Numerical control system parameter optimization iteration method

Abstract

The invention relates to the technical field of numerical control, in particular to a numerical control system parameter optimization iteration method. According to the technical scheme, the method comprises the following steps of 1, constructing an interpretation module and a motion module of a numerical control system, 2, inputting a machining program into the system, generating a control instruction and carrying out trial operation, 3, establishing a motor driving model according to trial operation data, 4, extracting feature points in a machining path, and 5, calculating the machining path according to the feature points. The method comprises the steps of constructing a feature judgment model for identifying an optimization demand area, debugging control parameters in combination with the feature judgment model to ensure error control, and adopting an optimization algorithm to automatically adjust the control parameters and iteratively optimize system performance based on feature points and optimization demands. The precision, the stability and the production efficiency of a numerical control system are remarkably improved, the requirement for manual intervention is reduced, the method adapts to complex machining tasks and dynamic load changes, the robustness and the automation level of the system are improved, and finally manufacturing enterprises are helped to improve the overall production capacity and the competitiveness.

Description

Technical Field

[0001] The present invention relates to the field of numerical control technology, and in particular to a method for iterative optimization of numerical control system parameters. Background Art

[0002] CNC systems are widely used in mechanical processing, automated production and other fields. They can accurately control the motion trajectory, speed and processing process of machine tools through preset program instructions. With the development of precision and automation in the manufacturing industry, the performance requirements of CNC systems are becoming higher and higher, including high precision, high efficiency and the ability to adapt to complex processes and dynamic changes. In order to meet these requirements, CNC systems must continuously optimize their control parameters to achieve better processing quality and higher production efficiency.

[0003] At present, the traditional parameter adjustment method of CNC system mainly relies on manual debugging and experience adjustment. This method is not only time-consuming but also difficult to ensure that each adjustment can reach the optimal state. Especially when facing complex processing tasks or dynamic loads, the efficiency and accuracy of manual adjustment are greatly problematic. In addition, due to the complexity and uncertainty of the operating environment of the CNC system, parameter optimization becomes particularly important as processing conditions change. Existing technologies have not yet been able to fully and automatically optimize parameters and achieve precise adjustment, especially under dynamic load changes, different processing tasks and complex working conditions, the optimization effect is difficult to guarantee.

[0004] In order to overcome this problem, it is particularly important to develop a solution that can optimize the parameters of the CNC system through automatic iteration. By establishing a mathematical model, capturing feedback signals, defining feature points and combining automated optimization algorithms, the accuracy and stability of the CNC system can be effectively improved, ensuring consistency and efficiency during the machining process.

[0005] To this end, we propose an iterative method for CNC system parameter optimization to solve the existing problems. Summary of the invention

[0006] The purpose of the present invention is to propose a method for iterative optimization of numerical control system parameters in view of the problems existing in the background technology.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for iterative optimization of numerical control system parameters, comprising the following steps, wherein step 1 establishes a system model: constructs an interpretation module and a motion module of the numerical control system, wherein the interpretation module is used to parse the input processing program and generate low-level control instructions; the motion module includes a driver, a motor and a mechanical load, and describes the dynamic behavior of the system through a transfer function to reflect the response characteristics of the motor drive and the mechanical load; step 2 imports the processing program and conducts a trial run: inputs the processing program of the part to be processed into the system, generates control instructions such as processing path and speed through the interpretation module, and conducts a trial run on the processing program, collects initial operation data such as tool path, processing speed, and system feedback signal, and provides a benchmark for subsequent optimization; step 3 establishes a motor drive model: collects trial The data of the system input instructions and feedback signals during operation are combined with mathematical modeling methods to establish a motor drive model to describe the dynamic characteristics of the motor and its load. The step 4 establishes a feature judgment model: extracts feature points in the processing path, and establishes a feature judgment model for identifying the optimization requirement area, including three-dimensional features, speed mutation features and plane features, which are used to assign optimization priorities. The step 5 debugs system parameters: debugs system parameters in combination with the feature judgment model, including adjusting control parameters such as proportional gain, integral time, and differential time to ensure that errors in the processing process are effectively controlled. The step 6 parameter optimization and automatic iteration: based on the feature points and optimization requirements in the processing process, an optimization algorithm is used to automatically adjust the control parameters, and the system performance is iteratively optimized until the predetermined accuracy and stability requirements are met.

[0008] Preferably, the interpretation module of the system model can parse the multi-axis linkage path information in the machining program, convert it into motor control instructions, and realize accurate interpolation of multi-dimensional space in combination with the motion module.

[0009] Preferably, the motion module is connected to the motor via a driver, the motor is coupled to the load, and the dynamic behavior of the motion module takes into account parameters such as motor inertia, damping, and load stiffness.

[0010] Preferably, the feature judgment model includes the following parts: extracting the complex features of the three-dimensional machining path by calculating the curvature of the tool path, and classifying the machining areas according to the curvature changes; marking the areas with larger curvature as high priority areas, and giving priority to parameter optimization to reduce machining errors;

[0011] During the machining process, the speed mutation in the path is detected, especially the speed section with sharp changes, which is marked as the mutation feature area. The control accuracy of these areas is high, and the control strategy needs to be further optimized;

[0012] By fitting the plane features in the machining path, we can identify whether the path is smooth. If the error is large, the system parameters in this area need to be optimized to ensure a smooth path during machining and avoid excessive vibration or error accumulation.

[0013] Preferably, the system parameter debugging process is based on the output of the feature judgment model, and gives priority to adjusting the control parameters of important feature areas during the processing. For complex path areas with large curvature, acceleration and deceleration stages, and sudden feature areas, the relevant proportional gain, time constant and differential control parameters are adjusted to achieve higher processing accuracy and process smoothness. The debugging process performs feedback and optimization in multiple processing tasks to ensure that the system parameters meet specific processing requirements.

[0014] Preferably, an iterative optimization method based on error feedback is used in the parameter optimization process. Specifically, the control parameters of the system are gradually adjusted by collecting the deviation between the feedback signal and the expected signal in real time. This iterative optimization method ensures that during the processing, the system can be dynamically adjusted according to the real-time feedback to improve the processing accuracy and the responsiveness of the system until the deviation is minimized and the process requirements are met. The optimization process does not require human intervention, and the production efficiency is improved through the automated optimization mechanism.

[0015] Preferably, the convergence condition of the optimization iteration is based on the error change rate of the feedback signal during the processing. When the error change is lower than the set threshold, it means that the system has achieved the optimization goal and completed the parameter adjustment. In addition, the system will also monitor the changes of multiple performance indicators in real time, such as processing accuracy, processing speed and energy consumption, to ensure that these indicators are within an acceptable range, thereby improving the overall efficiency and quality of processing.

[0016] Preferably, the parameters are adjusted during the optimization process by using a loss function that comprehensively evaluates system performance. The loss function not only considers machining accuracy, but also combines factors such as machining efficiency, system energy consumption, and mechanical vibration to balance multiple objectives to ensure that the final system can improve machining speed and reduce energy consumption while maintaining high accuracy. After multiple optimization iterations, the system parameters can adapt to various working conditions, thereby improving production flexibility and automation level.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] Improve system optimization accuracy: By establishing a motor drive model and a load coupling model based on a mathematical model, the dynamic response of the CNC system under different working conditions can be accurately described. Automated parameter optimization can ensure more accurate system parameter adjustment, effectively improve the control accuracy of the CNC system, and reduce human adjustment errors.

[0019] Reduce manual intervention and debugging time: Traditional parameter adjustment methods often rely on manual experience and a large number of manual tests. The adjustment process is cumbersome and takes a long time. By adopting the automatic iterative optimization method, adaptive adjustment of system parameters can be achieved in a shorter time, significantly reducing the need for manual intervention and the time cost of debugging and optimization.

[0020] Dynamic adaptation to load changes and complex process requirements: This method can adapt to complex processing tasks and dynamic load changes by capturing instructions and feedback signals and judging the system status through feature models. Even when the load conditions change greatly, the CNC system can dynamically adjust parameters based on feedback information to ensure the stability and consistency of the processing process;

[0021] Improve the robustness and stability of the system: The automatic iterative optimization method can gradually adjust the control parameters during multiple iterations to improve the system response speed and accuracy, especially in complex nonlinear or dynamically changing working environments. It enhances the robustness and adaptability of the CNC system and reduces the instability of the system caused by external disturbances or load changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0024] Embodiment 1

[0025] like Figure 1 As shown, a method for iterative optimization of numerical control system parameters proposed by the present invention comprises the following steps:

[0026] Step 1: Establish system model:

[0027] Construct the interpretation module and motion module of the numerical control system, wherein the interpretation module is used to parse the input processing program and generate low-level control instructions; the motion module includes a driver, a motor and a mechanical load, and describes the dynamic behavior of the system through a transfer function, reflecting the response characteristics of the motor drive and the mechanical load. The interpretation module can parse the multi-axis linkage path information in the processing program and convert it into a low-level motor control instruction suitable for the system execution, thereby realizing the synchronization and coordination between different axes. The analysis process includes the processing of elements such as tool path, speed, acceleration and workpiece coordinates, and generates accurate control signals through interpolation algorithms to ensure that the movement of multiple axes is accurate during the processing process. This module is responsible for parsing the processing program (such as G code or M code) input by the user into low-level control instructions that the system can execute. These instructions include control signals such as displacement, speed, acceleration, etc. The interpolation algorithm generates the corresponding interpolation trajectory by reading the path information (x, y, z) and speed information V (t).

[0028] The motion module consists of a motor, a driver, and a mechanical load. The coupling relationship between the motor and the load is modeled through the motion transfer function, taking into account factors such as the motor's moment of inertia, damping effect, and load stiffness, and then the dynamic response of the entire system is simulated and analyzed through this model. The motion module can respond to changes in the load in real time during system operation and adjust the motor output to ensure stability and accuracy under complex working conditions. It includes motors, drivers, and mechanical loads, and is the core part of the system to perform processing tasks. The transfer function M(s) is used to characterize the dynamic response characteristics of the entire system:

[0029]

[0030] Parameter explanation:

[0031] M(s): Motor gain coefficient, reflecting the amplification factor between motor input and output;

[0032] K m : Motor gain coefficient;

[0033] J: total inertia of the system, which determines the inertial response of the system;

[0034] D: Damping coefficient, which represents the energy loss and friction effect of the system;

[0035] K l : Load stiffness, which affects the constraint of mechanical load on the dynamic characteristics of the system.

[0036] Step 2: Import the machining program and test run it:

[0037] The processing program of the parts to be processed is input into the system, and the control instructions such as processing path and speed are generated through the interpretation module. The processing program is tested, and the initial operation data such as tool path, processing speed, system feedback signal, etc. are collected to verify the rationality of the processing path, ensure that there are no instructions that exceed the working scope, capture the initial operation data, and provide a benchmark value for subsequent optimization.

[0038] The test run data include: tool path (x(t), y(t), z(t));

[0039] Speed ​​V(t);

[0040] Feedback signals (position, velocity, acceleration).

[0041] Step 3: Build the motor drive model:

[0042] By collecting the data of system input instructions and feedback signals during the trial operation, the motor drive model is established in combination with mathematical modeling methods to describe the dynamic characteristics of the motor and its load, and the transfer function of the motor speed:

[0043]

[0044] The explanations of each parameter are as follows:

[0045] ω(s): Motor speed, which represents the angular velocity of the motor shaft.

[0046] V(s): The input voltage signal of the motor, usually regulated by a PWM (pulse width modulation) signal.

[0047] K m : The electromotive force constant of the motor, which represents the relationship between the speed and voltage of the motor output.

[0048] L: The inductance of the motor, which indicates the inductance value of the motor winding.

[0049] R: The resistance of the motor, which indicates the resistance value of the motor winding.

[0050] J: The motor’s moment of inertia, which represents the mass distribution of the motor shaft and affects the motor’s acceleration and deceleration characteristics.

[0051] B: The viscous damping coefficient of the motor, which represents the resistance the motor encounters during movement and is usually related to friction.

[0052] K e : The back electromotive force constant of the motor, which represents the inverse relationship between motor speed and voltage.

[0053] Speed ​​response ω(s): This is the output of the motor in response to an input voltage signal. Usually we want to control the motor speed to achieve a specific motion speed or position.

[0054] Voltage signal V(s): This is the control signal sent by the CNC system to the motor to control the drive of the motor. The system controls the movement of the motor by adjusting the voltage.

[0055] The transfer function of the system shows the relationship between the input voltage signal and the motor speed, which includes the electrical characteristics of the motor (inductance L and resistance R), mechanical characteristics (inertia J and damping B) and the electromotive force constant K of the motor. m and the back EMF constant K e .

[0056] There is usually a coupling relationship between the motor and the load, and changes in the load will affect the motion characteristics of the motor.

[0057] Load coupling model:

[0058]

[0059] The explanations of each parameter are as follows:

[0060] T(s): Torque response of the load, indicating the torque output of the load.

[0061] K L : The gain coefficient of the load, indicating the response characteristics of the load.

[0062] s: complex Laplace transform variable, representing the frequency domain characteristics of the system.

[0063] J L : The moment of inertia of the load, which indicates the mass distribution of the load and affects the acceleration and deceleration of the load.

[0064] B L : Viscous damping coefficient of the load, representing the frictional resistance of the load.

[0065] K m K e : The product of the motor's electromotive force constant and back electromotive force constant, which represents the coupling relationship between the motor and the load.

[0066] The torque response of the load represents the torque output of the system under the influence of the load. This output affects the working performance of the motor, so it needs to be modeled and optimized. The dynamic characteristics of the load are related to the rotational inertia and damping coefficient of the motor. The heavier the load or the greater the friction, the greater the torque the motor needs to provide to maintain motion stability.

[0067] By coupling the motor speed and load, the dynamic relationship between the motor and the load can be effectively described, ensuring that the motor can be dynamically adjusted according to actual load changes.

[0068] Step 4: Establish a feature judgment model:

[0069] Extract feature points in the machining path and establish a feature judgment model for identifying the optimization requirement area, including three-dimensional features, speed mutation features and plane features, to assign optimization priorities. The feature judgment model includes the following parts:

[0070] By calculating the curvature of the tool path, the complex features of the 3D machining path are extracted, and the machining areas are classified according to the curvature changes. For areas with large curvature, they are marked as high priority areas and parameter optimization is prioritized to reduce machining errors. The complex surface features extracted using the curvature formula are expressed as follows:

[0071]

[0072] K: Curvature, which indicates the curvature of the path. The greater the curvature, the more drastic the change in the path, and the higher the control requirements.

[0073] and are the velocity components of the point on the path in the x and y directions, respectively, that is, the first derivative of the path (the rate of change of position with time);

[0074] and are the acceleration components of the points on the path in the x and y directions, i.e., the second derivative of the path (the rate of change of velocity with time);

[0075] The sum of the squares of the path velocities, representing the intensity of the path's motion within the plane.

[0076] This formula calculates the curvature of the path in a two-dimensional plane, which can reflect the degree of curvature of the tool path.

[0077] When the curvature K exceeds a predetermined threshold, this area is usually considered to be a feature area that needs to be optimized first. This is because in these areas, the tool trajectory varies greatly and requires more precise control. Optimizing these high curvature areas can reduce tool path errors and ensure machining accuracy.

[0078] During the processing, the speed mutation in the path is detected, especially the speed section with sharp changes, which is marked as the mutation feature area. The control accuracy of these areas is high, and the control strategy needs to be further optimized. The speed mutation formula is:

[0079]

[0080] The explanations of each parameter are as follows:

[0081] a(t): acceleration, which is the rate of change of tool speed at time t. Acceleration reflects the speed of speed change and is a key indicator of speed mutation.

[0082] V(t): Instantaneous speed of the tool. The speed of the tool is one of the core parameters controlled by the CNC system, and the speed change rate will directly affect the processing quality.

[0083] The derivative of velocity represents the rate of change of velocity with time, that is, acceleration.

[0084] Sudden change feature area: When the acceleration a(t) exceeds a predetermined threshold, it indicates that the tool speed has suddenly changed at this point or in this area. This usually occurs at the turning point or acceleration / deceleration stage in the path.

[0085] Optimization significance: Parameter optimization is usually required in the mutation area, and the control system should respond smoothly to these areas to avoid vibration or error caused by speed mutation.

[0086] By fitting the plane features in the machining path, we can identify whether the path is smooth. If the error is large, the system parameters in this area need to be optimized to ensure a smooth path during machining and avoid excessive vibration or error accumulation. The plane fitting error formula is:

[0087]

[0088] The explanations of each parameter are as follows:

[0089] E fit : Plane fitting error, which indicates the deviation between the actual tool path and the ideal plane path.

[0090] p i : Actual point coordinates in the tool path.

[0091] The coordinates of the theoretical plane fitting points in the tool path, that is, the expected positions calculated based on the fitting results of the tool path.

[0092] N: The total number of sampling points on the path, used to calculate the fitting error.

[0093] Fitting error E fit : Smaller fitting errors mean that the deviation between the tool path and the predetermined plane trajectory is smaller, and the path is smoother. For areas with larger fitting errors, there may be machining errors or unstable movements. These areas need to be optimized to reduce path errors and improve system accuracy.

[0094] Through the comprehensive application of the above three feature models, the CNC system can identify the characteristics of different areas in the machining process and optimize parameters based on these characteristics:

[0095] For high curvature areas, the control parameters are adjusted to reduce the bending error of the path;

[0096] For areas with sudden speed changes, optimize acceleration and deceleration strategies to avoid sudden speed changes that may cause system instability;

[0097] For areas with large plane errors, the smoothness of the tool path is improved by adjusting the control strategy to reduce vibration and errors during machining.

[0098] Step 5: Debug system parameters:

[0099] The system parameters are debugged in combination with the feature judgment model, including adjusting the proportional gain, integral time, differential time and other control parameters to ensure that the errors in the processing process are effectively controlled. The system parameter debugging process is based on the output of the feature judgment model, and the control parameters of the important feature areas in the processing process are adjusted first. For complex path areas with large curvature, acceleration and deceleration stages, and sudden feature areas, the relevant proportional gain, time constant and differential control parameters are adjusted to achieve higher processing accuracy and process stability. The debugging process performs feedback and optimization in multiple processing tasks to ensure that the system parameters meet specific processing requirements.

[0100] PID controller can adjust according to the system error to achieve stable and accurate control goals. When debugging system parameters, the three parameters of PID controller - proportional gain K p , integration time T i and the derivative time T d Plays a vital role.

[0101] PID controller control formula:

[0102]

[0103] The meaning of each parameter is as follows:

[0104] u(t): The output signal of the controller, i.e. the system control quantity. This quantity determines the behavior of the motor or drive and is usually used to adjust the speed or position of the motor.

[0105] e(t): Error function, that is, the difference between the expected value of the system and the feedback signal. Specifically, e(t) = r(t) - y(t), where:

[0106] r(t) is the desired reference signal (such as the set target position or speed);

[0107] y(t) is the system feedback value (such as the current actual speed or position).

[0108] K p : Proportional gain, which indicates the degree of response of the control system to the current error. The function of the proportional controller is to calculate the control amount based on the current error. The larger the gain, the stronger the control system responds to the error. Adjusting the proportional gain helps to reduce the error, but too high a gain may cause excessive response or oscillation of the system.

[0109] T i : Integral time, which represents the controller's reaction to the accumulation of errors. The integral controller acts on the accumulation of historical errors and helps eliminate steady-state errors (i.e., small errors that exist for a long time). Longer integral times usually provide better error correction, but may result in slower system response.

[0110] T d : Derivative time, which indicates the controller's response to the error change rate. The differential controller adjusts the control amount according to the speed of error change, which can effectively suppress system oscillation and overshoot, especially in fast-changing systems. A larger derivative time can make the system more stable, but may be more sensitive to high-frequency noise.

[0111] Key parameter adjustments during debugging

[0112] Proportional gain K p : The controller's response strength to the current error is adjusted by adjusting the proportional gain. When the error is large, a higher proportional gain will increase the system's response speed. However, if the proportional gain is too large, it may cause the system to over-respond, oscillate or become unstable.

[0113] Integration time T i : Adjusting the integral time affects the system's ability to correct long-term small errors. A shorter integral time can quickly eliminate steady-state errors, but may increase the oscillation of the system. A longer integral action helps eliminate stable errors, but may cause the system to react slowly.

[0114] Derivative time T d :By adjusting the differential time, the controller can respond to the rate of change of the error and reduce the overshoot and oscillation of the system. When the differential time is longer, large error changes can be suppressed, but the impact of noise on the system may increase. Therefore, the differential time needs to be set carefully, especially in cases where the system is sensitive to high-frequency noise.

[0115] Debugging and Optimization Process

[0116] In actual debugging, the three parameters of the PID controller are usually adjusted step by step to balance the response speed, accuracy and stability. During the debugging process, the system adjusts these parameters according to the error between the feedback signal and the target signal during the processing to ensure that the CNC system can complete the processing task accurately and stably.

[0117] Through experiments and feedback, appropriate PID parameters can be selected according to the characteristics of different processing tasks (such as changes in cutting speed, complexity of the path, etc.).

[0118] During the adjustment process, automated debugging tools can be used to combine the system's real-time data and performance indicators for optimization, making the parameter adjustment process more efficient and accurate.

[0119] Step 6 Parameter optimization and automatic iteration:

[0120] Based on the characteristic points and optimization requirements in the processing process, the optimization algorithm is used to automatically adjust the control parameters, and the system performance is iteratively optimized until the predetermined accuracy and stability requirements are met. The iterative optimization method based on error feedback is used in the parameter optimization process. Specifically, the control parameters of the system are gradually adjusted by collecting the deviation between the feedback signal and the expected signal in real time. This iterative optimization method ensures that during the processing, the system can dynamically adjust according to the real-time feedback to improve the processing accuracy and the responsiveness of the system until the deviation is minimized and the process requirements are met. The optimization process does not require human intervention, and the production efficiency is improved through the automated optimization mechanism. During the optimization process, the parameters are adjusted through the loss function that comprehensively evaluates the system performance. The loss function not only considers the processing accuracy, but also combines factors such as processing efficiency, system energy consumption, and mechanical vibration, and balances between multiple objectives to ensure that the final system can improve the processing speed and reduce energy consumption while maintaining high accuracy. After multiple optimization iterations, the system parameters can adapt to various working conditions, thereby improving the flexibility and automation level of production.

[0121] In the process of parameter optimization, an objective function needs to be defined to quantify the performance of the system. This objective function is usually an error function or a loss function, which represents the difference between the system output and the desired target. Assume that the optimization goal is to minimize the total error of the system.

[0122] Objective function formula:

[0123]

[0124] The explanations of each parameter are as follows:

[0125] J(θ): objective function, which represents the performance measure of the system. The goal is to minimize this function to minimize the system error.

[0126] e i(θ): The error at the i-th iteration, which represents the difference between the target signal and the feedback signal. It can usually be position error, velocity error, or other quantized control error.

[0127] θ: The optimized parameter set represents the parameters that need to be adjusted in the control system. For example, these parameters can be the proportional gain, integral time, derivative time, etc. of the PID controller, or the parameters in the motor drive model.

[0128] N: The number of iterations or data points, which represents the total number of error calculations during the optimization process.

[0129] This function measures the error of the system, and the optimization goal is to minimize this function. By minimizing the objective function, the error of the system can be reduced, thereby improving the accuracy and stability of the CNC system. The error reflects the performance deviation of the system, and the optimization goal of the control parameters is to make the error as small as possible.

[0130] The convergence condition of the optimization iteration is based on the error change rate of the feedback signal during the processing. When the error change is lower than the set threshold, it means that the system has achieved the optimization goal and completed the parameter adjustment. In addition, the system will also monitor the changes of multiple performance indicators in real time, such as processing accuracy, processing speed and energy consumption, to ensure that these indicators are within an acceptable range, thereby improving the overall efficiency and quality of processing.

[0131] During the automatic iterative optimization process, the CNC system will continuously adjust the control parameters according to the set rules and optimization algorithms to minimize the system error.

[0132] Iteration stopping criteria:

[0133] |J(θ k+1 )-J(θ k )|<∈

[0134] The explanations of each parameter are as follows:

[0135] J(θ k+1 ) and J(θ k ): are the objective function values ​​of the k+1th iteration and the kth iteration respectively.

[0136] ∈: The set error threshold, which means that the iteration stops when the error change is less than the threshold.

[0137] When the change in the objective function is less than the preset threshold ∈, it means that the system has reached the optimal solution for optimization and stops further iteration. This usually means that the system has converged to a stable state.

[0138] Through the above steps, the control parameters of the CNC system are automatically iterated and optimized, the system error is gradually reduced, and the control accuracy is improved. Through iterative updates and feedback, the optimization process can ensure that the CNC system maintains stability and accuracy in dynamically changing processing tasks.

[0139] The above-mentioned specific embodiments are only several preferred embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above-mentioned embodiments, those skilled in the art can make various alternative improvements and combinations to the above-mentioned specific embodiments.

[0140] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. A method for iterative optimization of numerical control system parameters, comprising the following steps, characterized in that: The step 1 is to establish a system model: construct an interpretation module and a motion module of the CNC system, wherein the interpretation module is used to parse the input processing program and generate low-level control instructions; the motion module includes a driver, a motor and a mechanical load, and describes the dynamic behavior of the system through a transfer function to reflect the response characteristics of the motor drive and the mechanical load. The step 2 is to import the processing program and conduct a trial run: input the processing program of the part to be processed into the system, generate control instructions such as the processing path and speed through the interpretation module, and conduct a trial run on the processing program, collect initial operation data such as the tool path, processing speed, and system feedback signal, and provide a benchmark for subsequent optimization. The step 3 is to establish a motor drive model: by collecting data on the system input instructions and feedback signals during the trial run, combined with The mathematical modeling method is used to establish a motor drive model to describe the dynamic characteristics of the motor and its load. The step 4 is to establish a feature judgment model: extract feature points in the processing path, and establish a feature judgment model for identifying the optimization requirement area, including three-dimensional features, speed mutation features and plane features, which are used to assign optimization priorities. The step 5 is to debug system parameters: debug the system parameters in combination with the feature judgment model, including adjusting control parameters such as proportional gain, integral time, and differential time to ensure that errors in the processing process are effectively controlled. The step 6 is parameter optimization and automatic iteration: based on the feature points and optimization requirements in the processing process, an optimization algorithm is used to automatically adjust the control parameters, and the system performance is iteratively optimized until the predetermined accuracy and stability requirements are met.

2. A method for iterative optimization of numerical control system parameters according to claim 1, characterized in that: The interpretation module of the system model can parse the multi-axis linkage path information in the machining program, convert it into motor control instructions, and realize accurate interpolation in multi-dimensional space in combination with the motion module.

3. The method for iterative optimization of numerical control system parameters according to claim 1, characterized in that: The motion module is connected to the motor through a driver, and the motor is coupled to the load. The dynamic behavior of the motion module takes into account parameters such as motor inertia, damping, and load stiffness.

4. The method for iterative optimization of numerical control system parameters according to claim 1, characterized in that: The feature judgment model includes the following parts: extracting the complex features of the three-dimensional machining path by calculating the curvature of the tool path, and classifying the machining area according to the curvature change. For areas with larger curvature, they are marked as high-priority areas and parameter optimization is performed first to reduce machining errors; During the machining process, the speed mutation in the path is detected, especially the speed section with sharp changes, which is marked as the mutation feature area. The control accuracy of these areas is high, and the control strategy needs to be further optimized; By fitting the plane features in the machining path, we can identify whether the path is smooth. If the error is large, the system parameters in this area need to be optimized to ensure a smooth path during machining and avoid excessive vibration or error accumulation.

5. The method for iterative optimization of numerical control system parameters according to claim 1, characterized in that: The system parameter debugging process is based on the output of the feature judgment model, and gives priority to adjusting the control parameters of important feature areas in the processing process. For complex path areas with large curvature, acceleration and deceleration stages, and sudden feature areas, the relevant proportional gain, time constant and differential control parameters are adjusted to achieve higher processing accuracy and process stability. The debugging process performs feedback and optimization in multiple processing tasks to ensure that the system parameters meet specific processing requirements.

6. The method for iterative optimization of numerical control system parameters according to claim 1, characterized in that: An iterative optimization method based on error feedback is used in the parameter optimization process. Specifically, the control parameters of the system are gradually adjusted by collecting the deviation between the feedback signal and the expected signal in real time. This iterative optimization method ensures that during the processing, the system can be dynamically adjusted according to the real-time feedback to improve the processing accuracy and the responsiveness of the system until the deviation is minimized and the process requirements are met. The optimization process does not require human intervention, and the production efficiency is improved through the automated optimization mechanism.

7. The method for iterative optimization of numerical control system parameters according to claim 1, characterized in that: The convergence condition of the optimization iteration is based on the error change rate of the feedback signal during the processing. When the error change is lower than the set threshold, it means that the system has achieved the optimization goal and completed the parameter adjustment. In addition, the system will also monitor the changes of multiple performance indicators in real time, such as processing accuracy, processing speed and energy consumption, to ensure that these indicators are within an acceptable range, thereby improving the overall processing efficiency and quality.

8. The method for iterative optimization of numerical control system parameters according to claim 7, characterized in that: During the optimization process, parameter adjustment is performed through a loss function that comprehensively evaluates system performance. The loss function not only takes into account machining accuracy, but also combines factors such as machining efficiency, system energy consumption, and mechanical vibration, and balances multiple objectives to ensure that the final system can improve machining speed and reduce energy consumption while maintaining high accuracy. After multiple optimization iterations, the system parameters can adapt to various working conditions, thereby improving production flexibility and automation level.

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