A method and device for visual multi-degree-of-freedom positioning and clamping of a circular workpiece

Abstract

The application discloses a kind of circular workpiece visual multi-degree-of-freedom positioning clamping control method and device, belong to advanced control field, including the following steps: based on the visual positioning data of acquired circular workpiece, through multi-degree-of-freedom motion control algorithm, the positioning compensation quantity is solved, according to positioning compensation quantity, drive multi-degree-of-freedom positioning mechanism adjustment workpiece posture, make workpiece center and rotary table center coincide;Subsequently trigger clamp compression and real-time monitoring pressure to ensure clamping state, in clamping state, combined with machine tool geometric error and dynamic thermal drift information constructs multi-dimensional error comprehensive prediction model, calculates expected deviation value and carries out feedforward compensation to machining coordinate system;During indexing action, start non-contact monitoring simultaneously and obtain position data, based on the residual of position data and expected deviation value carries out asynchronous fine adjustment correction, and accordingly executes continuous processing, improve the accuracy of circular workpiece visual multi-degree-of-freedom positioning clamping control.

Description

Technical Field

[0001] This invention relates to the field of advanced control technology, and in particular to a method and device for visual multi-degree-of-freedom positioning and clamping control of circular workpieces. Background Technology

[0002] Round workpieces are widely used in machinery manufacturing, automotive parts, aerospace, rail transportation, and general equipment manufacturing. Typical products include flanges, impellers, ring parts, disc parts, and various rotating parts. These workpieces typically have high requirements for concentricity, positional accuracy, and contour precision, and their production often requires multiple processes such as drilling, tapping, grooving, milling, chamfering, and finishing.

[0003] In existing production methods, for round workpieces that require multiple processing steps, multiple machines are typically used for processing. The workpiece first undergoes preliminary processing on the first machine, and then is transferred by manual labor or a robotic arm to the second machine for subsequent processing.

[0004] To improve the automation level of circular workpiece processing, industrial vision positioning technology is gradually being introduced into existing technologies. This involves acquiring workpiece image information through industrial cameras, identifying the workpiece's center position, outer contour features, and orientation angles, and using the detection results to guide workpiece positioning or machining trajectory adjustment, thereby improving workpiece clamping accuracy and machining precision. Simultaneously, employing rotary tables or multi-station fixture structures allows the workpiece to complete multiple processing steps sequentially between different stations, reducing the number of devices and workpiece transfers.

[0005] For example, the Chinese invention patent application CN121918491A discloses a gear multi-axis collaborative compensation machining method and device based on screw error mapping, which includes: collecting measured data and calculating dynamic transmission error; establishing a gear contact analysis model and a dynamic model, and inverting the equivalent tooth profile error, including tooth profile / pitch error, under manufacturing feasible constraints; establishing a machining kinematic chain model consistent with the actual gear grinding machine structure based on screw theory, characterizing various machine tool errors as screw / displacement parameter disturbances, constructing a linear mapping relationship, and establishing an error propagation model; calculating the sensitivity matrix, identifying the key machine tool error sources and corresponding motion axes that have the most significant impact on tooth profile error; calculating the synchronous correction amount of each key axis and executing it in real time during machining to achieve active compensation.

[0006] For example, the adaptive optimization method for machining error compensation of a five-axis CNC machine tool published in Chinese Invention Patent Application No. CN120540201A includes: (1) using a sensor network arranged on the key components of the machine tool to collect data on the geometric error, thermal error, dynamic error and coupling error of the five-axis machine tool in real time; (2) by constructing a five-axis linkage nonlinear error compensation model, using the reverse error propagation algorithm to trace the source of the error generated during the machining process, and dynamically optimizing the compensation amount according to the source of the error; (3) establishing a causal relationship and probability distribution model of each error source based on a Bayesian network, updating the joint distribution of the error sources using real-time sensor data, predicting the comprehensive error through Bayesian inference, and adjusting the motion parameters of each axis.

[0007] The above-mentioned technology has at least the following technical problems:

[0008] In existing technologies, for circular workpiece clamping systems employing a three-station indexing structure for continuous machining, the workpiece needs to be periodically indexed between different stations via a rotary mechanism. Although the rotary mechanism has high positioning accuracy, it inevitably suffers from repetitive positioning errors, transmission backlash errors, and cumulative rotational errors during long-term operation. When the workpiece is continuously indexed between multiple stations, slight deviations can easily occur between the actual machining position and the theoretical machining position at each station. Simultaneously, differences in load conditions at different stations and cutting vibrations can cause changes in the workpiece's posture after indexing, gradually causing the machining center to deviate from its initial calibration position. This ultimately results in drift between the machining center and the vision positioning center, affecting the consistency and machining accuracy of multi-stage continuous machining, and leading to low accuracy in the visual multi-degree-of-freedom positioning and clamping control of circular workpieces. Summary of the Invention

[0009] To address the low accuracy of visual multi-degree-of-freedom positioning and clamping control for circular workpieces in existing technologies, this invention provides a method and apparatus for visual multi-degree-of-freedom positioning and clamping control of circular workpieces. The technical solution is as follows:

[0010] On the one hand, a method for visual multi-degree-of-freedom positioning and clamping control of circular workpieces is provided, the method comprising:

[0011] Step 1: Acquire visual positioning data of the circular workpiece, including the center coordinate deviation and rotation angle deviation of the workpiece at the clamping station; Step 2: Based on the visual positioning data, calculate the positioning compensation amount using a multi-degree-of-freedom motion control algorithm, which includes at least translational and rotational degrees of freedom in the plane; Step 3: Drive the multi-degree-of-freedom positioning mechanism to adjust the workpiece posture according to the positioning compensation amount, so that the center of the circular workpiece coincides with the rotation center of the rotary table; Step 4: After the posture adjustment is completed, trigger the fixture to perform the clamping action and monitor the clamping force in real time; Step 5: In the clamping state, construct a multi-dimensional error comprehensive prediction model by combining machine tool geometric error and dynamic thermal drift information, calculate the expected deviation value, and perform feedforward compensation on the machining coordinate system based on the expected deviation value; Step 6: During the rotation action of the rotary table, activate the non-contact monitoring device to acquire position data, calculate the residual based on the position data and the expected deviation value, perform asynchronous fine-tuning correction based on the residual, and perform continuous machining based on the corrected coordinate system.

[0012] On the other hand, a visual multi-degree-of-freedom positioning and clamping control device for circular workpieces is provided, the device comprising:

[0013] The system includes a visual positioning data acquisition module for acquiring visual positioning data of the circular workpiece, including the center coordinate deviation and rotation angle deviation of the workpiece at the clamping station; a positioning compensation calculation module for calculating the positioning compensation based on the visual positioning data using a multi-degree-of-freedom motion control algorithm, where the multi-degree-of-freedom motion control includes at least translational and rotational degrees of freedom in the plane; a posture adjustment control module for driving the multi-degree-of-freedom positioning mechanism to adjust the workpiece posture according to the positioning compensation, ensuring that the center of the circular workpiece coincides with the rotation center of the rotary table; and a clamping control module for... After the state adjustment is completed, the fixture is triggered to perform a clamping action, and the clamping force is monitored in real time. The feedforward compensation module is used to construct a multi-dimensional error comprehensive prediction model by combining machine tool geometric error and dynamic thermal drift information in the clamping state, calculate the expected deviation value, and perform feedforward compensation on the machining coordinate system based on the expected deviation value. The asynchronous fine-tuning correction module is used to activate a non-contact monitoring device to acquire position data during the rotation action of the rotary table, calculate the residual based on the position data and the expected deviation value, perform asynchronous fine-tuning correction based on the residual, and perform continuous machining based on the corrected coordinate system.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0015] 1. This invention provides a visual multi-degree-of-freedom positioning and clamping control method for circular workpieces. By acquiring visual positioning data of the circular workpiece and combining it with a multi-degree-of-freedom motion control algorithm to calculate compensation, it achieves high-precision automatic alignment and attitude adjustment between the workpiece center and the rotary table rotation center. Compared with existing technologies that rely on manual alignment or simple mechanical limits, resulting in low positioning accuracy and long processing time, this method significantly improves clamping efficiency and initial positioning accuracy. Then, by constructing a multi-dimensional error comprehensive prediction model and combining machine tool geometric errors and dynamic thermal drift information, it achieves feedforward compensation for the machining coordinate system. Compared with existing technologies that only perform static geometric compensation and ignore the problem of machining accuracy drifting over time due to thermal deformation, this method effectively ensures the dimensional stability of long-term multi-degree-of-freedom positioning and clamping control. Finally, by initiating non-contact monitoring to acquire position data during the indexing action and performing asynchronous fine-tuning correction based on the residual, it achieves the overlap of measurement and machining time windows and uninterrupted continuous machining. Compared with existing technologies that require physical contact measurement while the machine is stopped, resulting in machining cycle interruptions, this method greatly improves the dynamic response speed and overall machining efficiency of visual multi-degree-of-freedom positioning and clamping control.

[0016] 2. This invention acquires position data synchronously during dynamic indexing by using optical image acquisition equipment or a high-resolution circular grating ruler to obtain position data within the time window of the rotary table's rotational motion. Compared to the existing serial working mode that requires waiting for the rotary table to be fully locked and stationary before measurement can begin, this invention fully utilizes the rotary table's motion time and eliminates measurement waiting time. Then, during the rotary table's deceleration and locking process, position calculation is completed, causing the measurement time window to overlap with the indexing time window, thus achieving parallel data processing. Compared to the existing technology where measurement actions occupy the machine tool's time, this invention further reduces the auxiliary time for multi-degree-of-freedom positioning and clamping. Finally, subsequent corrections are made directly based on the calculation results, achieving seamless integration of indexing and measurement. Compared to the positioning and clamping control response delay caused by measurement lag in the existing technology, this invention significantly improves the production cycle time for multi-station continuous processing.

[0017] 3. By real-time monitoring of the deviation confidence level, cumulative number of processed parts, and cutting load fluctuations of the multi-dimensional error comprehensive prediction model, dynamic evaluation of the reliability of the current vision multi-degree-of-freedom positioning and clamping control state is achieved. Compared with the rigid mode of forced calibration using fixed time intervals or fixed processing quantities in existing technologies, unnecessary shutdown calibration is avoided when the positioning and clamping state is stable. Then, the spindle probe is triggered to perform physical calibration only when the deviation confidence level is lower than the preset deviation confidence level threshold, the cumulative number of processed parts reaches the set quantity, or the detected change in cutting load exceeds the preset change in cutting load threshold. This realizes an intelligent control strategy of on-demand calibration. Compared with the problem of existing technologies being unable to detect compensation failure caused by sudden load changes, this ensures the reliability of positioning and clamping under drastic changes in working conditions. Finally, physical detection is skipped in other states to maintain the prediction model compensation, maximizing continuous processing time. Compared with the efficiency loss caused by frequent probe contact measurement in existing technologies, this maximizes processing efficiency while ensuring positioning and clamping accuracy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a visual multi-degree-of-freedom positioning and clamping control method for a circular workpiece provided in this application embodiment;

[0020] Figure 2 A flowchart of the clamping control provided in the embodiments of this application;

[0021] Figure 3 A flowchart for asynchronous fine-tuning correction provided in the embodiments of this application;

[0022] Figure 4 A flowchart for physical calibration determination provided in the embodiments of this application. Detailed Implementation

[0023] Embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present disclosure are shown in the drawings, it should be understood that embodiments of the present disclosure may be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure.

[0024] It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure. In the description of the embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "this embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects.

[0025] Five-axis machining centers or multi-axis linkage machining centers are integrated with the pressing fixture through mechanical mounting interfaces, coordinate reference interfaces, and CNC control interfaces. Specifically, the pressing three-station fixture is rigidly fixed to the rotary table of the machine tool, i.e., the B or C axis, via T-bolts or quick-change interfaces. The fixture base is equipped with locating pin holes and mounting threaded holes to achieve precise positioning and reliable connection with the rotary table. The cylinder or servo driver of the fixture is connected to the machine tool I / O module or PLC controller via air pipes or cables to achieve control and feedback of clamping force and working status.

[0026] The pressure-type clamp includes a clamping body base, a workpiece positioning base, and a clamping execution unit. The clamping execution unit employs three sets of pressure assemblies in a triangular layout. Each set of pressure assemblies includes a drive mechanism for adjustable clamping force and a pressure head assembly with a floating buffer structure, used to apply controllable clamping force to the workpiece to achieve positioning and clamping. During workpiece installation, the workpiece is placed at any station by a robot or manually, and initial geometric positioning is achieved by the positioning base.

[0027] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0028] like Figure 1 The diagram shown is a flowchart of a visual multi-degree-of-freedom positioning and clamping control method for a circular workpiece provided in an embodiment of this application. The method includes the following steps:

[0029] Step 1: Obtain visual positioning data for the circular workpiece. The visual positioning data includes the center coordinate deviation and rotation angle deviation of the workpiece at the clamping station.

[0030] Furthermore, specific methods for obtaining visual positioning data for circular workpieces include:

[0031] A top-view image of a circular workpiece is acquired using an optical image acquisition device fixed to the processing area to ensure image quality meets the requirements of subsequent processing. The optical image acquisition device is a high-resolution industrial camera paired with a low-distortion lens to reduce the impact of optical distortion on image accuracy. In addition, to accommodate circular workpieces of different sizes and materials, a programmable LED ring light source is selected for the light source system, whose brightness and illumination angle can be adjusted according to specific working conditions. The selection of the installation position needs to comprehensively consider the field of view and imaging clarity. The camera is usually installed at a height of 500mm to 800mm from the workpiece surface, and the camera is precisely calibrated using a calibration plate to establish the mapping relationship between the pixel coordinate system and the world coordinate system. The settings of acquisition parameters, including exposure time, frame rate, and white balance, all need to be optimized experimentally to avoid image quality degradation caused by ambient light interference or motion blur.

[0032] After acquiring the top-view image of the circular workpiece, the Canny edge detection algorithm is used. Gaussian filtering is used to suppress noise, and the Sobel operator is used to calculate the image gradient magnitude and direction. Single-pixel-width edge lines are extracted through non-maximum suppression and double threshold connection. This can accurately identify the outer contour edge of the circular workpiece, laying a solid foundation for subsequent center coordinate fitting and edge angle feature calculation. The standard deviation σ of the Gaussian filter needs to be adjusted according to the image noise level, usually between 1.0 and 2.0, to achieve a balance between smoothing noise and preserving details. In the gradient calculation stage, the tightness of edge connection is controlled by setting threshold parameters. The high threshold is usually set to twice the low threshold to avoid edge breakage caused by excessively high thresholds.

[0033] Based on the outer contour features of the circular workpiece extracted by the Canny edge detection algorithm, the actual center coordinates and radius of the circular workpiece are fitted using the least squares method, and the edge angle features are calculated. The basic principle of least squares fitting is to minimize the sum of squared residuals between the fitted circle and the actual contour point set by optimizing the objective function, thereby obtaining the optimal center coordinates and radius. The edge angle feature is defined as the angle between the tangent direction at a certain point on the outer contour and the preset baseline, which is used to describe the rotational posture of the workpiece. The preset baseline usually depends on the specific processing requirements and workpiece characteristics, and is usually defined as horizontal or vertical. In this application, the baseline is set as the horizontal axis in the image coordinate system, that is, the positive direction of the X-axis.

[0034] The actual center coordinates are compared with the preset theoretical center coordinates, i.e., the corresponding coordinate axes are directly subtracted to calculate the center coordinate deviation. This effectively quantifies the center position deviation of the circular workpiece at the clamping station, providing reliable data support for subsequent multi-degree-of-freedom adjustments. The preset theoretical center coordinates are determined based on the workpiece's clamping station design parameters. The theoretical center coordinates are usually defined as the geometric center point of the clamping device on the work platform, which can be converted into its corresponding pixel coordinate value in the pixel coordinate system through the mapping relationship established by the calibration plate. The deviation between the actual center coordinates and the theoretical center coordinates needs to be represented in the form of a two-dimensional vector, i.e., the offsets in the X-axis and Y-axis directions are calculated separately.

[0035] Based on the angle between the edge angle feature and the theoretical angle, the rotation angle deviation is calculated, which is the direct difference between the edge angle feature and the theoretical angle. The theoretical angle is usually set to 0, that is, there is no rotation in the ideal state. This can accurately quantify the rotational attitude deviation of the circular workpiece and provide the necessary input parameters for subsequent multi-degree-of-freedom adjustment.

[0036] Step 2: Based on visual positioning data, calculate the positioning compensation amount using a multi-degree-of-freedom motion control algorithm. The multi-degree-of-freedom motion control includes at least translational and rotational degrees of freedom in the plane.

[0037] Furthermore, the steps for calculating the positioning compensation amount using a multi-degree-of-freedom motion control algorithm include:

[0038] To convert visual positioning data into control commands for a multi-degree-of-freedom positioning mechanism, a mapping matrix between the workpiece coordinate system and the machine tool coordinate system is established. The workpiece coordinate system has its origin at the actual center of the circular workpiece, with its X and Y axes parallel to the machine tool's worktable plane, and its Z axis perpendicular to the worktable plane and pointing upwards towards the workpiece. The machine tool coordinate system has its origin at the center of the machine tool spindle, and the direction of its coordinate axes is determined by the machine tool's mechanical structure. Based on this, the transformation relationship between the two coordinate systems is derived through homogeneous coordinate transformation, including translation and rotation transformations. The translation transformation parameters are provided by the center coordinate deviation in the visual positioning data, i.e., the deviation between the actual and theoretical center of the workpiece, while the rotation transformation parameters are determined by the rotation angle deviation. The accuracy of the mapping matrix directly affects the accuracy of the positioning compensation calculation. In practical applications, the mapping matrix needs to be calibrated through multiple calibration experiments to eliminate cumulative deviations caused by installation errors or mechanical wear. During the calibration process, multiple calibration points with known locations are first selected, and the actual coordinates of these points are obtained through a vision positioning system. These coordinates are then compared with the theoretical coordinates to calculate the error term of the mapping matrix. Optimization algorithms such as least squares or gradient descent are used to fit and correct the error term to eliminate the influence of cumulative deviations. Through homogeneous coordinate transformation, not only is the calculation accuracy of the positioning compensation improved, but a solid theoretical foundation is also laid for the subsequent construction of the inverse kinematics model. The calibrated mapping matrix can convert vision positioning data into compensation quantities in a high-precision machine tool coordinate system, providing a reliable guarantee for subsequent attitude adjustment.

[0039] By inputting the center coordinate deviation and rotation angle deviation into the mapping matrix, the theoretical displacements of the translational and rotational degrees of freedom in the plane can be calculated. Specifically, if the center coordinate deviation is (Δx, Δy) and the rotation angle deviation is Δθ, the theoretical displacements can be calculated using the following formula: Where (x,y,θ) represents the initial position and orientation of the workpiece in the machine tool coordinate system, and (x',y',θ') represents the compensated target position and orientation. This formula combines translation and rotation transformation matrices, and can comprehensively reflect the position and orientation changes of the workpiece in the actual machining space.

[0040] By combining the inverse kinematics model of the multi-degree-of-freedom positioning mechanism, the theoretical displacement is converted into pulse commands for each drive axis, which serve as positioning compensation quantities. Specifically, the inverse kinematics model is used to solve for the actual physical motion quantities that each drive joint of the multi-degree-of-freedom mechanism needs to perform based on the theoretical displacement quantity required by the end effector, i.e., the target pose. This includes the linear distance that each translation axis needs to move and the angle that each rotation axis needs to rotate. After calculating the physical motion quantities of each drive axis, the pulse equivalent of each servo drive system is combined with the pulse equivalent, i.e., the displacement or angular displacement accuracy corresponding to a single pulse, to convert the physical motion quantities into the corresponding number of pulses. This number serves as the positioning compensation quantity, i.e., the pulse command, for directly driving the servo motor.

[0041] The aforementioned inverse kinematics model is constructed using the DH parameter method. Its core lies in constructing a homogeneous transformation matrix between adjacent joints through DH parameters: For each joint of the multi-degree-of-freedom positioning mechanism, four DH parameters are defined, including link length, link torsion angle, joint offset, and joint angle, which determine the translation and rotation relationship between the current joint coordinate system and the previous joint coordinate system, and solidify it into a 4×4 homogeneous transformation matrix; by multiplying the transformation matrices of all joints in the mechanism in the order of the kinematic chain, the total transformation matrix describing the overall kinematic characteristics of the entire mechanism can be constructed; when the end target pose, i.e., the theoretical displacement, is known, the unknown joint variables in the total transformation matrix, i.e., the movement of the linear motor or the rotation of the rotary motor, can be deduced through matrix inversion or geometric algebra calculation, thereby realizing the accurate conversion from the theoretical displacement to the physical motion of each axis, and then to the final pulse command.

[0042] Step 3: Drive the multi-degree-of-freedom positioning mechanism to adjust the workpiece posture according to the positioning compensation amount, so that the center of the circular workpiece coincides with the rotation center of the rotary table; interact with the positioning compensation amount calculation module through the standardized interface, receive and parse the positioning compensation amount information, and convert it into specific motion control commands and send them to each drive axis; adjust the translational and rotational degrees of freedom in the plane in sequence to ensure the smoothness and coordination of the motion process.

[0043] To further ensure the accuracy of attitude adjustment, a real-time feedback mechanism is introduced. The actual motion state of each degree of freedom is collected by encoders and sensors and compared with theoretical values ​​to perform online compensation for motion errors. Encoders are usually installed on the rotating or linear moving parts of each drive shaft to record the actual displacement and angular displacement of each degree of freedom. Laser sensors and vision sensors can be selected. Laser sensors have the characteristics of non-contact measurement and can quickly acquire three-dimensional point cloud data of the workpiece surface, which is suitable for high-precision detection of complex curved surfaces. Vision sensors can extract workpiece edge features through image processing technology, providing additional geometric constraints for attitude adjustment.

[0044] like Figure 2 The flowchart of the clamping control shown is as follows: Step 4: After the attitude adjustment is completed, the clamp is triggered to perform the clamping action, and the clamping force is monitored in real time to ensure the stability and reliability of the clamping state.

[0045] Furthermore, the specific steps for triggering the clamp to perform the clamping action are as follows:

[0046] The PLC controller outputs a preset initial clamping force control command to the cylinder or servo driver of the fixture. The determination of the preset initial clamping force needs to take into account factors such as the material properties of the workpiece, processing requirements, and clamping contact area. It is set according to the theory of material mechanics. After the preset initial clamping force is determined, the control system needs to send a command to the cylinder or servo driver to achieve the precise output of the clamping force.

[0047] The clamping force is obtained from the real-time clamping force feedback of the clamping force sensor installed on the clamping end of the fixture; the clamping force refers to the mechanical force that the pressure head directly acts on the surface of the workpiece.

[0048] If the real-time clamping force remains stable within the target clamping force range within the preset time window, and the fluctuation amplitude of the real-time clamping force does not exceed the preset clamping force fluctuation threshold, the clamping state is determined to be normal. The target clamping force range and the preset clamping force fluctuation threshold are derived based on the material yield strength, clamping contact area, and maximum cutting force of the circular workpiece. The preset time window can be set to 500ms-1000ms. This time window excludes the dynamic oscillation period during the initial pressure build-up of the hydraulic system and only extracts the steady-state clamping force data in the later stage for judgment, ensuring the reliability of the clamping state confirmation.

[0049] If the real-time clamping force does not meet the above conditions, it indicates that there may be an abnormality in the clamping state, and corresponding measures need to be taken to ensure the safety of the machining process. Possible causes of abnormal clamping state include hydraulic system leakage, loose fixture mechanical structure, or uneven workpiece surface. For these problems, the following handling procedures should be performed according to the specific situation:

[0050] If the real-time clamping force value remains below the lower limit of the target clamping force range, and the fluctuation amplitude does not exceed the preset pressure value fluctuation threshold, it indicates that the clamping state may be low due to the workpiece not being fully positioned or insufficient system pressure. In this case, the step of sending control commands and outputting the preset initial clamping force is re-executed to attempt to restore the stability of the clamping state. If the real-time clamping force remains above the upper limit of the target clamping force range, the machine tool is controlled to alarm and stop, and the operator is prompted to check the status of the hydraulic system and the fixture mechanical structure. If the fluctuation amplitude of the real-time clamping force value exceeds the preset pressure value fluctuation threshold, it indicates that the workpiece surface is uneven, there is cutting chip interference on the contact surface, or the hydraulic system is leaking. In this case, the machine tool is also controlled to alarm and stop, and the operator is prompted to check the hydraulic system, the fixture mechanical structure, and the workpiece surface condition.

[0051] Step 5: Under clamping conditions, a multi-dimensional error prediction model is constructed by combining machine tool geometric errors and dynamic thermal drift information. The expected deviation value is calculated, and feedforward compensation is performed on the machining coordinate system based on the expected deviation value. Machine tool geometric errors include indexing error, eccentricity oscillation, and perpendicularity error. Indexing error is the angular deviation generated by the machine tool during the movement of the rotating axis or indexing device. It is usually measured by a laser interferometer or a high-precision angle encoder. Eccentricity oscillation error is usually measured by a double ball bar or a capacitive displacement sensor. By monitoring the displacement changes of the rotating parts at multiple positions, the radial and axial oscillation error components can be accurately separated. Perpendicularity error is usually measured by high-precision equipment such as an autocollimator or a laser tracker. The angular deviation data between the axes is collected at different positions. The acquisition of dynamic thermal drift information depends on temperature sensors distributed in key parts of the machine tool. The selection of their distribution positions needs to comprehensively consider the machine tool's characteristics. Factors such as the heat source distribution, heat conduction path, and heat deformation sensitive areas of the machine tool are measured and their data are fused using a thermal error model to predict the impact of thermal deformation on machining accuracy. Commonly used thermal error models include multiple linear regression models, neural network models, and support vector regression models. In the model building process, the input variables must first be determined based on the layout of the temperature sensors. Then, temperature and thermal deformation data under different working conditions are obtained through experimental calibration. These data are then used to optimize the model parameters. The establishment of model parameters includes steps such as kernel function selection, penalty factor adjustment, and insensitivity loss parameter setting. Finally, the model performance is evaluated using validation set data to ensure that it can accurately predict the impact of machine tool thermal deformation on machining accuracy. The expected deviation value refers to the offset between the actual position and the theoretical position of the workpiece after the indexing is predicted, calculated in advance by the multi-dimensional error comprehensive prediction model based on the current working conditions before the indexing action occurs.

[0052] Furthermore, constructing a multidimensional error comprehensive prediction model also includes the step of constructing a geometric error map:

[0053] The rotary table is driven to perform continuous scanning, and the indexing error, eccentric oscillation and perpendicularity error are extracted by combining the data from the laser interferometer. The indexing error refers to the positioning deviation of the rotary table at different angular positions, the eccentric oscillation reflects the radial runout of the table during rotation, and the perpendicularity error describes the perpendicularity deviation between the table plane and the spindle axis.

[0054] Based on the extracted error data, a multidimensional geometric error lookup table is constructed, indexed by rotation angle and outputting machine tool geometric error as the output vector, serving as a geometric error map. The design of the lookup table needs to comprehensively consider the spatial distribution characteristics and temporal correlation of different types of errors. The full rotation range of the rotary table is divided into several equally spaced angular intervals, each interval corresponding to a discrete angle value. For each angle value, the measured values ​​of the corresponding indexing error, eccentric oscillation error, and perpendicularity error are recorded. To improve the interpolation accuracy of the lookup table, cubic spline interpolation or Kriging interpolation methods can be used to smooth the discrete data, thereby achieving accurate prediction of error values ​​at any angular position. The main purpose of the multidimensional geometric error lookup table is to provide basic data support for subsequent multivariate coupled feedforward prediction. By querying this lookup table in real time, the machine tool geometric error at the current indexing angle can be obtained, thus providing a basis for dynamic compensation of the machining coordinate system.

[0055] Specifically, the input variables of the multidimensional error comprehensive prediction model mainly include the target indexing angle, machine tool geometric error parameters, and dynamic thermal drift information. The target indexing angle serves as an index parameter input, used to retrieve geometric error parameters such as indexing error, eccentricity error, and perpendicularity error corresponding to that angle position from a pre-built geometric error map. These geometric error parameters cover key indicators such as indexing error, eccentricity error, and perpendicularity error. These parameters are acquired through high-precision measuring equipment and pre-processed to eliminate the influence of outliers. Dynamic thermal drift information is provided by temperature sensors distributed across key parts of the machine tool and is converted into predicted thermal deformation values ​​after being fused by the thermal error model. The model's output variable is the expected deviation value, defined as the offset between the actual and theoretical positions of the workpiece, calculated in advance based on the current working conditions before the indexing action. The calculation of the expected deviation value not only needs to consider the influence of a single error source but also needs to comprehensively evaluate the coupling relationship between various errors, thereby achieving a comprehensive prediction of machining deviations.

[0056] The multidimensional error comprehensive prediction model adopts a mathematical structure based on the kinematics theory of multibody systems to achieve the fusion of geometric errors and dynamic thermal drift information. Specifically, the model first abstracts the machine tool as a multibody system and establishes the coordinate system of each moving component according to the kinematic chain configuration. On this basis, the model uses the kinematic Jacobi matrix to establish the differential mapping relationship between the positioning error of the translation axis, the indexing error of the rotation axis, and the thermal drift of each moving component and the end-effector pose deviation of the machine tool. The end-effector pose deviation is the comprehensive position and attitude offset of the tool center point in the machining coordinate system. By substituting various geometric errors and thermal errors into the Jacobi matrix for linear superposition and nonlinear coupling calculation, the comprehensive expected deviation value of the end-effector of the machine tool is finally derived. The derivation process of the model formula combines the actual motion characteristics of the machine tool and the error propagation law to ensure the accuracy and reliability of the prediction results.

[0057] The model parameters were determined by combining experimental calibration and data fitting methods to ensure the model's adaptability and prediction accuracy under different working conditions. During the experimental calibration process, a series of experimental schemes covering different processing conditions were designed to collect machine tool geometric error parameters, temperature data, and actual end-point deviation data under different target indexing angles. These data were then used to train and optimize the model. For data fitting, the least squares method was used to extract the optimal solution of the model parameters by performing polynomial or nonlinear fitting on the experimental data.

[0058] Step Six: During the rotation of the rotary table, a non-contact monitoring device is activated to acquire position data. The residual is calculated based on the position data and the expected deviation value. Asynchronous fine-tuning correction is performed based on the residual, and continuous machining is performed based on the corrected coordinate system. Asynchronous fine-tuning correction refers to the parallel acquisition and calculation of position data while the rotary table is performing the rotation physical action, and the correction of the coordinate system software offset is completed based on the residual at the moment of locking. Its asynchronous nature is reflected in the overlap of the time window between the measurement and calculation process and the rotation physical process, rather than sequential waiting.

[0059] Furthermore, the specific method for activating the non-contact monitoring device to acquire location data is as follows:

[0060] Within the time window during which the rotary table performs its rotational motion, position data is acquired using a non-contact monitoring device. The position data consists of multi-dimensional pose data containing planar coordinate displacement components and rotation angle components. The non-contact monitoring device is an optical image acquisition device or a high-resolution circular grating ruler.

[0061] Position calculation is completed during the deceleration and locking process of the rotary table, so that the measurement time window overlaps with the indexing time window.

[0062] Specifically, when using an optical image acquisition device, its working sequence is synchronized with the rotation process of the rotary table. That is, image acquisition is started immediately after the indexing action begins, and position calculation is completed during the deceleration and locking phase of the table. By performing image recognition on the feature markers on the fixture or workpiece, the actual position data, including planar coordinate deviation and rotation angle deviation, is calculated. To ensure the real-time performance and accuracy of the data, the data acquisition frequency is set to 100Hz, and a high-speed data processing unit is used to analyze the images in real time. When using a high-resolution circular grating ruler, its measurement resolution can reach 0.001, which can directly provide the angular displacement data of the rotary table. The monitoring device and the control system are connected through a high-speed communication interface to ensure that the data transmission delay is less than 1ms, which can effectively capture small position deviations during the indexing process and provide a reliable basis for subsequent residual calculation and correction.

[0063] like Figure 3The flowchart shown illustrates the asynchronous fine-tuning correction process. Further, the specific steps for performing asynchronous fine-tuning correction are as follows:

[0064] After acquiring the location data, the residual between the parallel monitored location data and the expected deviation value output by the multidimensional error comprehensive prediction model is obtained by difference calculation; the expected deviation value includes the predicted coordinate and predicted angle components.

[0065] If the residual is within the safety threshold, it indicates that the accuracy of the current machining coordinate system meets the requirements, and the machining coordinate system after feedforward compensation is directly adopted to enter the machining state. The safety threshold is set based on the machine tool repeatability and machining tolerance requirements. For example, if the machine tool repeatability is ±3μm and the machining tolerance requirement is 10μm, then the safety threshold is set to 5μm. The residual is negligible if it is less than this value, and must be corrected if it is greater than this value.

[0066] If the residual exceeds the safety threshold, coordinate system offset correction is performed at the moment of locking completion, without triggering the spindle probe to perform physical contact detection. The specific correction process is as follows: adjust the origin position of the machining coordinate system according to the residual direction, and update the coordinate system parameters through the CNC system without triggering the spindle probe's physical contact detection. Asynchronous fine-tuning correction can significantly improve machining accuracy without increasing additional detection time, and can also effectively reduce equipment wear caused by frequent physical detection, thereby extending the service life of the machine tool.

[0067] Specifically, at the moment the locking is completed, the offset that the origin of the coordinate system needs to be adjusted is calculated based on the direction of the residual. For example, if the residual is a deviation along the positive X-axis, the origin of the machining coordinate system is moved a corresponding distance along the negative X-axis to offset the deviation. The updated coordinate system parameters are loaded into the control system through the G-code or macro program interface of the CNC system, thereby completing the offset correction of the coordinate system, which significantly improves the correction efficiency and reduces equipment wear.

[0068] This application provides a visual multi-degree-of-freedom positioning and clamping control device for a circular workpiece, comprising:

[0069] The visual positioning data acquisition module is used to acquire visual positioning data of a circular workpiece. The visual positioning data includes the center coordinate deviation and rotation angle deviation of the workpiece at the clamping station, realizing non-contact high-frequency quantization acquisition of the workpiece's pose and significantly improving the initial positioning accuracy.

[0070] The positioning compensation calculation module is used to calculate the positioning compensation based on visual positioning data through a multi-degree-of-freedom motion control algorithm. The multi-degree-of-freedom motion control includes at least translational and rotational degrees of freedom in the plane, realizing a precise mapping from the visual feature space to the machine tool motion control space, and ensuring the synchronization and accuracy of multi-degree-of-freedom linkage adjustment.

[0071] The attitude adjustment control module is used to drive the multi-degree-of-freedom positioning mechanism to adjust the workpiece attitude according to the positioning compensation amount, so that the center of the circular workpiece coincides with the rotation center of the rotary table, realizing the physical concentric alignment of the circular workpiece and the rotary table, and ensuring the dynamic balance and stability of subsequent indexing processing.

[0072] The clamping control module is used to trigger the fixture to perform the clamping action after the posture adjustment is completed, and to monitor the clamping force in real time. It realizes closed-loop control and steady-state determination of clamping force, ensuring the authenticity and reliability of the clamping state and the safety of processing.

[0073] The feedforward compensation module is used to construct a multi-dimensional error comprehensive prediction model by combining machine tool geometric error and dynamic thermal drift information under clamping conditions, calculate the expected deviation value, and perform feedforward compensation on the machining coordinate system based on the expected deviation value. By introducing advanced control strategies, it realizes early intervention and active elimination of multi-source dynamic errors, effectively ensuring the dimensional stability of long-term multi-degree-of-freedom positioning and clamping control.

[0074] The asynchronous fine-tuning correction module is used to activate a non-contact monitoring device to acquire position data during the rotation of the rotary table. Based on the position data and the expected deviation value, the module calculates the residual, performs asynchronous fine-tuning correction based on the residual, and performs continuous machining based on the corrected coordinate system. Based on advanced control theory, it realizes the overlap of the measurement time window and the rotation time window and the software-level seamless correction, which improves the dynamic response speed and the efficiency of continuous machining without interruption of vision multi-degree-of-freedom positioning and clamping control.

[0075] Example 2: Based on Example 1, feedforward compensation is performed on the machining coordinate system, and the step of performing multivariable coupled feedforward prediction is also included:

[0076] In the multidimensional error comprehensive prediction model, a temperature field weight term, a load fluctuation term, and a long-period drift history factor are introduced. The temperature field weight term describes the influence of different temperature distributions on thermal drift, and its value is determined by the finite element analysis method. The load fluctuation term reflects the effect of cutting force changes on the deformation of the machine tool structure, and its real-time data is provided by a force sensor installed on the spindle. The long-period drift history factor refers to a characteristic parameter that reflects the slow and irreversible systematic positional offset trend caused by spindle bearing wear and guide rail micro-creep due to long-term operation of the machine tool. It is obtained through statistical analysis of historical machining data and is used to compensate for systematic deviations caused by machine tool aging or long-term operation.

[0077] Using the target rotation angle, machine tool geometric error, current temperature field, current load fluctuation, and long-period drift history factor as independent variables, a multivariate coupled feedforward prediction function is constructed, namely... Where ΔP(t) is the expected deviation value at time t, and T(t), L(t), and H(t) represent the state vectors of the current temperature field, load fluctuation, and long-period drift history factor, respectively. The function construction process comprehensively considers the nonlinear relationship between the variables and their coupling effect on the machining error. For example, the combined effect of the temperature field and load fluctuation may lead to nonlinear thermo-mechanical coupling deformation of the machine tool structure, while the long-period drift history factor further increases the complexity and accuracy of the model. In order to improve the solution efficiency of the function, the least squares method is used to fit the historical machining data, and the model parameters are optimized by combining the particle swarm optimization algorithm. The resulting multivariate coupled feedforward prediction function can not only accurately predict the machining error, but also has a strong generalization ability and can adapt to different machining conditions and machine tool types.

[0078] By solving the multivariable coupled feedforward prediction function, the expected deviation value is calculated, and the machining coordinate system of the next station is actively updated based on the expected deviation value. Before each indexing operation, the multivariable coupled feedforward prediction function is called to predict the deviation based on the target indexing angle, real-time monitored temperature field and load fluctuation data, and pre-calculated long-period drift history factor. The predicted deviation value is then applied to the adjustment of the machining coordinate system to ensure that the machining operation of the next station can be completed within the theoretical accuracy range. This advanced control strategy can also effectively cope with the machining requirements under complex working conditions, providing strong technical support for high-precision machining.

[0079] Example 3: Based on Example 1, step six further includes:

[0080] like Figure 4 The flowchart shown illustrates the physical calibration judgment process, which monitors the deviation confidence level, cumulative number of machined parts, and cutting load fluctuations of the multidimensional error comprehensive prediction model in real time. Deviation confidence level refers to the reliability index of the expected deviation value currently output by the multidimensional error comprehensive prediction model. It is obtained by statistically analyzing the deviation distribution between the model's predicted value and the actual measured value, with a value ranging from 0 to 1. A larger value indicates higher model prediction accuracy. The deviation confidence level is calculated based on fitting the probability density function of the model's prediction error, typically using a normal distribution or t-distribution assumption, and its parameters are determined using the maximum likelihood estimation method. The cumulative number of machined parts reflects the long-term operating status of the machine tool. Cutting load fluctuations are assessed by real-time acquisition of the three-phase current signal of the spindle motor using a Hall current sensor, and the root mean square value is calculated as a characteristic quantity of the cutting load. Since there is a strong correlation between spindle current and cutting force, the fluctuation amplitude of the current root mean square value can indirectly reflect the fluctuation of the cutting force. Cutting load fluctuations not only cause instantaneous deformation of the machine tool structure but may also trigger abnormal conditions such as tool wear or hard point collisions, thus significantly affecting machining accuracy.

[0081] When the deviation confidence level is lower than the preset deviation confidence level threshold, it indicates that the model may have a large deviation, requiring immediate physical calibration. The current machining task must be paused, and the machine tool's geometric errors must be recalibrated using precision measuring equipment such as a laser interferometer. Combined with real-time data on temperature field and load fluctuations, the parameters of the multi-dimensional error prediction model are updated. The preset deviation confidence level threshold can be set as the variance of the residuals from the last 50 indexing operations. When the cumulative number of machined parts reaches a set quantity, the spindle probe must be triggered for physical calibration. During calibration, the spindle probe comprehensively detects machine tool geometric errors, thermal drift, and force-induced errors, generating new error compensation parameters to eliminate systematic deviations caused by mechanical wear or thermal deformation accumulation. The set quantity is set to 200 parts, determined based on the tool life cycle or machine tool thermal cycle, serving as a maintenance node for forced physical reset. When the detected change in cutting load exceeds the preset cutting load change threshold, it indicates that the current... If the operating conditions exceed the model's applicable range, the spindle probe will be triggered to perform physical calibration. During calibration, the force sensor will provide real-time cutting load data. Based on this data, the load fluctuation term in the multidimensional error prediction model will be dynamically adjusted. The spindle probe will recalibrate the machine tool's geometric errors and thermal drift to eliminate instantaneous deformation caused by sudden changes in cutting load. In other states, physical detection will be skipped to maintain the compensation state of the multidimensional error prediction model. The preset cutting load change threshold is determined based on the nonlinear relationship between cutting force and machine tool structural deformation, using finite element analysis combined with experimental data. It can be set to 15% of the current average fluctuation of the spindle cutting current. Exceeding this threshold means that the tool may be worn or encounter a hard point, leading to sudden changes in force deformation. The above monitoring and calibration strategy based on advanced control theory can minimize unnecessary physical detection while ensuring machining accuracy, thereby improving production efficiency and reducing operating costs.

[0082] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the above functions can be divided into different functional modules to complete all or part of the functions described above.

[0083] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0084] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units, located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0085] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0086] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the solution, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for visual multi-degree-of-freedom positioning and clamping control of a circular workpiece, characterized in that, Includes the following steps: Step 1: Obtain visual positioning data for the circular workpiece, including the center coordinate deviation and rotation angle deviation of the workpiece at the clamping station; Step 2: Based on visual positioning data, calculate the positioning compensation amount using a multi-degree-of-freedom motion control algorithm. The multi-degree-of-freedom motion control includes at least translational and rotational degrees of freedom in the plane. Step 3: Drive the multi-degree-of-freedom positioning mechanism to adjust the workpiece posture according to the positioning compensation amount, so that the center of the circular workpiece coincides with the rotation center of the rotary table; Step 4: After the posture adjustment is completed, trigger the clamp to perform the clamping action and monitor the clamping force in real time; Step 5: Under clamping conditions, construct a multi-dimensional error comprehensive prediction model by combining machine tool geometric error and dynamic thermal drift information, calculate the expected deviation value, and perform feedforward compensation on the machining coordinate system based on the expected deviation value; Step Six: During the rotation of the rotary table, a non-contact monitoring device is activated to acquire position data. The residual is calculated based on the position data and the expected deviation value. Asynchronous fine-tuning correction is performed based on the residual, and continuous machining is performed based on the corrected coordinate system.

2. The method for visual multi-degree-of-freedom positioning and clamping control of a circular workpiece as described in claim 1, characterized in that: The specific methods for obtaining the visual positioning data of the circular workpiece include: A top view image of a circular workpiece is obtained using an optical image acquisition device fixed to the processing area; An edge detection algorithm is used to extract the outer contour features of a circular workpiece, and the actual center coordinates and edge angle features of the circular workpiece are obtained by fitting. The actual center coordinates are compared with the preset theoretical center coordinates to calculate the center coordinate deviation; The rotation angle deviation is calculated based on the angle between the edge angle characteristics and the theoretical angle.

3. The method for visual multi-degree-of-freedom positioning and clamping control of a circular workpiece as described in claim 2, characterized in that: The step of calculating the positioning compensation amount using a multi-degree-of-freedom motion control algorithm includes: Establish the mapping matrix between the workpiece coordinate system and the machine tool coordinate system; Input the center coordinate deviation and rotation angle deviation into the mapping matrix to calculate the theoretical displacement of each of the translational and rotational degrees of freedom in the plane. By combining the inverse kinematics model of the multi-degree-of-freedom positioning mechanism, the theoretical displacement is converted into pulse commands for each drive shaft, which serve as positioning compensation quantities.

4. The method for visual multi-degree-of-freedom positioning and clamping control of a circular workpiece as described in claim 1, characterized in that: The specific steps for the trigger clamp to perform the clamping action are as follows: The cylinder or servo driver controlling the fixture outputs a preset initial clamping force; Obtain the real-time clamping force fed back by the clamping force sensor installed on the clamping end of the fixture; If the real-time clamping force is stable within the target clamping force range within the preset time window, and the fluctuation amplitude of the real-time clamping force does not exceed the preset clamping force fluctuation threshold, the clamping state is determined to be normal; otherwise, the step of outputting the preset initial clamping force is re-executed or the machine tool is controlled to alarm and stop.

5. The method for visual multi-degree-of-freedom positioning and clamping control of a circular workpiece as described in claim 1, characterized in that: The construction of the multidimensional error comprehensive prediction model also includes the step of constructing a geometric error map: The rotary table is driven to perform continuous scanning, and the indexing error, eccentric oscillation and perpendicularity error are extracted by combining the data from the laser interferometer. Based on the extracted error data, a multidimensional geometric error lookup table indexed by rotation angle is constructed as a geometric error map.

6. The method for visual multi-degree-of-freedom positioning and clamping control of a circular workpiece as described in claim 1, characterized in that: The specific method for activating the non-contact monitoring device to acquire location data is as follows: During the time window when the rotary table performs the rotation action, position data is acquired using a non-contact monitoring device, which is an optical image acquisition device or a high-resolution circular grating ruler. Position calculation is completed during the deceleration and locking process of the rotary table, so that the measurement time window overlaps with the indexing time window.

7. The method for visual multi-degree-of-freedom positioning and clamping control of a circular workpiece as described in claim 6, characterized in that: The specific steps for asynchronous fine-tuning and correction are as follows: Calculate the residual between the location data monitored in parallel and the expected deviation value; If the residual is within the safety threshold, the machining coordinate system after feedforward compensation is directly used to enter the machining state; If the residual exceeds the safety threshold, coordinate system offset correction will be performed at the moment the locking is completed, and the spindle probe will not be triggered to perform physical touch detection.

8. The method for visual multi-degree-of-freedom positioning and clamping control of a circular workpiece as described in claim 7, characterized in that: Step six also includes: Real-time monitoring of the deviation confidence level, cumulative number of processed parts, and cutting load fluctuation of the multidimensional error comprehensive prediction model; The spindle probe is triggered to perform physical calibration only when the deviation confidence level is lower than the preset deviation confidence threshold, the cumulative number of processed parts reaches the set quantity, or the detected change in cutting load exceeds the preset change in cutting load threshold. In other states, physical detection is skipped, and the compensation state of the multidimensional error comprehensive prediction model is maintained.

9. The method for visual multi-degree-of-freedom positioning and clamping control of a circular workpiece as described in claim 8, characterized in that: The feedforward compensation of the machining coordinate system also includes the step of performing multivariable coupled feedforward prediction: In the multidimensional error comprehensive prediction model, a temperature field weight term, a load fluctuation term, and a long-period drift history factor are introduced; A multivariate coupled feedforward prediction function is constructed by using the target rotation angle, machine tool geometric error, current temperature field, current load fluctuation, and long-period drift history factor as independent variables. By solving the multivariate coupled feedforward prediction function, the expected deviation value is calculated, and the machining coordinate system of the next station is actively updated based on the expected deviation value.

10. A visual multi-degree-of-freedom positioning and clamping control device for a circular workpiece, characterized in that, include: The visual positioning data acquisition module is used to acquire visual positioning data of a circular workpiece, the visual positioning data including the center coordinate deviation and rotation angle deviation of the workpiece at the clamping station. The positioning compensation calculation module is used to calculate the positioning compensation based on visual positioning data and through a multi-degree-of-freedom motion control algorithm. The multi-degree-of-freedom motion control includes at least translational and rotational degrees of freedom in the plane. The attitude adjustment control module is used to drive the multi-degree-of-freedom positioning mechanism to adjust the workpiece attitude according to the positioning compensation amount, so that the center of the circular workpiece coincides with the rotation center of the rotary table. The clamping control module is used to trigger the fixture to perform the clamping action after the attitude adjustment is completed, and to monitor the clamping force in real time; The feedforward compensation module is used to construct a multi-dimensional error comprehensive prediction model by combining machine tool geometric error and dynamic thermal drift information under clamping conditions, calculate the expected deviation value, and perform feedforward compensation on the machining coordinate system based on the expected deviation value. The asynchronous fine-tuning correction module is used to activate a non-contact monitoring device to acquire position data during the rotation of the rotary table, calculate the residual based on the position data and the expected deviation value, perform asynchronous fine-tuning correction based on the residual, and perform continuous machining based on the corrected coordinate system.

Images