Multi-axis linkage precision drilling control system and method of numerical control six-face drill

Abstract

This invention discloses a multi-axis linkage precision drilling control system and method for CNC six-sided drilling, belonging to the field of CNC six-sided drilling control technology, including the following steps: S1: self-calibration before machining; S2: adaptive control during machining, integrating multi-source sensor data to perform plate deformation compensation, multi-axis dynamic feedforward and cross-coupling control, drill bit runout suppression, and thermal error correction; S3: verification and self-learning after machining, detecting characteristic hole position deviations; This invention integrates multi-source sensor real-time perception, model prediction and cross-coupling multi-axis linkage control, biomimetic flexible clamping dynamic adjustment, active suppression of drill bit runout and self-repair of chipping sound emission, real-time identification and strategy avoidance of internal defects through deep learning of machining acoustic patterns, and online updating of thermal errors and self-learning of process parameters based on recursive least squares and Bayesian optimization, forming a complete closed-loop system of pre-machining self-calibration, in-machining adaptive compensation, and post-machining self-learning.

Description

Technical Field

[0001] This invention relates to the field of CNC six-sided drilling control technology, and more specifically, to a multi-axis linkage precision drilling control system and method for CNC six-sided drilling. Background Technology

[0002] In existing CNC six-sided drilling processes, a G-code-based CNC controller, combined with a servo drive and PLC architecture, is typically used to achieve multi-axis linkage drilling. However, due to factors such as differences in dynamic response of each axis, mechanical backlash, and frictional nonlinearity, the drilling center is prone to offset and diagonal hole misalignment during multi-axis coordinated motion, and the positional accuracy can often only be maintained within ±0.1 to 0.2 mm. At the same time, for thin plates or large-sized plates, the elastic deformation caused by rigid clamping rebounds after processing, resulting in a significant deviation of the actual hole position from the target. Conventional systems lack the ability to perceive and compensate for plate deformation, drill wear runout, and thermal elongation of the lead screw guide. In addition, wear or radial runout of the drill bit after long-term high-speed processing cannot be corrected online, and processing parameters (speed, feed, etc.) mostly rely on manual experience to set, making it difficult to balance efficiency and quality. This makes existing six-sided drilling machines significantly inadequate in terms of long-term continuous processing, small-batch multi-variety production, and adaptability to internal defects (such as voids and metal embedded parts).

[0003] Therefore, we have made improvements to this and proposed a multi-axis linkage precision drilling control system and method for CNC six-sided drilling. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, the purpose of this invention is to provide a multi-axis linkage precision drilling control system and method for CNC six-sided drilling.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0006] Includes the following steps:

[0007] S1: Pre-process self-calibration, through multi-axis mechanical backlash identification and heat transfer model initialization, establishes an error compensation benchmark;

[0008] S2: Adaptive control during machining, integrating multi-source sensor data to perform sheet deformation compensation, multi-axis dynamic feedforward and cross-coupling control, drill bit runout suppression, and thermal error correction.

[0009] S3: Post-processing verification and self-learning, detecting deviations in feature hole positions, using recursive least squares method to update error model parameters online, and adjusting process parameters based on Bayesian optimization;

[0010] The multi-axis dynamic feedforward and cross-coupling control adopts model predictive control, which solves the acceleration sequence that minimizes the weighted sum of the position tracking error and contour error of each axis in each control cycle; the thermal error correction adopts a temperature and thermal elongation linear regression model to compensate for the command position of each axis in real time.

[0011] Preferably, the multi-source sensing data specifically includes the following sensors:

[0012] Laser displacement sensor is used to measure the deformation deviation of the plate surface;

[0013] A piezoelectric vibration sensor, mounted at the front end of the spindle, is used to extract the radial runout characteristics of the drill bit;

[0014] Temperature sensor arrays are respectively arranged on the X-axis lead screw nut seat, Y-axis guide rail slider, Z-axis slide plate and spindle housing;

[0015] An acoustic emission sensor, located in the same position as a vibration sensor, is used to detect stress waves from chipped blades.

[0016] A miniature microphone array, mounted on the spindle housing, is used to collect drilling audio.

[0017] A flexible clamping unit integrating a thin-film pressure sensor and a magnetorheological elastomer chuck, with each chuck independently controlling the adsorption pressure and magnetic field strength.

[0018] Preferably, the pre-processing self-calibration includes:

[0019] Multi-axis mechanical backlash identification: Control each axis to move forward and backward at different speeds, record the deviation between the commanded position and the actual position, establish the relationship between speed and backlash function, and compensate by looking up a table based on the current feed speed during real-time machining;

[0020] Heat transfer model initialization: Perform standard cyclic motion under cold conditions, record the readings of each temperature sensor and the thermal expansion of each axis, and establish a thermal expansion prediction model using multiple linear regression as follows:

[0021] ;

[0022] in, For the first thermal elongation of shaft , For the first The measured values ​​of a temperature sensor, For the number of temperature sensors, , The coefficients are used for regression. The obtained coefficients are saved, and the predicted thermal elongation value is calculated every 10 seconds during processing based on the current temperature, and the command position is corrected in real time.

[0023] Preferably, it also includes real-time compensation for sheet deformation and dynamic control of biomimetic flexible clamping:

[0024] Plate deformation compensation: Before drilling, a laser displacement sensor scans the plate surface along the machining path to obtain the deformation deviation between the actual height and the theoretical plane. The deviation is superimposed on the Z-axis command during drilling;

[0025] Bionic flexible clamping: Each magnetorheological elastomer suction cup is embedded with a thin-film pressure sensor. The controller independently adjusts the adsorption pressure and magnetic field strength of each suction cup according to the vibration spectrum of the plate and the current drilling position.

[0026] Preferably, the multi-axis dynamic feedforward and cross-coupling control adopts a strategy combining model predictive control and cross-coupling control, specifically including:

[0027] Establish a multi-axis discrete state-space model; solve the following constrained quadratic programming problem in each control cycle:

[0028] ;

[0029] in, This is a sequence of acceleration commands for each axis. For discrete time step index, To predict the time domain, For the desired position, For actual location, The contour error vector, , , This is the weight matrix. For prediction in the time domain;

[0030] Contour error is calculated in real time by a cross-coupled controller: ,in, Contour error vector For each axis tracking error vector, Let be the unit tangent vector of the desired trajectory;

[0031] The optimized first control input is applied to the servo drives of each axis to achieve coordinated control that minimizes position error and contour error.

[0032] Preferably, it also includes active suppression of drill bit yaw and instantaneous detection and self-repair of chipping:

[0033] Drill bit runout suppression: Vibration reference amplitude A0A0 is acquired during spindle idling; fundamental frequency amplitude is calculated in real time during drilling. ,like If necessary, reduce the spindle speed, increase the feed per revolution, and compensate for the reverse offset of the drilling position according to the yaw direction.

[0034] Self-repair of chipped blades: Short-time energy is calculated after the acoustic emission sensor signal is bandpass filtered. ,like If the threshold is exceeded and the duration is less than 1ms, the chipping is determined. Within 0.5ms, the following actions are performed: freeze the motion of each axis, use the accelerometer array to locate the chipping position, offset it in the opposite direction by 0.15mm, if the chipping size is greater than 0.05mm, activate the micro-pulse laser module to perform local reshaping, and after resuming processing, reduce the feed per revolution by 30% and increase the spindle speed by 15%.

[0035] Preferably, it also includes a real-time acoustic analysis step, which is executed in parallel with multi-axis control: a miniature microphone array collects drilling audio, extracts Mel frequency cepstral coefficient features, inputs them into a pre-trained one-dimensional convolutional neural network, and outputs defect category probabilities, including voids, scars, metal embedded parts, delamination, and normal.

[0036] Preferably, in the thermal error correction, the recursive least squares method with a forgetting factor is used to update the thermal model parameters online, and the update formula is:

[0037] ;

[0038] ;

[0039] in, For the first The model parameter vector at time step, For discrete time step index, For the regression vector, For temperature observation vector, This is the actual measured value of thermal elongation. Let covariance matrix be the variance matrix. The forgetting factor (values ​​range from 0.98 to 0.995). For the first The actual thermal elongation measurement value of the step;

[0040] By recursively updating, the thermal model adapts to environmental changes, ensuring that thermal compensation errors remain within acceptable limits during long-term processing.

[0041] Preferably, the self-optimization of process parameters in the post-processing verification and self-learning adopts the Bayesian optimization method:

[0042] Using a Gaussian process as a surrogate model, the covariance function adopts the Matérn kernel; the parameter combination for the next experiment is selected by improving the acquisition function through expectation; after each processing, the real quality index is added to the training set, the Gaussian process is updated, and suggested parameters are output for the next batch of processing;

[0043] The quality index is defined as the weighted sum of positional deviation, hole wall roughness, and machining cycle; Bayesian optimization continues until the parameters converge.

[0044] A multi-axis linkage precision drilling control system for a CNC six-sided drill includes the following modules:

[0045] The multi-source sensing unit includes a laser displacement sensor, a piezoelectric vibration sensor, a temperature sensor array, an acoustic emission sensor, a miniature microphone array, and a magnetorheological elastomer chuck array integrating a thin-film pressure sensor.

[0046] The execution unit includes a servo driver, a linear motor or ball screw, a high-speed electric spindle, a pneumatic clamp, and a micro-pulse laser module.

[0047] The control unit adopts an ARM+FPGA dual-core architecture motion controller based on EtherCAT bus, with a control cycle of ≤250μs. It has an embedded model predictive control algorithm library, recursive least squares online identification module, and a one-dimensional convolutional neural network accelerator.

[0048] The decision-making unit is an embedded industrial computer that runs a Bayesian optimization engine, a heat transfer model library, and a processing acoustic defect classification model.

[0049] The control unit is interconnected with the sensing unit, execution unit and decision-making unit to form a closed-loop control system that performs self-calibration before processing, adaptive compensation during processing and self-learning after processing.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] By integrating multi-source real-time sensing, model prediction and cross-coupled multi-axis linkage control, biomimetic flexible clamping dynamic adjustment, active suppression of drill bit runout and self-repair of chipping sound emission, real-time identification and strategy avoidance of internal defects through deep learning of machining acoustic patterns, and online updating of thermal errors and self-learning of process parameters based on recursive least squares and Bayesian optimization, a complete closed-loop system of pre-machining self-calibration, in-machining adaptive compensation, and post-machining self-learning is formed. This improves drilling position accuracy to within ±0.04mm, multi-axis synchronization error to below 0.02mm, thin plate perpendicularity error to no more than 0.03°, thermal drift of less than 0.03mm during long-term continuous machining, and ensures that more than 95% of the hole diameters are qualified even after drill bit chipping. The accuracy rate of identifying internal defects such as voids and metal embedded parts exceeds 98%. At the same time, it eliminates the need for frequent manual production changes and machine adjustments, significantly improving the machining accuracy, efficiency, reliability, and intelligence level of the six-sided drill. Attached Figure Description

[0052] Figure 1 A flowchart of a multi-axis linkage precision drilling control method for a CNC six-sided drill provided in this application. Detailed Implementation

[0053] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0054] A multi-axis linkage precision drilling control method for CNC six-sided drilling includes the following steps:

[0055] S1: Pre-process self-calibration, through multi-axis mechanical backlash identification and heat transfer model initialization, establishes an error compensation benchmark;

[0056] S2: Adaptive control during machining, integrating multi-source sensor data to perform sheet deformation compensation, multi-axis dynamic feedforward and cross-coupling control, drill bit runout suppression, and thermal error correction.

[0057] S3: Post-processing verification and self-learning, detecting deviations in feature hole positions, using recursive least squares method to update error model parameters online, and adjusting process parameters based on Bayesian optimization;

[0058] Multi-axis dynamic feedforward and cross-coupling control employs model predictive control, solving for the acceleration sequence that minimizes the weighted sum of position tracking error and contour error for each axis within each control cycle; thermal error correction uses a linear regression model of temperature and thermal elongation to compensate for the commanded position of each axis in real time.

[0059] Furthermore, multi-source sensing data specifically includes the following sensors:

[0060] Laser displacement sensors are installed at both ends of the gantry beam to measure the surface deformation deviation of the plates.

[0061] A piezoelectric vibration sensor with a sampling rate ≥10kHz is installed at the front end of the spindle to extract the radial runout characteristics of the drill bit.

[0062] Temperature sensor array, including PT1000 thermistors, is respectively arranged in the X-axis lead screw nut seat, Y-axis guide rail slider, Z-axis slide plate and spindle housing;

[0063] An acoustic emission sensor, with a frequency response range of 100kHz to 1MHz, is co-located with a vibration sensor and is used to detect chipping stress waves.

[0064] A miniature microphone array, consisting of three microphones arranged at 120°, is mounted on the spindle housing and used to collect drilling audio.

[0065] A flexible clamping unit integrating a thin-film pressure sensor and a magnetorheological elastomer chuck, with each chuck independently controlling the adsorption pressure and magnetic field strength.

[0066] Furthermore, pre-processing self-calibration includes:

[0067] Multi-axis mechanical backlash identification: Control each axis to move forward and backward at different speeds, record the deviation between the commanded position and the actual position, establish the relationship between speed and backlash function, and compensate by looking up a table based on the current feed speed during real-time machining;

[0068] Heat transfer model initialization: Perform standard cyclic motion under cold conditions, record the readings of each temperature sensor and the thermal expansion of each axis, and establish a thermal expansion prediction model using multiple linear regression as follows:

[0069] ;

[0070] in, For the first thermal elongation of shaft , For the first The measured values ​​of a temperature sensor, For the number of temperature sensors, , The coefficients are used for regression. The obtained coefficients are saved, and the predicted thermal elongation value is calculated every 10 seconds during processing based on the current temperature, and the command position is corrected in real time.

[0071] Furthermore, it also includes real-time compensation for sheet deformation and dynamic control of biomimetic flexible clamping:

[0072] Plate deformation compensation: Before drilling, a laser displacement sensor scans the plate surface along the machining path to obtain the deformation deviation between the actual height and the theoretical plane. During drilling, the Z-axis command is superimposed on this deviation; if the absolute value of the deformation gradient exceeds 0.05mm / mm, the rapid traverse speed is automatically reduced.

[0073] Bionic flexible clamping: Each magnetorheological elastomer chuck is embedded with a thin-film pressure sensor. The controller independently adjusts the adsorption pressure and magnetic field strength of each chuck according to the vibration spectrum of the plate and the current drilling position. When the main vibration frequency of the plate is detected to be close to the spindle speed frequency, the magnetic field of the chuck at the diagonal position is released alternately to break the resonance.

[0074] Furthermore, the multi-axis dynamic feedforward and cross-coupling control employs a strategy combining model predictive control and cross-coupling control, specifically including:

[0075] Establish a multi-axis discrete state-space model; solve the following constrained quadratic programming problem in each control cycle:

[0076] ;

[0077] in, This is a sequence of acceleration commands for each axis. For discrete time step index, To predict the time domain, For the desired position, For actual location, The contour error vector, , , This is the weight matrix. For prediction in the time domain;

[0078] Contour error is calculated in real time by a cross-coupled controller: ,in, Contour error vector For each axis tracking error vector, Let be the unit tangent vector of the desired trajectory;

[0079] The optimized first control input is applied to the servo drives of each axis to achieve coordinated control that minimizes position error and contour error.

[0080] Furthermore, it also includes active suppression of drill bit yaw and instantaneous detection and self-repair of chipping edges:

[0081] Drill bit runout suppression: Vibration reference amplitude A0A0 is acquired during spindle idling; fundamental frequency amplitude is calculated in real time during drilling. ,like If necessary, reduce the spindle speed, increase the feed per revolution, and compensate for the reverse offset of the drilling position according to the yaw direction, with the compensation amount not exceeding 0.02mm.

[0082] Self-repair of chipped blades: Short-time energy is calculated after the acoustic emission sensor signal is bandpass filtered. ,like If the threshold is exceeded and the duration is less than 1ms, the chipping is determined. Within 0.5ms, the following actions are performed: freeze the motion of each axis, use the accelerometer array to locate the chipping position, offset it in the opposite direction by 0.15mm, if the chipping size is greater than 0.05mm, activate the micro-pulse laser module to perform local reshaping, and after resuming processing, reduce the feed per revolution by 30% and increase the spindle speed by 15%.

[0083] Furthermore, it also includes a real-time analysis step of the machining acoustic signature, executed in parallel with multi-axis control:

[0084] A miniature microphone array collects drilling audio, extracts Mel frequency cepstral coefficient features, inputs them into a pre-trained one-dimensional convolutional neural network, and outputs defect category probabilities, including voids, scars, metal embedded parts, delamination, and normal.

[0085] If a cavity is detected, reduce the feed rate by 50% and increase the spindle speed by 20%. If the estimated cavity diameter is greater than 3mm, perform oblique offset within the same hole to form a gourd-shaped hole.

[0086] If a metal embedded part is detected, immediately stop feeding and trigger an alarm;

[0087] If the detection indicates layering, a chamfering path will be automatically executed after drilling to close the layered edges.

[0088] Furthermore, in the thermal error correction, the recursive least squares method with a forgetting factor is used to update the thermal model parameters online. The update formula is as follows:

[0089] ;

[0090] ;

[0091] in, For the first The model parameter vector at time step, For discrete time step index, For the regression vector, For temperature observation vector, This is the actual measured value of thermal elongation. Let covariance matrix be the variance matrix. The forgetting factor (values ​​range from 0.98 to 0.995). For the first The actual thermal elongation measurement value of the step;

[0092] By recursively updating, the thermal model adapts to environmental changes, ensuring that thermal compensation errors remain within acceptable limits during long-term processing.

[0093] Furthermore, the self-optimization of process parameters in post-processing verification and self-learning adopts the Bayesian optimization method:

[0094] Using a Gaussian process as a surrogate model, the covariance function adopts the Matérn kernel; the parameter combination for the next experiment is selected by improving the acquisition function through expectation; after each processing, the real quality index is added to the training set, the Gaussian process is updated, and suggested parameters are output for the next batch of processing;

[0095] The quality index is defined as the weighted sum of positional deviation, hole wall roughness, and machining cycle; Bayesian optimization continues until the parameters converge.

[0096] A multi-axis linkage precision drilling control system for a CNC six-sided drill includes the following modules:

[0097] The multi-source sensing unit includes a laser displacement sensor, a piezoelectric vibration sensor, a temperature sensor array, an acoustic emission sensor, a miniature microphone array, and a magnetorheological elastomer chuck array integrating a thin-film pressure sensor.

[0098] The execution unit includes a servo driver, a linear motor or ball screw, a high-speed electric spindle, a pneumatic clamp, and a micro-pulse laser module.

[0099] The control unit adopts an ARM+FPGA dual-core architecture motion controller based on EtherCAT bus, with a control cycle of ≤250μs. It has an embedded model predictive control algorithm library, recursive least squares online identification module, and a one-dimensional convolutional neural network accelerator.

[0100] The decision-making unit is an embedded industrial computer that runs a Bayesian optimization engine, a heat transfer model library, and a processing acoustic defect classification model.

[0101] The control unit is interconnected with the sensing unit, execution unit, and decision-making unit to form a closed-loop control system that performs self-calibration before processing, adaptive compensation during processing, and self-learning after processing.

[0102] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0103] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

Claims

1. A multi-axis linkage precision drilling control method for a CNC six-sided drill, characterized in that, Includes the following steps: S1: Pre-process self-calibration, through multi-axis mechanical backlash identification and heat transfer model initialization, establishes an error compensation benchmark; S2: Adaptive control during machining, integrating multi-source sensor data to perform sheet deformation compensation, multi-axis dynamic feedforward and cross-coupling control, drill bit runout suppression, and thermal error correction. S3: Post-processing verification and self-learning, detecting deviations in feature hole positions, using recursive least squares method to update error model parameters online, and adjusting process parameters based on Bayesian optimization; The multi-axis dynamic feedforward and cross-coupling control adopts model predictive control, which solves the acceleration sequence that minimizes the weighted sum of the position tracking error and contour error of each axis in each control cycle; the thermal error correction adopts a temperature and thermal elongation linear regression model to compensate for the command position of each axis in real time.

2. The multi-axis linkage precision drilling control method for a CNC six-sided drill according to claim 1, characterized in that, The multi-source sensing data specifically includes the following sensors: Laser displacement sensor is used to measure the deformation deviation of the plate surface; A piezoelectric vibration sensor, mounted at the front end of the spindle, is used to extract the radial runout characteristics of the drill bit; Temperature sensor arrays are respectively arranged on the X-axis lead screw nut seat, Y-axis guide rail slider, Z-axis slide plate and spindle housing; An acoustic emission sensor, located in the same position as a vibration sensor, is used to detect stress waves from chipped blades. A miniature microphone array, mounted on the spindle housing, is used to collect drilling audio. A flexible clamping unit integrating a thin-film pressure sensor and a magnetorheological elastomer chuck, with each chuck independently controlling the adsorption pressure and magnetic field strength.

3. The multi-axis linkage precision drilling control method for a CNC six-sided drill according to claim 1, characterized in that, The pre-processing self-calibration includes: Multi-axis mechanical backlash identification: Control each axis to move forward and backward at different speeds, record the deviation between the commanded position and the actual position, establish the relationship between speed and backlash function, and compensate by looking up a table based on the current feed speed during real-time machining; Heat transfer model initialization: Perform standard cyclic motion under cold conditions, record the readings of each temperature sensor and the thermal expansion of each axis, and establish a thermal expansion prediction model using multiple linear regression as follows: ； in, For the first Thermal elongation of shaft , For the first The measured values ​​of a temperature sensor, For the number of temperature sensors, , The coefficients are used for regression. The obtained coefficients are saved, and the predicted thermal elongation value is calculated every 10 seconds during processing based on the current temperature, and the command position is corrected in real time.

4. The multi-axis linkage precision drilling control method for a CNC six-sided drill according to claim 1, characterized in that, It also includes real-time compensation for sheet deformation and dynamic control of biomimetic flexible clamping: Plate deformation compensation: Before drilling, a laser displacement sensor scans the plate surface along the machining path to obtain the deformation deviation between the actual height and the theoretical plane. The deviation is superimposed on the Z-axis command during drilling; Bionic flexible clamping: Each magnetorheological elastomer suction cup is embedded with a thin-film pressure sensor. The controller independently adjusts the adsorption pressure and magnetic field strength of each suction cup according to the vibration spectrum of the plate and the current drilling position.

5. The multi-axis linkage precision drilling control method for a CNC six-sided drill according to claim 1, characterized in that, The multi-axis dynamic feedforward and cross-coupling control adopts a strategy that combines model predictive control and cross-coupling control, specifically including: Establish a multi-axis discrete state-space model; solve the following constrained quadratic programming problem in each control cycle: ； in, This is a sequence of acceleration commands for each axis. For discrete time step index, To predict the time domain, For the desired position, For actual location, The contour error vector, , , This is the weight matrix. For prediction in the time domain; Contour error is calculated in real time by a cross-coupled controller: ,in, Contour error vector For each axis tracking error vector, Let be the unit tangent vector of the desired trajectory; The optimized first control input is applied to the servo drives of each axis to achieve coordinated control that minimizes position error and contour error.

6. The multi-axis linkage precision drilling control method for a CNC six-sided drill according to claim 1, characterized in that, It also includes active suppression of drill bit yaw and instantaneous detection and self-repair of chipping: Drill bit runout suppression: The vibration reference amplitude A0A0 is collected when the spindle is idling; Real-time calculation of fundamental frequency amplitude during drilling ,like If necessary, reduce the spindle speed, increase the feed per revolution, and compensate for the reverse offset of the drilling position according to the yaw direction. Self-repair of chipped blades: Short-time energy is calculated after the acoustic emission sensor signal is bandpass filtered. ,like If the threshold is exceeded and the duration is less than 1ms, the chipping is determined. Within 0.5ms, the following actions are performed: freeze the motion of each axis, use the accelerometer array to locate the chipping position, offset it in the opposite direction by 0.15mm, if the chipping size is greater than 0.05mm, activate the micro-pulse laser module to perform local reshaping, and after resuming processing, reduce the feed per revolution by 30% and increase the spindle speed by 15%.

7. The multi-axis linkage precision drilling control method for a CNC six-sided drill according to claim 1, characterized in that, It also includes a real-time acoustic analysis step, which is executed in parallel with multi-axis control: a miniature microphone array collects drilling audio, extracts Mel frequency cepstral coefficient features, inputs them into a pre-trained one-dimensional convolutional neural network, and outputs defect category probabilities, including voids, scars, metal embedded parts, delamination, and normal.

8. The multi-axis linkage precision drilling control method for a CNC six-sided drill according to claim 1, characterized in that, In the thermal error correction, the recursive least squares method with a forgetting factor is used to update the thermal model parameters online. The update formula is as follows: ； ； in, For the first The model parameter vector at time step, For discrete time step index, For the regression vector, For temperature observation vector, This is the actual measured value of thermal elongation. Let covariance matrix be the variance matrix. The forgetting factor (values ​​range from 0.98 to 0.995). For the first The actual thermal elongation measurement value of the step; By recursively updating, the thermal model adapts to environmental changes, ensuring that thermal compensation errors remain within acceptable limits during long-term processing.

9. The multi-axis linkage precision drilling control method for a CNC six-sided drill according to claim 1, characterized in that, The self-optimization of process parameters in the post-processing verification and self-learning process adopts the Bayesian optimization method. A Gaussian process was used as the surrogate model, and the covariance function adopted the Matérn kernel; The parameter combination for the next experiment is selected by improving the acquisition function; after each processing, the actual quality index is added to the training set, the Gaussian process is updated, and suggested parameters are output for the next batch of processing. The quality index is defined as the weighted sum of positional deviation, hole wall roughness, and machining cycle; Bayesian optimization continues until the parameters converge.

10. A multi-axis linkage precision drilling control system for a CNC six-sided drill, comprising a multi-axis linkage precision drilling control method for a CNC six-sided drill according to any one of claims 1-9, characterized in that, Includes the following modules: The multi-source sensing unit includes a laser displacement sensor, a piezoelectric vibration sensor, a temperature sensor array, an acoustic emission sensor, a miniature microphone array, and a magnetorheological elastomer chuck array integrating a thin-film pressure sensor. The execution unit includes a servo driver, a linear motor or ball screw, a high-speed electric spindle, a pneumatic clamp, and a micro-pulse laser module. The control unit adopts an ARM+FPGA dual-core architecture motion controller based on EtherCAT bus, with a control cycle of ≤250μs. It has an embedded model predictive control algorithm library, recursive least squares online identification module, and a one-dimensional convolutional neural network accelerator. The decision-making unit is an embedded industrial computer that runs a Bayesian optimization engine, a heat transfer model library, and a processing acoustic defect classification model. The control unit is interconnected with the sensing unit, execution unit and decision-making unit to form a closed-loop control system that performs self-calibration before processing, adaptive compensation during processing and self-learning after processing.

Images