Intelligent Multi-axis Synchronous Control System and Method for CNC Machine Tools for Aluminum Processing

Abstract

The present invention belongs to the field of machine tool production control, and particularly relates to an intelligent multi-axis synchronous control system and method for numerically controlled machine tools for aluminum processing. The system integrates intelligent perception, error compensation, processing control, and interaction modules. Among them, the intelligent perception module real-time monitors and preprocesses multi-axis working state data through integrated sensors. The error compensation module uses an error monitoring unit and an error compensation unit to predict and dynamically compensate multi-axis errors based on an error model. The processing control module realizes precise synchronous control of the workpiece and the tool through three-dimensional modeling, machining trajectory generation, and feedback adjustment. The interaction module provides a visual display of the processing progress and real-time monitoring and early warning of faults. The present invention effectively improves the machining accuracy and efficiency of five-axis numerically controlled machine tools for aluminum, and enhances the intelligence and stability of the machining process.

Description

Technical Field

[0001] The present invention belongs to the field of machine tool production control, and particularly relates to an intelligent multi-axis synchronous control system and method for numerically controlled machine tools for aluminum processing. Background Art

[0002] In modern manufacturing, aluminum materials are widely used due to their light weight, high strength, and good corrosion resistance. Especially in fields such as aerospace, automotive manufacturing, and electronic equipment, there are extremely high requirements for the precision and efficiency of aluminum processing. As an important processing tool, five-axis numerically controlled machine tools can achieve precise processing of aluminum materials. However, due to the special properties of aluminum materials and the requirements for complex shapes, traditional numerically controlled machine tools face many challenges during the processing. For example, existing methods usually perform post-error compensation, but post-error compensation will cause error accumulation and reduce the processing precision. Existing pre-error prediction compensation methods can solve the problem of error accumulation, but it is very difficult to control the sequence of error compensation for each axis and the compensation error precision, resulting in the problem of asynchrony during the processing of each processing axis.

[0003] For example, the patent with the authorization announcement number CN110018669B discloses a decoupled contour error control method for five-axis numerically controlled machine tools, including: first, calculating the contour error vector and the tangential tracking error vector through an online five-axis contour error estimation method; then designing a stable normal contour error controller for each drive axis, with the contour error vector as the input and the normal contour error control component of each axis as the output; then designing a stable tangential tracking error controller for each drive axis, with the tangential tracking error vector as the input and the tangential tracking error control component of each axis as the output; finally, directly adding the normal contour error control component and the tangential tracking error control component of each axis to obtain the total control signal to control each drive axis.

[0004] For example, the patent with the publication number CN107479497A discloses a double-closed-loop compensation method for the contour error of a five-axis machining trajectory. This method is based on model prediction and feedback correction, estimates the motion positions of each physical axis at the next moment, calculates the contour error of the tool tip point and the tool axis direction at the next moment and the current moment through tangential inverse deduction and the Newton method, and predicts and compensates the contour error in the inner loop and performs feedback compensation on the contour error in the outer loop according to the inverse Jacobian matrix.

[0005] The above existing technologies have the following problems: The current error compensation methods are usually based on compensation after error generation, and the existing pre-compensation methods only predict and then compensate for the compensation error, but ignore the sequence of error compensation for each processing axis, resulting in asynchrony of each processing axis during the processing. To solve the above problems, the present invention provides an intelligent multi-axis synchronous control system and method for numerically controlled machine tools for aluminum processing. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention proposes an intelligent multi-axis synchronous control system and method for numerically controlled machine tools for aluminum processing. The system integrates intelligent perception, error compensation, processing control, and interaction modules. Among them, the intelligent perception module uses integrated sensors to monitor and preprocess the multi-axis working state data in real time. The error compensation module uses an error monitoring unit and an error compensation unit to predict and dynamically compensate multi-axis errors based on an error model. The processing control module realizes precise synchronous control of the workpiece and the tool through three-dimensional modeling, processing trajectory generation, and feedback adjustment. The interaction module provides a visual display of the processing progress and real-time monitoring and early warning of faults. This system effectively improves the aluminum processing accuracy and efficiency of five-axis numerically controlled machine tools and enhances the intelligence and stability of the processing process.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent multi-axis synchronous control system for numerically controlled machine tools for aluminum processing, comprising: an intelligent perception module, an error compensation module, and a processing control module;

[0009] The intelligent perception module includes a data acquisition unit; the data acquisition unit is used to collect the processing process data of the processing tool and the processing workpiece in real time through integrated sensors;

[0010] The processing control module includes a three-dimensional modeling unit, a processing trajectory generation unit, and a feedback adjustment unit; the three-dimensional modeling unit is used to construct an integrated three-dimensional model of the machine tool, the processing workpiece, and the processing tool through three-dimensional modeling software according to the attribute parameters of the machine tool, the processing workpiece, and the processing tool, and on the constructed integrated three-dimensional model, construct a multi-axis coordinate system of the machine tool, the processing workpiece, and the processing tool according to the actual positions of the processing workpiece and the processing tool; the processing trajectory generation unit is used to generate the processing trajectory parameters of the processing workpiece and the processing tool on multiple axes through a trajectory generation algorithm according to the constructed integrated three-dimensional model, processing process parameters, and error compensation instructions; the feedback adjustment unit is used to obtain the trajectory position error of the processing workpiece and the processing tool in the multi-axis trajectory after error compensation in real time, and feed the obtained error back to the error compensation unit to perform secondary compensation on the position error in the multi-axis trajectory;

[0011] The error compensation module includes an error monitoring unit and an error compensation unit;

[0012] An error monitoring unit, which is used to predict the real-time change trend of multi-axis errors and the error priority compensation degree according to the obtained machining process data of the machining tool and the workpiece through the built-in error offset model and error evaluation model, and feedback the calculated real-time change trend of multi-axis errors and the error priority compensation degree to the error compensation unit; an error compensation unit, which is used to generate an error compensation instruction according to the predicted real-time change trend of multi-axis errors and the priority compensation degree through the built-in enhanced servo compensation model, and perform priority pre-compensation on the errors of the machining tool and the workpiece in the corresponding coordinate axis directions.

[0013] Specifically, the multi-axis coordinate systems of the machine tool, the workpiece to be machined, and the machining tool include: the machine tool coordinate system o M , the workpiece coordinate system o W , and the tool coordinate system o T ; the corresponding translational axis coordinate systems X, Y, Z and rotational axis coordinate systems A and C are established according to the machine tool coordinate system, the workpiece coordinate system and the tool coordinate system.

[0014] Specifically, the machining process data includes the attribute data of the machine tool, the machining tool and the workpiece to be machined, the machining process parameters of the workpiece to be machined, the moving position error and rotational angle error data of the machining tool and the workpiece to be machined, the vibration data corresponding to the machine tool, the machining trajectory data of the machining tool and the workpiece to be machined in the translational axis coordinate system and the rotational axis coordinate system, and the machining tool torque information data;

[0015] The specific steps for the error monitoring unit to obtain the real-time change trend of multi-axis errors through the error offset model are as follows:

[0016] A1. Obtain the historical moving position error and rotational angle error data of the corresponding machining trajectory in the translational axis coordinate system and the rotational axis coordinate system during the machining process, the vibration data corresponding to the machine tool during the machining process, and the torque information data corresponding to the machining tool; the vibration data includes the vibration intensity and the vibration direction;

[0017] A2. Calculate the error change rates of the moving trajectory position error and rotational angle error on the translational axis and the rotational axis according to the obtained moving trajectory position error and rotational angle error data and their corresponding machining trajectory data for each axis, and align the calculated error change rates with the machine tool vibration data in time during the machining process;

[0018] A3. Build an error offset model based on the support vector machine. Input the aligned error change rate, the machine tool vibration data, the machining trajectory data on the translational and rotational axes corresponding to the error change rate, and the torque information data corresponding to the machining tool into the error offset model for training to obtain a trained error offset model. At the same time, within the error offset model, obtain the change trend formula of the error change rate on each translational or rotational axis with respect to the movement and rotation trajectories, the vibration intensity and vibration direction in the vibration data, and the torque information through the kernel function fitting in the support vector machine;

[0019] A4. Embed the trained error offset model into the error monitoring unit. Real-time obtain the vibration data during the machine tool operation, the trajectory data on each axis generated by the machining trajectory generation unit in real time, and the torque information data corresponding to each movement trajectory calculated according to the workpiece attribute data and machining process parameters, and calculate the predicted error change rate corresponding to the trajectory data on each axis generated by the machining trajectory generation unit;

[0020] A5. Calculate the predicted error change trend corresponding to the trajectory data on each axis generated by the machining trajectory generation unit according to the predicted error change rate.

[0021] Specifically, the specific steps for the error monitoring unit to obtain the error priority compensation degree through the error evaluation model include:

[0022] A6. For the error change rate obtained in A2, perform corresponding priority compensation degree annotation through the change direction of the error change rate, and align the annotated error change rate with the current translational and rotational axis trajectory data in time;

[0023] A7. Build an error evaluation model based on the random forest algorithm, and input the aligned and annotated error change rate and trajectory data into the error evaluation model for training to obtain a trained error evaluation model;

[0024] A8. Input the predicted error change rate corresponding to the trajectory data on each axis obtained in A4 and the corresponding trajectory data into the error evaluation model to obtain the error priority compensation degree and the error compensation rate corresponding to the error change rate at each time point of the machining trajectory on each translational or rotational axis.

[0025] Specifically, the enhanced servo compensation model in the error compensation unit includes a compensation adjustment layer; the steps for constructing the compensation adjustment layer include:

[0026] B1. Construct the input state at the current time t according to the obtained machining process data, the predicted error change trend corresponding to the trajectory data on each axis, the error priority compensation degree, and the error compensation rate where Xt represents the machining process data sequence obtained at the current time t, It represents the changing trend of the machining trajectory error corresponding to the predicted $i$-th translational or rotational axis. It represents the priority compensation degree of the machining trajectory error corresponding to the predicted $i$-th translational or rotational axis. It represents the compensation rate of the machining trajectory error corresponding to the predicted $i$-th translational or rotational axis.

[0027] B2. Set the ideal machining trajectory parameters. When the machining trajectory parameters of the machining tool and the workpiece obtained in real time on multiple axes are not equal to the ideal machining trajectory parameters, the error compensation information is triggered.

[0028] B3. Based on the constructed input state, error priority compensation degree, and error compensation rate, construct the joint execution action at the current moment. Among them, $at1$ represents the action of whether to perform error compensation. When $at1 = 0$, the error compensation action is not performed. When $at1 = 1$, the error compensation action is performed, and the corresponding instruction is generated; $at2$ represents that on the premise of $at1 = 1$, according to the obtained error priority compensation degree, the machining trajectory of the translational or rotational axis with a high error priority compensation degree is preferentially compensated for errors; $at3$ represents that on the premise of $at1 = 1$, according to the predicted error compensation rate of the machining trajectory of the $i$-th translational or rotational axis, the machining trajectory of the $i$-th translational or rotational axis is compensated for errors at the corresponding rate.

[0029] B4. Based on the constructed joint execution action, construct the reward function $R_t$, specifically:

[0030]

[0031] Among them, represents the error distance between the $j$-th ideal position point in the ideal machining trajectory parameters corresponding to the $i$-th translational or rotational axis at time $t$ and the position point of the machining tool or workpiece in the actual machining trajectory parameters corresponding to the $i$-th translational or rotational axis. $Ig$ represents the penalty term for the $g$-th incorrect execution of $at1$, and $G$ represents the total number of incorrect executions of $at1$. When $at1$ is incorrectly executed, $Ig = 1$, otherwise $Ig = 0$. $Iv$ represents the penalty term for the $v$-th incorrect execution of $at2$, and $V$ represents the total number of incorrect executions of $at2$. When $at2$ is incorrectly executed, $Iv = 1$, otherwise $Iv = 0$. represents the difference between the predicted error compensation rate and the actual error compensation rate on the $i$-th translational or rotational axis at time $t$; $k1$, $k2$, $k3$, $k4$ represent the gain coefficients corresponding to the reward function.

[0032] B5. Based on the reinforcement learning algorithm, construct a compensation adjustment layer, and input the obtained input state, joint execution action, and reward function at the current time $t$ into the compensation adjustment layer for training, and output the priority compensation adjustment amount and error compensation rate adjustment amount.

[0033] Specifically, the enhanced servo compensation model in the error compensation unit further includes a compensation control layer. The steps for constructing the compensation control layer are as follows:

[0034] B6. Initialize the control parameters within the compensation control layer based on the obtained machining process data, the predicted error change trends corresponding to the trajectory data on each axis, and the error priority compensation degree and error compensation rate. Then, use the obtained priority compensation adjustment amount and error compensation rate adjustment amount to calculate the error compensation instruction corresponding to the i-th translational or rotational axis. Perform priority pre-compensation on the machining trajectory parameter error in the direction of the i-th translational or rotational axis. Specifically:

[0035]

[0036] Among them, q1 represents the evaluation value obtained corresponding to the execution of at1. When at1 = 0, q1 = 0, and when at1 = 1, q1 = 1. q2 and q3 respectively represent the evaluation values obtained when executing at2 and at3. t0 represents the time length for the machining tool or the workpiece to move a complete machining trajectory on the i-th translational or rotational axis. Ii represents the priority compensation factor for the machining trajectory on the i-th translational or rotational axis. When Ii = 1, priority is given to compensating the machining trajectory on the i-th translational or rotational axis. When Ii = 0, no priority compensation is performed on the machining trajectory on the i-th translational or rotational axis. kp represents the proportionality coefficient within the initialized control parameters, ki represents the integral time coefficient within the initialized control parameters, and kd represents the differential time coefficient within the initialized control parameters.

[0037] Specifically, the specific steps for obtaining the ideal machining trajectory parameters in B2 are as follows:

[0038] C1. According to the integrated three-dimensional model constructed by the three-dimensional modeling unit, the machining process parameters of the workpiece, and the error compensation instruction, use the trajectory generation algorithm built into the virtual simulation software to generate corresponding machining trajectory parameters on each translational or rotational axis within the multi-axis coordinate system corresponding to the integrated three-dimensional model, and set the generated machining trajectory parameters as the ideal machining trajectory parameters.

[0039] C2. Incorporate the obtained ideal machining trajectory parameters into the controller corresponding to the five-axis CNC machine tool, and set the initial state of the error compensation instruction within the ideal machining trajectory parameters to the silent state. When the five-axis CNC machine tool starts working, the initial state of the error compensation instruction changes to the active state, and pre-compensation is performed on the machining trajectory parameters of the machining tool and the workpiece corresponding to the five-axis CNC machine tool, so that the real-time machining trajectory parameters of the machining tool and the workpiece within the multi-axis coordinate system are always equal to the ideal machining trajectory parameters in real time.

[0040] Specifically, the error compensation rate and the error change rate at each time point of the machining trajectory on each translational or rotational axis are equal.

[0041] An intelligent multi-axis synchronous control method for a numerically controlled machine tool for aluminum processing, the steps including:

[0042] S1. Obtain the machining process data of the machining tool and the workpiece to be machined, and perform preprocessing;

[0043] S2. Utilize the machine tool, machining tool and workpiece attribute data in the machining process data, construct an integrated three-dimensional model of the machine tool, machining tool and workpiece through three-dimensional modeling software, and construct a multi-axis coordinate system on the constructed integrated three-dimensional model according to the actual position information of the machining tool and the workpiece;

[0044] S3. On the constructed multi-axis coordinate system, according to the machining process parameters in the machining process data, generate the ideal machining trajectory parameters of the machining tool and the workpiece on each translational or rotational axis through a trajectory generation algorithm, and embed the obtained ideal machining trajectory parameters into the corresponding controller of the five-axis numerically controlled machine tool;

[0045] S4. When the five-axis numerically controlled machine tool starts to work, control the workpiece to be machined and the machining tool to perform corresponding machining through the built-in ideal machining trajectory parameters, and generate an error compensation instruction through the built-in enhanced servo compensation model to preferentially pre-compensate the position error and rotational angle error of the workpiece to be machined and the machining tool on the corresponding translational or rotational axis;

[0046] S5. Obtain the position error of the workpiece to be machined and the machining tool in the compensated multi-axis trajectory in real time through a feedback adjustment unit, and feed back the obtained error to an error compensation unit to perform secondary compensation on the position error in the multi-axis trajectory.

[0047] A computer-readable storage medium, on which computer instructions are stored, and when the computer instructions run, an intelligent multi-axis synchronous control method for a numerically controlled machine tool for aluminum processing is executed.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] The present invention preferentially compensates the machining trajectories on the translational or rotational axes with fast-changing error trends, that is, the translational or rotational axes that first generate errors and have a fast error growth rate, by strengthening the servo compensation model in combination with the change trend of the prediction error, the error priority compensation degree corresponding to the error change rate at each time point, and the rate of error compensation. This makes the machining trajectory parameters on each axis consistent with the ideal machining trajectory, enhances the consistency of the machining trajectories on each axis. At the same time, the machining trajectory parameters on each axis are compensated by the error compensation rate calculated by obtaining the error change rate on each axis respectively, so that the machining trajectories on each axis can be synchronously restored to the corresponding ideal machining trajectory parameters, enhancing the synchronism during the machining process of the five-axis CNC machine tool. Brief Description of the Drawings

[0050] Figure 1 FIG. is a block diagram of an intelligent multi-axis synchronous control system for an aluminum processing CNC machine tool according to Embodiment 1 of the present invention;

[0051] Figure 2 FIG. is a working structure diagram of the corresponding unit of the intelligent multi-axis synchronous control system for an aluminum processing CNC machine tool according to Embodiment 2 of the present invention;

[0052] Figure 3 FIG. is a flowchart of an intelligent multi-axis synchronous control method for an aluminum processing CNC machine tool according to Embodiment 3 of the present invention. Detailed Description of the Invention

[0053] Embodiment 1

[0054] Please refer to Figure 1 , an embodiment provided by the present invention: an intelligent multi-axis synchronous control system for an aluminum processing CNC machine tool, which is applied to the control of the aluminum processing process of an AC double-turntable five-axis machine tool, including: an intelligent perception module, an error compensation module, a machining control module, and an interaction module; the intelligent perception module real-time monitors and obtains the multi-axis working state data of the five-axis CNC machine tool through sensors, and performs fusion preprocessing on the collected working state data; the intelligent perception module includes a data acquisition unit and a preprocessing fusion unit;

[0055] The data acquisition unit is used to collect the machining process data of the machining tool and the workpiece in real time through an integrated sensor; further, the machining process data in this embodiment includes the attribute data of the machine tool, the machining tool, and the workpiece, the machining process parameters of the workpiece, the moving position error and rotational angle error data of the machining tool and the workpiece, the vibration data corresponding to the machine tool, the machining trajectory data of the machining tool and the workpiece in the translational axis coordinate system and the rotational axis coordinate system, and the machining tool torque information data; further, the attribute data of the workpiece in this embodiment is aluminum attribute data;

[0056] The preprocessing fusion unit is used to preprocess the collected data and integrate the preprocessed data into a unified format;

[0057] The processing control module is used for the planning and synchronous control of the movement trajectories of the workpiece and the processing tool on multiple axes; the processing control module includes a three-dimensional modeling unit, a processing trajectory generation unit, and a feedback adjustment unit; the three-dimensional modeling unit is used to construct an integrated three-dimensional model of the machine tool, the processing workpiece, and the processing tool through three-dimensional modeling software according to the attribute parameters of the machine tool, the processing workpiece, and the processing tool, and on the constructed integrated three-dimensional model, according to the actual positions of the processing workpiece and the processing tool, construct a multi-axis coordinate system for the machine tool, the processing workpiece, and the processing tool; further, the specific steps of the three-dimensional modeling unit in this embodiment include:

[0058] a1. Input the attribute data of the machine tool, the processing tool, and the processing workpiece in the preprocessed processing process data into the three-dimensional modeling software to establish an integrated three-dimensional model of the machine tool, the processing tool, and the processing workpiece; further, the attribute data of the machine tool, the processing tool, and the processing workpiece in this embodiment include the stroke range, speed, initial position of the machine tool, the processing tool, and the processing workpiece on each axis, the geometric shape, size, material properties, etc. of the processing workpiece, and the type, diameter, length, edge type, and cutting parameters of the processing tool; further, the three-dimensional modeling software includes: SolidWorks, Siemens NX, and CATIA;

[0059] a2. Based on the constructed integrated three-dimensional model, according to the actual positions of the processing workpiece and the processing tool, construct a multi-axis coordinate system for the machine tool, the processing workpiece, and the processing tool; further, in this embodiment, the multi-axis coordinate system of the machine tool, the processing workpiece, and the processing tool includes: the machine tool coordinate system o M the workpiece coordinate system o W the tool coordinate system o T ; establish the corresponding translational axis coordinate systems X, Y, Z and the rotational axis coordinate systems A and C according to the machine tool coordinate system, the workpiece coordinate system, and the tool coordinate system.

[0060] The processing trajectory generation unit is used to generate the processing trajectory parameters of the processing workpiece and the processing tool on multiple axes through a trajectory generation algorithm according to the constructed integrated three-dimensional model, the processing process parameters, and the error compensation instructions; further, the trajectory generation algorithms in this embodiment include the Dijkstra algorithm and the A* search algorithm.

[0061] The feedback adjustment unit is used to obtain in real time the trajectory position error of the processing workpiece and the processing tool in the multi-axis trajectory after error compensation, and feed back the obtained error to the error compensation unit to perform secondary compensation on the position error in the multi-axis trajectory.

[0062] An error compensation module is used for predicting translational or rotational errors and dynamically compensating the corresponding predicted errors. The error compensation module includes an error monitoring unit and an error compensation unit;

[0063] The error monitoring unit is configured to predict the real-time change trend of multi-axis errors and the error priority compensation degree through the built-in error offset model and error evaluation model based on the acquired position information and movement information data of the machining tool and the workpiece, and feedback the calculated real-time change trend of multi-axis errors and the error priority compensation degree to the error compensation unit. Further, in this embodiment, the specific steps for the error monitoring unit to obtain the real-time change trend of multi-axis errors through the error offset model are as follows:

[0064] A1. Obtain the historical movement position errors and rotational angle errors on the translational axis coordinate system and the rotational axis coordinate system corresponding to the machining trajectory during the machining process, the vibration data corresponding to the machine tool during the machining process, and the torque information data corresponding to the machining tool; the vibration data includes the vibration intensity and the vibration direction;

[0065] A2. Calculate the error change rates of the corresponding movement trajectory position errors and rotational angle errors on the translational axis and the rotational axis according to the obtained movement trajectory position errors and rotational angle errors data and their corresponding axis machining trajectory data, and align the calculated error change rates with the vibration data of the machine tool during the machining process in terms of time;

[0066] A3. Build an error offset model based on the support vector machine, input the aligned error change rates, the vibration data of the machine tool, the machining trajectory data on the translational axis and the rotational axis corresponding to the error change rates, and the torque information data corresponding to the machining tool into the error offset model for training to obtain the trained error offset prediction model. At the same time, in the error offset model, obtain the change trend formula of the error change rate on each translational or rotational axis with respect to the movement and rotation trajectories, the vibration intensity and direction in the vibration data, and the torque information through the kernel function fitting in the support vector machine;

[0067] A4. Incorporate the trained error offset model into the error monitoring unit, and in real time, obtain the vibration data during the operation of the machine tool, the trajectory data on each axis generated by the machining trajectory generation unit in real time, and the torque information data corresponding to each movement trajectory calculated according to the workpiece attribute data and the machining process parameters, and calculate the predicted error change rate corresponding to the trajectory data on each axis generated by the machining trajectory generation unit;

[0068] A5. Calculate the predicted error change trend corresponding to the trajectory data on each axis generated by the machining trajectory generation unit according to the predicted error change rate.

[0069] This process realizes precise error compensation by analyzing the position, vibration, and torque data during the machining process in real time, predicting the multi-axis error change trend using the trained error offset model, effectively improving the machining accuracy and efficiency, reducing the scrap rate, and at the same time optimizing the resource allocation and enhancing the intelligence level of the manufacturing system through the evaluation of error priorities.

[0070] Further, the specific steps for the error monitoring unit to obtain the error priority compensation degree through the error evaluation model include:

[0071] A6. For the error change rate obtained in A2, perform corresponding priority compensation degree labeling according to the change direction of the error change rate, and align the labeled error change rate with the corresponding current translational and rotational axis trajectory data in time; further, the specific steps for the corresponding priority compensation degree labeling in this embodiment include:

[0072] A61. Divide the obtained error change rate at each time point, take 0 as the starting point for the divided error change rate, calculate the difference between the change rate and 0, and use the obtained difference as the priority compensation degree label for corresponding priority compensation degree labeling;

[0073] A7. Construct an error evaluation model based on the random forest algorithm, and input the aligned and labeled error change rate and trajectory data into the error evaluation model for training to obtain the trained error evaluation model;

[0074] A8. Input the predicted error change rate corresponding to the trajectory data on each axis obtained in A4 and the corresponding trajectory data into the error evaluation model to obtain the error priority compensation degree and the error compensation rate corresponding to the error change rate at each time point of the machining trajectory on each translational or rotational axis.

[0075] In this embodiment, the priority error compensation is determined according to the obtained error priority compensation degree, and the trajectory with a high priority compensation degree is preferentially compensated for errors, so that the movement trajectories of each axis are kept consistent. Exemplarily, when an error occurs in the translational axis X and no errors occur in the translational axes Y and Z, the translational axis X can be preferentially compensated according to the error priority compensation degree at this time. For another example, when the translational axis X, the translational axis Y, and the translational axis Z are preferentially compensated for the maximum error priority compensation according to the calculated error priority compensation, that is, the translational axis with a fast error change is preferentially compensated, further ensuring the synchronous operation of the movement trajectories of each axis. The same applies to the rotary axes A and C. Further, in this embodiment, the error compensation rate at each time point of the machining trajectory on each translational or rotary axis is equal to the error change rate at each time point. Further, in this embodiment, the rate of error compensation for each translational or rotary axis is obtained through predictive calculation, and the error of the corresponding translational or rotary axis is compensated at the corresponding rate according to the obtained rate of error compensation. This avoids using the same compensation rate, resulting in incomplete error compensation or compensation exceeding the limit on some of the translational or rotary axes, so that the machining trajectories on all the translational or rotary axes can be restored to the ideal machining trajectory at the same time, and the machining processes on each translational or rotary axis can be kept consistent, enhancing the control accuracy and machining accuracy.

[0076] This process quantifies the priority of error compensation through an error evaluation model, ensures that key errors are corrected in a timely manner, maintains the consistency of the movement of each axis, and significantly enhances the stability of the machining process and the quality of the finished product. Especially in a complex and changeable machining environment, this dynamic error management strategy greatly improves production efficiency and part accuracy.

[0077] An error compensation unit is used to generate an error compensation instruction according to the real-time change trend and priority compensation degree of the multi-axis error obtained by prediction, and preferentially pre-compensate the errors of the machining tool and the workpiece in the corresponding coordinate axis direction through a built-in enhanced servo compensation model. Further, in this embodiment, the enhanced servo compensation model in the error compensation unit includes a compensation adjustment layer and a compensation control layer. The steps for constructing the compensation adjustment layer and the compensation control layer include:

[0078] B1. Construct the input state at the current time t according to the obtained machining process data, the predicted error change trend corresponding to the trajectory data on each axis, the error priority compensation degree, and the error compensation rate where Xt represents the machining process data sequence obtained at the current time t, represents the predicted error change trend of the machining trajectory corresponding to the i-th translational or rotary axis, represents the predicted error priority compensation degree of the machining trajectory corresponding to the i-th translational or rotary axis, represents the predicted error compensation rate of the machining trajectory corresponding to the i-th translational or rotary axis;

[0079] B2. Set the ideal machining trajectory parameters. When the machining trajectory parameters of the machining tool and the workpiece obtained in real time on multiple axes are not equal to the ideal machining trajectory parameters, error compensation information is triggered.

[0080] B3. Based on the constructed input state, error priority compensation degree, and error compensation rate, construct the joint execution action at the current moment. Among them, at1 represents whether to perform the error compensation action. When at1 = 0, the error compensation action is not performed. When at1 = 1, the error compensation action is performed, and the corresponding instruction is generated; at2 represents that on the premise of at1 = 1, according to the obtained error priority compensation degree, the translational or rotational axis machining trajectory with a higher error priority compensation degree is preferentially compensated for errors; at3 represents that on the premise of at1 = 1, according to the error compensation rate predicted for the machining trajectory of the i-th translational or rotational axis, the error compensation of the corresponding rate is performed on the machining trajectory of the i-th translational or rotational axis.

[0081] B4. Based on the constructed joint execution action, construct the reward function Rt, specifically:

[0082]

[0083] Among them, represents the error distance between the j-th ideal position point in the ideal machining trajectory parameters corresponding to the i-th translational or rotational axis at time t and the position point of the machining tool or workpiece in the actual machining trajectory parameters corresponding to the i-th translational or rotational axis. Ig represents the penalty term for the g-th incorrect execution of at1, and G represents the total number of incorrect executions of at1. When at1 is incorrectly executed, Ig = 1; otherwise, Ig = 0. Iv represents the penalty term for the v-th incorrect execution of at2, and V represents the total number of incorrect executions of at2. When at2 is incorrectly executed, Iv = 1; otherwise, Iv = 0. represents the difference between the predicted error compensation rate and the actual error compensation rate on the i-th translational or rotational axis at time t; k1, k2, k3, k4 represent the gain coefficients corresponding to the reward function. Further, in this embodiment, rewards are given for the error distance of the position points of the machining trajectory parameters, the difference between the error compensation rate and the actual error compensation rate, incorrect execution of error compensation, and incorrect execution of priority compensation. The smaller the error and the number of execution errors, the greater the reward, enabling the reinforcement servo compensation model to more accurately avoid errors and incorrect executions and further improve the control accuracy of the reinforcement servo compensation model.

[0084] B5. Based on the reinforcement learning algorithm, construct a compensation adjustment layer, and input the obtained input state, joint execution action, and reward function at the current time t into the compensation adjustment layer for training, and output the priority compensation adjustment amount and the error compensation rate adjustment amount.

[0085] B6. Initialize the control parameters in the compensation control layer based on the obtained machining process data, the changing trend of the prediction error corresponding to the trajectory data on each axis, and the error priority compensation degree and error compensation rate. Then, use the obtained priority compensation adjustment amount and error compensation rate adjustment amount to calculate the error compensation instruction corresponding to the i-th translational or rotational axis. Perform priority pre-compensation on the machining trajectory parameter error in the direction of the i-th translational or rotational axis. Specifically, Specifically:

[0086]

[0087] Among them, q1 represents the evaluation value obtained corresponding to the execution of at1. When at1 = 0, q1 = 0, and when at1 = 1, q1 = 1. q2 and q3 respectively represent the evaluation values obtained when executing at2 and at3. t0 represents the time length for the machining tool or workpiece to move a complete machining trajectory on the i-th translational or rotational axis. Ii represents the priority compensation factor of the machining trajectory on the i-th translational or rotational axis. When Ii = 1, the machining trajectory on the i-th translational or rotational axis is preferentially compensated. When Ii = 0, the machining trajectory on the i-th translational or rotational axis is not preferentially compensated. kp represents the proportionality coefficient in the initialized control parameters, ki represents the integral time coefficient in the initialized control parameters, and kd represents the differential time coefficient in the initialized control parameters.

[0088] This process realizes dynamic error management during the machining process by intelligently analyzing the prediction error trend and error priority compensation degree. It can not only accurately identify the key axes that need to be preferentially compensated according to the real-time changing trend of the error, but also optimize the compensation strategy through reinforcement learning, ensuring machining accuracy and efficiency, effectively reducing the cumulative error, improving the consistency and surface quality of complex workpieces. At the same time, by adaptively adjusting the compensation rate and priority, it can flexibly respond to different material characteristics and machining conditions, making the machining process on each translational or rotational axis always follow the ideal machining trajectory and maintain a synchronous state, making the machining process more accurate. In addition, in this embodiment, the synchronization of the machining process is achieved only by compensating the machining errors of each axis during the machining process, eliminating the transformation relationship between the axes and further reducing the complexity of the control method, improving the practicability of this control system.

[0089] Furthermore, the specific steps for obtaining the ideal machining trajectory parameters in B2 of this embodiment include:

[0090] C1. According to the integrated three-dimensional model constructed by the three-dimensional modeling unit, the processing process parameters of the workpiece to be processed, and the error compensation instruction, the corresponding processing trajectory parameters are generated on each translational or rotational axis in the multi-axis coordinate system corresponding to the integrated three-dimensional model through the trajectory generation algorithm built in the virtual simulation software, and the generated processing trajectory parameters are set as the ideal processing trajectory parameters;

[0091] C2. The obtained ideal processing trajectory parameters are built into the controller corresponding to the five-axis numerical control machine tool, and the initial state of the error compensation instruction in the ideal processing trajectory parameters is set to the silent state. When the five-axis numerical control machine tool starts to work, the initial state of the error compensation instruction becomes the active state, and pre-compensation is performed on the corresponding processing tool and the processing trajectory parameters of the workpiece to be processed of the five-axis numerical control machine tool, so that the real-time processing trajectory parameters of the processing tool and the workpiece to be processed in the multi-axis coordinate system are always equal to the ideal processing trajectory parameters in real time.

[0092] An interaction module for real-time display of the processing progress of the workpiece to be processed and monitoring and early warning of processing faults; the interaction module includes a visualization unit and a processing early warning unit;

[0093] The visualization unit is used for real-time display of the integrated three-dimensional model constructed during the processing, the processing trajectory parameters corresponding to each translational or rotational axis, and the processing progress; the processing early warning unit is used for real-time monitoring of the abnormalities and faults during the processing through the fault monitoring algorithm, and real-time early warning of the monitored abnormalities and faults through the interaction interface. Further, the fault monitoring algorithm in this embodiment includes the random forest algorithm and the support vector machine algorithm.

[0094] Embodiment 2

[0095] Please refer to Figure 2 , another embodiment provided by the present invention: The working process of the corresponding unit of the intelligent multi-axis synchronous control system for the aluminum processing numerical control machine tool includes:

[0096] First, the processing process data of the processing tool and the workpiece to be processed are collected in real time through the data acquisition unit, and the collected data is preprocessed and integrated in a unified format by the preprocessing and fusion unit;

[0097] Second, according to the attribute data in the obtained processing process data, an integrated three-dimensional model of the workpiece to be processed and the processing tool is constructed by the three-dimensional modeling unit, and on the constructed integrated three-dimensional model, a multi-axis coordinate system of the workpiece to be processed and the processing tool is constructed according to the actual positions of the workpiece to be processed and the processing tool. At the same time, according to the constructed integrated three-dimensional model and the processing process data in the obtained processing process data, on each coordinate axis of the multi-axis coordinate system corresponding to the integrated three-dimensional model, through the processing trajectory generation unit, the processing trajectory parameters of the workpiece to be processed and the processing tool on each coordinate axis in the multi-axis coordinate system are generated;

[0098] Third, the error monitoring unit predicts the error change trend, error priority compensation degree, and error compensation quantity based on the real-time obtained machining process data; according to the predicted error change trend, error priority compensation degree, error compensation quantity, and machining process data, the error compensation unit generates error compensation instructions in real time;

[0099] Fourth, when the five-axis CNC machine tool starts working, the generated error compensation instructions are used to perform real-time priority pre-compensation on the machining trajectory of the workpiece and the cutting tool on each translational or rotational axis according to the obtained machining trajectory parameters. At the same time, the feedback adjustment unit obtains the position error of the workpiece and the cutting tool in the compensated multi-axis trajectory in real time, and feeds the obtained error back to the error compensation unit to perform secondary compensation on the position error in the multi-axis trajectory;

[0100] Sixth, the visualization unit displays the integrated three-dimensional model, machining path, and machining progress in real time during the above first to fifth processes. At the same time, the machining warning unit monitors the abnormalities and faults in the first to sixth processes in real time, and issues real-time warnings for the monitored abnormalities or faults.

[0101] Embodiment 3

[0102] Please refer to Figure 3 , another embodiment provided by the present invention: an intelligent multi-axis synchronous control method for a CNC machine tool for aluminum processing, the steps include:

[0103] S1. Obtain the machining process data of the cutting tool and the workpiece, and perform preprocessing;

[0104] S2. Utilize the machine tool, cutting tool, and workpiece attribute data in the machining process data to construct an integrated three-dimensional model of the machine tool, cutting tool, and workpiece through 3D modeling software, and construct a multi-axis coordinate system on the constructed integrated three-dimensional model according to the actual position information of the cutting tool and the workpiece;

[0105] S3. On the constructed multi-axis coordinate system, according to the machining process parameters in the machining process data, generate the ideal machining trajectory parameters of the cutting tool and the workpiece on each translational or rotational axis through a trajectory generation algorithm, and embed the obtained ideal machining trajectory parameters into the corresponding controller of the five-axis CNC machine tool;

[0106] S4. When the five-axis CNC machine tool starts working, it controls the workpiece and the cutting tool for corresponding machining through the built-in ideal machining trajectory parameters, and generates an error compensation instruction through the built-in enhanced servo compensation model to preferentially pre-compensate the position error and rotation angle error of the workpiece and the cutting tool moving on the corresponding translational or rotational axes, so that the real-time machining trajectory parameters of the cutting tool and the workpiece in the multi-axis coordinate system are always equal to the ideal machining trajectory parameters in real time;

[0107] S5. The feedback adjustment unit is used to obtain the position error of the workpiece and the cutting tool in the compensated multi-axis trajectory in real time, and feed the obtained error back to the error compensation unit to perform secondary compensation on the position error in the multi-axis trajectory;

[0108] S6. The machining process from S1 to S5 is monitored in real time through a fault monitoring algorithm, and the monitored abnormalities and faults are warned in real time through an interactive interface.

[0109] Embodiment 4

[0110] A computer-readable storage medium, on which computer instructions are stored, and when the computer instructions run, they execute an intelligent multi-axis synchronous control method for a CNC machine tool for aluminum processing.

[0111] An electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements an intelligent multi-axis synchronous control method for a CNC machine tool for aluminum processing.

[0112] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments without departing from the spirit and scope protected by the present invention and the claims. All of these fall within the protection scope of the present invention.

Claims

1. Intelligent multi-axis synchronous control system for aluminum processing CNC machine tools, characterized in that: include: Intelligent sensing module, error compensation module and processing control module; The intelligent sensing module includes a data acquisition unit; the data acquisition unit is used to collect processing data of the processing tool and the processing workpiece in real time through an integrated sensor; The processing control module includes a three-dimensional modeling unit, a processing trajectory generating unit and a feedback adjustment unit; the three-dimensional modeling unit is used to construct an integrated three-dimensional model of the machine tool, the processing workpiece and the processing tool through a three-dimensional modeling software according to the attribute parameters of the machine tool, the processing workpiece and the processing tool, and construct a multi-axis coordinate system of the machine tool, the processing workpiece and the processing tool on the constructed integrated three-dimensional model according to the actual positions of the processing workpiece and the processing tool; the processing trajectory generating unit is used to generate processing trajectory parameters of the processing workpiece and the processing tool on multiple axes through a trajectory generating algorithm according to the constructed integrated three-dimensional model, processing process parameters and error compensation instructions; the feedback adjustment unit is used to obtain the trajectory position error of the processing workpiece and the processing tool in the multi-axis trajectory after error compensation in real time, and feed back the obtained error to the error compensation unit to perform secondary compensation on the position error in the multi-axis trajectory; The error compensation module includes an error monitoring unit and an error compensation unit; The error monitoring unit is used to predict the real-time change trend of multi-axis errors and the error priority compensation degree according to the acquired processing data of the processing tool and the processing workpiece through the built-in error offset model and the error evaluation model, and feed back the calculated real-time change trend of multi-axis errors and the error priority compensation degree to the error compensation unit; the error compensation unit is used to generate error compensation instructions according to the predicted real-time change trend of multi-axis errors and the priority compensation degree through the built-in enhanced servo compensation model, and perform priority pre-compensation for the processing tool and processing workpiece errors in the corresponding coordinate axis direction.

2. The intelligent multi-axis synchronous control system for aluminum processing CNC machine tools according to claim 1, characterized in that: The multi-axis coordinate system of the machine tool, the workpiece and the tool comprises: a machine tool coordinate system M , workpiece coordinate system o W , tool coordinate system o T ; According to the machine tool coordinate system, the workpiece coordinate system and the tool coordinate system, the corresponding translation axis coordinate systems X, Y, Z and rotation axis coordinate systems A and C are established.

3. The intelligent multi-axis synchronous control system for aluminum processing CNC machine tools according to claim 2, characterized in that: The processing data includes attribute data of the machine tool, the processing tool and the processing workpiece, processing parameters of the processing workpiece, movement position error and rotation angle error data of the processing tool and the processing workpiece, vibration data corresponding to the machine tool, processing trajectory data of the processing tool and the processing workpiece in the translation axis coordinate system and the rotation axis coordinate system, and torque information data of the processing tool; The specific steps of the error monitoring unit obtaining the real-time variation trend of the multi-axis error through the error offset model include: A1. Obtain historical moving position error and rotation angle error data of the corresponding machining trajectory in the translation axis coordinate system and the rotation axis coordinate system during the machining process, vibration data corresponding to the machine tool during the machining process, and torque information data corresponding to the machining tool; the vibration data includes vibration intensity and vibration direction; A2. According to the acquired moving trajectory position error and rotation angle error data and the corresponding machining trajectory data of each axis, the error change rate of the corresponding moving trajectory position error and rotation angle error on the translation axis and the rotation axis is calculated, and the calculated error change rate is aligned in time with the machine tool vibration data during the machining process; A3. Build an error offset model based on a support vector machine, input the aligned error change rate and machine tool vibration data, the machining trajectory data on the translation axis and the rotation axis corresponding to the error change rate, and the torque information data corresponding to the machining tool into the error offset model for training, and obtain the trained error offset model. At the same time, in the error offset model, obtain the error change rate on each translation or rotation axis with the movement and rotation trajectory, the vibration intensity and vibration direction in the vibration data, and the torque information change trend formula through the kernel function fitting in the support vector machine; A4. The trained error offset model is built into the error monitoring unit, and the vibration data of the machine tool during operation, the trajectory data on each axis generated by the machining trajectory generation unit in real time, and the torque information data corresponding to each moving trajectory calculated according to the workpiece attribute data and the machining process parameters are obtained in real time, and the predicted error change rate corresponding to the trajectory data on each axis generated by the machining trajectory generation unit is calculated; A5. According to the predicted error change rate, the predicted error change trend corresponding to the trajectory data on each axis generated by the machining trajectory generation unit is calculated.

4. The intelligent multi-axis synchronous control system for aluminum processing CNC machine tools according to claim 3, characterized in that: The specific steps of the error monitoring unit obtaining the error priority compensation degree through the error evaluation model include: A6, for the error change rate obtained in A2, the corresponding priority compensation degree is marked according to the change direction of the error change rate, and the marked error change rate is aligned with the corresponding current translation and rotation axis trajectory data in time; A7. Build an error assessment model based on the random forest algorithm, and input the error change rate and trajectory data of the aligned annotation into the error assessment model for training to obtain the trained error assessment model. A8. Input the predicted error change rate and the corresponding trajectory data corresponding to the trajectory data on each axis predicted in A4 into the error evaluation model to obtain the error priority compensation degree and the error compensation rate corresponding to the error change rate at each time point of the machining trajectory on each translational or rotational axis.

5. The intelligent multi-axis synchronous control system for aluminum material processing CNC machine tools according to claim 4, characterized in that: The enhanced servo compensation model in the error compensation unit includes a compensation adjustment layer; the steps of constructing the compensation adjustment layer include: B1. Construct the input state at the current time t based on the acquired processing data, the predicted error change trend corresponding to the trajectory data on each axis, the error priority compensation degree and the error compensation rate Among them, X t represents the processing data sequence obtained at the current time t, It indicates the predicted error change trend of the machining trajectory corresponding to the i-th translation or rotation axis. It indicates the priority compensation degree of the machining trajectory error corresponding to the predicted i-th translation or rotation axis. It represents the predicted rate of error compensation of machining trajectory corresponding to the i-th translation or rotation axis; B2. Setting the ideal machining trajectory parameters. When the machining trajectory parameters of the machining tool and the machining workpiece on the multi-axis obtained in real time are not equal to the ideal machining trajectory parameters, the error compensation information is triggered; B3. Construct the current moment joint execution action based on the constructed input state and error priority compensation degree and error compensation rate where a t1 Indicates whether to perform error compensation action. t1 = 0, no error compensation action is performed, a t1 =1, the error compensation action is executed and the corresponding instruction is generated; a t2 Indicates in a t1 = 1, according to the obtained error priority compensation degree, error compensation is performed first on the translation or rotation axis machining trajectory with high error priority compensation degree; a t3 Indicates in a t1 =1, according to the error compensation rate predicted by the i-th translational or rotational axis machining trajectory, the error compensation of the corresponding rate is performed on the i-th translational or rotational axis machining trajectory; B4. Construct reward function R based on the constructed joint execution action t , specifically: in, It represents the error distance between the jth ideal position point in the ideal machining trajectory parameter corresponding to the i-th translation or rotation axis at time t and the position point of the machining tool or workpiece in the i-th translation or rotation axis corresponding to the actual machining trajectory parameter, I g Indicates the g-th incorrect execution of a t1 The penalty term, G represents the error execution a t1 The total number of times when an error is executed t1 When g =1, otherwise I g =0,I v Indicates the vth incorrect execution of a t2 The penalty term, V represents the error execution a t2 The total number of times when an error is executed t2 When v =1, otherwise I v =0, represents the difference between the error compensation rate predicted on the i-th translation or rotation axis at time t and the actual error compensation rate; k1, k2, k3, k4 represent the gain coefficients corresponding to the reward function; B5. Construct a compensation adjustment layer based on the reinforcement learning algorithm, and input the current input state at time t, the joint execution action and the reward function into the compensation adjustment layer for training, and output the priority compensation adjustment amount and the error compensation rate adjustment amount.

6. The intelligent multi-axis synchronous control system for aluminum material processing CNC machine tools according to claim 5, characterized in that: The enhanced servo compensation model in the error compensation unit further includes a compensation control layer, and the steps of constructing the compensation control layer include: B6. Initialize the control parameters in the compensation control layer based on the acquired processing data, the predicted error change trend corresponding to the trajectory data on each axis, the error priority compensation degree and the error compensation rate, and use the acquired priority compensation adjustment amount and error compensation rate adjustment amount to calculate the error compensation instruction corresponding to the i-th translation or rotation axis. The machining trajectory parameter error in the direction of the i-th translation or rotation axis is pre-compensated first. Specifically: Among them, q1 means execution of a t1 Corresponding to the obtained evaluation value, a t1 =0 corresponds to q1 = 0, when a t1 =1 corresponds to q1=1, q2 and q3 respectively represent the execution of a t2 and a t3 The corresponding evaluation value is obtained when t0 represents the time length of a complete machining trajectory of a machining tool or a machining workpiece on the i-th translational or rotational axis. i Represents the priority compensation factor of the machining trajectory on the i-th translational or rotational axis. i = 1, the machining trajectory on the i-th translation or rotation axis is compensated first. i = 0, no priority compensation is performed on the machining trajectory on the i-th translation or rotation axis. p represents the proportional coefficient within the initialization control parameter, k i represents the integral time coefficient in the initialization control parameters, k d Represents the derivative time coefficient in the initialization control parameters.

7. The intelligent multi-axis synchronous control system for aluminum material processing CNC machine tools according to claim 6, characterized in that: The specific steps of obtaining the ideal machining trajectory parameters in B2 include: C1. According to the integrated three-dimensional model constructed by the three-dimensional modeling unit, the processing parameters of the workpiece and the error compensation instructions, the corresponding processing trajectory parameters are generated on each translation or rotation axis in the multi-axis coordinate system corresponding to the integrated three-dimensional model through the trajectory generation algorithm built into the virtual simulation software, and the generated processing trajectory parameters are set as ideal processing trajectory parameters; C2. The acquired ideal machining trajectory parameters are built into the controller corresponding to the five-axis CNC machine tool, and the initial state of the error compensation instruction in the ideal machining trajectory parameters is set to a silent state. When the five-axis CNC machine tool starts working, the initial state of the error compensation instruction becomes an activated state, and the machining trajectory parameters of the corresponding machining tools and machining workpieces of the five-axis CNC machine tool are pre-compensated, so that the real-time machining trajectory parameters of the machining tools and machining workpieces in the multi-axis coordinate system are equal to the ideal machining trajectory parameters in real time.

8. The intelligent multi-axis synchronous control system for aluminum material processing CNC machine tools according to claim 7, characterized in that: The error compensation rate of the machining trajectory on each translational or rotational axis at each time point is equal to the error change rate at each time point.

9. An intelligent multi-axis synchronous control method for aluminum processing CNC machine tools, which is implemented based on the intelligent multi-axis synchronous control system for aluminum processing CNC machine tools according to any one of claims 1 to 8, characterized in that the steps include: S1, obtaining the processing data of the processing tool and the processing workpiece, and preprocessing; S2, using the machine tool, machining tool and workpiece attribute data in the machining process data, constructing an integrated three-dimensional model of the machine tool, machining tool and workpiece through three-dimensional modeling software, and constructing a multi-axis coordinate system on the constructed integrated three-dimensional model according to the actual position information of the machining tool and the workpiece; S3. In the constructed multi-axis coordinate system, according to the machining process parameters in the machining process data, the ideal machining trajectory parameters of the machining tool and the machining workpiece on each translational or rotational axis are generated through the trajectory generation algorithm, and the obtained ideal machining trajectory parameters are built into the controller corresponding to the five-axis CNC machine tool; S4. When the five-axis CNC machine tool starts working, the workpiece and the tool are controlled to perform corresponding processing through the built-in ideal processing trajectory parameters, and the error compensation instructions are generated through the built-in enhanced servo compensation model to pre-compensate the position error and rotation angle error of the workpiece and tool on the corresponding translation or rotation axis. S5. The position errors of the workpiece and the tool in the multi-axis trajectory after compensation are acquired in real time through the feedback adjustment unit, and the acquired errors are fed back to the error compensation unit to perform secondary compensation on the position errors in the multi-axis trajectory.

10. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the intelligent multi-axis synchronous control method for aluminum processing CNC machine tools described in claim 9 is executed.

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