Automatic drilling and riveting machine executor normal alignment method and system

Abstract

The present application belongs to the field of automatic drilling and riveting of digital assembly of aircraft, and specifically discloses a normal alignment method and system for an automatic drilling and riveting machine executor, which comprises the following steps: first, collecting the original distance from the end face of a presser foot to the surrounding plane of a hole to be processed through a sensor; then, using a KF-MAF algorithm combining Kalman filtering and moving average filtering to suppress dynamic noise and obtain accurate distance information; subsequently, calculating the deviation of the feeding direction of the executor from the normal of the surface of the hole to be processed, and obtaining the target hole position angle error through a bilinear interpolation method; finally, controlling the rotation of the executor to compensate for the error, keeping the position of the tool center point unchanged, making the drill axis coincide with the local surface normal of the workpiece, and realizing the normal alignment. The present method requires less original information and is easy to obtain, can accurately and quickly complete the alignment, and ensures the hole making accuracy.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of automatic drilling and riveting of aircraft digital assembly, and particularly relates to a normal alignment method and system for an executor of an automatic drilling and riveting machine. BACKGROUND

[0002] In drilling and riveting processing, the normal alignment precision of the executor directly determines the hole making quality. The traditional contact type measurement (such as a length gauge) is easily affected by dust and chips, and is difficult to adapt to the complex workpiece surface. Although the non-contact laser displacement sensor overcomes the contact defects, the high-frequency electromagnetic interference of the drilling and riveting machine and the workpiece vibration will cause data distortion, and a single filtering algorithm is difficult to balance noise suppression and real-time performance. At the same time, the existing scheme measures each hole completely, and the multi-hole processing efficiency is low. The sensor installation and protection adaptability are poor, and it is difficult to meet the high-precision and high-efficiency processing requirements. SUMMARY

[0003] To this end, the application provides an automatic drilling and riveting machine executor normal alignment method and system to solve the above technical problems.

[0004] The application provides an automatic drilling and riveting machine executor normal alignment method, which comprises the following method steps:

[0005] Step S1, acquiring the original distance from the presser end face of the executor presser body to the surrounding plane of the hole to be processed through a sensor;

[0006] Step S2, using a KF-MAF algorithm combining Kalman filtering and moving average filtering to dynamically suppress the noise of the original distance to obtain accurate distance information;

[0007] Step S3, calculating the deviation of the end executor feeding direction from the surface normal at the hole to be processed according to the distance information;

[0008] Step S4, obtaining the angle error amount of the target hole position by a bilinear interpolation method;

[0009] Step S5, controlling the executor to rotate to compensate for the angle error, so that the drill axis coincides with the local surface normal of the workpiece while keeping the position of the tool center point unchanged, and the normal alignment is completed.

[0010] Further, the KF-MAF algorithm dynamically suppresses the noise of the original distance to obtain accurate distance information, which comprises: performing moving average on multiple frames of distance data within a preset sampling window to obtain smoothed observation values; using Kalman filtering to predict and update the observation values to output filtered distance data, wherein the observation noise covariance R of the Kalman filter is dynamically adjusted according to the data mutation.

[0011] Further, the angle error calculation includes calculating the rotation angle β around the Y axis and the rotation angle γ around the X axis, respectively, by the four-point height difference measured by the four laser displacement sensors, so that the plane where the sensors are located coincides with the target plane of the workpiece, and the calculation formula is:

[0012]

[0013]

[0014] wherein, is the projection length of the four laser displacement sensors in the tool axis direction, and EG and HF are the installation spacings of the sensors.

[0015] Further, the projection length is calculated by the following formula: wherein is the distance measured by the sensor, is the included angle between the laser beam and the tool axis.

[0016] Further, the observation noise covariance R of the Kalman filter is dynamically adjusted according to the data mutation, including when the current observation value Z k compared with the observation value Z k -1 of the previous moment, the mutation amplitude exceeds the preset threshold, it is determined that strong reflection or instantaneous electromagnetic pulse interference is encountered, the observation noise covariance R is temporarily increased to more than 10 times of the original value, so that the Kalman gain K is automatically reduced.

[0017] Further, the bilinear interpolation method is based on the angle error of the plurality of reference holes on the workpiece to calculate the correction angle of the target hole position.

[0018] Further, the state prediction model of the Kalman filter adopts a constant model, that is, the state transition matrix F is set to 1.

[0019] Further, when the to-be-processed hole is located in the rectangular region formed by the plurality of reference holes, steps S1 to S3 are skipped, and the rotation angle error amount required by the hole position is calculated by the bilinear interpolation formula according to the coordinates of the to-be-processed hole and the angle error amount of the plurality of reference holes.

[0020] Further, when the bilinear interpolation method is continuously used to process N hole positions, or when the workpiece clamping force is abnormal or the machine tool vibration amplitude exceeds the standard, a complete step S1 to step S5 is automatically triggered once to recalibrate the nearby reference hole data, so as to guarantee the normal accuracy in long-term continuous operation, wherein N≥10.

[0021] Another aspect of the present application also provides an automatic drilling and riveting machine executor normal alignment system, comprising:

[0022] The acquisition module is configured to acquire, by a sensor, an original distance from a presser foot end face of an actuator presser foot body to a plane around a hole to be machined;

[0023] The de-noising module is configured to perform dynamic noise suppression on the original distance by using a KF-MAF algorithm combining Kalman filtering and moving average filtering, to obtain accurate distance information.

[0024] The deviation calculation module is configured to calculate a deviation between a feeding direction of the end effector and a surface normal at the hole to be machined according to the distance information.

[0025] The angle error acquisition module is configured to acquire an angle error amount of the target hole position by using a bilinear interpolation method.

[0026] The normal alignment module is configured to control the actuator to rotate to compensate for the angle error, so that the drill bit axis coincides with the local surface normal of the workpiece under the premise of keeping the tool center point position unchanged, and the normal alignment is completed.

[0027] The present application effectively solves the pain points of the existing normal alignment technology by optimizing the sensor system, innovating the fusion filtering algorithm, improving the deviation calculation and compensation technology, and taking into account high precision, high efficiency, high reliability and wide adaptability, which can meet the stringent requirements of high-end equipment manufacturing in the fields of aerospace and rail transportation. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor by reading the following detailed description of the non-limiting embodiments with reference to the following drawings:

[0029] Other features, objects and advantages of the present application will become more apparent by reading the following detailed description of the non-limiting embodiments with reference to the accompanying drawings:

[0030] Figure 1 An automatic drilling and riveting machine actuator normal alignment method flow chart is provided for the embodiments of the present application.

[0031] Figure 2 A laser displacement sensor ranging schematic diagram is provided for the embodiments of the present application.

[0032] Figure 3 An angle deviation calculation geometry schematic diagram is provided for the embodiments of the present application.

[0033] Figure 4 A system schematic diagram is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0034] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0035] As shown in the drawings, Figure 1 The embodiments of the present application disclose a method 100 for normal alignment of an automatic drilling and riveting machine executor, comprising the following method steps:

[0036] Step S1: collecting, by a sensor, an original distance from a presser foot end face of an executor presser foot body to a surrounding plane of a hole to be processed;

[0037] Step S2: performing dynamic noise suppression on the original distance by using a KF-MAF algorithm combining Kalman filtering and moving average filtering to obtain accurate distance information;

[0038] Step S3: calculating, according to the distance information, a deviation of a feeding direction of the end executor from a normal of a surface at the hole to be processed;

[0039] Step S4: obtaining, by a bilinear interpolation method, an angle error amount of a target hole position;

[0040] Step S5: controlling the executor to rotate to compensate for the angle error, so that the drill bit axis coincides with the local surface normal of the workpiece under the premise of keeping the tool center point position unchanged, and the normal alignment is completed.

[0041] In some embodiments, for step S1, the present application takes four high-speed displacement sensors (preferably laser displacement sensors, which can be replaced by high-speed displacement length gauges) as core measurement elements, combines with exclusive installation structures and protection devices to ensure measurement stability.

[0042] Specifically, the four laser displacement sensors are uniformly installed around the presser foot body of the automatic drilling and riveting machine executor, and are installed at specific angles to adjust the laser beam emission angles of the sensors by pre-calibration tooling, so as to ensure that the four laser beams are accurately projected on the small planes around the hole to be processed, forming four measurement spots, and the spot spacing is strictly controlled within 25 mm. This design can avoid measurement failure caused by laser beams mistakenly entering the surface apertures and grooves of the workpiece, and at the same time, the four measurement points can comprehensively cover the peripheral area of the hole to be processed, accurately reflect the normal state of the workpiece surface, and provide complete data support for subsequent deviation calculation.

[0043] In view of the harsh processing environment (such as the aerospace panel batch drilling and riveting scene) with high dust concentration and strong electromagnetic interference (such as aluminum dust generated during the processing, and high-frequency electromagnetic radiation generated by the machine motor, frequency converter and other equipment), the application provides a built-in sensor installation scheme: embedding the laser displacement sensor into the preset installation groove in the presser body (the installation groove depth is greater than or equal to the sensor thickness + 5 mm, and the width gap between the sensor size is less than or equal to 1 mm), and sealing the gap between the sensor and the installation groove with a rubber protective sleeve.

[0044] In the built-in installation state, the sensor does not directly measure the distance from the workpiece surface, but indirectly feeds back the normal of the contact surface between the presser and the workpiece by detecting the inclination of the fixed plane in the presser.

[0045] In some embodiments, during the operation of the automatic drilling and riveting machine, high-frequency electromagnetic noise of 100 Hz-10 kHz is generated by the motor, frequency converter and other high-frequency electrical equipment, and under the action of cutting force and clamping force, the workpiece will generate a slight vibration of 0.01-0.1 mm. These interferences will cause the original distance data collected by the sensor to fluctuate, and if not effectively suppressed, it will directly affect the calculation accuracy of the normal deviation, and even cause the normal alignment to fail.

[0046] Therefore, the application proposes a KF-MAF dynamic noise suppression algorithm that combines Kalman filtering (KF) and moving average filtering (MAF), which fully utilizes the advantages of the two filtering algorithms to effectively suppress high-frequency electromagnetic noise and instantaneous vibration interference.

[0047] The four sensors are controlled to collect the distance data from the presser end face to the surrounding plane of the hole to be processed at a sampling frequency of 1 KHz, and a sampling window N=5 ms (i.e. each sensor continuously collects 5 frames of data within 5 ms) is set to form an original distance data set (where i=1, 2, 3, 4, respectively corresponding to the four sensors). The sampling frequency of 1 KHz can not only ensure the real-time performance of data collection (matching the processing pace of 0.5-1 s / hole of the drilling and riveting machine), but also avoid data redundancy caused by too high sampling frequency; the sampling window of 5 ms takes into account the data smoothness and response speed, and if the window is too large, the hysteresis will increase, and if it is too small, it will be difficult to effectively suppress high-frequency noise.

[0048] The moving average filtering can effectively smooth the high-frequency electromagnetic noise (the suppression rate of noise above 100 Hz can reach more than 80%) by taking the average value of multiple frames of data in the sampling window, and provide a stable observation basis for the subsequent Kalman filtering. The calculation formula of the observation value of the original distance data of the i-th sensor at time k is as follows:

[0049] As shown in Figure 2 , for the i-th sensor, the calculation formula of the observation value A laser displacement sensor in to a set of distances measured at time , whose observations can be expressed as:

[0050]

[0051] where is the observation at time , which is directly input to the KF update step in the form of an average output. This ensures that the KF uses smoothed data rather than noisy raw values.

[0052] where is the raw distance data collected by the i-th sensor at time t, and N is the size of the sampling window (5 ms)

[0053] The Kalman filter can effectively suppress transient vibration interference (such as 0.05-0.1 mm transient displacement of the workpiece due to sudden changes in cutting force) by establishing a state prediction model and dynamically tracking the trend of data changes. The present invention uses a constant model as the state transition equation of the Kalman filter (assuming that the distance data measured by the sensor remains stable for a short period of time, which is consistent with the actual working condition of the slow change of the relative position between the pressure foot and the workpiece in drilling and riveting processing), and the specific prediction steps are as follows:

[0054] The state transition matrix F is set to 1, i.e. it is assumed that the distance data at time k is consistent with the optimal estimate value at time k-1, and the state prediction formula is:

[0055]

[0056] where represents the predicted value of the distance at time to time , and is the optimal estimate value at time k-1 (i.e. the output value of the KF-MAF algorithm at the previous time). This design simplifies the calculation process while ensuring the prediction accuracy. If a more complex state transition model (such as a linear change model) is used, the computational complexity will increase, which may otherwise reduce the real-time performance.

[0057] The error covariance is used to quantify the uncertainty of the predicted value , and the calculation formula is:

[0058]

[0059] where is the error covariance of the optimal estimate value at time k-1, and Q is the process noise covariance (reflecting the intensity of slow-changing interference such as machine tool vibration and temperature drift).

[0060] For the state transition matrix, in the present application,

[0061] Optionally, to improve the adaptability of the algorithm to the working condition changes, the Q value adopts a dynamic adjustment strategy: by monitoring the motor speed of the drill and riveter in real time (obtained through the motor encoder), when the motor speed is less than or equal to 1500 rpm (low-speed working condition, weak vibration interference), the Q value is set to 1x10-6; when the motor speed is greater than 1500 rpm (high-speed working condition, enhanced vibration interference), the Q value is automatically increased to 1.2x10-6 (increased by 20%), ensuring that the prediction of the error covariance can match the actual interference intensity and avoiding the decline of the filtering effect caused by the fixed Q value.

[0062] At the same time, the initial value of the error covariance P is set to 1x10-5 (determined according to the factory accuracy of the sensor, usually the square of the resolution of the sensor), and as the filtering iteration proceeds, the P value will gradually converge to a stable range (1x10-6-5x10-6), reflecting that the estimation of the algorithm to the data uncertainty gradually becomes accurate.

[0063] Optionally, the predicted value is corrected in combination with the observation value obtained through the moving average filtering to obtain the optimal estimation value at time k (i.e. the final output of the KF-MAF algorithm, accurate distance data), and the specific updating steps are as follows:

[0064] Kalman gain used to weigh the reliability of the predicted value and the observation value, and the calculation formula is:

[0065]

[0066] Among them, H is the distance observation matrix, which is set to [1, 0] (mapping the state variable directly to the observation variable, which conforms to the physical process of directly measuring the distance by the sensor); R is the observation noise covariance (reflecting the intensity of transient interference such as high-frequency electromagnetic noise and workpiece surface reflection).

[0067] Optionally, the difference Δz between the current observation value and the observation value at the previous time is calculated in real time, that is, If Δz is less than or equal to 0.01 mm (normal fluctuation range, determined according to the accuracy of the sensor), it is determined that there is no strong interference, and the R value is set to 5x10-6; if Δz is greater than 0.01 mm (determined as encountering strong reflection or transient electromagnetic pulse interference, such as the existence of metal reflection points on the workpiece surface, causing the reflection intensity of the laser beam to suddenly change and the measured distance to jump), the R value is temporarily increased to 5x10-5 (i.e. 10 times the original value). ​​

[0068] The sudden increase in the value of R automatically reduces the Kalman gain, and the KF prioritizes the predicted model output z_(k|k) to avoid interference with the calculation of the normal angle.

[0069] Combining the Kalman gain and the observation residual The predicted value is corrected, and the calculation formula is:

[0070]

[0071] This formula fine-tunes the predicted value based on the difference between the observed value and the predicted value, so that the optimal estimate retains the stability of the predicted value and incorporates real-time information from the observed value, balancing noise suppression and data real-time performance.

[0072] Updating the error covariance of the optimal estimate at time k Providing a basis for the next time prediction, the calculation formula is:

[0073]

[0074] where I is the identity matrix. This formula adjusts the error covariance through the Kalman gain If is small (the observed value is low in reliability), then is close to , retaining the error estimate of the previous time; if is large (the observed value is high in reliability), then is reduced, reflecting the reduced uncertainty of the current optimal estimate.

[0075] In some embodiments, for step S3, based on the accurate distance data processed by the KF-MAF algorithm, the deviation between the actuator feed direction and the workpiece surface normal is calculated through geometric relationship derivation and bilinear interpolation, and the actuator rotation compensation angle error is controlled to ensure that the drill bit axis coincides with the local surface normal of the workpiece. The specific technical solutions are as follows:

[0076] Projection length calculation, as shown in Figure 3 , A, B, C, and D represent laser displacement sensors, the red dot line represents the laser beam direction, and the red dot represents the laser spot projected onto the wallboard, , represent the projection of the laser beam in the tool axis direction, respectively, and their lengths are , , and , according to the calibration results of the laser displacement sensor, we have:

[0077]

[0078] The laser beam of the laser displacement sensor is not emitted along the direction of the tool axis (there is an included angle ), so the distance measured by the sensor (the straight-line distance from the end face of the presser foot to the surface of the workpiece) needs to be converted into the projected length in the direction of the tool axis (the height of the measurement point along the tool axis, which is the core parameter for calculating the normal deviation).

[0079] wherein, is the included angle between the laser beam and the tool axis (measured by a special calibration tool, the accuracy grade of the calibration tool is not less than 1 / 3 of the measurement accuracy grade of the sensor, to ensure that the measurement error of is less than or equal to 0.1°, to avoid angle error leading to calculation deviation of the projected length).

[0080] The normal deviation of the reference hole and the angle error calculation assumes that the initial plane of the four sensors is (the plane in which the end face of the presser foot is located), and the target plane of the workpiece surface at the hole to be machined is (the target plane needs to be perpendicular to the tool axis, i.e., the normal direction coincides with the tool axis), and the essence of the normal alignment is to rotate the presser foot body so that coincides with after rotating around the Y axis and around the X axis, and are the angle error amounts that need to be compensated, and the specific derivation process is as follows:

[0081] First, calculate the height difference. According to the projected lengths of the four sensors , , and , calculate and (the corresponding sensors A, B, C, and D are distributed in clockwise order around the presser foot), calculate the height difference EI in the Y axis direction and the height difference HJ in the X axis direction between the initial plane and the target plane

[0082] :

[0083]

[0084] The installation spacing is measured as follows: EG is the installation spacing of the four sensors in the Y-axis direction (the distance between the midpoint of the line connecting sensors A and B and the midpoint of the line connecting sensors C and D), and HF is the installation spacing of the four sensors in the X-axis direction (the distance between the midpoint of the line connecting sensors A and D and the midpoint of the line connecting sensors B and C). EG and HF are measured using a digital caliper (accuracy ±0.001mm). Each spacing is measured three times, and the average value is taken as the final value to avoid the influence of single measurement errors on angle calculation.

[0085] Due to the angle error of normal alignment and Typically small (≤0.5°), the angle error can be calculated using a small-angle approximation (sinθ≈θ, where θ is in radians) and geometric relationships.

[0086]

[0087]

[0088] Calculated and Afterwards, it needs to be compared with the preset angle threshold (±0.5°): if the angle error is within the threshold range, it means that the normal deviation is small and subsequent compensation can be performed; if it exceeds the threshold, the system will immediately issue an alarm signal (audio and visual alarm, and at the same time display the "angle error exceeds threshold" prompt on the human-machine interface), suspend the drilling and riveting process, and wait for the operator to check the fault (such as loose sensor installation, severe workpiece deformation, etc.) before re-executing the measurement and calculation steps.

[0089] Optionally, in one embodiment, when continuously machining multiple holes (e.g., hundreds of holes are required for aerospace panel machining), performing a complete "data acquisition-KF-MAF filtering-angle calculation" process for each hole would result in low machining efficiency (the alignment time for each hole is approximately 1 second, and 500 holes would require an additional 500 seconds of machining time). Therefore, this embodiment employs bilinear interpolation to calculate the angle error of the target hole based on the angle error of multiple reference holes, significantly improving machining efficiency. The specific implementation is as follows:

[0090] Within the workpiece machining area, select no fewer than four evenly distributed reference holes (preferably forming a rectangular area to ensure coverage of the entire machining range). For each reference hole, follow the complete process of "data acquisition - KF-MAF filtering - angle error calculation" to obtain the angle error of each reference hole (β1,γ1), (β2,γ2), (β3,γ3), (β4,γ4), and record the coordinates of each reference hole (x1,y1), (x2,y2), (x3,y3), (x4,y4) (obtained through the CNC system of the drilling and riveting machine, with a coordinate accuracy of ±0.005mm).

[0091] Bilinear interpolation principle and calculation steps: for the target hole position (x, y) located in the reference hole rectangular area, the angle error amount β(x, y) and γ(x, y) are calculated by two-step linear interpolation:

[0092] X-axis direction interpolation: in the Y=y1 (upper edge reference hole connecting line) and Y=y3 (lower edge reference hole connecting line) two directions, the intermediate angle error amounts βx1 (at Y=y1) and βx2 (at Y=y3) are calculated according to the X coordinate x of the target hole position:

[0093]

[0094]

[0095] wherein x1, x2 are the X coordinates of the two reference holes on the upper edge, β1, β2 are the β values of the corresponding reference holes; x3, x4 (equal to x1, x2, because the reference holes at Y=y3 are aligned with the reference holes at Y=y1 in the X direction) are the X coordinates of the two reference holes on the lower edge, β3, β4 are the β values of the corresponding reference holes.

[0096] Y-axis direction interpolation: according to the Y coordinate y of the target hole position, βx1 and βx2 are interpolated to obtain the angle error amount β(x, y) of the target hole position:

[0097]

[0098] wherein y1, y3 are the Y coordinates of the reference holes on the upper and lower edges respectively. Similarly, the angle error amount γ(x, y) of the target hole position can be calculated according to the above steps.

[0099] Optionally, after continuously using the bilinear interpolation method to process N hole positions (N≥10, adjusted according to the processing precision requirement, the smaller N is, the higher the precision requirement is, such as N=5; the larger N is, the lower the precision requirement is, such as N=15), the system automatically triggers a complete "data acquisition-KF-MAF filtering-angle error calculation" process, recalibrates 1-2 reference holes nearby, and updates the reference hole angle error amount.

[0100] By real-time monitoring of the workpiece clamping force (using a pressure sensor, accuracy ±1%) and the machine tool vibration amplitude (using an acceleration sensor, range ±5g, resolution 0.001g), if the clamping force fluctuation exceeds ±5% (deviation of the rated clamping force, such as 1000N, fluctuation exceeds ±50N), or the machine tool vibration acceleration exceeds 0.1g (judged as abnormal vibration), the system immediately pauses the processing, triggers dynamic calibration, and reacquires the reference hole data, to ensure that the interpolation calculation is based on the latest workpiece and machine tool state, avoiding error accumulation.

[0101] In some embodiments, the angle error compensation is achieved by controlling the rotation mechanism of the drill driver executor, the core of which is to drive the foot body to rotate around the Y axis and the X axis by a corresponding angle while keeping the tool center point (TCP) position unchanged, so as to ensure that the drill axis coincides with the normal of the workpiece surface.

[0102] Figure 4 An automatic drill driver executor normal alignment system 400 is shown. The device embodiment corresponds to the method embodiment shown in Figure 1 The device embodiment specifically comprises:

[0103] The acquisition module 401 is configured to acquire the original distance from the foot end face of the executor foot body to the surrounding plane of the hole to be processed through a sensor;

[0104] The denoising module 402 is configured to use the KF-MAF algorithm combining Kalman filtering and moving average filtering to dynamically suppress the noise of the original distance, so as to obtain accurate distance information;

[0105] The deviation calculation module 403 is configured to calculate the deviation between the end executor feeding direction and the normal of the surface at the hole to be processed according to the distance information;

[0106] The angle error acquisition module 404 is configured to acquire the angle error amount of the target hole position by using the bilinear interpolation method;

[0107] The normal alignment module 405 is configured to control the executor to rotate to compensate for the angle error, so that the drill axis coincides with the normal of the local surface of the workpiece while keeping the tool center point position unchanged, and the normal alignment is completed.

[0108] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An automatic drilling and riveting machine executor normal alignment method, characterized in that, The method comprises the following steps: Step S1, collecting the original distance from the presser foot end face of the presser foot body of the actuator to the surrounding plane of the hole to be machined through a sensor; Step S2, performing dynamic noise suppression on the original distance by using a KF-MAF algorithm combining Kalman filtering and moving average filtering to obtain accurate distance information; Step S3, calculating the deviation of the feeding direction of the end effector from the surface normal at the hole to be machined according to the distance information; Step S4, obtaining the angle error amount of the target hole position by using a bilinear interpolation method; Step S5, controlling the actuator to rotate to compensate for the angle error, so that the drill bit axis coincides with the local surface normal of the workpiece under the premise of keeping the tool center point position unchanged, and normal alignment is completed. Wherein, the angle error calculation includes calculating the rotation angle β around the Y axis and the rotation angle γ around the X axis respectively by using the height difference of four points measured by the four laser displacement sensors, so that the plane where the sensor is located coincides with the target plane of the workpiece, and the calculation formula is: wherein, is the projected length of the four laser displacement sensors in the direction of the tool axis, and EG and HF are the sensor mounting spacings; projected length The projected length is calculated by the following equation: wherein is the distance measured by the sensor, is the angle between the laser beam and the tool axis; The observation noise covariance R of the Kalman filter is dynamically adjusted according to data mutation, including, when detecting that the mutation amplitude of the current observation value Z k Compared with the observation value Z k -1 exceeds the preset threshold, it is determined that strong reflection or instantaneous electromagnetic pulse interference is encountered, and the observation noise covariance R is temporarily increased to more than 10 times the original value, so that the Kalman gain K is automatically reduced.

2. The normal alignment method of the actuator of the automatic drilling and riveting machine according to claim 1, characterized in that, The KF-MAF algorithm performs dynamic noise suppression on the original distance to obtain accurate distance information, which comprises: performing moving average on multiple frames of distance data within a preset sampling window to obtain smoothed observation values; and predicting and updating the observation values by using Kalman filtering to output filtered distance data, wherein the observation noise covariance R of the Kalman filter is dynamically adjusted according to the data mutation.

3. The normal alignment method of the actuator of the automatic drilling and riveting machine according to claim 1, characterized in that, The bilinear interpolation method is based on the angle errors of multiple reference holes on the workpiece to calculate the correction angle of the target hole position.

4. The normal alignment method of the actuator of the automatic drilling and riveting machine according to claim 2, characterized in that, The state prediction model of the Kalman filter uses a constant model, that is, the state transition matrix F is set to 1.

5. The method of normal alignment of the actuator of the automatic drilling and riveting machine according to claim 1, characterized in that, Comprise: When the hole to be machined is located in the rectangular region formed by the multiple reference holes, steps S1 to S3 are skipped, and the required rotation angle error amount of the hole position is calculated by using a bilinear interpolation formula according to the coordinates of the hole to be machined and the angle error amounts of the multiple reference holes.

6. The automatic drilling and riveting machine executor normal alignment method according to claim 5, characterized in that, Further comprising, After continuously using the bilinear interpolation method to machine N hole positions, or when the workpiece clamping force is detected to be abnormal or the machine tool vibration amplitude exceeds the standard, a complete set of steps S1 to S5 is automatically triggered once to recalibrate the reference hole data in the vicinity, so as to ensure the normal accuracy in long-term continuous operation, wherein N≥10.

7. An automatic drilling and riveting machine effector normal alignment system, characterized by, Comprise: The acquisition module is configured to collect the original distance from the presser foot end face of the presser foot body of the actuator to the surrounding plane of the hole to be machined through a sensor; The denoising module is configured to perform dynamic noise suppression on the original distance by using a KF-MAF algorithm combining Kalman filtering and moving average filtering to obtain accurate distance information; The deviation calculation module is configured to calculate the deviation of the feeding direction of the end effector from the surface normal at the hole to be machined according to the distance information; The angle error acquisition module is configured to obtain the angle error amount of the target hole position by using a bilinear interpolation method. The normal alignment module is used for controlling the executor to rotate to compensate for the angle error, to make the drill bit axis coincide with the local surface normal of the workpiece, and to complete the normal alignment under the premise of keeping the tool center point position unchanged. The angle error calculation includes calculating the rotation angle β around the Y axis and the rotation angle γ around the X axis by using the height differences of four points measured by the four laser displacement sensors, so that the plane where the sensor is located coincides with the target plane of the workpiece, and the calculation formula is: wherein, is the projected length of the four laser displacement sensors in the direction of the tool axis, and EG and HF are the sensor mounting spacings; projected length The projected length is calculated by the following equation: wherein is the distance measured by the sensor, is the angle between the laser beam and the tool axis; The observation noise covariance R of the Kalman filter is dynamically adjusted according to data mutation, including, when detecting that the mutation amplitude of the current observation value Z k Compared with the observation value Z k -1 exceeds the preset threshold, it is determined that strong reflection or instantaneous electromagnetic pulse interference is encountered, and the observation noise covariance R is temporarily increased to more than 10 times the original value, so that the Kalman gain K is automatically reduced.

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