Dynamic control method for complex curved surface five-axis numerical control machining tool vector

Abstract

The invention discloses a dynamic control method for a complex curved surface five-axis numerical control machining tool vector, which comprises the following steps of: scanning the surface of a target workpiece by using a high-precision three-dimensional scanner to obtain point cloud data; based on the point cloud data, establishing a mathematical model S (u, v) of the target complex curved surface; a mathematical model based on the target complex curved surface; establishing a comprehensive dynamic model of the five-axis numerical control system; optimizing the initial tool posture in combination with the kinetic model; collecting dynamic operation data of the cutter and the workpiece; on the basis of the dynamic operation data, a model predictive control (MPC) algorithm is used for adjusting the machining path and the cutter direction in real time; according to the method, a comprehensive dynamic model of a five-axis numerical control system is established, dynamic coupling effects of a machine tool kinematic chain, cutter flexibility, cutting force, a temperature field and a stress field are brought into a unified framework, and complex dynamic behaviors are accurately described and controlled through multi-body dynamic modeling and an optimization algorithm.

Description

Technical Field

[0001] The invention relates to the technical field of dynamics control, and in particular to a dynamics control method for a tool vector in five-axis numerical control machining of complex curved surfaces. Background Art

[0002] With the development of high-end manufacturing technology, complex curved surface parts are increasingly used in aerospace, medical equipment, automobile manufacturing and other fields. Five-axis CNC machining technology has become the main means of machining complex curved surface parts due to its high degree of freedom and flexibility. However, in the actual machining process, the accuracy of tool direction (tool vector) control plays a key role in machining quality, surface roughness and part shape accuracy.

[0003] Complex surface machining involves multi-dimensional coupled dynamic behaviors, including the dynamic characteristics of the machine tool motion chain, the influence of tool flexibility on machining accuracy, mechanical vibration during the cutting process, thermal deformation and the combined effects of stress fields. Traditional methods are difficult to fully characterize and dynamically control these coupled behaviors, which often lead to uneven machining paths, large fluctuations in cutting forces, and strong tool vibrations, thereby reducing machining accuracy and surface quality. In addition, the dynamic changes in the CNC machining environment will make it difficult for the initially set machining parameters to meet actual needs, resulting in error accumulation and tool loss.

[0004] The existing tool vector control methods mainly rely on static geometric models, which cannot respond to changes in dynamic conditions during the machining process in real time, and lack online optimization and adjustment mechanisms. At the same time, in the five-axis machining path planning of complex surfaces, due to insufficient point cloud data preprocessing and limited accuracy of the path generation algorithm, it is difficult to take both machining efficiency and surface quality into account. Therefore, there is an urgent need for a tool vector dynamics control method that combines real-time online monitoring and dynamic modeling, which can comprehensively consider the dynamic response and environmental changes during the machining process and realize adaptive optimization control of the tool posture and path. Summary of the invention

[0005] The purpose of the present invention is to provide a dynamic control method for a tool vector in five-axis CNC machining of complex surfaces to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a dynamic control method for a tool vector in five-axis CNC machining of a complex curved surface, comprising the following steps: Use a high-precision 3D scanner to scan the surface of the target workpiece and obtain point cloud data; Based on the point cloud data, a mathematical model S(u,v) of the target complex surface is established by using a non-uniform rational B-spline (NURBS) fitting technique; Based on the mathematical model of the target complex surface, a smooth machining path is generated by using quintic spline interpolation, the machining path points and the tool contact normal vector are determined, the tool posture of each path point is calculated, and the tool vector direction is preliminarily generated; Based on the dynamic coupling effects of machine tool kinematic chain, tool flexibility, cutting force, temperature field and stress field, a comprehensive dynamic model of the five-axis CNC system is established; In combination with the dynamic model, the initial tool posture is optimized; A torque sensor, a vibration sensor and a temperature sensor are arranged on the machine tool, and an online monitoring system composed of the torque sensor, the vibration sensor and the temperature sensor is used to collect dynamic operation data of the tool and the workpiece in real time; Based on dynamic operation data, the model predictive control (MPC) algorithm is used to adjust the processing path and tool direction in real time.

[0007] Furthermore, the acquired point cloud data needs to go through the following processing steps: Use the nearest neighbor judgment method to identify and remove outliers and noise in the point cloud; Use median filtering or Gaussian filtering techniques to smooth the data; Use SIFT, a feature point analysis method, for preliminary alignment to ensure consistency of multi-view data; Implement the iterative closest point ICP algorithm to stitch multi-view point clouds and eliminate overlapping differences; Downsample the point cloud through a voxel grid; The reconstruction algorithm Poisson Surface Reconstruction is used to convert the point cloud into a polygonal mesh and construct a continuous Oberfläche.

[0008] Furthermore, based on the point cloud data, a mathematical model S(u,v) of the target complex surface is established by using a non-uniform rational B-spline (NURBS) fitting technique, including: Determine the parameter domain of the spline surface through initial parameterization, and calculate the control points and corresponding weights; Set the node vector and use the NURBS fitting method to generate the surface; The fitting error is minimized by iteratively adjusting the control points, weights, and knot vectors.

[0009] Furthermore, the mathematical model based on the target complex surface uses quintic spline interpolation to generate a smooth machining path, determines the machining path points and the tool contact normal vector, calculates the tool posture of each path point, and preliminarily generates the tool vector direction; including: According to the surface model, key path points are extracted therefrom, and interpolation nodes are set for the path points; Using the quintic spline interpolation algorithm, the smooth path coordinates of each point are calculated; At each point on the path, determine the tool's normal vector, which is the optimal direction for tool contact; Combined with the normal vector, the tool posture at each path point is calculated; The quintic spline interpolation calculates points on the path: P(t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5 Among them, the coefficient a i Determined based on the geometry and smoothness conditions of the path points.

[0010] Furthermore, the comprehensive dynamic model of the five-axis CNC system is established based on the dynamic coupling effect of the machine tool kinematic chain, tool flexibility, cutting force, temperature field and stress field; including: Analyze and model the dynamic characteristics of each kinematic chain of the machine tool and determine its mass, damping and stiffness parameters; Obtain its frequency response characteristics through finite element analysis or experimental data; Based on the tool-workpiece interaction analysis, the influence of cutting force is quantified, and the distribution model of temperature field and stress field is established by combining physical experiments and numerical simulations; Through the multi-body dynamics method, the above mechanical characteristics are integrated into a coupled model, and mathematical tools are used to describe its dynamic response; The accuracy of the model is verified through parameter identification and numerical simulation.

[0011] Furthermore, the kinetic model equation is as follows: M(q)q¨+C(q,q˙)q˙+G(q)= , Where M(q) is the mass matrix, C(q,q˙) is the Coriolis moment, G(q) is the gravity moment, is the control input.

[0012] Furthermore, the initial tool posture is optimized in combination with the dynamic model; including: Based on the pre-built dynamic model, the initial posture of the tool is decomposed into moments, and the inertia matrix, coupling moment and friction moment at the starting point of the machining path are calculated; Through the constrained optimization algorithm, the optimal initial tool posture is solved with the goal of minimizing the condition number of the tool posture; The optimization results are fed back to the initial setting parameters of the CNC system and dynamically adjusted in combination with the dynamics feedforward control module.

[0013] Furthermore, the initial tool posture is optimized in combination with the dynamic model, and the optimization objectives include minimizing cutting force fluctuation, tool vibration and error accumulation, and the tool direction is adjusted by the constrained gradient descent method, and the formula is as follows: J(θ)=α1⋅RMS(F cut )+α2⋅RMS(v tool )+α3⋅CE (θ) in: θ=[θx,θy,θz] is the tool posture parameter (expressed in the form of tool direction vector or Euler angle, etc.).

[0014] F cut is the cutting force, v tool is the tool vibration speed.

[0015] α1, α2, and α3 are weight coefficients that control the trade-offs between different optimization objectives.

[0016] RMS(F cut ) is the root mean square value of cutting force fluctuation, RMS(v tool ) is the root mean square value of tool vibration, CE (θ) is the accumulated amount of attitude error.

[0017] Furthermore, the dynamic operation data of the tool and the workpiece include cutting torque, spindle torque, tool vibration frequency and acceleration.

[0018] Furthermore, the processing path and tool direction are adjusted in real time based on the dynamic operation data using a model predictive control (MPC) algorithm; including: Input the dynamic model control (MPC) algorithm through the CNC system; Combine current data with the dynamics model to calculate the adjustment of machining path and tool direction in real time; The calculation results are fed back to the motion control module of the machine tool as control instructions to dynamically adjust the feed position and posture of the tool; In each control cycle, the model control parameters are updated according to the new dynamic operation data, and the adjustment process is repeated; The dynamics model and control signals are continuously updated through a feedback mechanism.

[0019] Technical effects and advantages of the present invention: 1. The present invention establishes a mathematical model of the target complex surface by utilizing high-precision point cloud data processing technology and NURBS surface fitting, which can accurately describe the surface shape; combined with the smooth processing path generated by quintic spline interpolation, it can effectively reduce the impact of path fluctuations on processing accuracy and improve surface quality.

[0020] 2. By installing torque, vibration, temperature and other sensors on the machine tool, an online monitoring system is constructed, and the MPC algorithm is combined to adjust the processing path and tool direction in real time, so that the system can dynamically respond to changes in the processing environment and reduce cutting force fluctuations and tool vibration.

[0021] 3. By optimizing the initial tool posture and combining the dynamic model to minimize cutting force fluctuations and vibrations, the tool wear rate can be significantly reduced, the tool life can be extended, and the processing cost can be reduced.

[0022] 4. By establishing a comprehensive dynamic model of the five-axis CNC system, the dynamic coupling effects of the machine tool motion chain, tool flexibility, cutting force, temperature field and stress field are incorporated into a unified framework, and the complex dynamic behavior is accurately described and controlled through multi-body dynamics modeling and optimization algorithms.

[0023] 5. The constrained gradient descent method is used to optimize the tool direction. The weight coefficient is used to flexibly adjust the balance of cutting force fluctuation, tool vibration and error accumulation to achieve global optimization of the processing path and tool posture, ensuring the dual satisfaction of processing efficiency and accuracy.

[0024] 6. The method of the present invention is applicable to five-axis CNC machining scenarios of various complex surfaces, including the machining of free-form surfaces, blade parts and other high-precision complex parts, providing reliable technical support for the field of precision manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a schematic flow chart of a method for controlling the dynamics of a tool vector in five-axis CNC machining of complex surfaces. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Reference Figure 1 The present invention proposes a method for controlling the dynamics of a tool vector in five-axis CNC machining of a complex curved surface, comprising the following steps: S1; Use a high-precision 3D scanner to scan the surface of the target workpiece and obtain point cloud data; In one embodiment, a high-precision three-dimensional scanner is used to scan the surface of a target workpiece to obtain point cloud data; including: S1.1; Use the nearest neighbor judgment method to identify and remove outliers and noise in the point cloud; S1.2; Use median filtering or Gaussian filtering technology to smooth the data; S1.3; Use SIFT, a feature point analysis method, for preliminary alignment to ensure consistency of multi-view data; S1.4; Implement the iterative closest point ICP algorithm to stitch multi-view point clouds and eliminate overlapping differences; S1.5; downsampling the point cloud through a voxel grid; The reconstruction algorithm Poisson Surface Reconstruction is used to convert the point cloud into a polygonal mesh and construct a continuous Oberfläche.

[0028] In the specific implementation process, first, the nearest neighbor judgment method (such as distance-based clustering or statistical filtering) is applied to identify and remove outliers and noise in the point cloud to ensure the validity and accuracy of the data. The point cloud data is smoothed using median filtering or Gaussian filtering technology to remove high-frequency noise while retaining the details of the surface morphology. The median filter suppresses outliers by replacing each point with the median of its neighborhood; the Gaussian filter achieves smoothing by weighted averaging of neighborhood data, and the feature point analysis method SIFT (scale-invariant feature transform) is applied to extract significant feature points in the point cloud and perform preliminary alignment. This step helps to process the consistency of multi-view point cloud data and ensure the accuracy of data alignment between different viewpoints. The iterative closest point (ICP) algorithm is used, which performs fine stitching by minimizing the distance between two sets of point clouds to eliminate overlapping differences caused by different scanning angles. The objective function of the ICP algorithm is; , where p i and q i Represent the points in the source point cloud and the target point cloud respectively, T is the transformation matrix, and N is the total number of points in the point cloud. The voxel grid method is used to downsample the point cloud data to reduce the density of the point cloud data while maintaining sufficient geometric information. This method achieves downsampling by dividing the space into voxels (volume pixels) and retaining the representative points in each voxel. The Poisson Surface Reconstruction algorithm is used to convert the downsampled point cloud into a polygonal mesh to generate a continuous surface model. This algorithm improves the quality of point cloud data by solving a Poisson equation to restore a continuous and smooth surface.

[0029] S2: Based on the point cloud data, a mathematical model S(u,v) of the target complex surface is established by using a non-uniform rational B-spline (NURBS) fitting technique; In one embodiment, the mathematical model S(u,v) of the target complex surface is established based on the point cloud data by using a non-uniform rational B-spline (NURBS) fitting technique, including: S2.1; Determine the parameter domain of the spline surface through initial parameterization, and calculate the control points and corresponding weights; S2.2; Set the node vector and use the NURBS fitting method to generate the surface; S2.3; Minimize the fitting error by iteratively adjusting the control points, weights and node vectors.

[0030] In the specific implementation process, we first use a parametric method (such as uniform parameterization, arc length parameterization or local weighting method) to assign parameter values ​​(u, v) to the point cloud data, determine the parameter domain of the spline surface, and then use the least squares method to calculate the control points P required for the NURBS surface. i,j and weight w i,j , initially construct the surface control grid, determine the node vectors U and V (corresponding to the parameters in the u and v directions respectively) according to the parameterized points, adopt a non-uniform node distribution method to make the surface adapt to the geometric characteristics of the point cloud data, and use the NURBS surface equation to calculate the surface model, , Among them, N i,p (u) and N j,q (v) p-order and q-order B-spline basis functions based on the node vector; calculating the error between the point cloud data and the initial fitting surface, and defining the error function; , Where Q k is a point cloud point, S(u k ,v k ) is the corresponding point of the surface. The error E is minimized by adjusting the control point Pi,j, the weight wi,j and the node vector U,V. Through the above steps, the NURBS surface generation based on point cloud data is completed, and the high-precision mathematical modeling of the target complex surface is achieved.

[0031] S3; based on the mathematical model of the target complex surface, a smooth machining path is generated by using quintic spline interpolation, the machining path points and the tool contact normal vector are determined, the tool posture of each path point is calculated, and the tool vector direction is preliminarily generated; In one embodiment, the mathematical model based on the target complex surface uses quintic spline interpolation to generate a smooth machining path, determines the machining path points and the tool contact normal vector, calculates the tool posture of each path point, and preliminarily generates the tool vector direction; including: S3.1; extracting key path points from the surface model and setting interpolation nodes for the path points; S3.2; using the quintic spline interpolation algorithm, calculate the smooth path coordinates of each point; S3.3; at each point on the path, determine the normal vector of the tool, that is, the optimal direction of tool contact; S3.4; Calculate the tool posture at each path point in combination with the normal vector; The quintic spline interpolation calculates points on the path: P(t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5 Among them, the coefficient a i Determined based on the geometry and smoothness conditions of the path points.

[0032] In the specific implementation process, according to the mathematical model S(u,v) of the complex surface, the key path points of the processing area are selected to ensure the coverage of the target surface. The parameter value t is assigned to each path point, which is usually evenly distributed or adjusted based on the processing requirements as the node of the quintic spline interpolation. The quintic spline interpolation formula is used: P(t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5 Determine the smooth path coordinates P(t) of each point, where the coefficient a i Through the geometric constraints of the path points and the smooth conditions (such as continuity), at each point P(t) on the path, the surface normal calculation formula is: , Determine the tool's normal vector N as the optimal direction for the tool to contact the surface. Combine the normal vector N and the tangent vector T of the machining path (which can be calculated by the derivative of the interpolation curve), and use the coordinate transformation method to calculate the tool posture matrix R for each path point to determine the tool vector direction, that is, the spatial direction vector or angle of the tool.

[0033] S4; Based on the dynamic coupling effect of machine tool kinematic chain, tool flexibility, cutting force, temperature field and stress field, a comprehensive dynamic model of the five-axis CNC system is established; In one embodiment, the comprehensive dynamic model of the five-axis CNC system is established based on the dynamic coupling effect of the machine tool kinematic chain, tool flexibility, cutting force, temperature field and stress field; including: S4.1; Analyze and model the dynamic characteristics of each kinematic chain of the machine tool and determine its mass, damping and stiffness parameters; S4.2; Obtain its frequency response characteristics through finite element analysis or experimental data; S4.3; Based on the tool-workpiece interaction analysis, quantify the influence of cutting force, and establish the distribution model of temperature field and stress field by combining physical experiments and numerical simulation; S4.4; Through the multi-body dynamics method, the above mechanical characteristics are integrated into a coupled model, and mathematical tools are used to describe its dynamic response; S4.5; Verify the accuracy of the model through parameter identification and numerical simulation.

[0034] In the specific implementation process, first, the dynamic characteristics of each kinematic chain of the machine tool are analyzed to clarify its mass, damping and stiffness parameters to ensure the accurate description of the behavior of each mechanical chain; secondly, finite element analysis or experimental data is used to evaluate the frequency response characteristics of the system to find the natural frequencies that may affect the dynamic performance, which is helpful to predict the vibration mode and system stability; next, by analyzing the interaction between the tool and the workpiece, the influence of the cutting force is quantified, and a detailed temperature field and stress field distribution model is constructed by combining physical experiments and numerical simulations to capture the influence of environmental factors during the cutting process; then, the multi-body dynamics method is applied to integrate the mechanical characteristics of the above aspects into a unified coupling model, and mathematical tools are used to describe its dynamic response to achieve reasonable prediction of complex coupled vibrations and dynamic loads; finally, parameter identification and numerical simulation are carried out, and the accuracy and applicability of the model are verified by adjusting the model parameters to ensure the reliability and guiding role of the model in actual processing conditions.

[0035] The kinetic model equation is as follows: M(q)q¨+C(q,q˙)q˙+G(q)= , Where M(q) is the mass matrix, C(q,q˙) is the Coriolis moment, G(q) is the gravity moment, is the control input.

[0036] S5; optimizing the initial tool posture in combination with the dynamic model; In one embodiment, the initial tool posture is optimized in combination with the dynamic model; comprising: S5.1; Based on the pre-built dynamic model, the initial posture of the tool is decomposed into torques, and the inertia matrix, coupling torque and friction torque at the starting point of the machining path are calculated; S5.2; by using the constrained optimization algorithm, the optimal initial tool posture is solved with the goal of minimizing the condition number of the tool posture; S5.3; Feedback the optimization results to the initial setting parameters of the numerical control system, and perform dynamic adjustments in combination with the dynamic feedforward control module.

[0037] The optimization objectives include minimizing cutting force fluctuation, tool vibration and error accumulation, and the tool direction is adjusted using the constrained gradient descent method. The formula is as follows: J(θ)=α1⋅RMS(F cut )+α2⋅RMS(v tool )+α3⋅CE (θ) in: θ=[θx,θy,θz] is the tool posture parameter (expressed in the form of tool direction vector or Euler angle, etc.).

[0038] F cut is the cutting force, v tool is the tool vibration speed.

[0039] α1, α2, and α3 are weight coefficients that control the trade-offs between different optimization objectives.

[0040] RMS(F cut ) is the root mean square value of cutting force fluctuation, RMS(v tool ) is the root mean square value of tool vibration, CE (θ) is the accumulated amount of attitude error.

[0041] In the specific implementation process, first, the pre-built dynamic model is used to decompose the torque, focusing on calculating the inertia matrix, coupling torque and friction torque at the starting point of the machining path. This analysis helps to identify the influencing factors and dynamic characteristics of the tool posture on the machining mechanics; then, the constrained optimization algorithm is used to minimize the condition number of the tool posture as the initial goal, and the tool initial posture parameters are optimized by defining relevant constraints to enhance the stability of the system by reducing the condition number; further, in order to minimize the cutting force fluctuation, minimize the tool vibration and minimize the error accumulation during the machining process, the constrained gradient descent method is used to finely adjust the tool direction, and the optimization formula is as above; in the iterative process, according to the optimized objective function, the tool direction is adjusted in real time to reduce the objective function value; finally, the calculated optimal initial posture is fed back to the setting parameters of the CNC system to ensure the high precision of the machine tool startup and operation, and combined with the dynamic feedforward control module, the posture in the actual machining process is dynamically adjusted to ensure the improvement of machining accuracy and surface quality. At the same time, through real-time monitoring and data feedback, iterative optimization ensures that the design goals are continuously met during the machining process.

[0042] S6; a torque sensor, a vibration sensor and a temperature sensor are set on the machine tool, and an online monitoring system composed of the torque sensor, the vibration sensor and the temperature sensor is used to collect dynamic operation data of the tool and the workpiece in real time; In one embodiment, the dynamic operation data of the tool and the workpiece include cutting torque, spindle torque, tool vibration frequency and acceleration.

[0043] In the specific implementation process, first, torque sensors, vibration sensors and temperature sensors are installed at key positions of the machine tool. Torque sensors are installed on the spindle and feed system to monitor the changes in cutting torque and spindle torque in real time, thereby reflecting the mechanical interaction between the tool and the workpiece; vibration sensors should be arranged close to the tool installation position and the workpiece clamping area to monitor the tool vibration frequency and acceleration and capture any unstable vibration or resonance effect generated during the processing; at the same time, temperature sensors are configured near the tool and the workpiece to obtain temperature changes in the cutting area, which helps to identify problems such as thermal expansion and thermal wear. Next, these sensor data are integrated into a centralized online monitoring system, and real-time data is transmitted to the central processing unit through the data acquisition module. The unit should be combined with data analysis software to process the cutting torque, spindle torque, tool vibration frequency and acceleration in real time to identify potential anomalies. When dynamic changes or vibration patterns exceeding the preset threshold are found during the processing process, the system can trigger an alarm or automatically adjust the processing parameters to ensure the stability of the processing process and the processing quality of the parts. In addition, through the accumulation and analysis of historical data, the processing parameter settings and maintenance plans are optimized to extend the life of equipment and tools and improve overall production efficiency.

[0044] S6: Based on dynamic operation data, the model predictive control (MPC) algorithm is used to adjust the processing path and tool direction in real time; In one embodiment, the process of adjusting the machining path and tool direction in real time based on the dynamic operation data and using a model predictive control (MPC) algorithm includes: S6.1; Input the dynamic model control (MPC) algorithm through the numerical control system; S6.2: Combine the current data with the dynamic model to calculate the adjustment amount of the machining path and tool direction in real time; S6.3: Feedback the calculation results as control instructions to the motion control module of the machine tool to dynamically adjust the feed position and posture of the tool; S6.4; in each control cycle, the model control parameters are updated according to the new dynamic operation data, and the adjustment process is repeated; S6.5; Continuously update the dynamic model and control signals through a feedback mechanism.

[0045] In the specific implementation process, first, the MPC algorithm based on the dynamic model is initialized and input into the CNC system. The algorithm integrates the dynamic characteristics and processing parameters of the machine tool to provide theoretical support for the subsequent path and posture adjustment. Then, the dynamic operation data from the torque, vibration and temperature sensors are obtained in real time and combined with the existing dynamic model. The current state is analyzed by the MPC algorithm to calculate the required adjustment amount of the processing path and tool direction to ensure the stability and accuracy of the processing. Then, the calculation results are used as specific control instructions and quickly fed back to the motion control module of the machine tool. This module dynamically adjusts the feed position and posture of the tool based on this to respond to any changes in the processing process in a timely manner. In each control cycle, the system further updates the model control parameters according to the latest dynamic operation data to ensure the continued effectiveness of the adjustment strategy, and continuously optimizes the processing path and tool posture through repeated control cycles. Finally, a continuous feedback mechanism is used to monitor and update the dynamic model and control signals to ensure the adaptive and precise control of the entire processing process. At the same time, the feedback of historical data also provides a reference basis for further refinement of the model and formulation of future control strategies. This dynamic adjustment method improves the responsiveness of the processing process and product quality.

[0046] The present invention uses high-precision point cloud data processing technology and NURBS surface fitting to establish a mathematical model of the target complex surface, which can accurately describe the surface shape; the smooth processing path generated by combining quintic spline interpolation can effectively reduce the impact of path fluctuations on processing accuracy and improve surface quality; by installing torque, vibration, temperature and other sensors on the machine tool, building an online monitoring system, and combining the MPC algorithm to adjust the processing path and tool direction in real time, the system can dynamically respond to changes in the processing environment and reduce cutting force fluctuations and tool vibrations; by optimizing the initial tool posture and combining the dynamic model to minimize cutting force fluctuations and vibrations, the tool wear rate can be significantly reduced, the tool life can be extended, and the processing cost can be reduced. ; By establishing a comprehensive dynamic model of the five-axis CNC system, the dynamic coupling effects of the machine tool motion chain, tool flexibility, cutting force, temperature field and stress field are included in a unified framework, and the complex dynamic behavior is accurately described and controlled through multi-body dynamics modeling and optimization algorithms; the constrained gradient descent method is used to optimize the tool direction, and the balance of cutting force fluctuation, tool vibration and error accumulation is flexibly controlled by the weight coefficient to achieve global optimization of the processing path and tool posture, ensuring the dual satisfaction of processing efficiency and accuracy; the method of the present invention is suitable for five-axis CNC processing scenarios of various complex surfaces, including the processing of free-form surfaces, blade-type parts and other high-precision complex parts, providing reliable technical support for the field of precision manufacturing.

[0047] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dynamic control method for a tool vector in five-axis CNC machining of complex surfaces, characterized in that: The steps include: Use a high-precision 3D scanner to scan the surface of the target workpiece and obtain point cloud data; Based on the point cloud data, a mathematical model S(u,v) of the target complex surface is established by using a non-uniform rational B-spline (NURBS) fitting technique; Based on the mathematical model of the target complex surface, a smooth machining path is generated by using quintic spline interpolation, the machining path points and the tool contact normal vector are determined, the tool posture of each path point is calculated, and the tool vector direction is preliminarily generated; Based on the dynamic coupling effects of machine tool kinematic chain, tool flexibility, cutting force, temperature field and stress field, a comprehensive dynamic model of the five-axis CNC system is established; In combination with the dynamic model, the initial tool posture is optimized; A torque sensor, a vibration sensor and a temperature sensor are arranged on the machine tool, and an online monitoring system composed of the torque sensor, the vibration sensor and the temperature sensor is used to collect dynamic operation data of the tool and the workpiece in real time; Based on dynamic operation data, the model predictive control (MPC) algorithm is used to adjust the processing path and tool direction in real time.

2. The method for dynamic control of tool vectors for five-axis CNC machining of complex curved surfaces according to claim 1, characterized in that: The acquired point cloud data undergoes the following processing steps: Use the nearest neighbor judgment method to identify and remove outliers and noise in the point cloud; Use median filtering or Gaussian filtering techniques to smooth the data; Use SIFT, a feature point analysis method, for preliminary alignment; Implement the iterative closest point ICP algorithm to stitch multi-view point clouds; Downsample the point cloud through a voxel grid; The point cloud is converted into a polygonal mesh using the reconstruction algorithm Poisson Surface Reconstruction.

3. The method for dynamic control of tool vectors for five-axis CNC machining of complex curved surfaces according to claim 1, characterized in that: Based on the point cloud data, a mathematical model S(u,v) of the target complex surface is established by using a non-uniform rational B-spline (NURBS) fitting technique, including: Determine the parameter domain of the spline surface through initial parameterization, and calculate the control points and corresponding weights; Set the node vector and use the NURBS fitting method to generate the surface; The fitting error is minimized by iteratively adjusting the control points, weights, and knot vectors.

4. The method for dynamic control of tool vectors for five-axis CNC machining of complex curved surfaces according to claim 1, characterized in that: The mathematical model based on the target complex surface uses quintic spline interpolation to generate a smooth machining path, determines the machining path points and the tool contact normal vector, calculates the tool posture of each path point, and preliminarily generates the tool vector direction; specifically: According to the surface model, key path points are extracted therefrom, and interpolation nodes are set for the path points; Using the quintic spline interpolation algorithm, the smooth path coordinates of each point are calculated; At each point on the path, determine the tool's normal vector, which is the optimal direction for tool contact; Combined with the normal vector, the tool posture at each path point is calculated; The quintic spline interpolation calculates points on the path: P(t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5 Among them, the coefficient a i Determined based on the geometry and smoothness conditions of the path points.

5. The method for dynamic control of tool vectors for five-axis CNC machining of complex curved surfaces according to claim 1, characterized in that: The comprehensive dynamics model of the five-axis CNC system is established based on the dynamic coupling effect of the machine tool kinematic chain, tool flexibility, cutting force, temperature field and stress field; including: Analyze and model the dynamic characteristics of each kinematic chain of the machine tool and determine its mass, damping and stiffness parameters; Obtain its frequency response characteristics through experimental data; Based on the tool-workpiece interaction analysis, combined with physical experiments and numerical simulations, the distribution model of temperature and stress fields is established; Through the multi-body dynamics method, the above mechanical characteristics are integrated into a coupled model, and mathematical tools are used to describe its dynamic response; The accuracy of the model is verified through parameter identification and numerical simulation.

6. The method for dynamic control of tool vectors for five-axis CNC machining of complex curved surfaces according to claim 1, characterized in that: The kinetic model equation is as follows: M(q)q¨+C(q,q˙)q˙+G(q)= , Where M(q) is the mass matrix, C(q,q˙) is the Coriolis moment, G(q) is the gravity moment, is the control input.

7. The method for dynamic control of tool vectors for five-axis CNC machining of complex curved surfaces according to claim 1, characterized in that: The step of optimizing the initial tool posture in combination with the dynamic model comprises: Based on the pre-built dynamic model, the initial posture of the tool is decomposed into moments, and the inertia matrix, coupling moment and friction moment at the starting point of the machining path are calculated; Through the constrained optimization algorithm, the optimal initial tool posture is solved with the goal of minimizing the condition number of the tool posture; The optimization results are fed back to the initial setting parameters of the CNC system and dynamically adjusted in combination with the dynamics feedforward control module.

8. The method for dynamic control of tool vectors for five-axis CNC machining of complex curved surfaces according to claim 1, characterized in that: The initial tool posture is optimized in combination with the dynamic model. The optimization objectives include minimizing cutting force fluctuation, tool vibration and error accumulation, and adjusting the tool direction using the constrained gradient descent method. The formula is as follows: J(θ)=α1⋅RMS(F cut )+α2⋅RMS(v tool )+α3⋅CE (θ) in: θ=[θx,θy,θz] is the tool posture parameter (expressed in the form of tool direction vector or Euler angle, etc.), F cut is the cutting force, v tool is the tool vibration speed, α1, α2, and α3 are weight coefficients for each item, which control the trade-off between different optimization objectives. RMS(F cut ) is the root mean square value of cutting force fluctuation, RMS(v tool ) is the root mean square value of tool vibration, CE (θ) is the accumulated amount of attitude error.

9. The method for dynamic control of tool vectors for five-axis CNC machining of complex curved surfaces according to claim 1, characterized in that: The dynamic operation data of the tool and the workpiece include cutting torque, spindle torque, tool vibration frequency and acceleration.

10. The method for dynamic control of tool vectors for five-axis CNC machining of complex curved surfaces according to claim 1, characterized in that: Based on the dynamic operation data, the model predictive control (MPC) algorithm is used to adjust the machining path and tool direction in real time; including: Input the dynamic model control (MPC) algorithm through the CNC system; Combine current data with the dynamics model to calculate the adjustment of machining path and tool direction in real time; The calculation results are fed back to the motion control module of the machine tool as control instructions to dynamically adjust the feed position and posture of the tool; In each control cycle, the model control parameters are updated according to the new dynamic operation data, and the adjustment process is repeated; The dynamics model and control signals are continuously updated through a feedback mechanism.

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