Precise part efficient machining system based on tool path intelligent optimization

Abstract

The invention relates to the technical field of precise numerical control machining and intelligent manufacturing, and discloses a tool path intelligent optimization-based precise part efficient machining system, which sequentially comprises a multi-source physical sensing module, a dynamic manifold construction module, a path autonomous evolution module and a Lie group error compensation module which are in data interaction connection, the multi-source physical sensing module is used for collecting multi-source sensing data in the machining process in real time and mapping the multi-source sensing data into a cutting impedance tensor with a direction attribute in combination with the feeding direction of a tool. And the dynamic manifold construction module. By establishing an error covariant mapping compensation model of a Lie group space, the problem of nonlinear coupling in traditional multi-axis machining error compensation is solved, a system extracts gradient change of a manifold physical field by using a covariant derivative, and the gradient change is accurately mapped into an error torque in a Lie algebra space through an adjoint effect, so that the error compensation precision of the multi-axis machining is improved. And the geometric accuracy and the motion stability of dynamic error compensation in complex curved surface machining are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of precision CNC machining and intelligent manufacturing technology, specifically to a high-efficiency machining system for precision parts based on intelligent toolpath optimization. Background Technology

[0002] Efficient machining of precision components is a core technological challenge in the field of high-end equipment manufacturing. As industries such as aerospace, medical devices, and mold manufacturing continue to increase their requirements for part precision, surface quality, and material machinability, traditional numerical control (CNC) machining technology is facing severe challenges.

[0003] Significant progress has been made in toolpath optimization in existing technologies, mainly in two aspects: First, offline geometric and physical simulation technology, such as commercial computer-aided manufacturing (CAM) software, generates smooth NURBS (non-uniform rational B-spline) toolpaths based on geometric models, and adjusts the feed rate in segments through pre-set cutting force or material removal rate (MRR) models to achieve macroscopic stability of the machining process. Second, online adaptive control (ACC) technology monitors the spindle load current or vibration signal, and linearly reduces the machine tool feed rate in real time when the signal exceeds a preset threshold to prevent tool damage or breakage caused by sudden overload.

[0004] However, existing technologies still have insurmountable limitations when applied to high-dynamic, micron-level precision machining. While offline simulation models can avoid macroscopic collisions in advance, they cannot predict the dynamic time-varying factors that exist in actual machining, such as the inhomogeneity of the blank material (e.g., hard spots), the accumulation of micro-wear of the tool during cutting, and the local deformation of the workpiece caused by the release of residual stress. When these dynamic factors occur, traditional ACC systems can only make reactive deceleration. Once the tool's spatial trajectory (geometric path) is generated, it is regarded as a rigid constraint and cannot be micro-adjusted. This strategy of adjusting only in the time domain (speed) and keeping it fixed in the spatial domain (path) cannot fundamentally avoid the changes in the direction of force, insufficient local stiffness, or resonant frequency regions during cutting. Especially for multi-axis linkage machining, traditional error compensation techniques often use linear superposition or Euler angles to describe the error, which is difficult to accurately handle the highly nonlinear coupling error between the rotary axis and the translational axis, resulting in insufficient compensation accuracy and easy to produce mathematical singularities under certain postures. Therefore, this invention designs a high-efficiency machining system for precision parts based on intelligent toolpath optimization to address the problems mentioned above. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a high-efficiency machining system for precision parts based on intelligent toolpath optimization. This system solves the problem that existing precision machining technologies are limited by static geometric planning, making it impossible to autonomously reconstruct and adaptively avoid spatial dimensions based on the dynamic time-varying characteristics of multi-dimensional physical fields such as real-time cutting force and vibration. As a result, it is difficult to simultaneously ensure machining efficiency and micron-level accuracy under complex nonlinear working conditions.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: a high-efficiency machining system for precision parts based on intelligent toolpath optimization, which sequentially includes a multi-source physical sensing module with data interaction connection, a dynamic manifold construction module, a path autonomous evolution module, and a Lie group error compensation module;

[0007] The multi-source physical sensing module is used to collect multi-source sensing data in real time during the machining process, and combine it with the tool feed direction to map the multi-source sensing data into a cutting impedance tensor with directional attributes.

[0008] The dynamic manifold construction module is used to receive the cutting impedance tensor and fuse it with the basic geometric constraints of the workpiece to construct an anisotropic Finsler manifold space, wherein the geometric metric field of the Finsler manifold space evolves in real time with the numerical change of the cutting impedance tensor to change the equivalent distance at various points in the space.

[0009] The path-auto-evolution module is used to plan an adaptive evolution toolpath that can avoid high curvature and high impedance regions in the Finsler manifold space by solving the geodesic differential equation for the minimum value of the energy functional.

[0010] The Lie group error compensation module is used to analyze the geometric variation of the adaptive evolution toolpath relative to the nominal path based on the differential mapping relationship between the Lie group space and the Lie algebra space, generate multi-axis linkage compensation instructions adapted to the kinematics of the multi-axis machine tool, and use the multi-axis linkage compensation instructions to drive the machine tool to perform machining in real time.

[0011] Preferably, the specific configuration for the multi-source physical sensing module to construct the cutting impedance tensor is as follows:

[0012] Real-time acquisition of spindle load current and tool tip vibration acceleration is used as the multi-source sensing data;

[0013] Calculate the current tool feed unit direction vector;

[0014] Using tensor product operations, the amplitude of the spindle load current is assigned a vector characteristic to the unit feed direction vector of the tool, and the amplitude of the tool tip vibration acceleration is used as an isotropic component to weight and synthesize the cutting impedance tensor.

[0015] Preferably, in the dynamic manifold construction module, the geometric metric field of the Finsler manifold space is composed of a linear superposition of Euclidean metric and the cutting impedance tensor;

[0016] The superposition process includes an anisotropy correction factor, which is dynamically calculated based on the angle between the tool feed direction and the principal axis of the workpiece material properties. This factor is used to adjust the weights of the cutting impedance tensor in different directions, thereby giving the Finsler manifold a spatially oriented metric property.

[0017] Preferably, the dynamic manifold building module is configured as follows:

[0018] When the tool feed direction is consistent with the weak stiffness direction of the workpiece material, the corresponding metric weight is increased by the anisotropy correction factor, resulting in an increase in the local spatial curvature of the Finsler manifold space in that direction.

[0019] Preferably, the specific configuration for the adaptive evolution toolpath generated by the path autonomous evolution module is as follows:

[0020] Based on the metric function and its partial derivatives of the Finsler manifold space, the jet coefficient characterizing the nonlinear connection is calculated;

[0021] Construct a second-order geodesic differential equation that incorporates the jetting coefficient and geometric tolerance zone constraints;

[0022] The second-order geodesic differential equation is solved by numerical integration to obtain the tool position sequence at the next moment. The tool position sequence constitutes the adaptive evolution toolpath with the minimum cutting potential energy within the tolerance zone constraint.

[0023] Preferably, the specific configuration for the Lie group error compensation module to generate the multi-axis linkage compensation command is as follows:

[0024] Calculate the covariant derivative of the metric tensor of the Finsler manifold space along the tool feed direction to extract the rate of change of the spatial gradient caused by the physical field;

[0025] Using the adjoint operator of the Lie group, the spatial gradient change rate is transformed from the tool local coordinate system to the machine tool base coordinate system, and combined with the machine tool servo stiffness matrix, the error torque in the Lie algebra space is generated.

[0026] Preferably, the Lie group error compensation module is further used for:

[0027] Using an exponential mapping function, the error torque is converted into a special Euclidean group. The correction matrix in;

[0028] The correction matrix is ​​superimposed on the pose matrix of the nominal machining path to obtain the final multi-axis linkage compensation command.

[0029] Preferably, the system further includes a symplectic geometry prediction and correction module for connection to the dynamic manifold construction module;

[0030] The symplectic geometry prediction and correction module is used to construct a dual symplectic space containing real and imaginary manifolds;

[0031] The real manifold is driven by the multi-source sensor data at the current moment, while the virtual manifold is constructed based on the machining history data of the previous toolpath and is used to store the historical momentum distribution of the cutting load.

[0032] Preferably, the operating mechanism of the symplectic geometric prediction and correction module is as follows:

[0033] Using the Hamiltonian flow evolution equation, the load state distribution in front of the current toolpath is deduced in the virtual manifold;

[0034] When a sudden change in cutting impedance is predicted ahead, a feedforward adjustment signal is generated and sent to the dynamic manifold construction module to adjust the construction parameters of the Finsler manifold space in advance, so that the manifold curvature is deformed in advance before the tool reaches the sudden change point.

[0035] Preferably, in a CNC machine tool, the system is integrated into the real-time control kernel of the CNC machine tool, and the calculation cycle of the path auto-evolution module and the Lie group error compensation module is synchronized with the interpolation cycle of the CNC machine tool.

[0036] This invention provides a high-efficiency machining system for precision parts based on intelligent toolpath optimization. It offers the following advantages:

[0037] 1. This invention introduces anisotropic Finsler manifold construction technology driven by multi-source physical fields, which breaks through the limitation of traditional toolpath planning relying solely on static geometric models. It transforms dynamic physical quantities such as cutting force and vibration into metric tensors of geometric space, and uses orientation correction factors to endow the manifold with anisotropic characteristics. This enables the machining system to achieve a qualitative leap from rigid execution to flexible adaptation in the machining process based on the material texture direction and real-time load changes.

[0038] 2. This invention solves the conflict and computational efficiency problems in multi-objective optimization by using a geodesic path auto-evolution mechanism based on variational principles. Unlike traditional methods that require complex weighted iterative solutions for efficiency, accuracy, and energy consumption, this endogenous optimization mechanism based on physical geometric field theory can generate the optimal toolpath that balances efficiency and quality without human intervention, significantly improving the level of intelligence in precision machining.

[0039] 3. This invention establishes a Lie group. The spatial error covariant mapping compensation model overcomes the nonlinear coupling problem in traditional multi-axis machining error compensation. The system uses covariant derivatives to extract the gradient changes of the manifold physical field and accurately maps them into error torque in Lie algebra space through adjoint effects. This effectively avoids the universal joint deadlock and singularity problems in five-axis linkage machining and greatly improves the geometric accuracy and motion stability of dynamic error compensation in complex surface machining.

[0040] 4. This invention solves the control delay problem caused by the lag in physical perception feedback by introducing a bidirectional prediction and correction mechanism based on symplectic geometry for virtual and real manifolds. The system utilizes the spatiotemporal correlation of the machining process to construct a dual symplectic space containing real and virtual manifolds, and uses the Hamiltonian flow equation to predict the load state in front of the current toolpath using historical data from the previous toolpath. This feedforward-feedback fusion control strategy enables the system to adjust manifold parameters and plan avoidance paths in advance before the tool contacts the difficult-to-machine area, effectively suppressing vibration and tool deflection caused by sudden load changes, and significantly improving the surface machining quality.

[0041] 5. This invention unifies physical perception, geometric evolution, and motion control within a high-dimensional differential geometric mathematical framework, achieving real-time and closed-loop computation at the edge. The core algorithms of the entire system (tensor construction, geodesic solution, and Lie group mapping) are all analytical or semi-analytical, with high computational efficiency and easy integration into the real-time kernel or edge computing unit of the CNC system. This tightly coupled architecture design ensures millisecond-level synchronization between data flow and control flow, enabling the system to perform high-dynamic precision machining in industrial settings. Attached Figure Description

[0042] Figure 1 This is one of the system flow diagrams of the present invention;

[0043] Figure 2 This is the second system flow diagram of the present invention;

[0044] Figure 3 This is the third system flow diagram of the present invention;

[0045] Figure 4 This is the fourth system flow diagram of the present invention. Detailed Implementation

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0047] Please see the appendix Figure 1 -Appendix Figure 4 The present invention provides a high-efficiency machining system for precision parts based on intelligent toolpath optimization. The system includes, in sequence, a multi-source physical sensing module with data interaction connection, a dynamic manifold construction module, a path autonomous evolution module, and a Lie group error compensation module.

[0048] The multi-source physical sensing module is used to collect multi-source sensor data in real time during the machining process. Combined with the tool feed direction, the multi-source sensor data is mapped into a cutting impedance tensor with directional attributes. The specific configuration for constructing the cutting impedance tensor by the multi-source physical sensing module is as follows: real-time acquisition of spindle load current and tool tip vibration acceleration as multi-source sensor data; calculation of the current tool feed unit direction vector; using tensor product operation, the amplitude of the spindle load current is assigned the vector characteristics along the tool feed unit direction vector, and the amplitude of the tool tip vibration acceleration is used as an isotropic component, and the cutting impedance tensor is weighted and synthesized.

[0049] Specifically, in the process of precision component machining, simple scalar signals (such as current values) cannot fully describe the spatial characteristics of the cutting process. This module aims to build a physical and mathematical model that can include magnitude and direction.

[0050] In practice, the system first collects the spindle load current through a current transformer installed on the machine tool spindle drive. Triaxial vibration signals are acquired by a wireless accelerometer mounted on the spindle housing or tool holder. At the same time, the CNC system kernel outputs the current feed rate vector of the tool in real time. and normalize it to a unit direction vector. To describe the directionality of cutting resistance (e.g., the tool experiences different forces at different angles of entry), this embodiment utilizes tensor product operations to construct second-order cutting, as shown in the following formula:

[0051]

[0052] It is the cutting force amplitude derived from the current model inversion. It is the amplitude of vibrational energy; It is a unit tensor; Represents the tensor cross product; , These are weighting coefficients. The physical meaning of this formula is that the cutting force is modeled as a force along the feed direction. The anisotropic field acts as a tensor, while the vibration is modeled as an isotropic field with all-directional diffusion. This provides the physical basis for the subsequent construction of geometric space.

[0053] The dynamic manifold construction module receives the cutting impedance tensor and integrates it with the workpiece's fundamental geometric constraints to construct an anisotropic Finsler manifold space. The geometric metric field of the Finsler manifold space evolves in real-time with the numerical change of the cutting impedance tensor, altering the equivalent distance at various points in the space. Within the dynamic manifold construction module, the geometric metric field of the Finsler manifold space is composed of a linear superposition of Euclidean metrics and the cutting impedance tensor. This superposition process includes an anisotropy correction factor, dynamically calculated based on the angle between the tool feed direction and the principal axes of the workpiece material properties. This factor adjusts the weights of the cutting impedance tensor in different directions, thereby assigning the Finsler manifold space direction-dependent metric properties. The dynamic manifold construction module is configured such that when the tool feed direction aligns with the weak stiffness direction of the workpiece material, the corresponding metric weight is increased through the anisotropy correction factor, leading to an increase in the local spatial curvature of the Finsler manifold space in that direction.

[0054] Specifically, this module can transform physical tensors into the curvature of geometric space. This embodiment does not use isotropic Riemannian geometry, but Finsler geometry, because the cutting stiffness of metallic materials (such as single-crystal alloys or composite materials) is often highly related to the cutting direction.

[0055] The system defines the Finsler metric function for the machining space. ,in For location, The core concept is to construct a dynamic metric tensor, which is the tangent vector (corresponding to the feed velocity). :

[0056] in For the measurement of the Euclidean background space, Anisotropy correction factor, The tool feed direction vector is The angle between the workpiece material and the principal axis of maximum stiffness (e.g., lattice direction or fiber direction);

[0057] In a preferred embodiment This means that when the tool cuts along the direction of the material's weakest stiffness (the direction in which tool deformation is most likely to occur), The value increases, causing the metric tensor to... In a geometric sense, this increase is equivalent to stretching the space of the region, making the equivalent distance through the region longer.

[0058] The path-autogeneration module is used to plan an adaptive evolution toolpath that avoids high-curvature, high-resistance regions in the Finsler manifold space by solving the geodesic differential equation that minimizes the energy functional. The specific configuration for generating the adaptive evolution toolpath by the path-autogeneration module is as follows: based on the metric function and its partial derivatives of the Finsler manifold space, the jetting coefficient characterizing the nonlinear connection is calculated; a second-order geodesic differential equation containing the jetting coefficient and geometric tolerance zone constraints is constructed; the second-order geodesic differential equation is solved by numerical integration to obtain the tool position sequence at the next moment. The tool position sequence constitutes the adaptive evolution toolpath with the minimum cutting potential energy within the tolerance zone constraint range.

[0059] Specifically, after constructing the changing geometric space, the system needs to find an optimal processing path. According to the principle of least action in physics, the trajectory of a particle in curved spacetime follows geodesic equations. The system generates the toolpath by solving the following second-order differential equation in real time:

[0060]

[0061] Here, It is the injection coefficient, which is determined by the metric function. The second-order partial derivative of represents the nonlinear connection (i.e., the curvature of space).

[0062]

[0063] It refers to the tolerance zone boundary constraint force. Specifically, the system defines a potential well in the normal direction of the surface to be machined, the width of which is equal to the tolerance zone range of the part. When the evolution path attempts to go beyond the tolerance zone, It increases exponentially, pushing the path back into the tolerance zone.

[0064] The Lie group error compensation module is used to analyze the geometric variation of the adaptively evolved toolpath relative to the nominal path based on the differential mapping relationship between the Lie group space and the Lie algebra space. This generates multi-axis linkage compensation commands adapted to the kinematics of multi-axis machine tools, and uses these commands to drive the machine tool to perform machining in real time. The specific configuration for generating multi-axis linkage compensation commands by the Lie group error compensation module is as follows: It calculates the covariant derivative of the metric tensor of the Finsler manifold space along the tool feed direction to extract the rate of change of the spatial gradient caused by the physical field; it uses the adjoint operator of the Lie group to transform the rate of change of the spatial gradient from the tool local coordinate system to the machine tool base coordinate system, and combines this with the machine tool servo stiffness matrix to generate the error torque in the Lie algebra space. The Lie group error compensation module is also used to convert the error torque into a special Euclidean group using an exponential mapping function. The correction matrix in the nominal machining path is superimposed onto the pose matrix of the nominal machining path to obtain the final multi-axis linkage compensation command.

[0065] Specifically, the path fine-tuning amount calculated above is a vector in the tangent space of the manifold. For the earth-shaft machine tool, directly superimposing the vectors will lead to coupling errors between the rotating axis and the half-moving axis. Therefore, this embodiment uses a Lie group. The theory is precisely mapped;

[0066] The system first calculates the covariant derivative of the manifold metric along the tool feed direction. This represents the rate of change of the spatial gradient caused by the physical field, which is then used to explore the adjoint effects of Lie groups. The gradient rate of change, established in the tool's local coordinate system, is transformed to the machine tool's base coordinate system (inertial frame), and then combined with the stiffness matrix of the machine tool servo system. The Lie algebra space was calculated. Error torque in :

[0067]

[0068] This error torque It is a 6-dimensional vector, with the first 3 dimensions representing linear velocity compensation, the last 3 dimensions representing angular velocity compensation, and finally, it is mapped exponentially. Convert the error torque back to a Lie group The homogeneous transformation matrix in the matrix is ​​then multiplied by left or right into the nominal pose matrix generated by the CAM. The final multi-axis linkage compensation command is obtained. This process ensures that the compensated motion strictly satisfies the rigid body kinematic constraints, avoiding the gimbal lock and singularity problems in traditional Euler angle compensation.

[0069] The system also includes a symplectic geometry prediction and correction module for connection with the dynamic manifold construction module. The symplectic geometry prediction and correction module is used to construct a dual symplectic space containing real and virtual manifolds. The real manifold is driven by multi-source sensor data at the current moment, while the virtual manifold is constructed based on the machining history data of the previous toolpath and is used to store the historical momentum distribution of the cutting load. The operating mechanism of the symplectic geometry prediction and correction module is as follows: using the Hamiltonian flow evolution equation, the load state distribution ahead of the current toolpath is deduced in the virtual manifold; when a cutting impedance abrupt change is predicted ahead, a feedforward adjustment signal is generated and sent to the dynamic manifold construction module to adjust the construction parameters of the Finsler manifold space in advance, so that the manifold curvature is deformed in advance before the tool reaches the abrupt change point.

[0070] Specifically, to address the control delay caused by sensor signal hysteresis, this system introduces a symplectic geometric prediction mechanism. The system constructs a dual symplectic space (phase space) containing two manifolds:

[0071] Real manifold: from the current time Driven by real-time sensor data;

[0072] Virtual manifold: Based on the previous line of toolpath ( The system constructs historical data, and because precision machining typically employs row cutting or ring cutting methods, the physical states between adjacent toolpaths are highly similar. Therefore, the system stores the load distribution of the previous toolpath as a generalized momentum field on a virtual manifold. When processing the current row, the system utilizes the Hamiltonian evolution equation:

[0073]

[0074] Anticipate the load state in front of the current tool position on the virtual manifold. When the prediction results indicate that the area ahead is about to enter a hard spot or discontinuous cutting zone (leading to...) When there is a sudden increase, the system generates a feedforward signal to increase the weighting coefficients of the above steps in advance. and This allows the Finsler manifold in the above steps to be mathematically raised before the tool actually contacts the hard point. Therefore, the generated geodesic path will make smooth attitude adjustments or avoidance actions in advance, thereby eliminating the hysteresis error in traditional feedback control and realizing intelligent machining with predictive capabilities.

[0075] A CNC machine tool, wherein the system is integrated and deployed in the real-time control kernel of the CNC machine tool, and the calculation cycle of the path auto-evolution module and the Lie group error compensation module is synchronized with the interpolation cycle of the CNC machine tool;

[0076] Specifically, since this system can be integrated into the real-time kernel or edge computing module of a high-performance CNC system, the operation cycle of each module is synchronized with the interpolation cycle (e.g., 1ms or 0.5ms), thus ensuring the real-time performance and response speed of the control.

[0077] In summary, this invention provides a high-efficiency precision component machining system based on intelligent toolpath optimization. By introducing anisotropic Finsler manifold construction technology driven by multi-source physical fields, it overcomes the limitations of traditional toolpath planning that relies solely on static geometric models. It transforms dynamic physical quantities such as cutting force and vibration into metric tensors in geometric space and uses direction correction factors to endow the manifold with anisotropic characteristics. This allows the machining system to adapt to material texture direction and real-time load changes, achieving a qualitative leap from rigid execution to flexible adaptation in the machining process. Through a geodesic path auto-evolution mechanism based on variational principles, it solves the conflict and computational efficiency problems in multi-objective optimization. Unlike traditional methods that require complex weighted iterative solutions for efficiency, accuracy, and energy consumption, this invention unifies all optimization objectives into a shortest path problem in manifold space. The tool's movement along the geodesic means automatically finding the trajectory with the minimum energy consumption (lowest impedance) while satisfying tolerance constraints. This endogenous optimization mechanism based on physical geometric field theory can generate the optimal toolpath that balances efficiency and quality without manual intervention, significantly improving the intelligence level of precision machining. Furthermore, by establishing Lie groups... The spatial error covariant mapping compensation model overcomes the nonlinear coupling problem in traditional multi-axis machining error compensation. The system uses covariant derivatives to extract the gradient changes of the manifold physical field and accurately maps them into error torque in Lie algebra space through adjoint effects. Compared with traditional Euler angle linear compensation, this Lie group manifold-based computation method strictly follows rigid body kinematics constraints, effectively avoiding universal joint deadlock and singularity problems in five-axis linkage machining, and significantly improving the geometric accuracy and motion stability of dynamic error compensation in complex surface machining.

[0078] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency machining system for precision parts based on intelligent toolpath optimization, characterized in that, The system comprises, in sequence, a multi-source physical sensing module for data interaction and connection, a dynamic manifold construction module, a path autonomous evolution module, and a Lie group error compensation module; The multi-source physical sensing module is used to collect multi-source sensing data in real time during the machining process, and combine it with the tool feed direction to map the multi-source sensing data into a cutting impedance tensor with directional attributes. The dynamic manifold construction module is used to receive the cutting impedance tensor and fuse it with the basic geometric constraints of the workpiece to construct an anisotropic Finsler manifold space, wherein the geometric metric field of the Finsler manifold space evolves in real time with the numerical change of the cutting impedance tensor to change the equivalent distance at various points in the space. The path-auto-evolution module is used to plan an adaptive evolution toolpath that can avoid high curvature and high impedance regions in the Finsler manifold space by solving the geodesic differential equation for the minimum value of the energy functional. The Lie group error compensation module is used to analyze the geometric variation of the adaptive evolution toolpath relative to the nominal path based on the differential mapping relationship between the Lie group space and the Lie algebra space, generate multi-axis linkage compensation instructions adapted to the kinematics of the multi-axis machine tool, and use the multi-axis linkage compensation instructions to drive the machine tool to perform machining in real time.

2. The high-efficiency machining system for precision parts based on intelligent toolpath optimization according to claim 1, characterized in that, The specific configuration for constructing the cutting impedance tensor by the multi-source physical sensing module is as follows: Real-time acquisition of spindle load current and tool tip vibration acceleration is used as the multi-source sensing data; Calculate the current tool feed unit direction vector; Using tensor product operations, the amplitude of the spindle load current is assigned a vector characteristic to the unit feed direction vector of the tool, and the amplitude of the tool tip vibration acceleration is used as an isotropic component to weight and synthesize the cutting impedance tensor.

3. The high-efficiency machining system for precision parts based on intelligent toolpath optimization according to claim 1, characterized in that, In the dynamic manifold construction module, the geometric metric field of the Finsler manifold space is composed of a linear superposition of Euclidean metric and cutting impedance tensor; The superposition process includes an anisotropy correction factor, which is dynamically calculated based on the angle between the tool feed direction and the principal axis of the workpiece material properties. This factor is used to adjust the weights of the cutting impedance tensor in different directions, thereby giving the Finsler manifold a spatially oriented metric property.

4. The high-efficiency machining system for precision parts based on intelligent toolpath optimization according to claim 3, characterized in that, The dynamic manifold building module is configured as follows: When the tool feed direction is consistent with the weak stiffness direction of the workpiece material, the corresponding metric weight is increased by the anisotropy correction factor, resulting in an increase in the local spatial curvature of the Finsler manifold space in that direction.

5. The high-efficiency machining system for precision parts based on intelligent toolpath optimization according to claim 1, characterized in that, The specific configuration for the adaptive evolution toolpath generated by the path-auto-evolution module is as follows: Based on the metric function and its partial derivatives of the Finsler manifold space, the jet coefficient characterizing the nonlinear connection is calculated; Construct a second-order geodesic differential equation that incorporates the jetting coefficient and geometric tolerance zone constraints; The second-order geodesic differential equation is solved by numerical integration to obtain the tool position sequence at the next moment. The tool position sequence constitutes the adaptive evolution toolpath with the minimum cutting potential energy within the tolerance zone constraint.

6. The high-efficiency machining system for precision parts based on intelligent toolpath optimization according to claim 1, characterized in that, The specific configuration for the Lie group error compensation module to generate the multi-axis linkage compensation command is as follows: Calculate the covariant derivative of the metric tensor of the Finsler manifold space along the tool feed direction to extract the rate of change of the spatial gradient caused by the physical field; Using the adjoint operator of the Lie group, the spatial gradient change rate is transformed from the tool local coordinate system to the machine tool base coordinate system, and combined with the machine tool servo stiffness matrix, the error torque in the Lie algebra space is generated.

7. The high-efficiency machining system for precision parts based on intelligent toolpath optimization according to claim 6, characterized in that, The Lie group error compensation module is also used for: Using an exponential mapping function, the error torque is converted into a special Euclidean group. The correction matrix in; The correction matrix is ​​superimposed on the pose matrix of the nominal machining path to obtain the final multi-axis linkage compensation command.

8. The high-efficiency machining system for precision parts based on intelligent toolpath optimization according to claim 1, characterized in that, The system also includes a symplectic geometry prediction and correction module for connection to the dynamic manifold construction module; The symplectic geometry prediction and correction module is used to construct a dual symplectic space containing real and imaginary manifolds; The real manifold is driven by the multi-source sensor data at the current moment, while the virtual manifold is constructed based on the machining history data of the previous toolpath and is used to store the historical momentum distribution of the cutting load.

9. The high-efficiency machining system for precision parts based on intelligent toolpath optimization according to claim 8, characterized in that, The operating mechanism of the symplectic geometric prediction and correction module is as follows: Using the Hamiltonian flow evolution equation, the load state distribution in front of the current toolpath is deduced in the virtual manifold; When a sudden change in cutting impedance is predicted ahead, a feedforward adjustment signal is generated and sent to the dynamic manifold construction module to adjust the construction parameters of the Finsler manifold space in advance, so that the manifold curvature is deformed in advance before the tool reaches the sudden change point.

10. A CNC machine tool, characterized in that, According to any one of claims 1-9, the precision component high-efficiency machining system based on intelligent toolpath optimization is integrated into the real-time control kernel of a CNC machine tool, and the calculation cycle of the path auto-evolution module and the Lie group error compensation module is synchronized with the interpolation cycle of the CNC machine tool.

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