Control method, device, equipment and storage medium for five-axis high-precision CNC machine tool

Abstract

The invention relates to the technical field of numerical control machine tools, and discloses a control method, device, equipment and storage medium for a five-axis high-precision numerical control machine tool. The method comprises: collecting temperature data and displacement data of the five-axis high-precision numerical control machine tool, and performing state space modeling to obtain a machine tool motion state model; calculating feed axis force data and thermal error influencing factors; inputting the feed axis force data and thermal error influencing factors into a compensation model, calculating real-time compensation parameters, and performing compensation control on the machine tool feed axis to obtain compensation control results; constructing an initial processing trajectory optimization model according to the compensation control results, and generating optimized processing parameters; performing segmented processing control based on the optimized processing parameters to obtain real-time processing data; performing model optimization on the initial processing trajectory optimization model based on the real-time processing data to obtain a target processing trajectory optimization model. The method improves the dynamic response performance and control accuracy of the five-axis high-precision numerical control machine tool.

Description

Technical Field

[0001] The present invention relates to the technical field of numerically controlled machine tools, and in particular to a control method, device, equipment and storage medium for a five-axis high-precision numerically controlled machine tool. Background Art

[0002] Five-axis high-precision CNC machine tools are widely used in the field of high-end manufacturing, and their processing accuracy and surface quality directly affect the performance and reliability of the product. However, in the actual processing process, due to the complexity of the machine tool structure and the characteristics of multi-axis linkage, the motion coupling of each axis, as well as the influence of nonlinear factors such as thermal deformation and cutting force, the processing error is difficult to effectively control, which affects the further improvement of processing accuracy.

[0003] Traditional five-axis CNC machine tool control methods mainly use a single compensation strategy, which makes it difficult to simultaneously deal with the coupling effects of multiple source errors such as thermal error, mechanical error, and dynamic error. At the same time, existing trajectory planning methods often use fixed processing parameters and lack the ability to adaptively adjust various disturbances during the processing process, resulting in overshoot and vibration problems when processing complex surfaces. In addition, existing control methods generally have problems such as insufficient modeling accuracy, limited compensation effect, and poor real-time performance. Especially in high-speed and high-precision processing, due to the lack of effective multi-source error coupling compensation mechanism and real-time optimization strategy, it is difficult to meet the dual requirements of processing accuracy and efficiency. Summary of the invention

[0004] The present invention provides a control method, device, equipment and storage medium for a five-axis high-precision CNC machine tool, which are used to improve the dynamic response performance and control accuracy of the five-axis high-precision CNC machine tool.

[0005] In a first aspect, the present invention provides a control method for a five-axis high-precision CNC machine tool, the control method for the five-axis high-precision CNC machine tool comprising:

[0006] Collect temperature and displacement data of a five-axis high-precision CNC machine tool, perform state space modeling, and obtain the machine tool motion state model;

[0007] Calculating the force data of the feed axis based on the machine tool motion state model, and calculating the thermal error influence factor according to the temperature data;

[0008] Inputting the feed axis force data and the thermal error influencing factor into the compensation model, calculating the real-time compensation parameters, and performing compensation control on the machine tool feed axis to obtain a compensation control result;

[0009] Constructing an initial machining trajectory optimization model according to the compensation control result, and generating optimized machining parameters;

[0010] Execute segmented processing control based on the optimized processing parameters to obtain real-time processing data;

[0011] The initial machining trajectory optimization model is optimized based on the real-time machining data to obtain a target machining trajectory optimization model.

[0012] In a second aspect, the present invention provides a control device for a five-axis high-precision CNC machine tool, the control device for the five-axis high-precision CNC machine tool comprising:

[0013] The acquisition module is used to collect the temperature data and displacement data of the five-axis high-precision CNC machine tool, and perform state space modeling to obtain the machine tool motion state model;

[0014] A calculation module, used for calculating the force data of the feed axis based on the machine tool motion state model, and calculating the thermal error influence factor according to the temperature data;

[0015] A compensation control module, used for inputting the force data of the feed shaft and the thermal error influencing factor into a compensation model, calculating real-time compensation parameters, and performing compensation control on the feed shaft of the machine tool to obtain a compensation control result;

[0016] A construction module, used to construct an initial processing trajectory optimization model according to the compensation control result, and generate optimized processing parameters;

[0017] A segmented processing module, used to perform segmented processing control based on the optimized processing parameters to obtain real-time processing data;

[0018] The optimization module is used to optimize the initial processing trajectory optimization model based on the real-time processing data to obtain a target processing trajectory optimization model.

[0019] The third aspect of the present invention provides a control device for a five-axis high-precision CNC machine tool, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the control device of the five-axis high-precision CNC machine tool executes the above-mentioned control method of the five-axis high-precision CNC machine tool.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is run on a computer, the computer executes the control method of the above-mentioned five-axis high-precision CNC machine tool.

[0021] In the technical solution provided by the present invention, by establishing a state space model of multi-source data fusion, combining the state observer and Lyapunov stability theory, the accurate description and prediction of the motion state of the machine tool is achieved, and a hierarchical compensation structure is adopted to organically combine mechanical compensation, thermal deformation compensation and dynamic comprehensive compensation. Through the adaptive weighted fusion mechanism, the coupling compensation problem of multi-source errors is effectively solved, and the compensation accuracy is significantly improved; based on the tool posture constraints and dynamic constraints, a multi-objective optimization trajectory planning model is constructed, and through weight adaptive adjustment, the comprehensive optimization of processing efficiency, surface quality and energy consumption is achieved; a segmented processing control strategy is proposed, and differentiated control parameters are designed for the rough processing and fine processing stages respectively, which effectively improves the material removal efficiency and ensures the processing accuracy at the same time; a trajectory optimization closed-loop control mechanism based on real-time processing data is established, and through real-time detection of contour errors and adaptive adjustment of parameters, the dynamic optimization of the processing process is achieved, and the stability of the processing quality is ensured; a multi-level cascade control structure is adopted, and through the position-speed-current three-loop compensation, combined with the feedforward and feedback bidirectional compensation mechanism, the dynamic response performance and control accuracy of the five-axis high-precision CNC machine tool are significantly improved.

[0022] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0023] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of an embodiment of a control method for a five-axis high-precision CNC machine tool in an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of an embodiment of a control device for a five-axis high-precision CNC machine tool in an embodiment of the present invention;

[0026] Figure 3 It is a schematic diagram of an embodiment of a control device for a five-axis high-precision CNC machine tool in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. 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.

[0028] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0029] To facilitate understanding of this embodiment, a control method for a five-axis high-precision CNC machine tool disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method comprises the following steps:

[0030] 101. Collect temperature data and displacement data of a five-axis high-precision CNC machine tool, perform state space modeling, and obtain a machine tool motion state model;

[0031] It is understandable that the execution subject of the present invention may be a control device of a five-axis high-precision CNC machine tool, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0032] Specifically, by installing temperature sensor arrays on the X-axis, Y-axis, Z-axis, B-axis and C-axis of the five-axis high-precision CNC machine tool, the machine tool is comprehensively monitored for temperature. These sensor arrays can collect temperature data of each axis in real time. The temperature data is preprocessed for abnormal values ​​to eliminate the influence of environmental noise and acquisition errors, and the temperature data is obtained. At the same time, the displacement sensors of the five-axis high-precision CNC machine tool are sampled in real time. These displacement sensors are installed on the X-axis, Y-axis, Z-axis, B-axis and C-axis respectively, and the displacement of each axis can be monitored in real time. During the sampling process, the displacement data is preprocessed to improve the accuracy of the data. By using Kalman filtering, the sampled displacement data is processed to effectively reduce noise and obtain more accurate displacement data. The temperature data and displacement data are input into the state observer, and the state observer simultaneously uses the stiffness coefficient of the feed system and the parameters of the servo system as the observed quantities to determine the initial state variables of the machine tool. The stiffness coefficient of the feed system directly affects the stability and accuracy of the machine tool motion, while the servo system parameters affect the servo control system's ability to compensate for motion errors. Based on the initial state variables, the state space expression of the system is established. The state space expression is used to describe the relationship between the input, state and output of the dynamic system, and can fully reflect the physical characteristics of the machine tool during motion. The state matrix dimension of the state space expression is 10×10, the input matrix dimension is 10×5, and the output matrix dimension is 5×10. This dimension setting can ensure that each motion axis of the five-axis machine tool and related temperature influencing factors are reasonably described and observed, and the initial equation of the state space is obtained. The convergence analysis of the initial state space equation is performed based on the Lyapunov stability theory. The Lyapunov stability theory is an important tool for judging the stability of a dynamic system. By performing a convergence analysis on the state space equation, it is ensured that the system can remain stable in the presence of various disturbances. The gain matrix of the observer is determined by the pole configuration method. The pole configuration method is a common method for designing state feedback in control theory. The dynamic characteristics of the system are changed by adjusting the pole position of the system, so that the gain matrix of the state observation equation can achieve optimal observation of the system. Compare the displacement data in the state observation equation with the actual collected machine tool motion data, perform online parameter identification, and continuously update and optimize the system model to better adapt to changes in the actual processing process. Parameter identification is performed using the recursive least squares method. The recursive least squares method is an algorithm suitable for online estimation. It gradually updates the model parameters based on the real-time collected data, so that the machine tool motion state model can accurately reflect the current machine tool state and motion characteristics.

[0033] 102. Calculate the feed axis force data based on the machine tool motion state model, and calculate the thermal error influence factor based on the temperature data;

[0034] Specifically, based on the motion state model of the machine tool, the current data of the feed motors of the X-axis, Y-axis, Z-axis, B-axis and C-axis in the five-axis high-precision CNC machine tool are sampled. The sampled current data is important information reflecting the working state of the feed motor. Since there is a certain functional relationship between the current and torque of the motor, the initial force data of each axis is obtained by converting the current-torque relationship. The high-frequency noise of the initial force data is filtered out, and unnecessary high-frequency interference is removed through filtering to obtain the target force data. According to the displacement data in the machine tool motion state model, the dynamic force compensation calculation is combined with the target force data to obtain the feed axis force data, which accurately describes the dynamic characteristics of the machine tool during the processing. In the actual processing process, the force state of the machine tool is constantly changing. By dynamically adjusting the target force in combination with the displacement data, more accurate feed axis force data is obtained. At the same time, the temperature data is arranged in chronological order to obtain temperature time series data. Since the temperature sensor will have zero drift when working for a long time, this drift will cause the measured temperature data to be inaccurate. The temperature time series data is corrected for zero drift to obtain the temperature change data. According to the temperature change data, the temperature change rate of each measuring point is calculated. The temperature change rate can reflect the trend of temperature change over time and is an important indicator for judging the impact of thermal error. In order to improve the accuracy of thermal error compensation, the temperature change rate is judged by threshold, and the measuring points whose temperature change rate exceeds a certain threshold are selected as key measuring points. At the same time, combined with the actual impact on the processing process, it is determined which key measuring points have the most significant impact on the thermal error, and the temperature key measuring point data is obtained. The correlation analysis of the temperature key measuring point data and the thermal deformation data of each axis is carried out to find out the relationship between temperature change and thermal deformation of each axis of the machine tool, determine which temperature measuring points have a significant impact on the thermal deformation of each axis, and obtain the temperature sensitive measuring points. Based on the temperature sensitive measuring point data, the temperature-thermal deformation response function is calculated. The temperature-thermal deformation response function is used to describe the influence of temperature change on the thermal deformation of the machine tool. The calculation of this function needs to consider the contribution of different measuring points to the deformation of the machine tool, so the weighted coefficient is introduced for calculation. Through the reasonable allocation of weighted coefficients, the influence of different measuring points on thermal deformation can be reflected more accurately, and the thermal error influencing factor can be obtained.

[0035] 103. Input the feed axis force data and thermal error influencing factors into the compensation model, calculate the real-time compensation parameters, and perform compensation control on the machine tool feed axis to obtain the compensation control result;

[0036] Specifically, the force data of the feed axis is input into the mechanical compensation layer, and the thermal error influencing factor is input into the thermal deformation compensation layer. In the mechanical compensation layer, the force data of the feed axis is decomposed according to the X-axis, Y-axis, Z-axis, B-axis and C-axis, and the force conditions of each axis are analyzed respectively. The force data of each axis is converted into the frequency domain to obtain the force spectrum data of each axis. The force spectrum data reflects the force conditions of the machine tool at different frequencies. Based on the spectrum data, the force characteristics of each axis are extracted, and these characteristics are vector-converted to obtain the force characteristic vector. At the same time, in the thermal deformation compensation layer, the thermal error influencing factor will be mapped by axis, and the thermal error compensation amount will be calculated for the X-axis, Y-axis, Z-axis, B-axis and C-axis respectively to obtain the thermal error compensation data of each axis. The thermal error compensation data reflects the impact of temperature changes on each axis of the machine tool. By accurately calculating these data, the processing errors caused by thermal deformation can be effectively compensated, and the processing accuracy and stability can be improved. In the dynamic comprehensive compensation layer, the force characteristic vector and the thermal error compensation data of each axis are adaptively weighted fused to obtain the compensation fusion data. By comprehensively considering the influence of both force and thermal deformation, the weighting coefficient is adaptively adjusted according to their contribution to the machining accuracy to ensure that the final compensation fusion data can optimally reflect the comprehensive state of the machine tool. Based on the compensation fusion data, the displacement compensation of the five axes is calculated respectively. In order to improve the accuracy of the compensation, the calculated displacement compensation is corrected, which is achieved through the compensation gain matrix. The role of the compensation gain matrix is ​​to make proportional corrections to the compensation amount according to the actual machining conditions and system characteristics to make the compensation more accurate. At the same time, since there is a certain motion coupling relationship between the five axes, the motion of each axis is not completely independent. Therefore, the coupling relationship between the axes is considered in the compensation correction process, so as to couple the compensation amount and finally obtain the real-time compensation parameters. The real-time compensation parameters are decomposed into feed speed compensation component and feed position compensation component. These two compensation components are bidirectionally compensated through the feedforward compensation channel and the feedback compensation channel respectively. The feedforward compensation channel is used to predictively compensate errors before the control system runs to reduce the occurrence of errors, while the feedback compensation channel corrects real-time errors during system operation to form a closed-loop control. According to the compensation control instructions, closed-loop servo control is performed on each feed axis of the five-axis high-precision CNC machine tool. In the servo controller, a three-loop control structure of position loop, speed loop and current loop is adopted, in which the position loop is used to ensure the accuracy of the processing position, the speed loop is used to smoothly control the change of speed, and the current loop is used to achieve precise control of the current of the drive motor to ensure the stability of the drive torque. In this control process, the compensation control instructions realize the compensation control of position, speed and current in turn through the inner and outer loops of the servo controller, so as to perform precise error compensation at all levels and obtain precise compensation control results.

[0037] 104. Construct an initial machining trajectory optimization model according to the compensation control result, and generate optimized machining parameters;

[0038] Specifically, the actual motion trajectory data of the X-axis, Y-axis, Z-axis, B-axis and C-axis are extracted according to the compensation control results. The actual motion trajectory data of each axis is mapped in posture space to obtain the initial posture data of the tool, which describes the geometric position and direction of the tool relative to the workpiece during the processing. Based on the initial posture data of the tool, the inclination angle and the cutting angle of the tool relative to the workpiece surface are calculated. The inclination angle and the cutting angle describe the geometric relationship of how the tool contacts and cuts the workpiece during the processing, thereby affecting the cutting force and processing quality. At the same time, the interference distance between the tool and the workpiece is calculated through the collision detection algorithm. The calculation of the interference distance is used to ensure that the tool and the workpiece will not have unexpected collisions during the processing, and the spatial position constraint of the tool is obtained, which effectively limits the posture of the tool so that it can avoid collision or damage while meeting the processing requirements. The spatial position constraint of the tool is decomposed into axial constraint components so that each axis can be analyzed in detail. The extreme value analysis of the axial constraint components is performed to detect the singular configuration and travel limit of each axis, and the posture constraint of the tool is obtained. Singular configuration refers to the situation that the movement of the machine tool will be out of control or the accuracy will decrease under certain special tool postures. By detecting singular configurations, we can avoid entering the unstable posture area during the processing. The travel limit analysis ensures that the movement of each axis does not exceed its physical travel range, thereby preventing mechanical collision and damage. For the compensation control results, the processing characteristics are analyzed to extract characteristic parameters such as the curvature, tangential velocity and normal acceleration of the processing trajectory. Curvature is a parameter that describes the geometric shape of the processing trajectory, and the tangential velocity and normal acceleration describe the motion state of the tool along the trajectory direction and perpendicular to the trajectory direction respectively. Based on these parameters, dynamic constraint parameters are established to describe the dynamic characteristics and motion restrictions of the tool during the processing. Based on the tool posture constraint conditions and dynamic constraint parameters, a multi-objective optimization function is constructed. Under the premise of meeting various constraints, the optimal processing trajectory is found. The weight coefficients of the three target items of processing efficiency, surface quality and energy consumption are set to 0.4, 0.4 and 0.2 respectively. By setting the weight coefficient, the efficiency, accuracy and energy consumption in the processing process are comprehensively considered to obtain the optimization model of the initial processing trajectory. In order to improve the optimization effect of the trajectory, the initial machining trajectory optimization model is segmented and optimized. For sections with large curvature changes, the trajectory is subdivided to ensure that the tool movement in these areas is smoother and more accurate. For the subdivided trajectory, local smoothing is performed to eliminate the sharp corners or mutations caused by the trajectory subdivision, and the segmented optimized trajectory data is obtained. The segmented optimized trajectory data is kinematically inversely solved to calculate the feed rate, acceleration and jerk of each axis. Kinematic inverse solution is a process of converting trajectory data into motion control instructions for each axis of the machine tool. The speed and acceleration information required for each axis when executing the optimized trajectory is obtained through inverse solution. In order to ensure the smoothness of the machine tool's movement, the trajectory planning data of each axis is calculated through the acceleration and deceleration optimization algorithm.The purpose of the acceleration and deceleration optimization algorithm is to control the speed change during the machining process, making the speed change smoother and avoiding sudden speed increase or decrease, thereby reducing the mechanical shock and vibration of the machine tool. According to the trajectory planning data, the feed speed, spindle speed and cutting amount of each axis are calculated, and these machining parameters are corrected in combination with the results of compensation control. Through the correction process, it is ensured that the machining parameters can adapt to the actual compensation requirements, thereby improving the machining accuracy and efficiency, and finally obtaining the optimized machining parameters.

[0039] 105. Perform segmented processing control based on optimized processing parameters to obtain real-time processing data;

[0040] Specifically, the optimized machining parameters are mapped according to the machining stages, and the entire machining process is divided into rough machining area and fine machining area according to the process requirements. The machining control parameter table is generated for different machining requirements to obtain segmented control data. For the machining control parameter table of the rough machining area, the cutting volume analysis is performed to calculate the volume of material removed per unit time. By calculating the material removal volume, the cutting efficiency can be effectively evaluated. In order to ensure the safety and efficiency of the cutting process, the cutting parameters are matched in combination with the upper limit of the machine tool spindle power to obtain the feed parameters suitable for rough machining. The upper limit of the spindle power is a limiting condition to ensure the safe and stable operation of the machine tool. By matching the cutting parameters, the optimal material removal rate is achieved without overloading. Based on the rough machining feed parameters, the feed speed and spindle speed are segmented and planned, and the maximum acceleration and deceleration threshold and the jerk threshold are set to obtain the speed planning curve of the rough machining section. The thresholds of acceleration and deceleration and jerk are set to prevent excessive impact of the machine tool during acceleration or deceleration, thereby improving the stability of the machining and the life of the machine tool. The obtained speed planning curve is used to guide the control of the servo drive system. By rationally planning the speed, efficient material removal can be achieved during the roughing process. The speed planning curve of the roughing section is input into the servo drive controller to perform the motion control of the roughing process. In this process, the current data and torque data of each axis are collected in real time to reflect the operating status of the machine tool during the roughing process. By analyzing these data, the load condition and dynamic characteristics of the machine tool are understood, and the process data of the roughing process are obtained. For the finishing area, the threshold of the cutting force is calculated based on the processing control parameter table. The magnitude of the cutting force directly affects the surface quality of the workpiece. By analyzing the mapping relationship between the cutting force and the surface quality, the appropriate cutting amount range is determined to obtain the feed parameters for finishing. The feed parameters for finishing are interpolated by trajectory calculation to generate accurate position instructions and speed instructions for each axis. Through interpolation calculation, it is ensured that the tool moves accurately along the predetermined trajectory to achieve the expected processing effect. The interpolated instructions are output as control signals through the feedforward controller to obtain the control instructions for finishing. These instructions are used to guide the servo system to perform motion control for finishing to achieve high-precision processing requirements. During the finishing process, motion control of finishing is performed, and dynamic cutting force data is collected at the same time, and these data are subjected to real-time spectrum analysis and amplitude monitoring. Spectral analysis is used to identify abnormal vibration or instability in the cutting process, and amplitude monitoring is used to detect the size of the cutting force to ensure that it operates within the set range, thereby ensuring the stability of the machining process and the quality of the workpiece surface. The finishing process data obtained from the analysis is used to evaluate the accuracy and stability of the machining. Feature extraction and data fusion are performed on the roughing process data and the finishing process data to obtain a feature vector that describes the entire machining process.Extract representative information from a large amount of raw data, such as the changing trend of cutting force, the fluctuation characteristics of current, etc., which reflect the important characteristics of the machining process. Data fusion combines the data of rough machining and fine machining to fully describe the entire machining process. Through the characteristic vector of the machining process, the process state space of the machining is constructed to obtain real-time machining data.

[0041] 106. The initial processing trajectory optimization model is optimized based on the real-time processing data to obtain the target processing trajectory optimization model.

[0042] Specifically, the actual trajectory data of the X-axis, Y-axis, Z-axis, B-axis and C-axis in the real-time machining data are compared with the theoretical trajectory generated by the initial machining trajectory optimization model to obtain the real-time contour error data of each axis. The real-time contour error data is statistically analyzed to calculate the straightness error, roundness error and angle error of each axis. The straightness error mainly describes the deviation of the tool in linear motion, the roundness error reflects the difference between the tool and the theoretical arc in circular motion, and the angle error describes the deviation of the tool in spatial angle change. The error is compared with the preset machining accuracy threshold to identify the axis with excessive error. The axis with excessive error refers to the axis whose error value exceeds the allowable range. These axes need to be further optimized to ensure that the machining accuracy meets the requirements. For the axis with excessive error, the machining feature analysis of the real-time machining data is performed to extract the dynamic features of the machining trajectory, such as the dynamic curvature, tangential velocity and normal acceleration of the trajectory. The dynamic curvature reflects the geometric change of the trajectory, the tangential velocity describes the speed of the tool moving along the trajectory, and the normal acceleration is related to the smoothness of the trajectory and the force characteristics of the tool. These characteristics reflect the dynamic behavior of the machine tool during the machining process and are potential causes of errors. According to the error association characteristics, the dynamic constraint parameters are corrected, and the velocity planning threshold and acceleration constraint in the initial machining trajectory optimization model are adjusted to obtain the corrected constraint parameters. Based on the corrected constraint parameters, the multi-objective optimization function in the initial machining trajectory optimization model is reconstructed. In the reconstruction process, the weight distribution of machining efficiency, surface quality and energy consumption is adjusted according to the actual machining requirements to obtain the corrected value of the optimization function. Among them, machining efficiency, surface quality and energy consumption are the three main optimization objectives. The weight distribution determines the priority between different objectives. Reasonable adjustment of the weight distribution makes the optimization model more in line with the specific requirements of the current machining. For example, more attention is paid to surface quality in fine machining, while machining efficiency is emphasized in rough machining. The corrected value of the optimization function is substituted into the initial machining trajectory optimization model for re-segmented optimization calculation. In the optimization process, the sections with large changes in trajectory curvature are subdivided and smoothed. Areas with large curvature are usually where more errors occur. Subdividing these areas allows the tool to have more precise control when passing through these areas, thereby reducing errors. Smoothing the subdivided trajectory makes the tool movement smoother, reduces vibration and acceleration mutations, and improves processing quality. After completing trajectory optimization, the optimized trajectory data is subjected to inverse kinematics calculations to generate new feed speed, acceleration, and jerk parameters for each axis, and obtain the optimized data for trajectory planning. Based on the optimized data for trajectory planning, the feed speed and spindle speed of each axis are recalculated, and parameter verification is performed in combination with the tool posture constraints to ensure that the generated parameters meet the actual mechanical and process constraints, and obtain the target machining trajectory optimization model.

[0043] In the embodiment of the present invention, by establishing a state space model of multi-source data fusion, combining the state observer and Lyapunov stability theory, the accurate description and prediction of the motion state of the machine tool is achieved, and a hierarchical compensation structure is adopted to organically combine mechanical compensation, thermal deformation compensation and dynamic comprehensive compensation. Through the adaptive weighted fusion mechanism, the coupling compensation problem of multi-source errors is effectively solved, and the compensation accuracy is significantly improved; based on the tool posture constraints and dynamic constraints, a multi-objective optimization trajectory planning model is constructed, and the comprehensive optimization of processing efficiency, surface quality and energy consumption is achieved through weight adaptive adjustment; a segmented processing control strategy is proposed, and differentiated control parameters are designed for the rough processing and fine processing stages respectively, which effectively improves the material removal efficiency and ensures the processing accuracy at the same time; a trajectory optimization closed-loop control mechanism based on real-time processing data is established, and the dynamic optimization of the processing process is achieved through real-time detection of contour errors and adaptive adjustment of parameters, and the stability of the processing quality is ensured; a multi-level cascade control structure is adopted, and through the position-speed-current three-loop compensation, combined with the feedforward and feedback bidirectional compensation mechanism, the dynamic response performance and control accuracy of the five-axis high-precision CNC machine tool are significantly improved.

[0044] In a specific embodiment, the process of executing step 101 may specifically include the following steps:

[0045] (1) Data collection and outlier preprocessing are performed on the temperature sensor array installed on the X-axis, Y-axis, Z-axis, B-axis, and C-axis of a five-axis high-precision CNC machine tool to obtain temperature data;

[0046] (2) Perform real-time sampling and Kalman filtering on the displacement sensors of the X-axis, Y-axis, Z-axis, B-axis, and C-axis of a five-axis high-precision CNC machine tool to obtain displacement data;

[0047] (3) Input the temperature data and displacement data into the state observer, and use the stiffness coefficient of the feed system and the servo system parameters as the observed quantities to obtain the initial state variables;

[0048] (4) Establish a state space expression for the initial state variables, where the dimension of the state matrix is ​​10×10, the dimension of the input matrix is ​​10×5, and the dimension of the output matrix is ​​5×10, and the initial state space equation is obtained;

[0049] (5) Based on Lyapunov stability theory, the convergence of the initial state space equation is analyzed, and the observer gain matrix is ​​determined by the pole placement method to obtain the state observation equation;

[0050] (6) The displacement data in the state observation equation is combined with the actual collected machine tool motion data for online parameter identification, and the machine tool motion state model is obtained by the recursive least squares method.

[0051] Specifically, the temperature data and displacement data of the X-axis, Y-axis, Z-axis, B-axis and C-axis of the machine tool are collected and processed. The collected temperature data is preprocessed for outliers. Through statistical analysis methods, such as median filtering or threshold screening based on normal distribution, unreasonable values ​​are removed to obtain temperature data. The displacement sensor of the machine tool is sampled in real time, and each axis is equipped with a displacement sensor to accurately measure the displacement of the machine tool movement. During the real-time sampling process, the displacement data will be interfered by noise, and the Kalman filter is used to process the data. Kalman filtering is a recursive algorithm for dynamic systems that can optimally estimate the true state of the system based on the state of the system and the observed data. Assume that the state equation of the system is:

[0052] ;

[0053] in, For the system at time The state vector of is the state transition matrix, is the input matrix, is the input signal, is the process noise. In the observation equation, the observed data of the system Described as:

[0054] ;

[0055] in, is the observation matrix, is the measurement noise. Through Kalman filtering, the real displacement data can be recursively estimated to make it more accurate and reliable. The collected temperature data and the displacement data processed by Kalman filtering are input into the state observer to estimate the internal state of the machine tool. In the state observer, the stiffness coefficient of the feed system and the parameters of the servo system are considered, which directly affect the motion characteristics of the machine tool. The stiffness coefficient of the feed system and the parameters of the servo system are used as the observed quantities to describe the dynamic characteristics of the machine tool and obtain the initial state variables. Establish a state space expression for the initial state variables. The state space expression is a mathematical model used to describe a dynamic system, which expresses the relationship between the state, input, and output of the system as a matrix equation. In this control method, the state matrix dimension of the state space model is , the dimension of the input matrix is , and the dimension of the output matrix is The initial state space equation is expressed as:

[0056] ;

[0057] ;

[0058] in, is the state vector, is the state matrix, whose dimension is , represents the internal state change of the system; is the input matrix, whose dimension is , represents the impact of input on the system state; is the input vector, representing the control input of the system; is the output vector, Matrix (dimension is and The matrix describes the input-output relationship. In order to ensure the stability of the state-space equation in practical applications, the convergence analysis of the initial state-space equation is performed based on the Lyapunov stability theory. The Lyapunov stability theory is used to determine the stability of the system. By constructing the Lyapunov function , if the function is positive definite in the entire state space of the system, and its derivative If it is negative definite in all states, the system is asymptotically stable. This means that the system can return to equilibrium after being disturbed by external interference. On this basis, the gain matrix of the observer is determined by the pole configuration method. The pole configuration method is a common method for control system design, which changes the dynamic response characteristics of the system by adjusting the position of the system poles. By selecting a suitable gain matrix, the response speed and stability of the system meet the requirements of machining accuracy, and the state observation equation is obtained. The displacement data in the state observation equation is identified online with the actual machine tool motion data collected, and the system model is updated in real time so that it can reflect the current actual state. The parameters of the model are continuously adjusted through the recursive least squares method to minimize the error between the output of the model and the actual observed data. The recursive least squares method gradually adjusts the model parameters by minimizing the sum of squared errors. Assume that the output of the model is The actual output is , the error is:

[0059] ;

[0060] The recursive least squares method continuously adjusts the parameters so that the error By minimizing the above factors, an accurate machine tool motion state model is obtained. This model can describe the dynamic characteristics of a five-axis high-precision CNC machine tool under various working conditions.

[0061] In a specific embodiment, the process of executing step 102 may specifically include the following steps:

[0062] (1) Based on the machine tool motion state model, the feed motor current data of the X-axis, Y-axis, Z-axis, B-axis and C-axis in the five-axis high-precision CNC machine tool are sampled and the current-torque relationship is converted to obtain the initial force data of each axis;

[0063] (2) The high-frequency noise of the initial force data of each axis is filtered out to obtain the target force data. The dynamic force compensation calculation is performed based on the displacement data in the machine tool motion state model and the target force data to obtain the feed axis force data.

[0064] (3) Arrange the temperature data in time series to obtain temperature time series data, and perform zero drift correction on the temperature time series data to obtain temperature change data;

[0065] (4) Calculate the temperature change rate of each measuring point based on the temperature change data, and perform threshold judgment and key measuring point judgment on the temperature change rate to obtain the temperature key measuring point data;

[0066] (5) Perform correlation analysis on the data of key temperature measurement points and the thermal deformation data of each axis to obtain the data of temperature-sensitive measurement points. Perform temperature-thermal deformation response function calculation based on the data of temperature-sensitive measurement points, and obtain the thermal error influencing factor through weighted coefficient calculation.

[0067] Specifically, based on the machine tool motion state model, the feed motor current data of the X-axis, Y-axis, Z-axis, B-axis and C-axis in the five-axis high-precision CNC machine tool is sampled. The feed motor current data reflects the actual operating state of each axis and the load conditions of the motor during operation. By collecting these current data, the current-torque relationship is converted to obtain the initial force data of each axis. The relationship between current and torque is expressed by the following formula:

[0068] ;

[0069] in, Indicates The motor output torque of each axis, is the torque constant of the motor, For the The motor current of each axis. Through this relationship, the torque of each axis is calculated from the collected current data to obtain the initial force data. The high-frequency noise of the initial force data of each axis is filtered out, and a low-pass filter is used to remove the high-frequency noise to obtain the target force data. The low-pass filter sets a cutoff frequency to filter out signals higher than this frequency, effectively reducing high-frequency interference and noise in the system. Based on the displacement data in the machine tool motion state model and combined with the target force data, dynamic force compensation calculation is performed to reduce the dynamic force changes caused by external interference or non-ideal operation, thereby ensuring motion stability during processing. Assume that the calculation formula for the compensation torque is:

[0070] ;

[0071] in, represents the compensation force, is the mass matrix, is the damping matrix, is the stiffness matrix, is the acceleration, For speed, is the displacement. By calculating these physical parameters, the force data required by the feed axis is obtained so as to accurately compensate and control the movement of the machine tool. The temperature data of the machine tool is processed. The temperature data is arranged in time series to obtain the temperature time series data. Zero drift correction is performed on the temperature time series data. Using the baseline adjustment method, it is assumed that Represents the original temperature data, and through zero drift correction, the temperature change data is obtained:

[0072] ;

[0073] in, is the corrected temperature change, for a certain moment The measured temperature, is the baseline temperature. The temperature change rate of each measuring point is calculated based on the temperature change data. By calculating the temperature change rate, a threshold judgment is made on the temperature change, and the measuring points with more drastic temperature changes are screened out. These measuring points are key measuring points. The judgment of key measuring points is based on the set change rate threshold. If the temperature change rate of a certain measuring point exceeds this threshold, it is marked as a key measuring point. A correlation analysis is performed on the temperature key measuring point data and the thermal deformation data of each axis to find out the relationship between temperature changes and thermal deformation of each axis of the machine tool. Thermal deformation is a physical deformation caused by temperature changes in various parts of the machine tool. Correlation analysis is used to determine which temperature measuring points have a significant correlation with the thermal deformation of a specific axis. Assume that the correlation coefficient between the temperature data of a certain measuring point and the displacement deformation data of a certain axis is ,if If the value is high, it means that this temperature measurement point has a significant impact on the thermal deformation of the shaft. These temperature measurement points are temperature sensitive measurement points. Based on the temperature sensitive measurement point data, the temperature-thermal deformation response function is calculated to obtain the specific impact of temperature change on thermal deformation. Assume that the response function is:

[0074] ;

[0075] in, For the Thermal deformation of each axis, is the thermal deformation coefficient, For the The temperature change of each temperature sensitive measuring point. Through this relationship, the thermal deformation of each axis caused by temperature change is calculated. In order to obtain a more comprehensive thermal error influencing factor, the data of all temperature sensitive measuring points are weighted. The weighting coefficient for each measuring point, thermal error influence factor It is expressed as:

[0076] ;

[0077] in, represents the final thermal error influence factor, is the total number of temperature sensitive measuring points, is the weight of each sensitive measuring point to the total error. The weighting coefficient is determined according to the contribution of each measuring point to the total thermal deformation of the system, so that the thermal error influencing factor finally calculated is more accurate and representative.

[0078] In a specific embodiment, the process of executing step 103 may specifically include the following steps:

[0079] (1) The compensation model is divided into a mechanical compensation layer, a thermal deformation compensation layer, and a dynamic comprehensive compensation layer. The force data of the feed axis is input into the mechanical compensation layer, and the thermal error influencing factor is input into the thermal deformation compensation layer.

[0080] (2) In the mechanical compensation layer, the force data of the feed axis is decomposed into the X-axis, Y-axis, Z-axis, B-axis and C-axis respectively, and the force data of each axis is converted into the frequency domain to obtain the force spectrum data of each axis. Based on the force spectrum data of each axis, the force characteristics of each axis are extracted and vectorized to obtain the force feature vector;

[0081] (3) In the thermal deformation compensation layer, the thermal error influencing factors are mapped by axes, and the thermal error compensation amounts of the X-axis, Y-axis, Z-axis, B-axis, and C-axis are generated accordingly to obtain the thermal error compensation data of each axis;

[0082] (4) In the dynamic comprehensive compensation layer, the force characteristic vector and the thermal error compensation data of each axis are adaptively weighted fused to obtain the compensation fusion data;

[0083] (5) The displacement compensation of the five axes is calculated based on the compensation fusion data. The displacement compensation is corrected by the compensation gain matrix and the compensation correction is performed in combination with the motion coupling relationship of each axis to obtain the real-time compensation parameters.

[0084] (6) Decomposing the real-time compensation parameters into a feed speed compensation component and a feed position compensation component, performing bidirectional compensation through a feedforward compensation channel and a feedback compensation channel, and obtaining a compensation control instruction;

[0085] (7) According to the compensation control instructions, closed-loop servo control is performed on each feed axis of the five-axis high-precision CNC machine tool, and the three-loop compensation of position, speed and current is realized through the inner and outer loops of the servo controller to obtain the compensation control result.

[0086] Specifically, the force data of the feed axis is input into the mechanical compensation layer, and the thermal error influencing factor is input into the thermal deformation compensation layer. The entire compensation process is divided into multiple links, and the mechanical compensation layer, thermal deformation compensation layer and dynamic comprehensive compensation layer respectively undertake different compensation tasks. In the mechanical compensation layer, the force data of the feed axis is decomposed according to the X-axis, Y-axis, Z-axis, B-axis and C-axis. The force data of each axis represents the effect of external cutting force, friction force, etc. on the machine tool axis during the processing process. By decomposing these data, the force state of each axis is analyzed separately. The force data of each axis is converted into the frequency domain, and the fast Fourier transform is used to convert the force signal in the time domain to the frequency domain to obtain the force spectrum data of each axis, revealing the periodic components and frequency characteristics in the force signal. The force characteristics after frequency domain conversion are described by the following formula:

[0087] ;

[0088] in, represents the force signal in the frequency domain, is the force signal in the time domain, is an imaginary unit, is the frequency. By extracting the features of the force spectrum data of each axis, the main force frequency components and corresponding amplitudes of each axis are obtained. Based on these characteristic data, the force is vector-converted to obtain a vector describing the force characteristics of each axis, namely, the force characteristic vector. In the thermal deformation compensation layer, the input thermal error influencing factors need to be mapped according to different axes to generate the thermal error compensation amounts of the X-axis, Y-axis, Z-axis, B-axis and C-axis. The thermal error influencing factors describe the impact of temperature changes on the geometry and motion accuracy of the machine tool. By mapping the thermal error influencing factors to each axis, the thermal deformation amount that needs to be compensated for each axis due to temperature changes is calculated respectively, and the thermal error compensation data of each axis is obtained. Assume that the calculation formula for the thermal error compensation amount is:

[0089] ;

[0090] in, For the Thermal error compensation for each axis, is the thermal sensitivity coefficient, which indicates the influence of temperature on the deformation of the shaft. is the temperature change. Through this calculation method, the thermal deformation compensation of each axis is obtained. In the dynamic comprehensive compensation layer, the force characteristic vector and the thermal error compensation data of each axis are adaptively weighted fused to obtain the compensation fusion data. By assigning different weights to the force characteristics and thermal deformation compensation, the contribution of each compensation amount can be dynamically adjusted according to the current processing conditions. Assume that the fused compensation data is:

[0091] ;

[0092] in, For the Comprehensive compensation data for each axis, is the force eigenvector, is the thermal error compensation data, and are adaptive weight coefficients. These weight coefficients are dynamically adjusted according to the real-time processing conditions to ensure the best comprehensive compensation effect. Based on the compensation fusion data, the displacement compensation of the five axes is calculated respectively. The calculation of the displacement compensation is corrected by the compensation gain matrix. The compensation gain matrix is ​​to optimize the compensation effect and ensure that the amplitude and direction of the compensation meet the actual needs of the processing. Assume that the compensation gain matrix is , the displacement compensation amount is expressed as:

[0093] ;

[0094] in, is the displacement compensation of each axis, is the compensation gain matrix, is the comprehensive compensation vector. When calculating the displacement compensation, the motion coupling relationship between the axes is considered. There is a certain coupling between the axes of the five-axis linkage CNC machine tool. For example, the rotation of the B axis and the C axis will affect the displacement of the X, Y, and Z axes. In the compensation correction process, the coupling relationship is modeled and corrected to ensure the coordination and accuracy of the compensation, and the real-time compensation parameters are obtained. The real-time compensation parameters are decomposed into feed speed compensation components and feed position compensation components. The feed speed compensation component is used to adjust the motion speed of each axis to reduce the error caused by dynamic force or thermal deformation, and the feed position compensation component is used to adjust the target position of each axis to improve the machining accuracy. These compensation components are compensated bidirectionally through the feedforward compensation channel and the feedback compensation channel. Feedforward compensation is used to apply compensation in advance before the control command is output to reduce the occurrence of errors, while feedback compensation is to correct the error after real-time measurement to achieve closed-loop control. Through these two compensation channels, compensation control instructions are generated. According to the generated compensation control instructions, closed-loop servo control is performed on each feed axis of the five-axis high-precision CNC machine tool. In servo control, a three-loop control structure of position-speed-current is adopted. The position loop is used to ensure the displacement accuracy of each axis, the speed loop is used to control the movement speed of each axis, and the current loop is used to control the output torque of the drive motor. Through the inner and outer loops of the servo controller, the three-loop compensation of position, speed and current is achieved respectively, thereby ensuring that the machine tool maintains high precision and high stability during the processing process and obtains compensation control results.

[0095] In a specific embodiment, the process of executing step 104 may specifically include the following steps:

[0096] (1) According to the compensation control results, the actual motion trajectory data of the X-axis, Y-axis, Z-axis, B-axis and C-axis are extracted respectively, and the actual motion trajectory data of each axis is mapped into the posture space to obtain the initial posture data of the tool;

[0097] (2) Based on the initial posture data of the tool, the inclination angle and cutting angle of the tool relative to the workpiece surface are calculated, and the interference distance between the tool and the workpiece is calculated through the collision detection algorithm to obtain the spatial position constraint conditions of the tool;

[0098] (3) Decompose the tool spatial position constraints into axial constraint components, perform extreme value analysis on the axial constraint components, detect the singular configurations and travel limits of each axis, and obtain the tool attitude constraints;

[0099] (4) Analyze the machining characteristics of the compensation control results, extract the curvature, tangential velocity, and normal acceleration of the machining trajectory, and establish dynamic constraint parameters;

[0100] (5) Based on the tool posture constraints and dynamic constraint parameters, a multi-objective optimization function was constructed, and the weight coefficients of the machining efficiency term, surface quality term, and energy consumption term were set to 0.4, 0.4, and 0.2, respectively, to obtain the initial machining trajectory optimization model;

[0101] (6) Performing segmented optimization calculation on the initial machining trajectory optimization model, subdividing the trajectory in the section with large curvature change, and locally smoothing the subdivided trajectory to obtain segmented optimized trajectory data;

[0102] (7) Perform kinematic inverse solution on the segmented optimized trajectory data, calculate the feed rate, acceleration and jerk of each axis, and calculate the trajectory planning data of each axis through the acceleration and deceleration optimization algorithm;

[0103] (8) The feed speed, spindle speed, and cutting amount of each axis are calculated based on the trajectory planning data, and the parameters are corrected in combination with the compensation control results to obtain the optimized processing parameters.

[0104] Specifically, the actual motion trajectory data of the X-axis, Y-axis, Z-axis, B-axis and C-axis of the five-axis high-precision CNC machine tool are extracted from the compensation control results. The motion trajectory data is mapped into the posture space, and the actual motion data of each axis is converted into specific data describing the position and posture of the tool in three-dimensional space to obtain the initial posture data of the tool. The initial posture data of the tool refers to the geometric position and direction of the tool relative to the machine tool reference system at the initial position. Based on the initial posture data of the tool, the inclination angle and cutting angle of the tool relative to the workpiece surface are calculated. The inclination angle describes the angular relationship between the tool and the workpiece surface, which directly affects the direction and magnitude of the cutting force. The cutting angle describes how the tool cuts into the workpiece surface. Assume that the normal vector of the tool relative to the workpiece surface is , the tool direction vector is , the tool tilt angle The vector angle formula is:

[0105] ;

[0106] in, is the dot product of the normal vector and the direction vector, and are the modulus of the normal vector and the direction vector, respectively. This formula is used to determine the inclination of the tool's posture relative to the workpiece. In order to ensure the safety and accuracy of the machining process, the interference distance between the tool and the workpiece is calculated by the collision detection algorithm. The purpose of collision detection is to detect whether the tool has unexpected contact with the workpiece during the machining process, so as to determine the spatial position constraints of the tool, which describe the position limits of the tool without collision. The spatial position constraints of the tool are decomposed into axial constraint components, and the motion of the X-axis, Y-axis, Z-axis, B-axis and C-axis are analyzed respectively. The extreme value analysis of the axial constraint components is performed to detect whether each axis has reached the limit position of motion or entered a singular configuration in actual operation. Singular configuration refers to the control failure or precision reduction of the machine tool under certain special postures. It occurs at the position where the motion coupling between multiple axes is strong. Detecting singular configurations can avoid situations that are not conducive to machining accuracy. At the same time, the stroke limit analysis can ensure that the motion of each axis does not exceed the physical stroke limit, thereby preventing damage to mechanical components. Through analysis, the posture constraints describing the range of motion of the tool in space are obtained. The processing characteristics of the compensation control results are analyzed to extract the curvature, tangential velocity and normal acceleration of the processing trajectory. The curvature describes the geometric characteristics of the trajectory. The higher the curvature, the greater the curvature of the trajectory, which will cause changes in the force on the tool during processing. The tangential velocity is the linear velocity of the tool along the processing trajectory, which directly affects the cutting efficiency and surface quality. The normal acceleration describes the movement change of the tool in the normal direction and is closely related to the stability of the processing. Based on these characteristics, dynamic constraint parameters are established to describe the force and motion constraints during the processing. Based on the tool posture constraints and dynamic constraint parameters, a multi-objective optimization function is constructed. The processing efficiency, surface quality and energy consumption are comprehensively considered to find an optimal processing solution. In order to achieve this goal, the weights of the processing efficiency term, surface quality term and energy consumption term are set to 0.4, 0.4 and 0.2 respectively to ensure that efficiency and quality are of equal importance, while energy consumption is relatively lower. Let the optimization objective function be:

[0107] ;

[0108] in, is the processing efficiency index, is the surface quality index, is the energy consumption index. By setting these weights, it is ensured that the efficiency, quality and energy consumption are balanced during the optimization process, and the initial machining trajectory optimization model is obtained. The initial machining trajectory optimization model is segmented and optimized, especially in areas with large curvature changes, the trajectory is subdivided to ensure that the tool moves more smoothly and accurately in complex areas. The trajectory is subdivided in sections with large curvature to reduce surface defects caused by sudden acceleration or deceleration of the tool. In order to improve the smoothness of the trajectory, the subdivided trajectory is locally smoothed to obtain segmented optimized trajectory data. The segmented optimized trajectory data is subjected to kinematic inverse solution to calculate the feed rate, acceleration and jerk of each axis. The trajectory data is converted into specific motion control instructions for each axis of the machine tool so that the control system can accurately execute these trajectories. Assume that the motion equations of each axis are expressed as:

[0109] ;

[0110] in, For the Axis in time location, represents the inverse kinematics function, is the position vector of the target trajectory. By performing an inverse operation on the trajectory data, the speed and acceleration information required by each axis when executing the optimized trajectory is obtained. The trajectory planning data of each axis is calculated using the acceleration and deceleration optimization algorithm. The role of the acceleration and deceleration optimization algorithm is to reasonably control the change of speed when each axis moves to ensure the stability of the machining process and avoid excessive acceleration or deceleration from having an adverse effect on the machine tool and the workpiece. Through this process, the trajectory planning data of each axis is obtained, including feed speed, spindle speed, and cutting amount. According to the trajectory planning data, the feed speed, spindle speed, and cutting amount of each axis are calculated, and these machining parameters are corrected in combination with the results of compensation control. The results of compensation control are used to correct the influence of factors such as thermal deformation and dynamic characteristics on the machining trajectory, so as to ensure that the actual machining is more consistent with the theoretical planning. The optimized machining parameters finally obtained can ensure that while meeting the machining accuracy, an efficient and low-energy machining process is achieved.

[0111] In a specific embodiment, the process of executing step 105 may specifically include the following steps:

[0112] (1) Map the optimized machining parameters according to the machining stages, generate machining control parameter tables for the rough machining area and the fine machining area, and obtain segmented control data;

[0113] (2) Analyze the cutting volume of the machining control parameter table of the rough machining area, calculate the material removal volume per unit time, and match the cutting parameters according to the upper limit of the spindle power to obtain the rough machining feed parameters;

[0114] (3) Based on the roughing feed parameters, the feed speed and spindle speed are segmented and planned, and the maximum acceleration and deceleration thresholds and jerk thresholds are set to obtain the roughing section speed planning curve;

[0115] (4) Input the rough machining speed planning curve into the servo drive controller, execute rough machining motion control, and collect the current data and torque data of each axis to obtain the rough machining process data;

[0116] (5) Calculate the cutting force threshold based on the machining control parameter table of the finishing area, determine the cutting amount range according to the mapping relationship between cutting force and surface quality, and obtain the finishing feed parameter;

[0117] (6) Perform trajectory interpolation calculation on the finishing feed parameters to generate position instructions and speed instructions for each axis, and output control signals through the feedforward controller to obtain finishing control instructions;

[0118] (7) Execute finishing motion control according to finishing control instructions, collect dynamic cutting force data, and perform real-time spectrum analysis and amplitude monitoring on the cutting force data to obtain finishing process data;

[0119] (8) Feature extraction and data fusion are performed on the rough machining process data and the fine machining process data to obtain the machining process feature vector, and the process state space is constructed through the machining process feature vector to obtain real-time machining data.

[0120] Specifically, the optimized machining parameters are mapped according to different machining stages, and the entire machining process is divided into two areas: rough machining and fine machining. Machining control parameter tables are generated for these two areas respectively, and segmented control data is obtained. Rough machining and fine machining stages have different goals and requirements. Rough machining mainly focuses on material removal rate and machining efficiency, while fine machining focuses on surface quality and accuracy. Different control parameters are set for these two stages to meet their respective process requirements. In the rough machining stage, the cutting volume analysis is performed according to the machining control parameter table of the rough machining area. The purpose of the cutting volume analysis is to determine the volume of material removed per unit time. This indicator is very important for evaluating the efficiency of rough machining. Assuming the cutting depth is , cutting width is , the feed speed is , then the material removal volume per unit time is expressed as:

[0121] ;

[0122] in, Represents the material removal volume. By calculating the material removal volume, the roughing stage is ensured to be carried out with maximum efficiency, while taking into account the upper power limit of the machine tool spindle to prevent overload. According to the upper power limit of the spindle, the cutting parameters are matched to obtain suitable roughing feed parameters to ensure that the machine tool operates within the power limit and achieves efficient material removal. Based on the roughing feed parameters, the feed speed and spindle speed are planned in sections. During the planning process, the maximum acceleration and deceleration threshold and the jerk threshold are set to ensure that the machine tool can maintain smooth acceleration and deceleration during roughing, and prevent excessive acceleration or jerk from causing impact and wear on the machine tool. Suppose the feed speed is , the acceleration is , the jerk is These parameters must satisfy the following constraints:

[0123] ;

[0124] in, is the maximum acceleration threshold, is the maximum jerk threshold. By setting these constraints, the speed planning curve of the roughing section is obtained to describe the speed changes of each axis during the roughing process and ensure that it operates within the physical capacity of the machine tool. The speed planning curve of the roughing section is input into the servo drive controller to perform roughing motion control. During this process, the current data and torque data of each axis are collected in real time. The current data reflects the load required by the drive motor during the processing, while the torque data directly reflects the size of the cutting force. These data are used to evaluate the performance and stability of the machine tool during the roughing process. In the finishing stage, the threshold of the cutting force is calculated based on the machining control parameter table of the finishing area. Cutting force is an important factor affecting surface quality and must be ensured to be within a certain range. By analyzing the mapping relationship between cutting force and surface quality, the appropriate cutting amount range is determined to obtain the feed parameters for the finishing stage. Assume that the cutting force is , cutting depth is , then the relationship between cutting force and cutting depth is expressed as:

[0125] ;

[0126] in, is the cutting force coefficient, and its magnitude depends on the hardness of the material and the geometry of the tool. By setting a reasonable cutting depth, the cutting force is ensured to be within a safe range, and the finishing feed parameters are obtained. The finishing feed parameters are subjected to trajectory interpolation to generate specific position instructions and speed instructions for each axis. Trajectory interpolation is a method for finely controlling the tool path. Through interpolation calculation, the tool is ensured to move accurately along the predetermined trajectory. The instructions obtained by interpolation are input into the feedforward controller to output a control signal to generate a finishing control instruction. The role of the feedforward controller is to predict and compensate for errors in advance, reduce errors in processing and improve surface quality. According to the finishing control instructions, the finishing motion control is performed, and dynamic cutting force data is collected at the same time. The collected cutting force data is subjected to real-time spectrum analysis and amplitude monitoring. The spectrum analysis helps to identify whether there are abnormal vibration signals in the processing process, while the amplitude monitoring is used to ensure that the size of the cutting force runs within the set range, thereby ensuring the stability and surface quality of the finishing process. Finishing process data is an important basis for judging processing quality, helping to optimize processing parameters and reduce processing defects. The process data of rough machining and fine machining are extracted and fused to obtain the feature vector of the machining process. The features that have the greatest impact on the machining results are extracted from the original data, such as current fluctuation characteristics, torque change trends, and cutting force spectrum characteristics. By extracting and fusing these features, a comprehensive feature vector describing the entire machining process is constructed. Assume that the feature vector of the machining process is , contains multiple characteristic components, such as is the current characteristic, is the torque characteristic, is the cutting force characteristic, the characteristic vector is expressed as:

[0127] ;

[0128] The process state space of the entire processing is constructed through the processing feature vector. The state space describes all important features of the processing process and is a mathematical model used to monitor and optimize the processing. The establishment of the state space model helps to identify abnormal conditions in the processing process and adjust the processing parameters in real time to ensure the stability and high precision of the processing process.

[0129] In a specific embodiment, the process of executing step 106 may specifically include the following steps:

[0130] (1) Compare the actual trajectory data of the X-axis, Y-axis, Z-axis, B-axis, and C-axis in the real-time machining data with the theoretical trajectory generated by the initial machining trajectory optimization model to obtain the real-time contour error data of each axis;

[0131] (2) Perform statistical analysis on the real-time contour error data, calculate the straightness error, roundness error, and angle error of each axis, and compare them with the preset machining accuracy threshold to obtain the axis with excessive error.

[0132] (3) For the axis with excessive error, the real-time machining data is analyzed for machining characteristics, and the dynamic curvature, tangential velocity, and normal acceleration of the machining trajectory are extracted to obtain the error correlation characteristics.

[0133] (4) Correct the dynamic constraint parameters according to the error correlation characteristics, adjust the velocity planning threshold and acceleration constraint in the initial machining trajectory optimization model, and obtain the corrected constraint parameters;

[0134] (5) Reconstruct the multi-objective optimization function in the initial machining trajectory optimization model based on the corrected constraint parameters, adjust the weight distribution of the machining efficiency term, surface quality term, and energy consumption term, and obtain the corrected value of the optimization function;

[0135] (6) Substitute the optimization function correction value into the initial machining trajectory optimization model, re-perform the segmented optimization calculation, subdivide and smooth the trajectory curvature change section, obtain the optimized trajectory data, and perform kinematic inverse solution operation on the optimized trajectory data to generate new feed speed, acceleration and jerk parameters for each axis to obtain trajectory planning optimization data;

[0136] (7) Recalculate the feed speed and spindle speed of each axis based on the trajectory planning optimization data, and perform parameter verification in combination with the tool posture constraint conditions to obtain the target machining trajectory optimization model.

[0137] Specifically, the actual motion trajectory data of the X-axis, Y-axis, Z-axis, B-axis and C-axis are extracted from the real-time processing data, and these data are compared with the theoretical trajectory generated by the initial processing trajectory optimization model to obtain the real-time contour error data of each axis. Contour error is a measure that describes the difference between the actual trajectory and the theoretical trajectory. By comparison, the deviation in the processing process is found. The real-time contour error data is calculated by measuring the difference between the actual position of the tool and the target position. Assuming the actual position is , the theoretical position is , then the contour error It is expressed as:

[0138] ;

[0139] in, For the Axis in time The contour error, is the theoretical trajectory, is the actual trajectory. The real-time contour error data is statistically analyzed to calculate the straightness error, roundness error and angle error of each axis. Straightness error refers to the deviation of the tool along the straight path, roundness error reflects the degree of deviation of the tool along the arc path, and angle error describes the deviation of the tool posture during multi-axis linkage. These errors are closely related to machining accuracy, so they need to be compared with the preset machining accuracy threshold to determine whether there is a situation where the error limit is exceeded. By comparison, the axis with excessive error is obtained, that is, the axis with an error exceeding the predetermined allowable range. For the axis with excessive error, the real-time machining data is subjected to machining feature analysis to extract the dynamic characteristics of the machining trajectory. The dynamic curvature, tangential velocity and normal acceleration of the machining trajectory are extracted. Curvature Describes the geometric changes of the machining trajectory and is an important feature of the trajectory curve. A larger curvature means a more severe trajectory bend. Describes the speed of the tool on the trajectory, while the normal acceleration It reflects the acceleration of the tool in the direction perpendicular to the tangent, which is directly related to the cutting force and machining stability. By extracting these features, the error correlation features that describe the force and motion state during the machining process are obtained. According to the extracted error correlation features, the dynamic constraint parameters are corrected to adjust the speed planning threshold and acceleration constraint in the initial machining trajectory optimization model. The speed planning threshold and acceleration constraint are important parameters in machining trajectory optimization, which determine the maximum speed and acceleration that the tool can achieve during the motion process. Assume that the original speed planning threshold is , the acceleration constraint is , then the corrected parameters are expressed as:

[0140] ;

[0141] in, and are the corrections for velocity and acceleration, respectively. These corrections are calculated based on the error correlation characteristics to ensure a more stable machining process. The multi-objective optimization function in the initial machining trajectory optimization model is reconstructed based on the corrected constraint parameters. In the multi-objective optimization, the three objectives of machining efficiency, surface quality and energy consumption are considered simultaneously. In order to meet the needs of error correction, the weight distribution of these objective items is adjusted to obtain the corrected value of the optimization function. Suppose the optimization objective function is:

[0142] ;

[0143] in, To optimize the objective function, is the processing efficiency index, is the surface quality index, is the energy consumption indicator, , , are the weights of processing efficiency, surface quality and energy consumption respectively. In the correction process, the weight of surface quality item is appropriately increased. , to ensure that the machining accuracy is improved when the error exceeds the limit. Substitute the correction value of the optimization function into the initial machining trajectory optimization model, and re-perform the segmented optimization calculation. Especially in sections where the trajectory curvature changes greatly, the trajectory is subdivided to ensure that the tool can achieve more precise control in these areas. Smooth the subdivided trajectory to reduce the discontinuity of the trajectory and excessive curvature changes, and obtain the optimized trajectory data to ensure that the tool can run smoothly during the machining process and improve the stability and quality of the machining. Perform inverse kinematics calculations on the optimized trajectory data to calculate the new feed rate, acceleration, and jerk parameters of each axis. Convert the trajectory coordinates in the three-dimensional space into motion instructions for each machine tool axis so that the machine tool can accurately execute these trajectories. Assume that the position vector is , the inverse kinematics function is , then Displacement of the axis It is expressed as:

[0144] ;

[0145] Through the inverse solution process, the feed speed and acceleration information of each axis are obtained, and the trajectory planning optimization data is generated. Based on the trajectory planning optimization data, the feed speed of each axis and the spindle speed are recalculated, and the parameters are checked in combination with the tool posture constraints. The tool posture constraints describe the posture limits that the tool can reach during the processing process, including the tilt angle, the cutting angle, etc. The purpose of these constraints is to prevent the tool from entering an unreasonable posture during the processing, thereby avoiding collisions and errors. By combining the trajectory planning data with the tool posture constraints, parameter verification is performed to ensure that the feed speed of each axis and the spindle speed meet the processing requirements, and finally the target processing trajectory optimization model is obtained.

[0146] The control method of the five-axis high-precision CNC machine tool in the embodiment of the present invention is described above. The control device of the five-axis high-precision CNC machine tool in the embodiment of the present invention is described below. Figure 2 , an embodiment of the control device of the five-axis high-precision CNC machine tool in the embodiment of the present invention includes:

[0147] The acquisition module 201 is used to acquire the temperature data and displacement data of the five-axis high-precision CNC machine tool, and perform state space modeling to obtain a machine tool motion state model;

[0148] A calculation module 202 is used to calculate the force data of the feed axis based on the machine tool motion state model, and calculate the thermal error influence factor according to the temperature data;

[0149] The compensation control module 203 is used to input the force data of the feed axis and the thermal error influencing factor into the compensation model, calculate the real-time compensation parameters, and perform compensation control on the feed axis of the machine tool to obtain the compensation control result;

[0150] A construction module 204 is used to construct an initial processing trajectory optimization model according to the compensation control result and generate optimized processing parameters;

[0151] The segment processing module 205 is used to perform segment processing control based on the optimized processing parameters to obtain real-time processing data;

[0152] The optimization module 206 is used to optimize the initial processing trajectory optimization model based on the real-time processing data to obtain a target processing trajectory optimization model.

[0153] Through the collaborative cooperation of the above components, by establishing a state space model of multi-source data fusion, combining the state observer and Lyapunov stability theory, the accurate description and prediction of the motion state of the machine tool is achieved. The hierarchical compensation structure is adopted to organically combine mechanical compensation, thermal deformation compensation and dynamic comprehensive compensation. Through the adaptive weighted fusion mechanism, the coupling compensation problem of multi-source errors is effectively solved, and the compensation accuracy is significantly improved; based on the tool posture constraint and dynamic constraint, a multi-objective optimization trajectory planning model is constructed, and the comprehensive optimization of machining efficiency, surface quality and energy consumption is achieved through weight adaptive adjustment; a segmented machining control strategy is proposed, and differentiated control parameters are designed for the rough machining and fine machining stages respectively, which effectively improves the material removal efficiency and ensures the machining accuracy at the same time; a trajectory optimization closed-loop control mechanism based on real-time machining data is established, and the dynamic optimization of the machining process is achieved through real-time detection of contour errors and adaptive adjustment of parameters, ensuring the stability of machining quality; a multi-level cascade control structure is adopted, and through the position-speed-current three-loop compensation, combined with the feedforward and feedback two-way compensation mechanism, the dynamic response performance and control accuracy of the five-axis high-precision CNC machine tool are significantly improved.

[0154] above Figure 2 The control device of the five-axis high-precision CNC machine tool in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The control device of the five-axis high-precision CNC machine tool in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0155] Figure 33 is a structural schematic diagram of a control device for a five-axis high-precision CNC machine tool provided by an embodiment of the present invention. The control device 300 for the five-axis high-precision CNC machine tool may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 may be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the control device 300 of the five-axis high-precision CNC machine tool. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the control device 300 of the five-axis high-precision CNC machine tool to implement the steps of the control method of the above-mentioned five-axis high-precision CNC machine tool.

[0156] The control device 300 of the five-axis high-precision CNC machine tool may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that Figure 3 The control device structure of the five-axis high-precision CNC machine tool shown does not constitute a limitation on the control device of the five-axis high-precision CNC machine tool provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0157] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the control method of the five-axis high-precision CNC machine tool.

[0158] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0159] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0160] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for a five-axis high-precision CNC machine tool, characterized in that: The method comprises: Collect temperature and displacement data of five-axis high-precision CNC machine tools, perform state space modeling, and obtain the machine tool motion state model; Calculating the force data of the feed axis based on the machine tool motion state model, and calculating the thermal error influence factor according to the temperature data; Inputting the feed axis force data and the thermal error influencing factor into the compensation model, calculating the real-time compensation parameters, and performing compensation control on the machine tool feed axis to obtain a compensation control result; Constructing an initial machining trajectory optimization model according to the compensation control result, and generating optimized machining parameters; Execute segmented processing control based on the optimized processing parameters to obtain real-time processing data; The initial machining trajectory optimization model is optimized based on the real-time machining data to obtain a target machining trajectory optimization model.

2. The control method of a five-axis high-precision CNC machine tool according to claim 1, characterized in that: The temperature data and displacement data of the five-axis high-precision CNC machine tool are collected, and state space modeling is performed to obtain a machine tool motion state model, including: The temperature sensor arrays installed on the X-axis, Y-axis, Z-axis, B-axis and C-axis of the five-axis high-precision CNC machine tool are used for data collection and abnormal value preprocessing to obtain temperature data; Performing real-time sampling and Kalman filtering on the displacement sensors of the X-axis, Y-axis, Z-axis, B-axis and C-axis of the five-axis high-precision CNC machine tool to obtain displacement data; Input the temperature data and the displacement data into a state observer, and use the stiffness coefficient of the feeding system and the servo system parameters as quantities to be observed to obtain initial state variables; Establishing a state space expression for the initial state variables, wherein the dimension of the state matrix is ​​10×10, the dimension of the input matrix is ​​10×5, and the dimension of the output matrix is ​​5×10, to obtain an initial state space equation; Based on Lyapunov stability theory, the convergence analysis of the initial state space equation is performed, and the observer gain matrix is ​​determined by the pole placement method to obtain the state observation equation; The displacement data in the state observation equation and the actually collected machine tool motion data are used for online parameter identification, and the machine tool motion state model is obtained by recursive least square method.

3. The control method of the five-axis high-precision CNC machine tool according to claim 2 is characterized in that: The calculating of the feed axis force data based on the machine tool motion state model and the calculating of the thermal error influence factor according to the temperature data include: Based on the machine tool motion state model, the feed motor current data of the X-axis, Y-axis, Z-axis, B-axis and C-axis in the five-axis high-precision CNC machine tool are sampled and converted into a current-torque relationship to obtain initial force data of each axis; High-frequency noise is filtered out on the initial force data of each axis to obtain target force data, and dynamic force compensation calculation is performed based on the displacement data in the machine tool motion state model in combination with the target force data to obtain the feed axis force data; Arranging the temperature data in time series to obtain temperature time series data, and performing zero drift correction on the temperature time series data to obtain temperature variation data; Calculating the temperature change rate of each measuring point according to the temperature change data, and performing threshold judgment and key measuring point judgment on the temperature change rate to obtain temperature key measuring point data; A correlation analysis is performed on the temperature key measuring point data and the thermal deformation data of each axis to obtain temperature sensitive measuring point data, and a temperature-thermal deformation response function is calculated based on the temperature sensitive measuring point data to obtain a thermal error influencing factor through weighted coefficient calculation.

4. The control method of a five-axis high-precision CNC machine tool according to claim 3, characterized in that: The feed axis force data and the thermal error influencing factor are input into a compensation model, real-time compensation parameters are calculated, and compensation control is performed on the machine tool feed axis to obtain a compensation control result, including: The compensation model is divided into a mechanical compensation layer, a thermal deformation compensation layer and a dynamic comprehensive compensation layer. The force data of the feed shaft is input into the mechanical compensation layer, and the thermal error influencing factor is input into the thermal deformation compensation layer. In the mechanical compensation layer, the force data of the feed axis is decomposed according to the X-axis, Y-axis, Z-axis, B-axis and C-axis respectively, and the force data of each axis is converted into the frequency domain to obtain the force spectrum data of each axis, and the force characteristics of each axis are extracted and vectorized based on the force spectrum data of each axis to obtain the force characteristic vector; In the thermal deformation compensation layer, the thermal error influencing factor is mapped by axis, and the thermal error compensation amounts of the X-axis, Y-axis, Z-axis, B-axis and C-axis are generated accordingly to obtain the thermal error compensation data of each axis; In the dynamic comprehensive compensation layer, the force characteristic vector and the thermal error compensation data of each axis are adaptively weighted fused to obtain compensation fusion data; The displacement compensation amounts of the five axes are calculated respectively according to the compensation fusion data, the displacement compensation amounts are corrected by the compensation gain matrix, and the compensation correction is performed in combination with the motion coupling relationship of each axis to obtain real-time compensation parameters; Decomposing the real-time compensation parameter into a feed speed compensation component and a feed position compensation component, performing bidirectional compensation through a feedforward compensation channel and a feedback compensation channel, and obtaining a compensation control instruction; According to the compensation control instruction, closed-loop servo control is performed on each feed axis of the five-axis high-precision CNC machine tool, and three-loop compensation of position-speed-current is realized through the inner loop and outer loop of the servo controller to obtain a compensation control result.

5. The control method of a five-axis high-precision CNC machine tool according to claim 4, characterized in that: The initial machining trajectory optimization model is constructed according to the compensation control result, and the optimized machining parameters are generated, including: According to the compensation control result, the actual motion trajectory data of the X-axis, Y-axis, Z-axis, B-axis and C-axis are respectively extracted, and the actual motion trajectory data of each axis is mapped into a posture space to obtain the initial posture data of the tool; The tool's inclination angle and cutting angle relative to the workpiece surface are calculated based on the tool's initial posture data, and the interference distance between the tool and the workpiece is calculated by a collision detection algorithm to obtain a tool spatial position constraint condition; Decomposing the tool spatial position constraint condition into an axial constraint component, performing extreme value analysis on the axial constraint component, detecting the singular configuration and travel limit of each axis, and obtaining the tool posture constraint condition; Performing machining feature analysis on the compensation control result, extracting the curvature, tangential velocity and normal acceleration of the machining trajectory, and establishing dynamic constraint parameters; Based on the tool posture constraint conditions and the dynamic constraint parameters, a multi-objective optimization function is constructed, and the weight coefficients of the processing efficiency item, the surface quality item, and the energy consumption item are set to 0.4, 0.4, and 0.2, respectively, to obtain an initial processing trajectory optimization model; The initial machining trajectory optimization model is subjected to segmented optimization calculation, the trajectory is subdivided in the section with large curvature change, and the subdivided trajectory is locally smoothed to obtain segmented optimized trajectory data; Performing kinematic inverse solution on the segmented optimized trajectory data, calculating the feed speed, acceleration and jerk of each axis, and calculating the trajectory planning data of each axis through an acceleration and deceleration optimization algorithm; The feed speed, spindle speed and cutting amount of each axis are calculated according to the trajectory planning data, and the parameters are corrected in combination with the compensation control result to obtain the optimized processing parameters.

6. The control method of a five-axis high-precision CNC machine tool according to claim 5, characterized in that: The step of performing segmented processing control based on the optimized processing parameters to obtain real-time processing data includes: The optimized processing parameters are mapped into different areas according to the processing stages, and processing control parameter tables are generated for the rough processing area and the fine processing area respectively to obtain segmented control data; Performing a cutting amount analysis on the machining control parameter table of the rough machining area, calculating the material removal volume per unit time, and matching the cutting parameters according to the upper limit value of the spindle power to obtain the rough machining feed parameters; Based on the rough machining feed parameters, the feed speed and the spindle speed are segmented and planned, and the maximum acceleration and deceleration threshold and the jerk threshold are set to obtain a rough machining segment speed planning curve; Inputting the rough machining section speed planning curve into the servo drive controller, executing rough machining motion control, and collecting current data and torque data of each axis to obtain rough machining process data; Calculating a cutting force threshold based on the machining control parameter table of the finishing area, and determining a cutting amount range according to a mapping relationship between cutting force and surface quality to obtain a finishing feed parameter; Performing trajectory interpolation operation on the finishing feed parameters to generate position instructions and speed instructions for each axis, and outputting control signals through a feedforward controller to obtain finishing control instructions; Execute finishing motion control according to the finishing control instruction, collect dynamic cutting force data, and perform real-time spectrum analysis and amplitude monitoring on the cutting force data to obtain finishing process data; Feature extraction and data fusion are performed on the rough machining process data and the fine machining process data to obtain a machining process feature vector, and a process state space is constructed through the machining process feature vector to obtain real-time machining data.

7. The control method of a five-axis high-precision CNC machine tool according to claim 6, characterized in that: The step of optimizing the initial machining trajectory optimization model based on the real-time machining data to obtain a target machining trajectory optimization model includes: Comparing the actual trajectory data of the X-axis, Y-axis, Z-axis, B-axis and C-axis in the real-time processing data with the theoretical trajectory generated by the initial processing trajectory optimization model to obtain real-time contour error data of each axis; Performing statistical analysis on the real-time contour error data, calculating the straightness error, roundness error and angle error of each axis, and comparing them with the preset machining accuracy threshold to obtain the axis with excessive error; For the axis with excessive error, performing machining feature analysis on the real-time machining data, extracting the dynamic curvature, tangential velocity and normal acceleration of the machining trajectory, and obtaining error correlation features; The dynamic constraint parameters are corrected according to the error correlation characteristics, and the speed planning threshold and acceleration constraint in the initial machining trajectory optimization model are adjusted to obtain corrected constraint parameters; Reconstructing the multi-objective optimization function in the initial machining trajectory optimization model based on the modified constraint parameters, adjusting the weight distribution of the machining efficiency item, the surface quality item and the energy consumption item, and obtaining a modified value of the optimization function; Substituting the optimization function correction value into the initial machining trajectory optimization model, re-performing segmented optimization calculation, subdividing and smoothing the trajectory curvature change section to obtain optimized trajectory data, and performing kinematic inverse solution operation on the optimized trajectory data to generate new feed speed, acceleration and jerk parameters of each axis to obtain trajectory planning optimization data; The feed speed and spindle speed of each axis are recalculated according to the trajectory planning optimization data, and parameter verification is performed in combination with the tool posture constraint conditions to obtain a target machining trajectory optimization model.

8. A control device for a five-axis high-precision CNC machine tool, characterized in that: The device is used to execute the control method of a five-axis high-precision CNC machine tool according to any one of claims 1 to 7, comprising: The acquisition module is used to collect the temperature data and displacement data of the five-axis high-precision CNC machine tool, and perform state space modeling to obtain the machine tool motion state model; A calculation module, used for calculating the force data of the feed axis based on the machine tool motion state model, and calculating the thermal error influence factor according to the temperature data; A compensation control module, used for inputting the force data of the feed shaft and the thermal error influencing factor into a compensation model, calculating real-time compensation parameters, and performing compensation control on the feed shaft of the machine tool to obtain a compensation control result; A construction module, used to construct an initial processing trajectory optimization model according to the compensation control result, and generate optimized processing parameters; A segmented processing module, used to perform segmented processing control based on the optimized processing parameters to obtain real-time processing data; The optimization module is used to optimize the initial processing trajectory optimization model based on the real-time processing data to obtain a target processing trajectory optimization model.

9. A control device for a five-axis high-precision CNC machine tool, characterized in that: The control device of the five-axis high-precision CNC machine tool comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instruction in the memory so that the control device of the five-axis high-precision CNC machine tool executes the control method of the five-axis high-precision CNC machine tool according to any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the control method of the five-axis high-precision CNC machine tool as described in any one of claims 1 to 7 is implemented.

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