Impeller five-axis machining unbalance control method

Abstract

The application discloses a kind of impeller five-axis machining unbalance control method, comprising: acquisition main shaft speed, cutting parameter, blade surface design data and other key information establish original set, construct five-axis linkage unbalance coupling model and determine unbalance influence factor distribution;Through blade surface deviation unbalance compensation algorithm correction unbalance, utilize processing flutter unbalance suppression algorithm dynamic adjustment cutting parameter and weaken flutter influence;Start five-axis machining unbalance diagnosis simulation system output diagnosis result and optimization direction, finally adjust processing parameter and compensation strategy to realize accurate control.The application is through parameter acquisition, deviation analysis, algorithm operation, simulation verification and dynamic adjustment closed loop link, systematic integration multi-factor coupling analysis and real-time feedback mechanism, overall improve the accuracy and adaptability of impeller five-axis machining unbalance control, provide reliable technical support for high-precision impeller processing.

Description

Technical Field

[0001] This invention relates to the field of five-axis impeller machining technology, and in particular to a method for controlling the imbalance in five-axis impeller machining. Background Technology

[0002] As a core component in aerospace, energy, and power industries, the machining accuracy of impellers directly affects the operating efficiency and stability of equipment. Five-axis machining technology, with its advantages of high flexibility and high machining accuracy, has become the mainstream machining method for complex curved impeller surfaces. However, during five-axis machining, the interaction of multiple factors such as the coordinated motion of the linkage shafts, the complex curvature of the blade surface, and machining chatter can easily lead to the accumulation of imbalance, causing vibration and noise during impeller rotation, exacerbating tool wear and machine tool damage, and even affecting the overall operational safety. With the increasing demands on impeller performance from high-end equipment, there is an urgent need to construct an imbalance control system that takes into account the coupled effects of multiple factors, dynamic compensation, and precise suppression. This system aims to solve the constraints of imbalance in five-axis machining on machining quality and efficiency, and meet the mass production needs of high-precision impellers.

[0003] Existing technologies have significant shortcomings in controlling the imbalance during five-axis impeller machining: On the one hand, traditional control methods often adjust independently for a single imbalance cause, failing to fully consider the coupling relationship between the motion error of the five-axis linkage, blade profile deviation, and machining chatter. This results in a lack of systematic control strategies, making it difficult to eliminate the imbalance caused by the superposition of multiple factors at its root. On the other hand, existing technologies lack a closed-loop control mechanism that integrates data acquisition, model analysis, dynamic compensation, and simulation diagnosis. The compatibility between compensation algorithms and suppression strategies is insufficient, and an effective feedback link for imbalance state diagnosis and parameter optimization has not been established. This results in a lag in the control process, making it impossible to respond in real time to the dynamic changes of imbalance factors during machining, and making it difficult to achieve precise control of the imbalance. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a method for controlling the unbalance in five-axis machining of impellers.

[0005] The technical solution adopted in this invention is a method for controlling the imbalance in five-axis impeller machining, comprising the following steps: S1, acquiring the spindle speed, tool cutting parameters, blade profile design data, five-axis linkage motion parameters, and machining coordinate system reference data during the five-axis impeller machining process, and establishing an original information set including each calibration parameter; S2, based on the original information set, constructing a five-axis linkage imbalance coupling model, and determining the distribution of imbalance influencing factors by analyzing the correlation between the motion error of each linkage shaft and the imbalance; S3, employing a blade profile deviation imbalance compensation algorithm, combining actual measurement data and design data of the blade profile. S4. Using a machining chatter imbalance suppression algorithm, vibration and cutting force signals are collected in real time during the machining process, and cutting parameters are dynamically adjusted to weaken the imbalance amplification effect caused by chatter; S5. The five-axis machining imbalance diagnosis simulation system is started, the corrected parameter data is input for simulation calculation, and the imbalance state diagnosis results and optimization directions are output; S6. Based on the simulation diagnosis results, the five-axis machining motion parameters and compensation strategies are adjusted to control the imbalance of the impeller five-axis machining. All steps form a closed-loop control link through real-time parameter interaction and feedback mechanism, which runs through the entire impeller machining process.

[0006] Furthermore, the expression for the five-axis linkage imbalance coupling model is: ,in, This represents the total amount of five-axis linkage imbalance coupling; This refers to the coupling coefficient for linkage imbalance; Let be the velocity of the i-th linkage axis; Let be the angular displacement deviation of the i-th linkage axis; This represents the peak value of the cutting force. This refers to geometric accuracy deviation; Main axis angular velocity; The moment of inertia of the impeller; This represents the dynamic stiffness fluctuation.

[0007] Furthermore, the expression for the blade profile deviation imbalance compensation algorithm is as follows: ,in, This is the compensation amount for blade profile deviation imbalance; This is the compensation coefficient; This refers to the actual blade profile data; Design profile data for the blade; The angle between the surface normal and the compensation direction; The gradient of the actual surface data; This refers to the blade surface curvature factor. The time derivative of the actual surface data; The time derivative of the design surface data; To compensate for the calculation cycle.

[0008] Furthermore, the expression for the processing chatter imbalance suppression algorithm is: ,in, For flutter imbalance suppression force; The inhibition coefficient; The amplitude of the vibration; It is the angular frequency of vibration; For time; The vibration phase angle; For cutting force ； The main axis angular frequency; The damping ratio; The k-th order flutter mode coefficients; This represents the k-th order flutter frequency offset.

[0009] Furthermore, the simulation calculation of the five-axis machining imbalance diagnosis simulation system is expressed as follows: ,in, This is the value of the imbalance diagnosis result; Diagnostic coefficient; The simulation weight for the j-th processing parameter; This represents the actual measured value of the j-th processing parameter; This represents the total number of processing parameters. This is the error redundancy. This is the sensor sensitivity factor.

[0010] Furthermore, the comprehensive parameter calculation for the unbalance control of the five-axis machining of the impeller is expressed as follows: ,in, The output value is controlled by the unbalance quantity. For control coefficients; The stiffness coefficient of a five-axis machine tool; The impeller's rotating mass; Let be the adjustment factor for the p-th control loop; Let be the response time of the p-th control loop; To control the total number of steps.

[0011] Further, step S3 includes the following sub-steps: S31, collecting actual profile data of each blade of the impeller using a 3D scanning device, registering the collected data with the design data in the same coordinate system, extracting the profile deviation value of each sampling point, and forming a deviation data matrix; S32, based on the deviation data matrix, filtering out sampling points that exceed a preset deviation threshold, analyzing the correlation between the deviation distribution characteristics and the blade spatial position, and determining the deviation concentration area; S33, inputting the deviation data into the blade profile deviation imbalance compensation algorithm, combining the imbalance influence factor output by the five-axis linkage imbalance coupling model, and calculating the compensation amount corresponding to each deviation sampling point; S34, generating motion adjustment commands recognizable by the five-axis machine tool according to the compensation amount distribution law, and offsetting the imbalance caused by the profile deviation through the micro-displacement adjustment of the linkage axis.

[0012] Further, step S4 includes the following sub-steps: S41, deploying vibration sensors and cutting force sensors at the spindle and tool holder of the five-axis machine tool to collect vibration acceleration signals and cutting force signals during the machining process in real time, converting them into digital signals and transmitting them to the data processing module; S42, filtering the collected digital signals to remove environmental interference signals, extracting characteristic frequency components related to chatter, and determining preliminary judgment indicators for chatter occurrence; S43, inputting the characteristic frequency components and judgment indicators into the machining chatter imbalance suppression algorithm, and calculating the parameter adjustment amount required for chatter suppression by combining the current spindle speed and cutting parameters; S44, dynamically adjusting the spindle speed, feed rate, and depth of cut according to the parameter adjustment amount, weakening chatter energy through parameter optimization, and suppressing further expansion of imbalance.

[0013] Further, S5 includes the following sub-steps: S51, integrating the compensated parameters output from S3, the adjusted machining parameters output from S4, and the original machining information set to form a simulation input dataset, and importing it into the five-axis machining imbalance diagnosis simulation system according to a preset format; S52, building a virtual machine tool model, impeller model, and machining environment model consistent with the actual machining scenario in the simulation system, setting the simulation step size and iteration number, and starting the imbalance simulation calculation; S53, monitoring the trend of imbalance of the virtual impeller, the motion error of each linkage axis, and the chatter suppression effect in real time during the simulation, and recording the calibration simulation data and intermediate calculation results; S54, based on the simulation data and intermediate results, using a feature extraction algorithm to analyze the main causes of imbalance, generating a diagnostic report including the imbalance state level and optimization parameter suggestions, and transmitting it to the subsequent control link.

[0014] A method for controlling the unbalance in five-axis machining of impellers is implemented through different units, including: a five-axis linkage parameter acquisition and preprocessing unit, a five-axis linkage unbalance coupling model construction and analysis unit, a blade profile deviation unbalance compensation algorithm calculation unit, a machining chatter unbalance suppression algorithm execution unit, a five-axis machining unbalance diagnosis simulation system operation unit, and an unbalance closed-loop control and adjustment unit.

[0015] The five-axis linkage parameter acquisition and preprocessing unit and the five-axis linkage imbalance coupling model construction and analysis unit are connected bidirectionally to transmit the acquired raw parameters to the model construction unit for imbalance factor analysis. The five-axis linkage imbalance coupling model construction and analysis unit transmits data unidirectionally to the blade profile deviation imbalance compensation algorithm calculation unit and the machining chatter imbalance suppression algorithm execution unit, respectively, and outputs imbalance influence factors. The blade profile deviation imbalance compensation algorithm calculation unit and the machining chatter imbalance suppression algorithm execution unit both interact bidirectionally with the five-axis machining imbalance diagnosis simulation system operation unit, transmitting corrected parameters and adjustment amounts and receiving simulation feedback. The five-axis machining imbalance diagnosis simulation system operation unit is connected unidirectionally to the imbalance quantity closed-loop control and adjustment unit, and outputs diagnostic results and optimization suggestions. The imbalance quantity closed-loop control and adjustment unit establishes a feedback link with the five-axis linkage parameter acquisition and preprocessing unit in reverse to dynamically adjust the machining parameters. Each unit shares parameters in real time and works collaboratively through the data bus to jointly complete the control of the impeller five-axis machining imbalance.

[0016] Beneficial Effects: This invention proposes a method for controlling the imbalance in five-axis impeller machining. This method overcomes the limitations of traditional single-factor control by incorporating the coupling relationship of imbalance across the five axes into a holistic analysis. A specially constructed model clarifies the correlation between the motion of each linkage axis, blade profile deviation, and machining chatter, fundamentally solving the problem of one-sided control caused by the failure to consider multi-factor coupling in existing technologies. Simultaneously, it integrates the entire process of data acquisition, model analysis, dynamic compensation, chatter suppression, simulation diagnosis, and parameter adjustment, forming a real-time interactive feedback mechanism. Through the synergistic effect of blade profile deviation compensation and machining chatter suppression algorithms, it dynamically responds to changes in imbalance factors during machining, effectively compensating for the lack of closed-loop control and response lag in existing technologies. This method, through a step-by-step detailed process of parameter acquisition, deviation correction, chatter suppression, and simulation diagnosis, combined with a multi-unit collaborative working mode, achieves comprehensive and dynamic control of the imbalance, significantly improving control accuracy and adaptability. It ensures optimal matching of key parameters during machining, significantly reduces the accumulation of imbalance caused by the superposition of multiple factors, and provides reliable support for high-precision impeller machining. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 This is a flowchart of method step S3 of the present invention; Figure 3 This is a flowchart of method step S4 of the present invention; Figure 4 This is a flowchart of step S5 of the method of the present invention; Figure 5 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, a method for controlling the imbalance in five-axis machining of an impeller includes the following steps: S1. Acquire spindle speed, tool cutting parameters, blade profile design data, five-axis linkage motion parameters, and machining coordinate system reference data during the five-axis machining of the impeller, and establish a raw information set including each calibration parameter; S2. Based on the raw information set, construct a five-axis linkage imbalance coupling model, and determine the distribution of imbalance influencing factors by analyzing the correlation between the motion error of each linkage axis and the imbalance amount; S3. Use a blade profile deviation imbalance compensation algorithm, combined with the deviation value between the actual measured data and the design data of the blade profile, to initially correct the imbalance amount; S4. Utilize a machining chatter imbalance suppression algorithm to collect vibration signals and cutting force signals in real time during the machining process, and dynamically adjust the cutting parameters to weaken the imbalance amplification effect caused by chatter; S5. Start the five-axis machining imbalance diagnosis simulation system, input the corrected parameter data for simulation calculation, and output the imbalance state diagnosis results and optimization direction; S6. Based on the simulation diagnosis results, adjust the five-axis machining motion parameters and compensation strategy to control the imbalance amount in the five-axis machining of the impeller. All steps form a closed-loop control link through a real-time parameter interaction and feedback mechanism, running through the entire impeller machining process.

[0020] Step S1 involves the comprehensive collection and integration of key information for the five-axis machining of the impeller. During implementation, the five-axis machine tool's built-in parameter monitoring module, high-precision data acquisition card, and 3D measurement equipment work together. Specifically, real-time data is collected for spindle speeds ranging from 1000 to 8000 RPM. Tool cutting parameters include core indicators such as cutting speed, feed rate, and depth of cut. The feed rate is precisely recorded in increments of 0.01 to 0.2 per tooth. Blade profile design data includes geometric parameters such as the number of blades, blade curvature, inlet and outlet angles, and chord length. The five-axis linkage motion parameters involve the travel distances of the X, Y, and Z axes and the rotation angles of the A and C axes. The accuracy is controlled within 0.001, and the rotation angle accuracy is maintained within 0.001 radians. The machining coordinate system reference data is based on the machine tool origin to determine the coordinate information of key references such as the impeller mounting surface and positioning holes. All collected data is transmitted to the data storage unit after analog-to-digital conversion, and is classified and archived according to parameter type, forming a complete set of original information including spindle operation, tool cutting, blade design, linkage shaft movement and coordinate system reference. This step provides comprehensive and accurate data support for subsequent model construction, algorithm calculation and parameter adjustment, ensuring that subsequent links are carried out based on real machining scenario data and avoiding poor control effect due to missing or biased information.

[0021] Step S2, based on the raw information collected in S1, initiates the model building process. During implementation, data cleaning is performed on each parameter to remove abnormal fluctuations. Then, a correlation analysis algorithm is used to uncover the intrinsic relationship between the motion parameters of each linkage axis and the imbalance. Specifically, the quantitative relationship between the movement errors of the X, Y, and Z axes, and the rotation errors of the A and C axes, and the imbalance is analyzed. Movement errors are monitored in real-time using a laser interferometer, while rotation errors are collected using a circular grating encoder. Combined with key parameters such as peak cutting force, geometric accuracy deviation, spindle angular velocity, impeller moment of inertia, and dynamic stiffness fluctuations, a multidimensional model is constructed. A five-axis linkage imbalance coupling model was developed. During the model construction process, multiple sets of comparative experiments were set up. By changing the value of a single parameter, the change law of the imbalance was observed, the influence weight of each parameter on the imbalance was determined, and the distribution characteristics of the imbalance influencing factors were clarified. The imbalance influencing factors include linkage axis motion error factors, cutting parameter influence factors, geometric accuracy deviation factors, etc. Each factor is assigned a corresponding weight coefficient according to the degree of influence. This step provides a clear target direction for the application of subsequent compensation and suppression algorithms through systematic analysis of the imbalance mechanism under the coupling effect of multiple parameters, making subsequent control measures more targeted.

[0022] Step S3 employs a specially designed blade profile deviation imbalance compensation algorithm for initial correction. During this process, a 3D laser scanning device is used to perform a full-range scan of the impeller during processing, with a scanning density set to 10 to 20 sampling points per square millimeter. This acquires actual measurement data for each blade profile. Subsequently, the actual measurement data and design data are imported into the same coordinate system for registration. The least squares method is used to calculate the profile deviation value for each sampling point. The deviation value calculation is based on the design data, comparing the spatial position differences between the actual sampling points and their corresponding design points one by one to form a complete data matrix including deviation information for all sampling points. The data matrix is ​​then input into the blade profile deviation imbalance compensation algorithm. During the algorithm's operation, parameters such as the angle between the profile normal and the compensation direction, the gradient of the actual profile data, and the blade profile curvature factor are combined to calculate the compensation amount corresponding to each deviation sampling point through multiple rounds of iteration. The calculation of the compensation amount fully considers the spatial surface characteristics of the blade profile to ensure that the compensation direction is compatible with the geometric shape of the blade profile, avoiding new imbalances caused by improper compensation. This step effectively reduces the imbalance caused by profile machining errors by accurately capturing blade profile deviations and implementing targeted compensation, laying the foundation for subsequent precise control.

[0023] Step S4 utilizes a machining chatter imbalance suppression algorithm for dynamic adjustment. During implementation, piezoelectric vibration sensors and resistance strain gauge cutting force sensors are deployed at the spindle end and tool holder connection of the five-axis machine tool, respectively. The vibration sensor sampling frequency is set to 10-50 kHz, and the cutting force sensor sampling frequency is set to 5-20 kHz. Real-time acquisition of radial and axial vibration acceleration signals along the spindle and cutting force signals at the tool cutting edge is performed. The acquired analog signals are amplified and filtered by a signal conditioner, then converted into digital signals by a data acquisition card and transmitted to the central processing unit. The central processing unit performs spectral analysis on the digital signals, extracting 10-10 kHz signals. The characteristic frequency components related to chatter within the 000 Hz range are used to determine the amplitude, phase, and other criteria for chatter occurrence. These characteristic parameters are then input into the machining chatter imbalance suppression algorithm. The algorithm, combined with current machining parameters such as spindle speed, feed rate, and depth of cut, calculates the corresponding parameter adjustment amounts through a dynamic optimization algorithm. The adjustment amounts are output in the form of spindle speed increase / decrease, feed rate correction ratio, and depth of cut adjustment value. Subsequently, the adjustment commands are sent to each actuator through the machine tool CNC system to adjust the relevant machining parameters in real time. This step effectively weakens chatter energy transmission and suppresses further expansion of imbalance by monitoring chatter signals in real time and dynamically optimizing parameters.

[0024] Step S5 initiates the five-axis machining imbalance diagnosis simulation system to perform simulation calculations. During implementation, the compensated blade profile parameters output by S3, the adjusted machining parameters output by S4, and the raw information collected by S1 are first integrated. This data is then standardized according to the simulation system's specified data format, converting it into a system-recognizable dataset. The dataset is then imported into the simulation system, where a virtual model consistent with the actual machining scenario is built. This model includes the five-axis machine tool's structural model, the impeller's 3D solid model, the tool model, and the machining environment model. The machine tool's structural model accurately reproduces the motion relationships and stiffness characteristics of each linkage axis. The impeller model includes detailed geometric features of the blades, and the machining environment model simulates the temperature during actual machining. Environmental conditions such as humidity were set, and then the simulation step size was set to 0.001 seconds, with 1000 to 5000 iterations. The imbalance simulation was then started. During the simulation, the system monitored the unbalance change curve, motion error values ​​of each linkage shaft, and chatter suppression effect data in real time during the virtual impeller rotation. Key data was recorded every 100 iterations. After the simulation, the data was analyzed using a feature extraction algorithm to identify the main causes of the imbalance, classify the imbalance according to its severity, and generate a diagnostic report including the imbalance state level, optimization suggestions for each parameter, and adjustment direction. This step uses virtual simulation to achieve early prediction of the imbalance state and output optimization suggestions, providing a scientific basis for subsequent actual parameter adjustments.

[0025] Step S6 implements the final adjustment based on the simulation diagnostic results to achieve precise control of the imbalance. During implementation, the diagnostic report output from S5 is received first. The data parsing module extracts key information from the report, such as the imbalance level, optimization suggestions for each parameter, and adjustment direction. Combining the quality requirements of impeller machining with the machine tool's operating limits, the feasibility of the optimization suggestions is verified, and the adjustment range of the five-axis machining motion parameters and compensation strategies is determined. The motion parameter adjustment involves the traverse speed and acceleration of the X, Y, and Z axes, and the rotational speed and acceleration of the A and C axes. The compensation strategy adjustment includes the weight allocation for blade profile deviation compensation and the response threshold for chatter suppression. Subsequently, the parameter configuration module converts the adjusted parameters into executable instructions for the machine tool's CNC system. The code and instruction code are arranged according to the machining process sequence to ensure that the adjustment of each parameter is synchronized and coordinated to avoid parameter conflicts. Then, the instruction code is sent to the actuator of the five-axis machine tool. The actuator adjusts the motion state and compensation strategy of the linkage axis in real time according to the instruction. At the same time, the real-time monitoring module continuously collects the adjusted imbalance data, linkage axis motion error data and machining quality data, and compares them with the preset control target to form a closed-loop link of parameter adjustment and data feedback. This link runs through the entire process of impeller blank clamping, roughing, semi-finishing and finishing. Through multiple rounds of dynamic adjustment, the imbalance is ensured to be controlled within the preset range. This step achieves precise matching of machining parameters and compensation strategy through closed-loop control mechanism to ensure the final quality of impeller five-axis machining.

[0026] Preferably, the expression for the five-axis linkage imbalance coupling model is: ,in, This represents the total amount of five-axis linkage imbalance coupling; This refers to the coupling coefficient for linkage imbalance; Let be the velocity of the i-th linkage axis; Let be the angular displacement deviation of the i-th linkage axis; This represents the peak value of the cutting force. This refers to geometric accuracy deviation; Main axis angular velocity; The moment of inertia of the impeller; This represents the dynamic stiffness fluctuation.

[0027] Specifically, the five-axis linkage imbalance coupling model is based on the imbalance generation mechanism of multi-parameter synergy in five-axis machining. It collects imbalance data under different linkage axis motion states and cutting conditions through extensive orthogonal experiments. Multiple regression analysis is used to explore the linear and nonlinear correlations between each parameter and the total imbalance. Since linkage axis motion error is the core cause of imbalance, a product of the motion velocity and angular displacement deviation of each linkage axis is first introduced. Then, considering the superimposed influence of cutting force and geometric accuracy deviation on imbalance, a product term of these two is added. Simultaneously, the spindle angular velocity, impeller moment of inertia, and dynamic stiffness fluctuation jointly determine the dynamic characteristics of imbalance; therefore, a square root term of their product is introduced. The range of values ​​for the three coupling coefficients is determined through experimental data fitting: the first coefficient is between 0.1 and 0.3, the second coefficient is between 0.2 and 0.4, and the third coefficient is between [missing value]. Between 0.05 and 0.15, the speed of the linkage axis is selected as 30% to 80% of the rated speed of the machine tool. The angular displacement deviation is controlled within the allowable machining error range of 0.001 to 0.005. The peak cutting force is determined based on the cutting performance of the tool material and impeller material and is in the range of 1000 to 5000. The geometric accuracy deviation does not exceed the maximum deviation value required by the design of 0.01. The spindle angular velocity corresponds to the actual rotational speed of 1000 to 8000 during machining. The moment of inertia of the impeller is calculated through a three-dimensional model. The dynamic stiffness fluctuation is obtained from the dynamic characteristic test of the machine tool and is in the range of 5% to 15%. The establishment of this model realizes the quantitative calculation of the total imbalance under multi-parameter coupling. During implementation, the measured values ​​of each parameter are first input, and the total imbalance coupling is obtained through model calculation, which provides a quantitative basis for subsequent compensation and suppression, and ensures accurate control of the five-axis linkage imbalance.

[0028] Preferably, the expression for the blade profile deviation imbalance compensation algorithm is: ,in, This is the compensation amount for blade profile deviation imbalance; This is the compensation coefficient; This refers to the actual blade profile data; Design profile data for the blade; The angle between the surface normal and the compensation direction; The gradient of the actual surface data; This refers to the blade surface curvature factor. The time derivative of the actual surface data; The time derivative of the design surface data; To compensate for the calculation cycle.

[0029] Specifically, the blade profile deviation imbalance compensation algorithm is based on the mapping relationship between profile deviation and imbalance amount. By collecting imbalance data under different profile deviation states, an error backpropagation method is used to establish a correlation model between deviation and compensation amount. Since the difference between the actual profile data and the design data is the core of the deviation, a product term of the difference and the cosine of the normal angle is first introduced. Then, considering the influence of profile gradient and curvature factor on compensation accuracy, a product term is added. Simultaneously, the time change rate of profile data directly affects the compensation timeliness, so its integral term is introduced. Three compensation coefficients are determined through multiple sets of experimental fitting, with values ​​ranging from 0.3 to 0.5, 0.1 to 0.2, and 0.08 to 0.12, respectively. The actual profile... Data is acquired through 3D scanning at a density of 10 to 20 sampling points per square millimeter. The design profile data is the theoretical design value. The included normal angle is calculated from the profile normal vector and the compensation direction vector, ranging from 0 to 90 degrees. The profile gradient is solved based on the deviation data matrix. The curvature factor is determined according to the geometric characteristics of the blade profile, ranging from 0.01 to 0.1. The time derivative reflects the rate of change of the profile data with the machining process. The integration period is consistent with the machining cycle of 1 to 5 seconds. The establishment of this algorithm realizes accurate compensation for imbalance caused by profile deviation. During implementation, the input parameter data is used to obtain the compensation amount through algorithm calculation, which is then converted into machine tool motion commands to correct the deviation and effectively reduce the imbalance caused by profile error.

[0030] Preferably, the expression for the processing chatter imbalance suppression algorithm is: ,in, For flutter imbalance suppression force; The inhibition coefficient; The amplitude of the vibration; It is the angular frequency of vibration; For time; The vibration phase angle; For cutting force ； The main axis angular frequency; The damping ratio; The k-th order flutter mode coefficients; This represents the k-th order flutter frequency offset.

[0031] Specifically, the chatter imbalance suppression algorithm is based on the coupling mechanism of chatter and imbalance. It collects vibration, cutting force, and machining parameter data during chatter occurrence and establishes a suppression force calculation model using spectral analysis and phase matching methods. Since vibration amplitude and phase directly determine chatter intensity, a product term of vibration amplitude and phase functions is first introduced. Then, considering the synergistic effect of cutting force, spindle angular frequency, and damping ratio, a ratio-related term is added. Simultaneously, since different chatter modes contribute differently to imbalance, a summation term of modal coefficients and frequency offset is introduced. Three suppression coefficients are determined through experimental data fitting, with values ​​ranging from 0.2 to 0.4, 0.15 to 0.25, and 0.05 to 0.1, respectively. Among the parameters, vibration amplitude and phase are measured using vibration sensors at frequencies ranging from 10 to 50 kHz. The sampling frequency of Hertz was analyzed to obtain an amplitude range of 0.01 to 0.1. The angular frequency corresponds to the actual spindle speed of 1000 to 8000 rpm. The damping ratio was obtained through machine tool dynamic characteristic testing and ranged from 0.05 to 0.2. The cutting force was the real-time value collected by the sensor at a sampling frequency of 5 to 20 kHz. The modal coefficient was determined through modal analysis experiments and ranged from 0.1 to 0.3. The frequency offset was the difference between the actual chatter frequency and the natural frequency, ranging from 10 to 100 Hz. The algorithm was established to achieve dynamic suppression of chatter-induced imbalance. During implementation, the data of each parameter were collected in real time, and the parameter adjustment amount corresponding to the suppression force was obtained through algorithm calculation. The machining parameters such as spindle speed and feed rate were dynamically optimized to weaken the transmission of chatter energy and suppress the expansion of imbalance.

[0032] Preferably, the simulation calculation of the five-axis machining imbalance diagnosis simulation system is expressed as follows: ,in, This is the value of the imbalance diagnosis result; Diagnostic coefficient; The simulation weight for the j-th processing parameter; This represents the actual measured value of the j-th processing parameter; This represents the total number of processing parameters. This is the error redundancy. This is the sensor sensitivity factor.

[0033] Specifically, the simulation calculation of the five-axis machining imbalance diagnosis simulation system is based on the collaborative diagnosis logic of multiple parameters. By integrating the output data of the coupling model, compensation algorithm, and suppression algorithm, a diagnostic result calculation model is established using weighted fusion and probabilistic statistical methods. Since the synergistic effect of the total coupling amount, compensation amount, and suppression force directly determines the imbalance state, a ratio-related term of the three is first introduced. Then, considering the influence of the weight of each machining parameter and the actual measured value on the diagnostic accuracy, a product term of the two is added. At the same time, the error redundancy and sensor sensitivity affect the diagnostic accuracy, so a logarithmic term of their product is introduced. Three diagnostic coefficients are determined through fitting multiple sets of simulation experiments, with values ​​between 0.4 and 0.6, 0.2 and 0.3, and 0.1 and 0.15, respectively. The weights of the machining parameters are determined according to the effect of each parameter on the imbalance. The impact level was determined to be in the range of 0.05 to 0.2. The actual measured values ​​were real-time collected parameter data. The total number of parameters included 30 to 50 key parameters such as spindle, tool, and linkage axis. The error redundancy was the maximum allowable error range of 0.005 to 0.02. The sensor sensitivity was the inherent characteristic parameter of the sensor in the range of 0.1 to 0.5. The establishment of this model realized the comprehensive diagnosis and accurate prediction of the imbalance state. During implementation, the output data of each link was first imported into the simulation system in the prescribed format. Combined with the built virtual machining scene model, the simulation step size of 0.001 seconds and the number of iterations of 1000 to 5000 were set. The model calculation output the imbalance state level and parameter optimization suggestions, providing a scientific basis for subsequent actual adjustments and improving the pertinence of the control strategy.

[0034] Preferably, the comprehensive parameter calculation for the impeller five-axis machining imbalance control is expressed as follows: ,in, The output value is controlled by the unbalance quantity. For control coefficients; The stiffness coefficient of a five-axis machine tool; The impeller's rotating mass; Let be the adjustment factor for the p-th control loop; Let be the response time of the p-th control loop; To control the total number of steps.

[0035] Specifically, the comprehensive parameter calculation for the unbalance control of the five-axis impeller machining is based on the collaborative logic of the entire process control. By integrating the core outputs of the coupling model, compensation algorithm, suppression algorithm, and simulation diagnosis, a multi-factor weighted summation method is used to establish the control output calculation model. Since the total coupling quantity is the fundamental source of the unbalance, its weighting term is introduced first. Then, considering the synergistic correction effect of the compensation quantity and the suppression force, a weighting term for their product is added. Simultaneously, the machine tool stiffness coefficient and the impeller rotational mass affect the control limit, so a weighting term for the square root of their product is introduced. The adjustment factors and response times of each control link determine the control efficiency, so a weighting term for their product summation is added. Four control coefficients are determined through fitting multiple sets of closed-loop control experiments, with values ​​ranging from 0.2 to 0.4, 0.15 to 0.25, and 0.1, respectively. The machine tool stiffness coefficient, obtained through machine tool performance testing, is between 1000 and 5000, ranging from 0.2 to 0.05 to 0.1. The impeller rotational mass, calculated through a three-dimensional model, is between 1 and 10. The control adjustment factor is determined according to the control priority of each link, ranging from 0.1 to 0.3. The response time is between 0.1 and 1 second from data input to command output for each link. The total number of control links includes 6 to 8 core links such as parameter acquisition, model analysis, and compensation calculation. The establishment of this model realizes the comprehensive quantitative output of unbalance control. During implementation, the input of various parameter data is used to obtain the control output value through model calculation. Based on this, the machining parameters and compensation strategies are adjusted to achieve synergistic optimization of parameters in each link, ensuring the accuracy and stability of unbalance control.

[0036] Preferred, such as Figure 2 As shown, step S3 includes the following sub-steps: S31, collecting actual profile data of each blade of the impeller using a 3D scanning device, registering the collected data with the design data in the same coordinate system, extracting the profile deviation value of each sampling point, and forming a deviation data matrix; S32, based on the deviation data matrix, filtering out sampling points that exceed a preset deviation threshold, analyzing the correlation between the deviation distribution characteristics and the blade spatial position, and determining the deviation concentration area; S33, inputting the deviation data into the blade profile deviation imbalance compensation algorithm, combining the imbalance influence factor output by the five-axis linkage imbalance coupling model, and calculating the compensation amount corresponding to each deviation sampling point; S34, generating motion adjustment commands that can be recognized by the five-axis machine tool according to the distribution law of the compensation amount, and offsetting the imbalance caused by the profile deviation through the micro-displacement adjustment of the linkage axis.

[0037] Specifically, step S3 utilizes four sub-steps to compensate for blade profile deviation imbalances, with a progressive implementation process and precisely controllable parameters. Step S31 involves comprehensive data acquisition from the impeller during processing using a high-precision 3D laser scanning device. The sampling frequency of the scanning device is set to 5000 to 10000 times per second, with a sampling density controlled at 10 to 20 sampling points per square millimeter, ensuring coverage of all key curved surface areas of the blade. After acquisition, the actual profile data and design data are imported into the same processing coordinate system for registration, with registration accuracy controlled within 0.001. The profile deviation value of each sampling point is extracted through point-by-point comparison, and the data is categorized and organized according to blade sequence number and sampling point location to form a deviation data matrix. Step S32, based on the generated deviation data matrix, sets a deviation threshold in the range of 0.005 to 0.01, filters out abnormal sampling points exceeding this threshold, and uses spatial clustering analysis to group these abnormal sampling points. The distribution density of deviation values ​​in each group is analyzed in relation to the spatial position of the blade, clarifying the blade regions with concentrated deviations and the trend of deviation changes, providing target areas for subsequent compensation. S33 inputs the filtered deviation data matrix into the blade profile deviation imbalance compensation algorithm, and simultaneously imports the imbalance influence factor output by the five-axis linkage imbalance coupling model. The algorithm iterates every 0.1 seconds, combining parameters such as the angle between the profile normal and the compensation direction, and the profile curvature factor, to calculate the compensation amount corresponding to each deviation sampling point one by one, with the calculation accuracy of the compensation amount controlled within 0.0001. S34 generates recognizable linkage axis motion adjustment commands according to the motion control protocol of the five-axis machine tool based on the calculated compensation amount distribution law. The commands include small movement distances of the X, Y, and Z axes and small rotation angles of the A and C axes. These commands are sent to each actuator through the machine tool CNC system to achieve synchronous small-scale adjustment of the linkage axes, accurately offsetting the imbalance caused by the profile deviation. These four sub-steps form a complete link from data acquisition to command execution, ensuring the accuracy and pertinence of the compensation process.

[0038] Preferred, such as Figure 3 As shown, step S4 includes the following sub-steps: S41, deploying vibration sensors and cutting force sensors at the spindle and tool holder of the five-axis machine tool to collect vibration acceleration signals and cutting force signals during the machining process in real time, converting them into digital signals and transmitting them to the data processing module; S42, filtering the collected digital signals to remove environmental interference signals, extracting characteristic frequency components related to chatter, and determining preliminary judgment indicators for chatter occurrence; S43, inputting the characteristic frequency components and judgment indicators into the machining chatter imbalance suppression algorithm, and calculating the parameter adjustment amount required for chatter suppression by combining the current spindle speed and cutting parameters; S44, dynamically adjusting the spindle speed, feed rate, and depth of cut according to the parameter adjustment amount, weakening chatter energy through parameter optimization, and suppressing further expansion of imbalance.

[0039] Specifically, step S4 utilizes four sub-steps to achieve real-time and dynamic chatter suppression during machining, achieving chatter suppression through multi-device collaboration and dynamic parameter optimization. S41 deploys piezoelectric vibration sensors and resistance strain gauge cutting force sensors at the spindle end, tool holder connection, and table side of the five-axis machine tool. The sampling frequency of the vibration sensors is set to 10 to 50 kHz, and the sampling frequency of the cutting force sensors is set to 5 to 20 kHz. The sensor measurement accuracy is controlled within 0.1% of full scale. Real-time acquisition of vibration acceleration signals along the radial and axial directions of the spindle and cutting force signals at the tool cutting edge is performed during machining. The acquired analog signals are amplified and filtered by a signal conditioner, then converted into digital signals by a high-speed data acquisition card and transmitted to the central processing unit for storage and analysis. The S42 central processing unit employs a digital filtering algorithm to denoise the acquired digital signals, removing low-frequency interference signals below 10 Hz and high-frequency noise signals above 1000 Hz. It then performs spectral analysis on the filtered signals using a Fast Fourier Transform to extract characteristic frequency components related to chatter within the 10 to 1000 Hz range. This determines key indicators such as the amplitude, phase, and frequency of chatter occurrence, establishing a chatter state characteristic database. The S43 unit inputs the extracted characteristic frequency components and indicators into a chatter imbalance suppression algorithm, simultaneously importing current spindle speed, feed rate, and depth of cut parameters. The algorithm, based on multi-objective optimization logic, calculates the parameter adjustments required for chatter suppression. These adjustments are determined based on spindle speed increases / decreases of 5% to 15%, feed rate correction ratios of 10% to 20%, and depth of cut adjustments of 0.01 to 0.05. The S44 sends parameter adjustment commands to each actuator in real time through the machine tool's CNC system, dynamically adjusting the spindle speed, feed rate, and depth of cut. During the adjustment process, it maintains the coordinated matching of each parameter to avoid new imbalances caused by sudden changes in a single parameter. Through continuous signal acquisition and parameter adjustment, it forms a closed-loop control for chatter suppression, effectively reducing chatter energy transmission.

[0040] Preferred, such as Figure 4As shown, S5 includes the following sub-steps: S51, integrating the compensated parameters output from S3, the adjusted machining parameters output from S4, and the original machining information set to form a simulation input dataset, and importing it into the five-axis machining imbalance diagnosis simulation system according to a preset format; S52, building a virtual machine tool model, impeller model, and machining environment model consistent with the actual machining scenario in the simulation system, setting the simulation step size and iteration number, and starting the imbalance simulation calculation; S53, monitoring the trend of imbalance of the virtual impeller, the motion error of each linkage axis, and the chatter suppression effect in real time during the simulation, and recording the calibration simulation data and intermediate calculation results; S54, based on the simulation data and intermediate results, using a feature extraction algorithm to analyze the main causes of imbalance, generating a diagnostic report including the imbalance state level and optimization parameter suggestions, and transmitting it to the subsequent control link.

[0041] Specifically, step S5 utilizes four sub-steps to construct the entire process of five-axis machining imbalance diagnosis simulation, achieving accurate prediction of imbalance states through virtual simulation. S51 first integrates the compensated blade profile parameters output from S3, the adjusted machining parameters output from S4, and the raw information set collected from S1. This data is standardized according to the simulation system's data interface requirements, converting the parameter format to ASCII code, forming a simulation input dataset including categories such as spindle operation, tool cutting, blade profile, and linkage axis movement. This dataset is then imported into the five-axis machining imbalance diagnosis simulation system via a data transmission protocol. In step S52, the simulation system uses 3D modeling software to build a virtual model consistent with the actual machining scenario. The five-axis machine tool structure model accurately reproduces the motion constraints and stiffness characteristics of each linkage axis. The impeller 3D solid model is constructed at a 1:1 scale according to the design drawings, including detailed geometric features and material properties of the blades. The tool model matches the type and size parameters of the tools used in actual machining. The machining environment model simulates environmental conditions such as temperature and humidity during actual machining. After the model is built, mesh generation is performed, with the mesh cell size controlled within the range of 0.1 to 0.5. S53 sets the simulation parameters, setting the simulation step size to 0.001 seconds, the number of iterations to 1000-5000, and the simulation accuracy level to high. It then starts the imbalance simulation. During the simulation, the system calculates the changes in the virtual impeller's imbalance, the motion errors of each linkage shaft, and the chatter suppression effect in real time according to the set step size. Key data is recorded every 100 iterations, forming a simulation process data log. S54, based on the simulation process data log, uses a feature extraction algorithm to analyze the trend, peak value, and fluctuation frequency of the imbalance, identifying the main causes of the imbalance. The imbalance is classified into three levels: slight, moderate, and severe, generating a diagnostic report including the imbalance status level, optimization suggestions for each parameter, adjustment direction, and implementation priority. This diagnostic report is transmitted to subsequent control links through a data interface, providing a scientific basis for adjusting actual processing parameters.

[0042] like Figure 5 As shown, a method for controlling the unbalance in five-axis machining of impellers is implemented through different units, including: a five-axis linkage parameter acquisition and preprocessing unit, a five-axis linkage unbalance coupling model construction and analysis unit, a blade profile deviation unbalance compensation algorithm calculation unit, a machining chatter unbalance suppression algorithm execution unit, a five-axis machining unbalance diagnosis simulation system operation unit, and an unbalance closed-loop control and adjustment unit. The five-axis linkage parameter acquisition and preprocessing unit and the five-axis linkage imbalance coupling model construction and analysis unit are connected bidirectionally to transmit the acquired raw parameters to the model construction unit for imbalance factor analysis. The five-axis linkage imbalance coupling model construction and analysis unit transmits data unidirectionally to the blade profile deviation imbalance compensation algorithm calculation unit and the machining chatter imbalance suppression algorithm execution unit, respectively, and outputs imbalance influence factors. The blade profile deviation imbalance compensation algorithm calculation unit and the machining chatter imbalance suppression algorithm execution unit both interact bidirectionally with the five-axis machining imbalance diagnosis simulation system operation unit, transmitting corrected parameters and adjustment amounts and receiving simulation feedback. The five-axis machining imbalance diagnosis simulation system operation unit is connected unidirectionally to the imbalance quantity closed-loop control and adjustment unit, and outputs diagnostic results and optimization suggestions. The imbalance quantity closed-loop control and adjustment unit establishes a feedback link with the five-axis linkage parameter acquisition and preprocessing unit in reverse to dynamically adjust the machining parameters. Each unit shares parameters in real time and works collaboratively through the data bus to jointly complete the control of the impeller five-axis machining imbalance.

[0043] The formula in this invention can integrate different scalar and vector parameters for unified calculation. Its core lies in eliminating parameter attribute differences through standardization, coupling relationship modeling, and dimensional adaptation mechanisms, ensuring the rationality of the computational logic and the validity of the results. First, for vector parameters (such as the motion velocity of the linkage shaft, the surface normal vector, and vibration acceleration signals), their magnitude, projection components, or direction coefficients are extracted and converted into scalar form. For example, the motion velocity vector of the five-axis linkage shaft is projected onto the main axis direction of the machining coordinate system to obtain a scalar value characterizing the motion intensity, thus providing a basis for the coordinated calculation of vector parameters with scalar parameters (such as the main axis angular velocity, cutting depth, and stiffness coefficient). Second, based on the imbalance coupling mechanism of five-axis impeller machining, the influence path and weight of different types of parameters on the imbalance are clarified. For example, the cutting force signal with vector attributes is correlated with the dynamic stiffness fluctuation of scalar attributes, and the results are determined through experimental fitting. The coupling coefficient between the two allows the effects of different attribute parameters to be quantified and superimposed. Finally, through dimensional normalization, each parameter is transformed into a dimensionless coefficient or a physical quantity of uniform magnitude. For example, the vector difference of blade profile deviation and the curvature factor in scalar form are both normalized to the 0-1 interval. Then, combined with the law of imbalance influence, a mathematical relationship is constructed to ensure that scalars (such as damping ratio and moment of inertia) and vector-derived scalars (such as vibration amplitude and displacement deviation magnitude) can achieve synergistic effects in the formula through weighting, multiplication, and summation. This accurately quantifies the total imbalance, compensation, and control output value under the coupling of multiple factors, and ultimately achieves the organic integration and efficient calculation of different attribute parameters.

[0044] A method for controlling the imbalance in five-axis impeller machining is proposed. Through a specially designed model, it deeply analyzes the coupling relationship between the motion of the five-axis linkage, blade profile deviation, and machining chatter. Instead of adjusting isolated imbalance causes, it achieves systematic control over the influencing factors of imbalance, effectively solving the problem of one-sided control caused by neglecting the superimposed effects of multiple factors in existing technologies. Simultaneously, it integrates key aspects such as parameter acquisition, model analysis, dynamic compensation, chatter suppression, simulation diagnosis, and parameter optimization to form a real-time interactive feedback mechanism. This ensures that the control strategy can dynamically respond to changes in imbalance factors during machining, completely overcoming the shortcomings of existing technologies in terms of control lag and insufficient adaptability.

[0045] In the blade profile deviation correction stage, this method achieves precise cancellation of imbalance caused by deviation by accurately collecting data, screening deviation areas, calculating targeted compensation amounts, and converting them into machine tool motion commands. In the process of suppressing machining chatter, it effectively weakens the amplification effect of chatter on imbalance by acquiring signals in real time, extracting feature components, calculating adjustment amounts, and dynamically optimizing machining parameters. With the help of virtual verification and diagnostic analysis of the simulation system, imbalance risks are predicted in advance and optimization suggestions are output, providing a scientific basis for subsequent parameter adjustments. Finally, the dynamic adaptation of machining parameters is achieved through a closed-loop control link, comprehensively improving the stability and accuracy of the unbalance control of the five-axis machining of impellers, and providing strong support for the efficient machining of high-precision impellers.

[0046] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0047] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the imbalance in five-axis machining of an impeller, characterized in that, Includes the following steps: S1, acquire spindle speed, tool cutting parameters, blade profile design data, five-axis linkage axis motion parameters and machining coordinate system reference data during the five-axis machining of the impeller, and establish a set of original information including each calibration parameter; S2. Based on the original information set, a five-axis linkage imbalance coupling model is constructed. By analyzing the correlation between the motion error of each linkage axis and the imbalance amount, the distribution of imbalance influencing factors is determined. S3 employs a blade profile deviation imbalance compensation algorithm, which combines the deviation values ​​between actual measured data and design data of the blade profile to make preliminary corrections to the imbalance. S4 utilizes a machining chatter imbalance suppression algorithm to collect vibration and cutting force signals in real time during the machining process and dynamically adjust cutting parameters to weaken the imbalance amplification effect caused by chatter. S5, start the five-axis machining imbalance diagnosis simulation system, input the corrected parameter data to perform simulation calculations, and output the imbalance state diagnosis results and optimization directions; S6, based on the simulation diagnosis results, adjusts the five-axis machining motion parameters and compensation strategy to control the unbalance of the impeller five-axis machining. All steps form a closed-loop control link through the real-time parameter interaction and feedback mechanism, which runs through the entire impeller machining process.

2. The method for controlling the imbalance in five-axis impeller machining according to claim 1, characterized in that, The expression for the five-axis linkage imbalance coupling model is: ,in, This represents the total amount of five-axis linkage imbalance coupling; This refers to the coupling coefficient for linkage imbalance; Let be the velocity of the i-th linkage axis; Let be the angular displacement deviation of the i-th linkage axis; This represents the peak value of the cutting force. This refers to geometric accuracy deviation; Main axis angular velocity; The moment of inertia of the impeller; This represents the dynamic stiffness fluctuation.

3. The method for controlling the imbalance in five-axis impeller machining according to claim 1, characterized in that, The expression for the blade profile deviation imbalance compensation algorithm is as follows: ,in, This is the compensation amount for blade profile deviation imbalance; This is the compensation coefficient; This refers to the actual blade profile data; Design profile data for the blade; The angle between the surface normal and the compensation direction; The gradient of the actual surface data; This refers to the blade surface curvature factor. The time derivative of the actual surface data; The time derivative of the design surface data; To compensate for the calculation cycle.

4. The method for controlling the imbalance in five-axis impeller machining according to claim 1, characterized in that, The expression for the processing chatter imbalance suppression algorithm is: ,in, For flutter imbalance suppression force; The inhibition coefficient; The amplitude of the vibration; It is the angular frequency of vibration; For time; The vibration phase angle; For cutting force ； The main axis angular frequency; The damping ratio; The k-th order flutter mode coefficients; This represents the k-th order flutter frequency offset.

5. The method for controlling the imbalance in five-axis impeller machining according to claim 1, characterized in that, The simulation calculation of the five-axis machining imbalance diagnosis simulation system is expressed as follows: ,in, This is the value of the imbalance diagnosis result; Diagnostic coefficient; The simulation weight for the j-th processing parameter; This represents the actual measured value of the j-th processing parameter; This represents the total number of processing parameters. This is the error redundancy. This is the sensor sensitivity factor.

6. The method for controlling the imbalance in five-axis machining of an impeller according to claim 1, characterized in that, The comprehensive parameter calculation for the unbalance control of the five-axis machining of the impeller is expressed as follows: ,in, The output value is controlled by the unbalance quantity. For control coefficients; The stiffness coefficient of a five-axis machine tool; The impeller's rotating mass; Let be the adjustment factor for the p-th control loop; Let be the response time of the p-th control loop; To control the total number of steps.

7. The method for controlling the imbalance in five-axis impeller machining according to claim 1, characterized in that, S3 includes the following steps: S31. The actual profile data of each blade of the impeller is collected by a three-dimensional scanning device. The collected data is registered with the design data in the same coordinate system, and the profile deviation value of each sampling point is extracted to form a deviation data matrix. S32, Based on the deviation data matrix, filter out sampling points that exceed the preset deviation threshold, analyze the correlation between deviation distribution characteristics and blade spatial position, and determine the deviation concentration area; S33, input the deviation data into the blade profile deviation imbalance compensation algorithm, combine it with the imbalance influence factor output by the five-axis linkage imbalance coupling model, and calculate the compensation amount corresponding to each deviation sampling point; S34 generates motion adjustment commands that can be recognized by the five-axis machine tool according to the distribution law of compensation amount, and offsets the imbalance caused by surface deviation through the micro displacement adjustment of the linkage axis.

8. The method for controlling the imbalance in five-axis machining of an impeller according to claim 1, characterized in that, S4 includes the following steps: S41, deploy vibration sensors and cutting force sensors at the spindle and tool holder of the five-axis machine tool to collect vibration acceleration signals and cutting force signals in real time during the machining process, convert them into digital signals and transmit them to the data processing module; S42, filter the acquired digital signal to remove environmental interference signals, extract characteristic frequency components related to flutter, and determine preliminary indicators for the occurrence of flutter; S43, input the characteristic frequency components and judgment index into the machining chatter imbalance suppression algorithm, and calculate the parameter adjustment amount required for chatter suppression by combining the current spindle speed and cutting parameters; S44 dynamically adjusts the spindle speed, feed rate, and depth of cut based on parameter adjustment amounts. By optimizing parameters, chatter energy is reduced, and the further expansion of misalignment is suppressed.

9. The method for controlling the imbalance in five-axis machining of an impeller according to claim 1, characterized in that, S5 includes the following steps: S51 integrates the compensated parameters output by S3, the adjusted machining parameters output by S4, and the original machining information set to form a simulation input dataset, which is then imported into the five-axis machining imbalance diagnosis simulation system according to a preset format. S52, build a virtual machine tool model, impeller model and processing environment model that are consistent with the actual processing scenario in the simulation system, set the simulation step size and number of iterations, and start the imbalance simulation calculation; S53: During the simulation, the unbalance change trend of the virtual impeller, the motion error of each linkage shaft and the chatter suppression effect are monitored in real time, and the calibration simulation data and intermediate calculation results are recorded. S54, based on simulation data and intermediate results, uses a feature extraction algorithm to analyze the main causes of imbalance, generates a diagnostic report including the imbalance state level and optimization parameter suggestions, and transmits it to the subsequent control link.

10. A method for controlling the imbalance in five-axis machining of an impeller according to any one of claims 1-9, characterized in that, This method is implemented through different units, including: a five-axis linkage parameter acquisition and preprocessing unit, a five-axis linkage imbalance coupling model construction and analysis unit, a blade profile deviation imbalance compensation algorithm calculation unit, a machining chatter imbalance suppression algorithm execution unit, a five-axis machining imbalance diagnosis simulation system operation unit, and an imbalance closed-loop control and adjustment unit. The five-axis linkage parameter acquisition and preprocessing unit and the five-axis linkage imbalance coupling model construction and analysis unit are connected bidirectionally to transmit the acquired raw parameters to the model construction unit for imbalance factor analysis. The five-axis linkage imbalance coupling model construction and analysis unit transmits data unidirectionally to the blade profile deviation imbalance compensation algorithm calculation unit and the machining chatter imbalance suppression algorithm execution unit, respectively, and outputs imbalance influence factors. The blade profile deviation imbalance compensation algorithm calculation unit and the machining chatter imbalance suppression algorithm execution unit both interact bidirectionally with the five-axis machining imbalance diagnosis simulation system operation unit, transmitting corrected parameters and adjustment amounts and receiving simulation feedback. The five-axis machining imbalance diagnosis simulation system operation unit is connected unidirectionally to the imbalance quantity closed-loop control and adjustment unit, and outputs diagnostic results and optimization suggestions. The imbalance quantity closed-loop control and adjustment unit establishes a feedback link with the five-axis linkage parameter acquisition and preprocessing unit in reverse to dynamically adjust the machining parameters. Each unit shares parameters in real time and works collaboratively through the data bus to jointly complete the control of the impeller five-axis machining imbalance.

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