Active control method for surface residual stress in CNC milling process

Abstract

This invention discloses an active control method for surface residual stress in CNC milling. By performing range analysis on process parameters and surface residual stress data, the method measures the influence of different process parameters on surface residual stress, identifying key factors for surface residual stress control as the control object. When the monitored surface residual stress value exceeds the expected range, the current process parameters and the prediction results of the surface residual stress monitoring model are used as the input to the NMPC model. Compared with traditional methods, this method considers the dynamic characteristics of milling surface residual stress and can output the optimal control strategy for the current moment. Based on the optimal control strategy output by the NMPC controller, the adjustment amount of the process parameters is converted into control instructions that can be executed by the machine tool PLC, and the control instructions are issued through the CNC machine tool communication module to achieve control of surface residual stress.

Description

Technical Field

[0001] This invention belongs to the field of surface residual stress control technology, specifically a method for actively controlling surface residual stress in CNC milling processes. Background Technology

[0002] During milling, the workpiece is subjected to the combined effects of multiple physical fields, including force, heat, mechanical load, and thermal load, resulting in self-balancing residual stress on the workpiece surface. The state of residual stress affects the performance of the component. If the cutting surface is in a state of residual tensile stress, the fatigue strength of the component decreases, affecting its service life; conversely, when the cutting surface is in a state of residual compressive stress, the fatigue strength of the component increases, its corrosion resistance improves, and its service life is extended. Therefore, controlling the residual stress generated on the workpiece surface during milling is of great significance for controlling the surface integrity of the milled workpiece and improving its machining performance.

[0003] Currently, there are methods and applications for quality control in cutting processes. For example, Chinese patent application CN201410312703.8 discloses an adaptive intelligent control method for variable speed grinding of rolls, which considers the changes in the axial stiffness of the rolls and uses a fuzzy logic system to control the grinding feed, thereby improving the surface roughness and ensuring machining quality. Chinese patent application CN202310556185.3 discloses an adaptive surface roughness control system for five-axis milling machines based on digital twins. This system predicts surface roughness by inputting depth of cut, rotational speed, feed, and vibration into a neural network prediction model. Then, it inputs vibration and surface roughness into a fuzzy control model to obtain rotational speed and feed. The obtained rotational speed and feed are then used to control the machining parameters of the five-axis milling machine through a CNC system, thereby achieving machining feedback to improve part quality.

[0004] In summary, most existing machining quality control methods adjust process parameters using methods such as fuzzy logic control, thereby controlling surface quality such as surface roughness by changing operating conditions. Therefore, existing machining quality control methods have the following shortcomings: Because residual stress on milled surfaces has nonlinear and time-varying characteristics, it exhibits a stronger linear correlation with process parameters (controlled objects) compared to control targets such as surface roughness, making it difficult to establish control rules for residual stress based on process parameters such as spindle speed. Furthermore, traditional PID control, fuzzy logic control, and other control methods struggle to consider or need to simplify the nonlinear and dynamic characteristics of residual stress on the milling process, leading to deviations or performance degradation in the control strategy output by the control model when operating conditions are adjusted. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method for actively controlling surface residual stress in CNC milling, which solves the problems of existing quality control methods in the field of cutting machining being difficult to establish control strategies and insufficient consideration of dynamic characteristics, and ultimately achieves active control of surface residual stress in the milling process.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for actively controlling surface residual stress during CNC milling includes the following steps:

[0008] Step 1: Range Analysis of Controlled Objects

[0009] Range analysis was used to calculate the range of surface residual stress data measured under process parameters at different factor levels, to measure the degree of influence of different process parameters on surface residual stress, and to identify key factors for surface residual stress control, which can then be used as process parameters for control and adjustment during milling.

[0010] Step 2: Nonlinear Model Predictive Control

[0011] Based on the process parameters selected through range analysis of the controlled object, an NMPC controller output control strategy is adopted; the nonlinear model predictive control of surface residual stress includes the following steps:

[0012] 21) Utilize process parameters and acquire multi-directional cutting force data through sensors;

[0013] 22) Establish a data-driven surface residual stress monitoring model;

[0014] 23) Using multi-directional cutting force data as input, the residual stress in the current machining process is continuously estimated during the cutting process to obtain the monitoring value of surface residual stress;

[0015] 24) Based on the monitored value of surface residual stress, determine whether the current surface residual stress is within the set target range: if yes, do not trigger the control command, and repeatedly execute step 211), using the surface residual stress monitoring model to continuously estimate the state of residual stress during the processing; if no, trigger the control command and execute step 215).

[0016] 25) The surface residual stress is optimized in the rolling time domain by using nonlinear model predictive control to obtain the optimal control strategy output by the NMPC controller.

[0017] Step 3: CNC machine tool communication module

[0018] Based on the optimal control strategy output by the NMPC controller, the adjustment amount of process parameters is converted into control instructions that can be executed by the machine tool PLC, and the control instructions are sent through the CNC machine tool communication module to achieve the control of surface residual stress.

[0019] Furthermore, in step one, the process parameters include the cutting speed v. c Feed rate f, depth of cut a p and cutting width a e ;

[0020] The steps of range analysis for controlled objects are as follows:

[0021] 11) Based on the cutting speed v c Feed rate f, depth of cut a p and cutting width a e A four-factor, five-level orthogonal experiment was set up, and residual stress values ​​of the milled surface were collected under different parameters;

[0022] 12) Perform range analysis on the experimental results.

[0023] 121) Calculate the mean of the experimental results for each level of each factor;

[0024] 122) Calculate the range of the average values ​​of each factor;

[0025] 123) Compare the ranges of each factor. The factor with the largest range is the one that has the most significant impact on surface residual stress, thus identifying the key factors for controlling surface residual stress.

[0026] Furthermore, in step 22), the surface residual stress monitoring model is as follows:

[0027] Rs t =f NN (v c ,f,a p ,a e ,F t )

[0028] Where: Rs t F represents the residual surface stress obtained by cutting the controlled object at the current time t. t The cutting force signal data collected by the sensor at the current time t; v c f is the cutting speed; f is the feed rate; a p For the depth of cut; a e This represents the cutting width.

[0029] Furthermore, in step 23), the method for estimating the residual stress state and obtaining the monitored value of the surface residual stress is as follows:

[0030] Rst+N =f NN (v c +Δμ(m),f,a p ,a e ,F t+N )

[0031] F t =f ANN (v c ,f,a p ,a e )

[0032] Where: Rs t+N F represents the residual stress state of the controlled object at the next moment t+N; t+N The cutting force state is indicated at the next moment t+N; Δμ(m) represents the adjustment amount of the process parameters; f ANN (·) represents the mapping relationship between process parameters and cutting force established based on artificial neural network (ANN); N is the time step.

[0033] Furthermore, in step 25), the method for optimizing the rolling time domain solution of surface residual stress using nonlinear model predictive control is as follows:

[0034] 251) Set optimization goals

[0035] The optimization objective is set as the adjustment amount Δμ of the process parameter selected by the range analysis, and the adjustment amount of the process parameter is minimized to min(Δμ), so as to minimize the impact on the stability of the milling process while ensuring that the surface residual stress is kept within the expected range during the machining process.

[0036] 252) Set constraints

[0037] The constraints for rolling optimization during the NMPC controller solution process for residual stress on the milled surface are set as follows:

[0038] Rs t+N =f NN (v c +Δμ(m),f,a p ,a e ,F t+N )≤0

[0039] Where: Rs t+N F represents the residual stress state of the controlled object at the next moment t+N; t+N The cutting force state is indicated at the next moment t+N; Δμ(m) represents the adjustment amount of the process parameters; f ANN (·) represents the mapping relationship between process parameters and cutting force established based on artificial neural network (ANN); N is the time step;

[0040] 253) Optimization Solution

[0041] Transform the constrained optimization process into an unconstrained optimization process:

[0042] min(Δμ)stf NN (v c +Δμ(m),f,a p ,a e ,F t+N )≤0

[0043] By introducing a barrier function into the constrained optimization process described above, the constraints are incorporated into the objective function, resulting in an unconstrained optimization problem:

[0044]

[0045] Where: θ is the barrier parameter; log(―f NN (v c +Δμ(m),f,a p ,a e ,F t+N ) i ) is the barrier function;

[0046] 254) Iterative solution: Select an initial point and initial obstacle parameter θ, and gradually reduce the obstacle parameter to make the iteration point gradually approach the constraint boundary, and finally obtain the optimal solution that satisfies the convergence condition.

[0047] Furthermore, in step 254), since each control cycle requires solving an optimization problem, the optimization problems in adjacent time steps are similar; the previous optimization result Δμ N―1 Δμ is the initial value for the current optimization problem. N This reduces the number of iterations required for the optimization algorithm.

[0048] Furthermore, in step three, the method for controlling surface residual stress by issuing control commands through the CNC machine tool communication module is as follows:

[0049] 31) Assign IP addresses to the host computer and the CNC machine tool PLC;

[0050] 32) Create a host computer client socket, establish a connection with the CNC machine tool's PLC based on the TCP protocol, and initiate a server socket connection request by specifying the IP address and port number of the CNC machine tool's PLC;

[0051] 33) Modify the process parameter multiplier parameters stored in the PLC according to the cyclically sent control instructions, and achieve surface residual stress control by continuously adjusting the process parameters.

[0052] The beneficial effects of this invention are as follows:

[0053] This invention provides an active control method for surface residual stress in CNC milling. By performing range analysis on process parameters and surface residual stress data, it measures the influence of different process parameters on surface residual stress, identifying key factors for controlling surface residual stress as control objects. When the monitored surface residual stress value exceeds the expected range, the current process parameters and the prediction results of the surface residual stress monitoring model are used as inputs to the NMPC model. Compared with traditional methods, this method considers the dynamic characteristics of milling surface residual stress and can output the optimal control strategy for the current moment. Based on the optimal control strategy output by the NMPC controller, the adjustment amount of the process parameters is converted into control instructions that can be executed by the machine tool PLC, and the control instructions are issued through the CNC machine tool communication module to achieve control of surface residual stress. In summary, this invention's active control method for surface residual stress in CNC milling, by analyzing the control objects of surface residual stress and considering the dynamic changes of cutting force during milling, actively controls the surface residual stress of the workpiece during milling. This solves the problems of existing quality control methods in the cutting machining field, such as the difficulty in establishing control strategies and insufficient consideration of dynamic characteristics, ultimately achieving active control of surface residual stress in the milling process.

[0054] In addition, the surface residual stress control strategy output by this invention takes into account minimizing the amount of process parameter adjustment, thus avoiding milling stability problems caused by excessive process parameter adjustment.

[0055] The surface residual stress control method proposed in this invention establishes communication with the CNC machine tool PLC based on TCP / IP, which enables the rolling issuance of control commands and monitoring of control feedback. Attached Figure Description

[0056] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

[0057] Figure 1 This is a flowchart of the active control method for surface residual stress in CNC milling process according to the present invention;

[0058] Figure 2 A flowchart of the NMPC iterative solution process;

[0059] Figure 3 This is a flowchart of CNC machine tool communication based on TCP / IP.

[0060] Figure 4 The average value of each process parameter for surface residual stress;

[0061] Figure 5 The results are for the verification of the residual stress control model for milled surfaces. Detailed Implementation

[0062] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0063] like Figure 1 As shown, the active control method for surface residual stress in CNC milling machining in this embodiment mainly includes three stages: range analysis of the controlled object, nonlinear model predictive control (NMPC) control, and CNC machine tool communication module. Specifically, the active control method for surface residual stress in CNC milling machining in this embodiment includes the following steps.

[0064] Step 1: Range Analysis of Controlled Objects

[0065] Range analysis is used to calculate the range of surface residual stress data measured under process parameters at different factor levels. This measures the degree of influence of different process parameters on surface residual stress and identifies key factors for controlling surface residual stress, which can then be used as process parameters for control and adjustment during milling.

[0066] Specifically, the residual stress on the milled surface is affected by the cutting speed v c Feed rate f, depth of cut a p Cutting width a e The process parameters are affected by factors such as cutting speed v, which are included in this embodiment. c Feed rate f, depth of cut a p and cutting width a e To select the active control target for residual stress on the milled surface, range analysis was performed on the control target.

[0067] In this embodiment, the method steps for range analysis of the controlled object are as follows.

[0068] 11) Based on the cutting speed v c Feed rate f, depth of cut a p and cutting width a e A four-factor, five-level orthogonal experiment was set up, and residual stress values ​​of the milled surface were collected under different parameters.

[0069] 12) Perform range analysis on the experimental results.

[0070] 121) Calculate the mean of the experimental results for each level of each factor.

[0071] 122) Calculate the range of the average values ​​of each factor.

[0072] 123) Compare the ranges of each factor. The factor with the largest range is the one that has the most significant impact on surface residual stress, thus identifying the key factors for controlling surface residual stress. These key factors are then used as process parameters to be controlled and adjusted during the milling process, achieving effective control of surface residual stress.

[0073] Step 2: Nonlinear Model Predictive Control

[0074] The process parameters selected based on the range analysis of the controlled object are used as the controlled object, and the NMPC controller outputs the control strategy. In this embodiment, the nonlinear model predictive control of surface residual stress includes the following steps.

[0075] 21) Utilize process parameters and obtain multi-directional cutting force data through sensors.

[0076] 22) Establish a data-driven surface residual stress monitoring model.

[0077] In this embodiment, the surface residual stress monitoring model is as follows:

[0078] Rs t =f NN (v c ,f,a p ,a e ,F t )

[0079] Where: Rs t F represents the residual surface stress obtained by cutting the controlled object at the current time t; t The cutting force signal data collected by the sensor at the current time t; v c f is the cutting speed; f is the feed rate; a p For the depth of cut; a e This represents the cutting width.

[0080] 23) Using multi-directional cutting force data as input, the residual stress in the current machining process is continuously estimated during the cutting process to obtain the monitoring value of surface residual stress.

[0081] In this embodiment, state estimation of residual stress requires obtaining the future control input result process parameter adjustment amount Δμ(m) based on future state prediction. In this embodiment, the method for predicting the future state process to obtain the monitored value of surface residual stress is as follows:

[0082] Rs t+N =f NN (v c +Δμ(m),f,a p ,a e ,F t+N )

[0083] To obtain the modified spindle speed v c F in the future state t+N An artificial neural network (ANN) is used to establish the mapping relationship between process parameters and cutting force.

[0084] F t =f ANN (v c ,f,a p ,a e )

[0085] Where: Rs t+N F represents the residual stress state of the controlled object at the next moment t+N; t+N The cutting force state is indicated at the next moment t+N; Δμ(m) represents the adjustment amount of the process parameters; f ANN (·) represents the mapping relationship between process parameters and cutting force established based on artificial neural network (ANN); N is the time step, and in this embodiment, N = 1s.

[0086] In the rolling time-domain optimization process, the residual stress value at the current moment is obtained through the prediction model within each time step N. Based on the current process parameters and the multi-directional cutting force data obtained through sensors as input, the residual stress value of the surface is predicted. During the cutting process, the residual stress in the current machining process is continuously estimated.

[0087] 24) Based on the monitored value of surface residual stress, determine whether the current surface residual stress is within the set target range: if yes, do not trigger the control command, and repeatedly execute step 211), using the surface residual stress monitoring model to continuously estimate the state of residual stress during the processing; if no, trigger the control command and execute step 215).

[0088] 25) The surface residual stress is optimized in the rolling time domain by using nonlinear model predictive control to obtain the optimal control strategy output by the NMPC controller.

[0089] In this embodiment, the method steps for using nonlinear model predictive control to perform rolling time-domain optimization of surface residual stress are as follows:

[0090] 251) Set optimization goals

[0091] Since excessive adjustment of process parameters during milling can affect cutting stability, the optimization objective in the surface residual stress control process based on NMPC is set as the process parameter adjustment amount Δμ selected by range analysis, and the adjustment amount of process parameters is minimized to min(Δμ) in order to minimize the impact on the stability of the milling process while ensuring that the surface residual stress is kept within the expected range during the machining process.

[0092] 252) Set constraints

[0093] To ensure that the residual stress on the milled surface is controlled within the expected range, taking the condition of compressive stress as an example, the constraint conditions for rolling optimization during the NMPC controller solution process for residual stress on the milled surface are set as follows:

[0094] Rs t+N =f NN (v c +Δμ(m),f,a p ,a e ,F t+N )≤0

[0095] Where: Rs t+N F represents the residual stress state of the controlled object at the next moment t+N; t+N The cutting force state is indicated at the next moment t+N; Δμ(m) represents the adjustment amount of the process parameters; f ANN (·) represents the mapping relationship between process parameters and cutting force established based on artificial neural network (ANN); N is the time step.

[0096] 253) Optimization Solution

[0097] The constraint problem is solved using the interior point method, and the calculation is accelerated by using the solver IPOPT (Interior Point OPTimizer), transforming the constraint optimization process into an unconstrained optimization process:

[0098] min(Δμ)stf NN (v c +Δμ(m),f,a p ,a e ,F t+N )≤0

[0099] By introducing a barrier function into the constrained optimization process described above, the constraints are incorporated into the objective function, resulting in an unconstrained optimization problem:

[0100]

[0101] Where: θ is the barrier parameter; log(―f NN (v c +Δμ(m),f,a p ,a e ,F t+N ) i ) is the barrier function.

[0102] 254) Iterative solution: Select an initial point and initial obstacle parameter θ. By gradually decreasing the obstacle parameter, the iteration point gradually approaches the constraint boundary, and finally obtains the optimal solution that satisfies the convergence condition, such as... Figure 2 As shown.

[0103] Furthermore, to quickly solve the optimization problem, since an optimization problem needs to be solved in each control cycle, and the optimization problems in adjacent time steps are similar, the previous optimization result Δμ is used. N―1 Δμ is the initial value for the current optimization problem. N This reduces the number of iterations required for the optimization algorithm.

[0104] Step 3: CNC machine tool communication module

[0105] Based on the optimal control strategy output by the NMPC controller, the adjustment quantities of process parameters are converted into control instructions that the machine tool PLC can execute. These control instructions are then sent through the CNC machine tool communication module to control surface residual stress. The CNC machine tool communication and control instruction sending process includes IP allocation, TCP connection, data request and response, real-time transmission of control instructions, and status monitoring, such as... Figure 2 As shown.

[0106] In this embodiment, the method for controlling surface residual stress by issuing control commands through the CNC machine tool communication module is as follows:

[0107] 31) Assign IP addresses to the host computer and the CNC machine tool PLC;

[0108] 32) Create a host computer client socket, establish a connection with the CNC machine tool's PLC based on the TCP protocol, and initiate a server socket connection request by specifying the IP address and port number of the CNC machine tool's PLC;

[0109] 33) Modify the process parameter multiplier parameters stored in the PLC according to the cyclically sent control instructions, and achieve surface residual stress control by continuously adjusting the process parameters.

[0110] During this process, the variable values ​​corresponding to the control target in the PLC are continuously monitored to ensure the smooth issuance of control commands.

[0111] The following uses aluminum alloy milling as an example to illustrate the active control method for surface residual stress in the CNC milling process of the present invention.

[0112] 1. Experimental environment for aluminum alloy milling

[0113] To demonstrate the active control method for surface residual stress in CNC milling proposed in this embodiment, a milling experiment is used as an example. The equipment used in the experiment is shown in Table 1.

[0114] Table 1. Main equipment used in the experiment

[0115]

[0116] The aluminum alloy milling experiment used an 8mm diameter solid carbide end mill. The workpiece was 7075 aluminum alloy, with dimensions of 50mm × 20mm × 20mm. Dry milling was performed on the workpiece, and the selected cutting parameters were cutting speed v, feed rate f, and depth of cut a. p Cutting width a e Using orthogonal experimental design, an experimental group for milling aluminum alloy components was designed. The orthogonal experimental parameters are shown in Table 2. A total of 25 milling experiments were conducted, and the specific milling experimental parameters are shown in Table 3. After the milling experiments were completed, the residual stress on the milled surface was obtained using a stress testing system.

[0117] Table 2. Parameter settings for orthogonal experiments

[0118]

[0119] 2. Range analysis of process parameters

[0120] Based on the above orthogonal experimental data, the residual stress under different process parameters was analyzed by comparing the range of the residual stress data using the range analysis method. The analysis results are shown in Table 3. Figure 3 As shown.

[0121] Table 3. Results of Range Analysis in Orthogonal Experiments

[0122]

[0123]

[0124] As shown in Table 3, the results of the orthogonal experimental range analysis indicate that among spindle speed, feed rate, cutting width, and cutting depth, the cutting depth has the greatest impact on surface residual stress, followed by spindle speed. Considering that the machining stage with the greatest impact on the final surface residual stress of the workpiece is finishing, it is difficult to guarantee the dimensional requirements of the workpiece by selecting the cutting depth as the control object. Therefore, the spindle speed is selected as the control object for active regulation of surface residual stress.

[0125] 3. Performance of the NMPC-based residual stress control model for milled surfaces

[0126] To verify the effectiveness of the residual stress control model for milled surfaces, a CNC milling process was simulated based on the established ANN model. To improve learning accuracy, neural network architectures with different hierarchical structures were designed, and the optimal model structure was selected based on the lowest mean square error (MSE). A set of process parameters was selected for verification, and the relationship between surface residual stress and spindle speed and its adjustment was as follows: Figure 5As shown in the figure. The surface residual stress control simulation can control and maintain the surface residual stress within the expected range, effectively achieving the control of surface residual stress.

[0127] To further verify the effectiveness of the active control method for surface residual stress, a comparative experiment was set up. The experimental parameters are shown in Table 4. The comparative experiments included a processing method with fixed processing parameters and a processing method with active control of processing parameters.

[0128] Table 4. Parameter settings for the comparative experiment on active control of surface residual stress

[0129]

[0130] The residual stress on the workpiece surface after milling was measured using an X-ray Proto residual stress testing and analysis system. Considering the accuracy of the residual stress measurement, the final result was the average of multiple measurements. Furthermore, to better compare the results of fixed machining parameters and active control, the residual stress in the same area of ​​the milled workpiece surface was selected for comparison. The results of the comparative experiment are shown in Table 5. From the residual stress values ​​of different machining methods in the table, it can be seen that the residual stress was changed from residual tensile stress to residual compressive stress. The proposed active control method can effectively control the residual stress within the set range.

[0131] Table 5 Comparative Experimental Results of Active Control of Surface Residual Stress

[0132]

[0133] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for actively controlling surface residual stress during CNC milling, characterized in that: Includes the following steps: Step 1: Range Analysis of Controlled Objects Range analysis was used to calculate the range of surface residual stress data measured under process parameters at different factor levels, to measure the degree of influence of different process parameters on surface residual stress, and to identify key factors for surface residual stress control, which can then be used as process parameters for control and adjustment during milling. Step 2: Nonlinear Model Predictive Control Based on the process parameters selected through range analysis of the controlled object, a nonlinear model predictive controller output control strategy is adopted; the nonlinear model predictive control of surface residual stress includes the following steps: 21) Utilize process parameters and obtain multi-directional cutting force data through sensors; 22) Establish a data-driven surface residual stress monitoring model; 23) Using multi-directional cutting force data as input, the residual stress in the current machining process is continuously estimated during the cutting process to obtain the monitoring value of surface residual stress; 24) Based on the monitored value of surface residual stress, determine whether the current surface residual stress is within the set target range: if yes, do not trigger the control command, and repeatedly execute step 21), continuously estimate the state of residual stress during the processing using the surface residual stress monitoring model; if no, trigger the control command and execute step 25). 25) The surface residual stress is optimized in the rolling time domain using nonlinear model predictive control to obtain the optimal control strategy output by the nonlinear model predictive controller; Step 3: CNC machine tool communication module The optimal control strategy based on the output of the nonlinear model predictive controller converts the adjustment of process parameters into control instructions that can be executed by the machine tool PLC, and sends the control instructions through the CNC machine tool communication module to control the residual stress on the surface.

2. The method for actively controlling surface residual stress in CNC milling process according to claim 1, characterized in that: In step one, the process parameters include cutting speed. Feed rate Depth of cut and cutting width ; The steps of range analysis for controlled objects are as follows: 11) Based on the cutting speed Feed rate Depth of cut and cutting width A four-factor, five-level orthogonal experiment was set up, and residual stress values ​​of the milled surface were collected under different parameters; 12) Perform range analysis on the experimental results. 121) Calculate the mean of the experimental results for each level of each factor; 122) Calculate the range of the average values ​​of each factor; 123) Compare the ranges of each factor. The factor with the largest range is the one that has the most significant impact on surface residual stress, thus identifying the key factors for controlling surface residual stress.

3. The method for actively controlling surface residual stress in CNC milling process according to claim 1, characterized in that: In step 22), the surface residual stress monitoring model is as follows: in: Indicates the current time of the controlled object Surface residual stress obtained from cutting; For the current moment Cutting force signal data acquired by the sensor; This refers to the cutting speed; For feed rate; This refers to the depth of cut. This represents the cutting width.

4. The method for actively controlling surface residual stress in CNC milling process according to claim 3, characterized in that: In step 23), the method for estimating the residual stress state and obtaining the monitored value of the surface residual stress is as follows: in: Indicates the next moment of the controlled object The residual stress state; Indicates the next moment The cutting force state; Indicates the amount of process parameter adjustment; This represents the mapping relationship between process parameters and cutting force established based on an artificial neural network (ANN). For time steps.

5. The method for actively controlling surface residual stress in CNC milling process according to claim 1, characterized in that: In step 25), the method for optimizing the rolling time-domain solution of surface residual stress using nonlinear model predictive control is as follows: 251) Set optimization goals The optimization objective is set as the adjustment amount of the process parameters selected for range analysis. And minimize the adjustment of process parameters. This is to minimize the impact on the stability of the milling process while ensuring that the surface residual stress remains within the expected range during machining; 252) Set constraints The constraints for rolling optimization during the solution process of the nonlinear model predictive controller for residual stress on the milled surface are set as follows: in: Indicates the next moment of the controlled object The residual stress state; Indicates the next moment The cutting force state; Indicates the amount of process parameter adjustment; This represents the mapping relationship between process parameters and cutting force established based on an artificial neural network (ANN). For time steps; 253) Optimization Solution Transform the constrained optimization process into an unconstrained optimization process: By introducing a barrier function into the constrained optimization process described above, the constraints are incorporated into the objective function, resulting in an unconstrained optimization problem: in: These are obstacle parameters; For barrier functions; 254) Iterative solution: Choosing the initial point and initial obstacle parameters By gradually reducing the obstacle parameters, the iteration points gradually approach the constraint boundary, and finally the optimal solution that satisfies the convergence condition is obtained.

6. The method for actively controlling surface residual stress in CNC milling process according to claim 5, characterized in that: In step 254), since each control cycle requires solving an optimization problem, the optimization problems at adjacent time steps are similar; the previous optimization result is then used... As the initial value of the current optimization problem This reduces the number of iterations required for the optimization algorithm.

7. The method for actively controlling surface residual stress in CNC milling process according to claim 1, characterized in that: In step three, the method for controlling surface residual stress by issuing control commands through the CNC machine tool communication module is as follows: 31) Assign IP addresses to the host computer and the CNC machine tool PLC; 32) Create a host computer client socket, establish a connection with the CNC machine tool's PLC based on the TCP protocol, and initiate a server socket connection request by specifying the IP address and port number of the CNC machine tool's PLC; 33) Modify the process parameter multiplier parameters stored in the PLC according to the cyclically sent control instructions, and achieve surface residual stress control by continuously adjusting the process parameters.

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