of the system response. For example, during two machining processes in different regions but with similar forces, if the trend of the image grayscale change is highly consistent, the additional verification feature shows a high degree of consistency.

[0044] Compensating the stability verification evaluation results according to additional verification features means taking the newly extracted verification features as correction factors and adding them to the original stability evaluation system to improve its accuracy. If it is considered that the clamping in a certain stage is unstable in the preliminary evaluation, but the additional verification features show that the force response in this stage is highly consistent with other normal stages, the original judgment can be corrected and compensated. For example, if the original deviation value is 0.08 and the additional verification features show that the similarity is more than 90%, the deviation value can be corrected to 0.04 to avoid false alarms.

[0045] Further, this application also includes: sorting the sequential grayscale images in chronological order into frame images; calculating the centroid of the grayscale image and the geometric center of the grayscale image for each frame image, performing the deviation comparison between the centroid of the grayscale image and the geometric center of the grayscale image, and establishing the characteristic of the center offset of the hot zone; setting the regional width value of the boundary region, and extracting the grayscale data of the boundary region of each frame image within the regional width value; calculating the grayscale mean value for each boundary region based on the grayscale data, generating a set of grayscale mean values mapped to the boundary region; and calculating the neighborhood gradient using the set of grayscale mean values to establish the characteristic of the boundary clamping jump.

[0046] Specifically, the two-dimensional grayscale images continuously collected during the processing are arranged in chronological order into a series of frames, reflecting the pressure distribution state of the workpiece contact surface at different time points. Each frame represents an instantaneous two-dimensional force field state. Through sorting, the evolution process of the pressure field over time can be intuitively captured. For example, the pressure distribution is sparse at the beginning of the processing, while the grayscale is dense at the cutting peak stage, providing a logical basis for the independent analysis of each frame image and the identification of dynamic trends.

[0047] Calculate the centroid of the grayscale image and the geometric center of the grayscale image for each frame image. The centroid of the grayscale image refers to the weighted average position of all pixel grayscale values in the image, representing the position of the main pressure concentration area; the geometric center is the geometric center point on the image plane, which is determined only by the image shape without considering the grayscale distribution. The deviation distance between the two center points can be used as a measure of the hot zone offset to determine whether the pressure center deviates from the structural center. The deviation comparison quantifies the coordinate difference between the two. If the offset continuously increases over time, it may indicate uneven force on the workpiece or displacement occurring.

[0048] Set the regional width value of the boundary region, which is used to expand a fixed-width range inward from the four sides of the image. Then, a 10-pixel-wide region is taken from the image edge towards the center as the object of boundary analysis. Extract the grayscale values of each frame image in the boundary region and calculate the grayscale mean value of this region to obtain a set of average value data corresponding to each boundary region, representing the overall force level of the edge region at this time point, forming a set of grayscale mean values that can change over time, reflecting the change trend of the pressure in the boundary region during the entire processing.

[0049] Using the grayscale mean set, further calculate the grayscale change rate between adjacent time points, that is, perform neighborhood gradient calculation. The gradient refers to the change rate of grayscale values. By analyzing the change rate, the mutation points of the force on the boundary can be captured. Establishing the boundary holding jump feature is to identify the positions and times of abnormal changes, which is used to reflect whether the workpiece is loose or the local force is unstable in the edge area.

[0050] Furthermore, this application also includes: obtaining the stable holding pressure of the calibrated test workpiece, converting the stable holding pressure into the corresponding target grayscale value; after configuring the local window, constructing the local neighborhood of the pixel points in each frame of the image; calculating the local mean and local standard deviation of the local neighborhood, and using the local mean, local standard deviation, and the target grayscale value for fusion calculation to establish a binary segmentation threshold; performing binary processing on each frame of the image based on the binary segmentation threshold to establish a binary image; performing image evaluation on the binary image through the connectivity index to establish the integrity feature of the grayscale connected domain.

[0051] Specifically, after the workpiece is clamped, the stable and uniform standard pressure value applied by the holding system to the workpiece in a static state is obtained as the stable holding pressure of the calibrated test workpiece, which serves as the benchmark for judging the normal holding state. Using the pressure-grayscale mapping relationship of the sensor, the stable holding pressure is mapped to a specific grayscale level in the image, which is used for comparison and judgment in the subsequent image processing to help determine which areas belong to the normal holding state.

[0052] Subsequently, a smaller rectangular area, such as a 5-pixel × 5-pixel square area, is selected in the entire image to establish a local neighborhood around each pixel point. The construction of the local neighborhood helps to analyze the image characteristics around each pixel, without being interfered by the global characteristics of the entire image, thereby improving the processing fineness and sensitivity, and is especially suitable for detecting local abnormal changes.

[0053] Then, in each local neighborhood, calculate the local mean and local standard deviation. The local mean represents the average level of the pixel grayscale in the neighborhood, and the local standard deviation represents the degree of grayscale dispersion, which is the magnitude of the fluctuation. Combining the obtained target grayscale value, establish a binary segmentation threshold for the pixels in this neighborhood through a fusion calculation formula, which is used as a judgment criterion to divide the current pixel into two categories: "normal holding" or "abnormal holding". For example, if the target grayscale value is 180, the local mean is 160, and the standard deviation is 20, the threshold obtained after fusion may be 170, and the pixels below 170 will be regarded as abnormal areas.

[0054] Then, each frame of the image is processed using a binary segmentation threshold to perform binarization, that is, each pixel in the grayscale image is converted into one of two states: black or white. Black usually represents the abnormal pressing area, and white represents the normal pressing area, forming a binary image, which is the basis for subsequent connectivity analysis. The advantage of binarization is that it greatly reduces the image complexity, making the structural feature analysis clearer and the calculation efficiency higher.

[0055] Finally, an image evaluation is performed on the binary image, and a connectivity index is used to measure the continuity and integrity of the white area in the image. Connectivity refers to whether pixels can be connected into a continuous area through adjacent pixels, such as analyzing the number and shape of its connected components. If the white area is highly coherent and concentrated, it indicates that the pressing is overall uniform and stable; on the contrary, if there are a large number of scattered white isolated blocks, there may be problems such as local pressing looseness and abnormal fixture contact, thus establishing the integrity feature of the gray connected domain.

[0056] Furthermore, this application also includes: after performing connected domain segmentation on the binary image, obtaining a connected domain quantity index; obtaining the total connected area of all connected domains, and establishing a maximum connected ratio index based on the total connected area; using the connected domain quantity index and the connected ratio index as the connectivity index to complete the image evaluation.

[0057] Specifically, the continuous white pixel areas in the image after black-and-white binarization are identified, and the total number of connected regions in the image is counted. The concept of a connected domain means a group of same-color pixel blocks that can be continuously connected in the image through up, down, left, right, or diagonal directions. White represents the normal pressing area, so each connected domain represents a locally normally stressed area. The fewer the number of connected domains, usually the more concentrated and complete the pressing area; while a larger number may indicate scattered pressing, uneven stress, or abnormal phenomena.

[0058] Immediately afterwards, the number of pixels of all connected white pixel blocks is accumulated to obtain a total area value. Then, the total connected area is divided by the total pixel area of the entire image to obtain a ratio, called the maximum connected ratio index, which is used to describe the proportion of the pressing area in the image. If the total number of pixels in the image is 100,000 pixels and the total white area is 60,000 pixels, then the maximum connected ratio is 0.6. The larger the ratio, the more areas are in a normally stressed state; on the contrary, the smaller the ratio, the lower the coverage rate of the pressing area, and there may be a large number of uncompacted or loose parts.

[0059] Finally, the two results of the number of connected components index and the maximum connected ratio index are used together as the connectivity index to complete the evaluation of the structural integrity of the entire image. The connectivity index is a comprehensive index that reflects whether the pressing area is concentrated and whether the force-bearing area is extensive. For example, if an image has 1 connected component and a connected ratio of 0.9, it means that almost the entire pressing surface is uniformly stressed; while if another image has 15 connected components and a connected ratio of only 0.4, it indicates the existence of multiple scattered force-bearing areas and a large area that is not effectively pressed.

[0060] Furthermore, this application also includes: horizontally and vertically flipping each frame of the image, and calculating the absolute difference in grayscale between the original image and the mirror image; generating horizontal and vertical influence factors using the workpiece feature extraction results of the calibrated test workpiece; calculating the symmetry imbalance feature using the influence factors and the absolute difference in grayscale.

[0061] Specifically, each frame of the image is horizontally and vertically flipped, and the original grayscale image is mirror-transformed along the horizontal and vertical directions respectively. Horizontal flipping moves the pixels on the left side of the image to the right side and the right side to the left side; vertical flipping turns the image upside down, moving the pixels at the top to the bottom and the bottom to the top, for analyzing the symmetry of the image itself in the spatial structure and judging whether the grayscale distributions on both sides or up and down of the image are consistent. Subsequently, the absolute difference in grayscale between the flipped mirror image and the original image at the corresponding pixel positions is calculated, that is, taking the absolute value after subtracting each pixel by pixel, which represents the magnitude of the difference on the axis of symmetry of the image. The smaller the difference, the more symmetric the image is.

[0062] Next, in order to improve the analysis accuracy, the results of workpiece feature extraction are introduced to generate horizontal and vertical influence factors. Workpiece feature extraction refers to identifying the key structural features of the calibrated test workpiece, such as contour, center point, boundary range, etc., which can reveal the physical symmetry characteristics of the workpiece. The influence factor is a set of weight parameters calculated from the features, used to describe the importance or deviation degree of the symmetric structure of the workpiece in the horizontal and vertical directions. Combining the influence factor with the absolute difference in grayscale, through methods such as weighting or normalization, the symmetry imbalance feature is calculated, which reflects the left-right or up-down imbalance degree of the image during the force application or pressing process, and measures the symmetry stability of the pressing structure. For example, if the overall absolute difference in grayscale between the left and right mirror images is less than 10 and the left-right symmetry of the workpiece is strong, the symmetry imbalance value may be less than 0.1, indicating uniform stress; but if the difference is as high as more than 30 and the weight of the influence factor is high, the symmetry imbalance value will increase significantly, indicating uneven structural pressing, which may affect the processing accuracy.

[0063] Furthermore, this application also includes: extracting the gray values of feature points over time from the sequential gray-scale images to establish a three-dimensional matrix; extracting typical gray-scale trajectories based on the three-dimensional matrix, and performing evolutionary form clustering analysis using the typical gray-scale trajectories to establish dynamic trajectory features; and supplementing and adding the dynamic trajectory features to the feature recognition results.

[0064] Specifically, select pixel points at specific positions in multiple consecutive gray-scale images and track the gray-scale changes of the pixel points in different time frames. The gray value is the numerical value representing brightness in the image, where 0 represents black and 255 represents white. After summarizing the gray-scale changes of the feature points at different time points, a three-dimensional matrix with spatial position and time as the three-dimensional coordinate axes is formed to describe the brightness change trajectory during the workpiece clamping process.

[0065] Next, typical gray-scale trajectories can be extracted based on the three-dimensional matrix, that is, identifying representative gray-scale change patterns from numerous change trajectories. For example, in the stable clamping area, the gray values of some pixels may remain continuously around 220, while in the unstable area, they may fluctuate significantly between 180 and 230. By performing evolutionary form clustering analysis on the trajectories, that is, classifying the trajectories into several categories according to the change trends and forms, such as stable rising type, severe oscillation type, or periodic fluctuation type, dynamic trajectory features are established, which can then reflect the possible stable areas, unstable areas, or potential abnormal areas during the cover plate clamping process.

[0066] Finally, the extracted dynamic trajectory features are added to the existing feature recognition results as a dynamic supplement to two-dimensional features such as hot zone offset and connectivity integrity in the gray-scale images, making the evaluation results of the entire system more comprehensive, including both the instantaneous features of the clamping force in the spatial distribution and the trend features evolving over time.

[0067] In summary, the stability evaluation method for cover plate clamping provided by this application has the following technical effects: by achieving the technical goal of multi-dimensional fusion modeling and stability intelligent evaluation based on workpiece structure features and processing dynamic processes, the technical effects of improving the recognition accuracy of clamping stability, reducing the misjudgment rate, and enhancing the system's adaptability to complex processing environments are achieved.

[0068] Embodiment 2, based on the same inventive concept as the stability evaluation method for cover plate clamping in the foregoing embodiment, this application also provides a stability evaluation device for cover plate clamping. Please refer to the appendix Figure 2, including: a fixing module 11, which is used to load a calibration test workpiece onto a target fixture and fix it with a cover plate after setting a sensor array on the contact surface of the calibration test workpiece; a machining module 12, which is used to activate the sensor array and control the machine tool to start and perform machining after confirming the completion of the cover plate assembly; a reading module 13, which is used to read the monitoring data of the sensor array in real time, establish a contact two-dimensional calibration force field, and normalize the contact two-dimensional calibration force field into a time-series grayscale image; a feature recognition module 14, which is used to recognize features such as the center offset of the hot zone, the boundary holding jump feature, the local maximum clustering feature, the integrity feature of the grayscale connected domain, and the symmetry imbalance feature of the time-series grayscale image, and establish a feature recognition result; a signal establishment module 15, which is used to synchronously obtain the acquisition information of a three-axis accelerometer set on the fixture, establish a micro-vibration signal, and use the micro-vibration signal as a first verification signal; a monitoring module 16, which is used to monitor key points of the workpiece with a laser displacement sensor, establish a workpiece time-series displacement, and use the workpiece time-series displacement as a second verification signal; a verification module 17, which is used to stably verify the feature recognition result through the first verification signal and the second verification signal, and output a stability verification evaluation result.

[0069] Furthermore, the stability evaluation device for cover plate pressing is further used for: obtaining a zero-point grayscale image corresponding to the time series zero point; calculating the grayscale mean value of the zero-point grayscale image, and using the grayscale mean value to perform a traversal deviation comparison of the zero-point grayscale image to generate a zero-point pressure deviation map; performing feature extraction of the calibration test workpiece, and establishing a zero-point feature recognition result by using the workpiece feature extraction result and the zero-point pressure deviation map.

[0070] Furthermore, the stability evaluation device for cover plate pressing is further used for: performing control information interaction of the machine tool system to obtain machining control information; obtaining the tool feed corresponding to the tool path mapping according to the machining control information, and performing influence compensation of the tool feed based on the workpiece feature extraction result and the tool path to establish a true feed; using the true feed to perform machining fitting of the calibration test workpiece fixed by the pressing plate to generate a time-series compensation force field; normalizing the time-series compensation force field, and correcting the time-series grayscale image, and establishing a feature recognition result with the corrected time-series grayscale image.

[0071] Furthermore, the stability evaluation device for cover plate pressing is further used for: establishing force similarity position points at different time nodes by using the true feed, the cover plate structure, and the workpiece feature extraction result; performing comparison verification of the time-series grayscale image through the force similarity position points to establish additional verification features; compensating the stability verification evaluation result according to the additional verification features.

[0072] Furthermore, the stability evaluation device for cover plate pressing is also used for: sorting the sequential grayscale images in chronological order into frame images; calculating the center of gravity of the grayscale image and the geometric center of the grayscale image for each frame image, performing the deviation comparison between the center of gravity of the grayscale image and the geometric center of the grayscale image, and establishing the center offset feature of the hot zone; setting the regional width value of the boundary region, and extracting the grayscale data of the boundary region of each frame image within the regional width value; calculating the grayscale mean value of each boundary region based on the grayscale data, and generating a grayscale mean value set mapped to the boundary region; performing neighborhood gradient calculation using the grayscale mean value set, and establishing the boundary pressing jump feature.

[0073] Furthermore, the stability evaluation device for cover plate pressing is also used for: obtaining the stable pressing pressure of the calibrated test workpiece, and converting the stable pressing pressure into the corresponding target grayscale value; after configuring the local window, constructing the local neighborhood of the pixel points within each frame image; calculating the local mean value and the local standard deviation of the local neighborhood, and performing fusion calculation using the local mean value, the local standard deviation, and the target grayscale value to establish the binary segmentation threshold; performing binary processing on each frame image based on the binary segmentation threshold to establish a binary image; performing image evaluation on the binary image through the connectivity index to establish the integrity feature of the grayscale connected domain.

[0074] Furthermore, the stability evaluation device for cover plate pressing is also used for: after performing connected domain segmentation on the binary image, obtaining the connected domain quantity index; obtaining the total connected area of all connected domains, and establishing the maximum connected ratio index according to the total connected area; using the connected domain quantity index and the connected ratio index as the connectivity index to complete the image evaluation.

[0075] Furthermore, the stability evaluation device for cover plate pressing is also used for: performing horizontal flipping and vertical flipping on each frame image, and calculating the absolute grayscale difference between the original image and the mirror image; generating horizontal and vertical influence factors using the workpiece feature extraction result of the calibrated test workpiece; calculating the symmetry imbalance feature using the influence factors and the absolute grayscale difference.

[0076] Furthermore, the stability evaluation device for cover plate pressing is also used for: extracting the grayscale values of the feature points changing with time using the sequential grayscale images to establish a three-dimensional matrix; extracting the typical grayscale trajectories based on the three-dimensional matrix, and performing evolutionary morphology clustering analysis using the typical grayscale trajectories to establish the dynamic trajectory feature; supplementing and adding the dynamic trajectory feature to the feature recognition result.

[0077] In this specification, the various embodiments are described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The method and specific examples for evaluating the stability of cover plate pressing in the foregoing Embodiment 1 are equally applicable to the device for evaluating the stability of cover plate pressing in this embodiment. Through the foregoing detailed description of the method for evaluating the stability of cover plate pressing, those skilled in the art can clearly understand the device for evaluating the stability of cover plate pressing in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated herein.

[0078] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A stability assessment method for cover plate pressing, characterized in that: include: After the sensor array is set on the contact surface of the calibration test workpiece, the calibration test workpiece is loaded into the target fixture and fixed by a cover plate; After confirming that the cover plate is assembled, activating the sensor array, and controlling the machine tool to start and perform machining; Reading the monitoring data of the sensor array in real time, establishing a contact two-dimensional scalar force field, and normalizing the contact two-dimensional scalar force field into a time-series grayscale image; The time series grayscale image is subjected to hot zone center offset feature, boundary pressure jump feature, local maximum clustering feature, grayscale connected domain integrity feature, and symmetry imbalance feature recognition to establish a feature recognition result; Synchronously acquiring the collected information of the three-axis accelerometer disposed on the fixture, establishing a micro-vibration signal, and using the micro-vibration signal as a first verification signal; Using a laser displacement sensor to monitor key points of a workpiece, establishing a time-series displacement of the workpiece, and using the time-series displacement of the workpiece as a second verification signal; The feature recognition result is stably verified by using the first verification signal and the second verification signal, and a stability verification evaluation result is output.

2. The stability evaluation method for cover plate pressing according to claim 1, characterized in that: The establishing of feature recognition results also includes: Obtain a zero-point grayscale image corresponding to the time series zero point; Calculating the grayscale mean of the zero-point grayscale image, and using the grayscale mean to perform ergodic deviation comparison of the zero-point grayscale image to generate a zero-point pressure deviation map; Perform feature extraction of the calibration test workpiece, and use the workpiece feature extraction results and the zero-point pressure deviation map to establish the zero-point feature recognition result.

3. The stability evaluation method for cover plate pressing according to claim 2, characterized in that: Before establishing the feature recognition result, the method further includes: Execute control information interaction of machine tool system and obtain processing control information; Acquire the tool feed amount mapped with the tool trajectory according to the machining control information, perform the influence compensation of the tool feed amount based on the workpiece feature extraction result and the tool trajectory, and establish the real feed amount; Using the actual feed amount to perform machining fitting of a calibration test workpiece fixed by a pressing plate, generating a time-series compensation force field; After the time series compensation force field is normalized, the time series grayscale image is corrected, and a feature recognition result is established using the corrected time series grayscale image.

4. The stability evaluation method for cover plate pressing according to claim 3, characterized in that: The output stability verification evaluation results include: Using the actual feed amount, cover plate structure and workpiece feature extraction results, similar force position points at different time nodes are established; Comparison and verification of the time-series grayscale images are performed through the similar force position points to establish additional verification features; The stability verification evaluation result is compensated according to the additional verification feature.

5. The stability evaluation method for cover plate pressing according to claim 1, characterized in that: The step of identifying the hot zone center offset feature, boundary pressure jump feature, local maximum clustering feature, grayscale connected domain integrity feature, and symmetry imbalance feature of the time series grayscale image includes: Sorting the time-series grayscale images into frame images in chronological order; For each frame of image, the centroid of the grayscale image and the geometric center of the grayscale image are calculated, and a deviation comparison between the centroid of the grayscale image and the geometric center of the grayscale image is performed to establish a hot zone center offset feature; Setting a region width value of the boundary region, and extracting grayscale data of the boundary region of each frame of image within the region width value; Calculate the grayscale mean value of each boundary area based on the grayscale data to generate a grayscale mean value set mapped to the boundary area; The grayscale mean set is used to calculate the neighborhood gradient and establish the boundary pressure jump feature.

6. The stability evaluation method for cover plate pressing according to claim 1, characterized in that: The identifying of the hot zone center offset feature, the boundary pressure jump feature, the local maximum clustering feature, the grayscale connected domain integrity feature, and the symmetry imbalance feature of the time series grayscale image also includes: Acquire a stable holding pressure of a calibration test workpiece, and convert the stable holding pressure into a corresponding target grayscale value; After configuring the local window, construct the local neighborhood of the pixel points in each frame image; Calculate the local mean and local standard deviation of the local neighborhood, and use the local mean, local standard deviation, and the target gray value to fuse and calculate to establish a binary segmentation threshold; Binarize each frame of image based on the binary segmentation threshold to create a binary image; The image evaluation of the binary image is performed through the connectivity index to establish the grayscale connected domain integrity feature.

7. The stability evaluation method for cover plate pressing according to claim 6, characterized in that: The performing of image evaluation of the binary image by using the connectivity index to establish grayscale connected domain integrity features includes: After performing connected domain segmentation on the binary image, a connected domain quantity index is obtained; Obtaining the total connected area of ​​all connected domains, and establishing a maximum connected ratio index according to the total connected area; The connected domain quantity index and the connected ratio index are used as connectivity indexes to complete image evaluation.

8. The stability assessment method for cover plate pressing according to claim 6, characterized in that: The identifying of the hot zone center offset feature, the boundary pressure jump feature, the local maximum clustering feature, the grayscale connected domain integrity feature, and the symmetry imbalance feature of the time series grayscale image also includes: Flip each frame of image horizontally and vertically, and calculate the absolute grayscale difference between the original image and the mirror image; Generate horizontal and vertical influence factors using workpiece feature extraction results of the calibration test workpiece; The symmetry imbalance feature is calculated using the influencing factor and the grayscale absolute difference.

9. The stability evaluation method for cover plate pressing according to claim 1, characterized in that: The establishing of feature recognition results also includes: Extracting the grayscale values ​​of feature points that change over time using the time-series grayscale image to establish a three-dimensional matrix; Extracting typical grayscale trajectories based on the three-dimensional matrix, and using the typical grayscale trajectories to perform evolutionary morphological clustering analysis to establish dynamic trajectory features; The dynamic trajectory feature is supplemented and added to the feature recognition result.

10. A stability assessment device for cover plate pressing, characterized in that: The steps for implementing the stability assessment method for cover plate pressing as claimed in any one of claims 1 to 9 include: A fixing module, used for loading the calibration test workpiece into a target fixture and fixing it through a cover plate after setting a sensor array on the contact surface of the calibration test workpiece; A processing module is used to activate the sensor array and control the machine tool to start and perform machining after confirming that the cover plate is assembled; A reading module, used for reading the monitoring data of the sensor array in real time, establishing a contact two-dimensional scalar force field, and normalizing the contact two-dimensional scalar force field into a time-series grayscale image; A feature recognition module is used to identify the hot zone center offset feature, boundary pressure jump feature, local maximum clustering feature, grayscale connected domain integrity feature, and symmetry imbalance feature of the time series grayscale image, and establish a feature recognition result; A signal establishment module, used for synchronously acquiring the collected information of the three-axis accelerometer arranged on the fixture, establishing a micro-vibration signal, and using the micro-vibration signal as the first verification signal; A monitoring module, used to monitor key points of a workpiece using a laser displacement sensor, establish a time-series displacement of the workpiece, and use the time-series displacement of the workpiece as a second verification signal; The verification module is used to perform stability verification on the feature recognition result through the first verification signal and the second verification signal, and output a stability verification evaluation result.

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