CNC machine tool motion control system

Abstract

The invention relates to the technical field of motion control, in particular to a CNC machine tool motion control system which comprises a cutting force feedback monitoring module, a multi-axis load coordination module, a spindle vibration suppression module and a dynamic path optimization module. According to the method, the cutting force fluctuation of each axle can be accurately mastered by monitoring the axle cutting force in real time and analyzing the load change, the load and the feeding speed of the axle are adjusted according to real-time feedback, machining errors caused by uneven loads are reduced, and the vibration amplitude and frequency can be calculated and the rotating speed of the main shaft can be adjusted by monitoring the vibration data of the main shaft in real time. The resonance phenomenon is effectively restrained, the machining stability is improved, meanwhile, dynamic path optimization is conducted, the dynamic response problem caused by improper path setting is avoided by evaluating the dynamic response of a machine tool and adjusting the machining path, the cutting direction and the feeding speed in real time, the machining precision and stability are remarkably improved, and the machining efficiency is improved. And the complex machining environment adaptability of the machine tool is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motion control, and in particular to a CNC machine tool motion control system. Background Art

[0002] The field of motion control technology involves the technology of controlling the motion trajectory, speed and position of each moving part in the mechanical system. It mainly focuses on how to accurately adjust the movement of mechanical equipment through the synergy of control hardware and software to achieve efficient, precise and stable operation. It is widely used in automation, manufacturing, robotics, aerospace, medical and other fields. The core lies in how to achieve precise adjustment of the dynamic behavior of the equipment through motor drive, sensor feedback and control algorithm. With the advancement of technology, intelligent and high-precision motion control systems have gradually become the core competitiveness of modern manufacturing.

[0003] Among them, CNC machine tool motion control system is an important application in the field of motion control technology, which aims to achieve complex processing tasks by accurately controlling the various moving parts of CNC machine tools (such as axes, tools, etc.). Its main uses include automated processing, precision manufacturing, mass production, etc., which can greatly improve processing accuracy and production efficiency. CNC machine tool motion control system ensures that the movement of each axis of the machine tool reaches the predetermined trajectory and position by integrating sensors, drive devices and control software. It is widely used in many industries with high precision requirements such as metal processing, mold manufacturing, and aviation parts processing.

[0004] Existing CNC machine tool motion control systems rely on preset loads and path control, and lack the ability to respond quickly to real-time working condition changes. Load distribution is usually based on static settings, and cannot promptly respond to uneven loads or spindle vibration problems that occur during processing. Therefore, load imbalance and vibration suppression can only rely on manual adjustment, which can easily cause processing errors and quality fluctuations. Path planning fails to take into account the dynamic response characteristics of the machine tool, resulting in the inability to flexibly respond to the needs of different processing stages during multi-process processing, increasing the risk of vibration and resonance. The static setting method limits the adaptability of the system, resulting in insufficient accuracy and stability during the processing process, thereby reducing production efficiency and increasing equipment failure rates. In particular, system deviations are prone to occur during long-term high-load operation, affecting processing quality and shortening the service life of the machine tool. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a CNC machine tool motion control system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A CNC machine tool motion control system comprises:

[0007] The cutting force feedback monitoring module installs a cutting force sensor at the axle position of the CNC machine tool to obtain the cutting force signal of the axle in real time, record the load change of each axle, analyze the cutting force fluctuation of the axle, and obtain the cutting force feedback data of the machine tool;

[0008] The multi-axis load coordination module calculates the load of the axle in real time based on the cutting force feedback data of the machine tool, compares the axle load with the set load value, determines the load imbalance according to the comparison result, adjusts the feed speed of each axis according to the current load ratio of the axle, performs load balancing, and generates a load distribution result;

[0009] The spindle vibration suppression module monitors the vibration data of the machine tool spindle in real time through a vibration sensor according to the load distribution result, calculates the vibration amplitude of the machine tool spindle, adjusts the machine tool spindle speed in real time according to the vibration amplitude calculation result, adjusts the cutting force distribution according to the vibration characteristics, adjusts the spindle operation state to suppress the resonance phenomenon, and generates spindle vibration adjustment data;

[0010] The dynamic path optimization module performs dynamic modal analysis based on the spindle vibration adjustment data, evaluates the dynamic response of the machine tool in the differential processing stage, adjusts the processing path of each process in real time according to the evaluation results, optimizes the cutting direction and feed speed, and obtains dynamic path planning data.

[0011] As a further solution of the present invention, the step of obtaining the cutting force feedback data of the machine tool is:

[0012] Select cutting force sensors compatible with CNC machine tool axles, install them at key locations on the axles, conduct initial functional verification of the cutting force sensors, and output the sensor configuration after the test is completed;

[0013] According to the sensor configuration completed in the test, the standard operation process of the machine tool is run to collect the axle cutting force data in real time, perform data cleaning and formatting, and generate processed axle cutting force data;

[0014] According to the processed axle cutting force data, the formula is used:

[0015]

[0016] Calculated standard axle cutting force data F adjusted , and obtain the machine tool cutting force feedback data, where F raw Represents the axle cutting force data after processing.

[0017] As a further solution of the present invention, the step of comparing the axle load with the set load value is:

[0018] Calling the cutting force feedback data of the machine tool, recording the cutting force of each axle during the machining process, and forming a real-time cutting force data set;

[0019] Based on the real-time cutting force data set, the formula is adopted:

[0020]

[0021] Calculate the actual load ratio L for each axle i , get the axle load ratio data, where F i Represents the real-time cutting force data value, F set Set the load value for the target axle;

[0022] The axle load ratio data is analyzed, the actual load of each axle is compared with the set load, overloaded and underloaded axles are identified and marked, and an axle load status report is generated.

[0023] As a further solution of the present invention, the steps of obtaining the load distribution result are:

[0024] Analyze the axle load status report, mark the axles with uneven load, record the current load ratio of the corresponding axles, analyze the deviation from the standard load value, and generate uneven load marking data;

[0025] According to the uneven load marking data, the feed speed of the marked axle is adjusted using the formula:

[0026]

[0027] Calculate the adjusted feed rate V adj , generate the adjusted feed speed data, where V orig is the original feed speed, ΔL is the difference between the load ratio and the set load, L set is the set load standard;

[0028] The adjusted feed speed data is applied to perform load distribution adjustment on all axles, dynamically balance the workload of the axles, and generate a load distribution result.

[0029] As a further solution of the present invention, the steps for calculating the vibration amplitude of the machine tool spindle are:

[0030] In combination with the load distribution result, a vibration sensor is installed at the position of the machine tool spindle to capture the spindle vibration signal in real time, extract time series data, and obtain time series vibration data;

[0031] Performing spectrum analysis on the time series vibration data, extracting key frequency components, analyzing energy distribution under difference frequencies, and obtaining spectrum energy distribution data;

[0032] According to the spectrum energy distribution data, the formula is adopted:

[0033]

[0034] Calculate the root mean square amplitude A of the vibration and generate the vibration amplitude data of the machine tool spindle, where s i Represents a single vibration sample value, s represents the average of the sample values, and N represents the total number of samples.

[0035] As a further solution of the present invention, the step of acquiring the spindle vibration adjustment data is:

[0036] According to the vibration amplitude data of the machine tool spindle, the current mechanical operation state is evaluated, and whether the spindle speed needs to be adjusted is analyzed in real time, and speed adjustment decision data is generated through preset judgment criteria;

[0037] According to the speed adjustment decision data, the spindle speed is adjusted, the cutting force distribution is adjusted synchronously, the current vibration characteristics are matched, and the adjusted spindle operating parameters are obtained;

[0038] According to the adjusted spindle operating parameters, the formula is used:

[0039]

[0040] Calculate the adjusted spindle responsiveness value R, suppress the resonance phenomenon, and generate the spindle vibration adjustment data. Δv is the change in vibration amplitude, which represents the difference in vibration amplitude measured before and after, and Δt is the time interval for the adjustment operation.

[0041] As a further solution of the present invention, the evaluation steps of the dynamic response of the machine tool during the differential processing stage are:

[0042] According to the spindle vibration adjustment data, Fourier transform is performed to convert the time domain signal into a frequency domain signal to obtain a spectrum distribution result;

[0043] Using the frequency spectrum distribution result, a modal analysis is performed to analyze the natural frequency and vibration mode of the machine tool spindle to obtain a modal analysis result;

[0044] Based on the modal analysis results, combined with the operating parameters of the processing stage, the dynamic response evaluation is carried out using the formula:

[0045]

[0046] The dynamic response value DRE(f,m,p) of the machine tool in the differential processing stage is calculated to obtain the dynamic response evaluation data, where f represents the frequency, m represents the vibration mode value, and p represents the parameter value of the processing stage.

[0047] As a further solution of the present invention, the step of acquiring the dynamic path planning data is:

[0048] Analyze the dynamic response evaluation data, identify the vibration sensitive area of ​​the machine tool in the different processing stages, and generate the vibration sensitive area analysis result by combining the natural frequency of the machine tool and the vibration mode during actual operation;

[0049] According to the analysis result of the vibration sensitive area, the machining path is adjusted, the cutting direction and the feed speed are changed, and an optimized machining path is obtained;

[0050] Based on the optimized machining path, the operating parameters of the machine tool are adjusted by dynamic planning, the cutting depth and speed are optimized, and dynamic path planning data is generated.

[0051] Compared with the prior art, the advantages and positive effects of the present invention are:

[0052] In the present invention, by real-time monitoring of the cutting force of the axle and analyzing the load changes, the cutting force fluctuation of each axle can be accurately grasped, and the load and feed speed of the axle can be adjusted according to real-time feedback to reduce the processing error caused by uneven load. By real-time monitoring of the spindle vibration data, the vibration amplitude and frequency can be calculated and the spindle speed can be adjusted to effectively suppress the resonance phenomenon and improve the processing stability. At the same time, dynamic path optimization is performed. By evaluating the dynamic response of the machine tool, the processing path, cutting direction and feed speed are adjusted in real time to avoid dynamic response problems caused by improper path settings, significantly improve the processing accuracy and stability, and enhance the ability of the machine tool to adapt to complex processing environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a system flow chart of the present invention;

[0054] Figure 2 It is a flow chart for obtaining the cutting force feedback data of the machine tool of the present invention;

[0055] Figure 3 A flow chart showing the comparison between the axle load and the set load value of the present invention;

[0056] Figure 4 A flow chart for obtaining the load distribution result of the present invention;

[0057] Figure 5 The figure is a flow chart for calculating the vibration amplitude of the machine tool spindle of the present invention;

[0058] Figure 6 A flowchart for obtaining spindle vibration adjustment data of the present invention;

[0059] Figure 7 A flow chart for evaluating the dynamic response of the machine tool during the differential processing stage of the present invention;

[0060] Figure 8 This is a flow chart for obtaining dynamic path planning data of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0063] See also Figure 1 , a CNC machine tool motion control system comprises:

[0064] The cutting force feedback monitoring module installs a cutting force sensor at the axle position of the CNC machine tool to obtain the cutting force signal of the axle in real time, record the load change of each axle, analyze the cutting force fluctuation of the axle, and obtain the cutting force feedback data of the machine tool;

[0065] The multi-axis load coordination module calculates the load of the axle in real time based on the machine tool cutting force feedback data, compares the axle load with the set load value, determines the load imbalance according to the comparison result, adjusts the feed speed of each axis according to the current load ratio of the axle, performs load balancing, and generates load distribution results;

[0066] The spindle vibration suppression module monitors the vibration data of the machine tool spindle in real time through the vibration sensor according to the load distribution result, calculates the vibration amplitude of the machine tool spindle, adjusts the machine tool spindle speed in real time according to the vibration amplitude calculation result, adjusts the cutting force distribution according to the vibration characteristics, adjusts the spindle operation state to suppress the resonance phenomenon, and generates spindle vibration adjustment data;

[0067] The dynamic path optimization module performs dynamic modal analysis based on the spindle vibration adjustment data, evaluates the dynamic response of the machine tool during the differential processing stage, adjusts the processing path of each process in real time according to the evaluation results, optimizes the cutting direction and feed speed, and obtains dynamic path planning data.

[0068] The machine tool cutting force feedback data includes the axle cutting force value, axle load change rate and cutting force fluctuation amplitude record; the load distribution results include the axle load distribution record, load imbalance ratio and feed speed adjustment value; the spindle vibration adjustment data includes the spindle vibration amplitude record, spindle vibration frequency and spindle speed adjustment amplitude record; the dynamic path planning data includes the process path adjustment amount, cutting direction adjustment amount and feed speed optimization value.

[0069] See also Figure 2 , the steps for obtaining the machine tool cutting force feedback data are:

[0070] Select cutting force sensors compatible with CNC machine tool axles, install them at key locations on the axles, conduct initial functional verification of the cutting force sensors, and output the sensor configuration after the test is completed;

[0071] The process of selecting a high-precision cutting force sensor that is compatible with the CNC machine tool axle includes determining the sensor model and specifications to ensure its compatibility with the axle interface. During the installation process, it is necessary to ensure that the sensor and the axle are tightly connected without gaps to avoid vibration and offset that affect the accurate collection of data. System-level connection and preliminary functional testing include circuit connection, signal testing and data transmission testing to ensure that the sensor's output signal is stable and meets the preset technical parameters. The installation and preliminary testing of the sensor system are completed to ensure its performance and reliability in actual operation.

[0072] According to the sensor configuration completed by the test, the machine tool standard operating procedures are run to collect the axle cutting force data in real time, perform data cleaning and formatting, and generate processed axle cutting force data;

[0073] The standard operating procedures of CNC machine tools include real-time data capture and preliminary signal processing of sensors. Signal capture should be performed synchronously when the machine tool is running to ensure the real-time and continuity of the data. Preliminary processing includes signal amplification, filtering and digitization to facilitate subsequent data analysis and processing. By running the machine tool for standardized test operations, data can be repeatedly collected under controlled conditions to ensure the consistency and repeatability of the acquired data. Finally, processed axle cutting force data is generated to provide accurate data support.

[0074] According to the processed axle cutting force data, the formula is used:

[0075]

[0076] Calculated standard axle cutting force data F adjusted , and obtain the machine tool cutting force feedback data, where F raw represents the axle cutting force data after processing;

[0077] Fraw represents the original axle cutting force data obtained from the sensor, let F raw =100 Newtons. The sine function sin extracts the periodic change characteristics of the data. The absolute value ensures the positive value during the processing. The square root processing provides suppression of large values ​​and enhances the sensitivity of the formula to high values. The natural logarithm ln emphasizes the sensitivity adjustment of low-value data. First, the sine value is calculated, sin(100), and the absolute value is 0.9848. The square root calculation The result is 10, the numerator is 0.9848, and the denominator is 1+10, which is 11. The fraction calculation is about 0.0895. The natural logarithm part is ln(1+100), which is ln(101), which is about 4.615. Finally, add these two parts together, 0.0895+4.615=4.7045, and the final adjusted cutting force F adjusted It is 4.7045 Newtons; the result shows that under the given initial cutting force conditions, the adjusted cutting force is obtained through nonlinear adjustment and sensitivity analysis. This data can be used to further optimize the cutting parameters of the machine tool to improve the machining accuracy and efficiency.

[0078] See also Figure 3 , the steps for comparing the axle load with the set load value are:

[0079] Call the machine tool cutting force feedback data to record the cutting force of each axle during the machining process to form a real-time cutting force data set;

[0080] The acquired machine tool cutting force feedback data is continuously monitored by high-precision sensors. The specific cutting force of each axle during processing is recorded in real time, including information such as force size, direction, point of application, axle identification and cutting force value. The data is transmitted to the central database via Bluetooth or Wi-Fi for real-time updating. Each piece of data is timestamped to ensure the timeliness and accuracy of the information. Abnormal values, such as sudden increases or decreases in force, are screened out through the data analysis module. The purified data set provides precise basic input for subsequent steps to ensure that the operating load of each axle is within the optimal range of safety and efficiency.

[0081] Based on the real-time cutting force data set, the formula is adopted:

[0082]

[0083] Calculate the actual load ratio L for each axle i , get the axle load ratio data, where F i Represents the real-time cutting force data value, F set Set the load value for the target axle;

[0084] Set F i= 150 Newtons, which is the average value of the real-time cutting force data obtained from the sensor, F set = 200 Newtons, as the standard load setting for the axle. Calculated by the formula This shows that the actual load of the current axle is 75% of the set load. This result shows that the current load condition of the axle is within the safe range, but close to the upper limit, and attention may need to be paid to future load adjustments to avoid overloading.

[0085] Analyze axle load ratio data, compare actual load of each axle with set load, identify and mark overloaded and underloaded axles, and generate axle load status report;

[0086] Based on the obtained axle load ratio data, a data integrity check is first performed to ensure that all axle data is correctly calculated and not lost. The real-time load of each axle is compared with the set standard. The load ratio of each axle is analyzed by a dedicated software module to identify overloaded or underloaded axles. The status of each axle is marked as "normal", "overloaded" or "underloaded" according to its load ratio. During the process, historical load data will be taken into account to identify trends in load changes, thereby providing more dynamic load management. The generated axle load status report provides workshop managers with instant and accurate production line status information.

[0087] See also Figure 4 , the steps to obtain the load distribution results are:

[0088] Analyze the axle load status report, mark the axles with uneven load, record the current load ratio of the corresponding axles, analyze the deviation from the standard load value, and generate uneven load marking data;

[0089] Analyze the axle load status report, identify axles with uneven loads, and mark the current load ratio of these axles and the deviation from the standard load value, providing necessary data input for the calculation of feed speed adjustment. In this process, the data analysis module accurately identifies the axles that need to be adjusted by comparing the real-time load data of the axles with the preset standard load value, thereby ensuring the targetedness and effectiveness of the adjustment measures. Through this analysis, the generated uneven load marking data not only reflects the load condition of each axle, but also indicates the risk points that lead to mechanical failure or reduced efficiency, providing decision support for subsequent adjustment operations.

[0090] According to the uneven load marking data, adjust the feed speed of the marked axle using the formula:

[0091]

[0092] Calculate the adjusted feed rate V adj , generate the adjusted feed speed data, where V origis the original feed speed, ΔL is the difference between the load ratio and the set load, L set is the set load standard;

[0093] V orig is the original feed rate of the axle, which is 200 mm / min, ΔL is the load deviation, assuming that one axle is 10% lower than the predetermined load, i.e. -0.1, L set is the set load ratio, usually 1. Substituting into the formula, we get This result shows that in order to adjust the axle load to the set level, the feed speed needs to be reduced from 200mm / min to 180mm / min. Through this adjustment, the axle overload or underload can be effectively avoided, and the machining accuracy and production efficiency of the machine tool can be improved.

[0094] Apply the adjusted feed speed data to adjust the load distribution of all axles, dynamically balance the workload of the axles, and generate load distribution results;

[0095] Apply the adjusted feed speed data to perform load balancing operations on all axles to ensure that the load of each axle is closer to the set standard load value. This step not only improves the working efficiency of the entire machine tool, but also reduces equipment wear and failure rate. The final load distribution result records in detail the load value and feed speed of each axle before and after adjustment, providing accurate data support for workshop management. This information is of great value for adjusting production plans, optimizing resource allocation, and preventing equipment failures, ensuring the stability and efficiency of the production process.

[0096] See also Figure 5 , the calculation steps of the machine tool spindle vibration amplitude are:

[0097] Combined with the load distribution results, a vibration sensor is installed at the spindle position of the machine tool to capture the spindle vibration signal in real time, extract the time series data, and obtain the time series vibration data;

[0098] Installing vibration sensors on the machine tool spindle and configuring them according to the load distribution results to capture the vibration signals from the machine tool spindle in real time is a meticulous and critical step. The selection of vibration sensors needs to consider their frequency response range and sensitivity to adapt to the dynamic range of machine tool operation. When installing the sensor, it is necessary to ensure that it is firmly fixed to the spindle to avoid small changes in the sensor position caused by mechanical vibration, which will directly affect the accuracy of the data. When collecting data, a high-speed ADC (analog to digital converter) is configured to capture high-frequency vibration signals. At the same time, an anti-aliasing filter is set to filter out frequency components that exceed the sensor response range. In addition, the appropriate data transmission rate and caching mechanism must be set in the real-time data acquisition system to cope with the large amount of data streams received from multiple sensors. Data preprocessing includes signal amplification, filtering and denoising to ensure the quality and availability of data in subsequent analysis.

[0099] Perform spectrum analysis on time series vibration data, extract key frequency components, analyze energy distribution under difference frequencies, and obtain spectrum energy distribution data;

[0100] When extracting key frequency components, the process involves converting time series data into a frequency domain representation. Using the Fast Fourier Transform (FFT) algorithm, the process can efficiently extract frequency domain information from time domain signals. Before performing the FFT, it is necessary to ensure that the data is properly windowed to reduce spectral leakage. Window functions such as Hanning windows or Hamming windows are used to smooth the data endpoints and improve the resolution and accuracy of the spectrum. The resulting spectrum shows the amplitude of different frequency components, so that it is possible to identify which frequencies are generated by the normal operation of the machine tool spindle and which may be abnormal vibrations caused by wear or damage. By analyzing the energy of each major frequency component, the intensity and possible impact of the vibration can be quantitatively evaluated. For example, if the vibration amplitude of a certain frequency component suddenly increases, it indicates the failure of a certain part of the machine tool, which is very important for the maintenance of the machine tool, helping technicians to formulate maintenance plans and timely intervention to avoid machine tool failures and production line downtime.

[0101] According to the spectrum energy distribution data, the formula is used:

[0102]

[0103] Calculate the root mean square amplitude A of the vibration and generate the vibration amplitude data of the machine tool spindle, where s i Represents a single vibration sample value, represents the average of the sample values, and N represents the total number of samples;

[0104] There are three vibration sample values ​​10, 12 and 9, the average value is (10+12+9) / 3=10.33, and the vibration amplitude is calculated as follows:

[0105] s1=10,s2=12,s3=9

[0106]

[0107] The results show that the vibration amplitude of the spindle is 1.247. The numerical results are very critical in the operation monitoring of machine tools. They can help maintenance personnel evaluate whether the operation status of the machine tool is normal and whether further maintenance or adjustment is needed.

[0108] See also Figure 6 , the steps to obtain the spindle vibration adjustment data are:

[0109] According to the vibration amplitude data of the machine tool spindle, the current mechanical operation status is evaluated, and the spindle speed needs to be adjusted in real time. The speed adjustment decision data is generated through the preset judgment criteria;

[0110] The vibration amplitude data of the machine tool spindle is used to evaluate the current mechanical operating status and monitor the vibration level in real time. The sensor continuously records the vibration data of the spindle and sends it to the central processing unit via wireless transmission. The data waveform can be analyzed in real time and abnormal vibration patterns can be automatically identified. Based on the real-time analysis of the vibration amplitude results, the system can quickly decide whether the spindle speed needs to be adjusted. The adjustment decision is executed by automatic control. When the vibration exceeds the safety threshold, the speed adjustment instruction will be activated to reduce or increase the speed to restore the spindle to the ideal working state. The entire process is precisely controlled by the dynamic data acquisition system to ensure the safety and efficiency of machine tool operation. The generated speed adjustment decision data will be recorded and analyzed for future maintenance and adjustment strategy formulation.

[0111] According to the speed adjustment decision data, the spindle speed is adjusted, the cutting force distribution is adjusted synchronously, the current vibration characteristics are matched, and the adjusted spindle operating parameters are obtained;

[0112] According to the speed adjustment decision data, the spindle speed is automatically adjusted and the cutting force distribution is adjusted synchronously, including a real-time dynamic feedback system and advanced algorithms to ensure the accuracy and response speed of the adjustment. During the process, each step of the adjustment is based on the analysis of the data collected from real-time monitoring to adapt to changes in cutting conditions. The dynamic adjustment of cutting parameters and loading conditions is based on the goal of continuously optimizing the cutting efficiency of the machine tool and reducing the vibration amplitude, including adjusting the spindle speed and cutting depth. The adjusted spindle operating parameters are rigorously tested to verify their effectiveness in vibration control. During the process, the optimal cutting force distribution and speed setting can be calculated in real time to minimize vibration and improve cutting efficiency. The final operating parameters are regarded as key outputs. The parameters directly affect the operating efficiency and processing quality of the machine tool, ensuring the stability and accuracy of the entire production process.

[0113] According to the adjusted spindle operating parameters, the formula is used:

[0114]

[0115] Calculate the adjusted spindle response value R, suppress the resonance phenomenon, and generate the spindle vibration adjustment data. Δv is the change in vibration amplitude, which means the difference between the vibration amplitudes measured before and after. Δt is the time interval for the adjustment operation.

[0116] During the adjustment process, the vibration amplitude is reduced from 10mm / s to 2mm / s, the time interval is 5 seconds, and the calculation process is as follows:

[0117]

[0118] The results show that the spindle responsiveness after adjustment is 0.566, which reflects the effectiveness and response speed of the adjustment measures. The high responsiveness value indicates that the adjustment effect is significant, which can quickly reduce the vibration amplitude and enhance the stability and machining accuracy of the machine tool.

[0119] See also Figure 7 , the evaluation steps of the dynamic response of the machine tool during the differential processing stage are:

[0120] According to the spindle vibration adjustment data, Fourier transform is performed to convert the time domain signal into a frequency domain signal to obtain the spectrum distribution result;

[0121] In the process of implementing Fourier transform, it is necessary to collect the vibration adjustment data of the machine tool's spindle, including collecting vibration signals under different operating conditions. The signal is usually obtained from the machine tool spindle through an acceleration sensor, and then the time series data is input into the Fourier transform algorithm to convert the time domain signal into a frequency domain signal to analyze its spectrum distribution. The conversion involves discretizing the signal, sampling and calculating the amplitude and phase of each frequency component. The obtained spectrum distribution results show in detail how the vibration energy is distributed at each frequency, which is the key data basis for the next step of modal analysis. The accuracy of the spectrum analysis directly affects the validity of the modal analysis results. It is necessary to ensure the accurate collection and processing of vibration data in order to accurately identify the natural frequency and vibration characteristics of the machine tool spindle.

[0122] Using the spectrum distribution results, perform modal analysis to analyze the natural frequency and vibration mode of the machine tool spindle and obtain the modal analysis results;

[0123] Using the data obtained from the spectrum distribution, modal analysis is performed to analyze the natural frequency and vibration mode of the machine tool spindle. It is a technology that uses spectrum data to identify the dynamic characteristics of the system, including calculating the natural frequency, damping ratio and vibration mode shape of the system. First, it is necessary to identify obvious energy peaks from the spectrum distribution. The peaks correspond to the natural frequencies of the system, and then further calculations are performed to determine the vibration mode corresponding to each frequency. The vibration mode is described by the system's response to specific frequency inputs, showing the vibration form of the system at these frequencies. The results of modal analysis not only reflect the dynamic behavior of the machine tool in normal operation, but can also be used to diagnose potential structural problems such as imbalance, relaxation or other forms of mechanical degradation, thereby providing important information for machine tool maintenance and fault diagnosis. This analysis process requires precise processing and calculation to ensure reliable modal analysis results.

[0124] Based on the modal analysis results, combined with the operating parameters of the processing stage, the dynamic response evaluation is carried out using the formula:

[0125]

[0126] Calculate the dynamic response value DRE (f, m, p) of the machine tool in the differential processing stage to obtain the dynamic response evaluation data, where f represents the frequency, m represents the vibration mode value, and p represents the parameter value of the processing stage;

[0127] Assuming f (frequency) is 150 Hz, determined by the peak value of the vibration data in the spectrum analysis, m (vibration mode) is determined as the first main mode, with a value of 1, and p (processing stage parameter) represents the load size of the machine tool in a specific processing task, which is taken as 500 N. The dynamic response evaluation value can be calculated:

[0128]

[0129] This shows that at the natural frequency of 150 Hz, the dynamic response intensity of the machine tool is 1.8257, which means that the machine tool spindle is highly sensitive to changes in external load at this frequency, which is crucial for further adjustment and optimization of machine tool performance.

[0130] See also Figure 8 ,The steps for obtaining dynamic path planning data are:

[0131] Analyze dynamic response evaluation data, identify vibration-sensitive areas of the machine tool at different processing stages, and generate vibration-sensitive area analysis results by combining the natural frequency of the machine tool and the vibration mode during actual operation;

[0132] When analyzing vibration-sensitive areas, it is first necessary to integrate dynamic response evaluation data. The data provides the vibration response intensity of the machine tool spindle at different processing stages. By analyzing the data, the vibration sensitivity of the machine tool under specific loads and speeds can be determined. The analysis process involves cluster analysis of the data to identify which frequencies and load conditions have the most significant vibrations. Sensitive areas indicate that the machine tool may face performance degradation or failure risks under these conditions. Therefore, the generated vibration-sensitive area analysis results will directly affect the subsequent cutting path adjustment strategy to ensure that these high-risk areas are avoided during the processing process, thereby optimizing the overall performance and processing quality of the machine tool.

[0133] According to the analysis results of the vibration-sensitive area, the machining path is adjusted, the cutting direction and feed speed are changed, and the optimized machining path is obtained;

[0134] According to the analysis results of vibration-sensitive areas, the implemented cutting path adjustment plan needs to comprehensively consider the relationship between vibration data and cutting parameters, reduce or avoid processing in high vibration areas by adjusting the processing path, simulate the impact of different cutting paths on vibration, and optimize the cutting direction and feed speed to ensure that the adjustment can minimize vibration without sacrificing processing efficiency. The optimized cutting path not only reduces the wear of the machine tool, but also improves the quality and precision of the processed parts, which directly affects the performance and reliability of the final product.

[0135] Based on the optimized machining path, dynamic planning is used to adjust the machine tool operating parameters, optimize cutting depth and speed, and generate dynamic path planning data;

[0136] The optimized cutting path is converted. The process involves encoding the adjusted cutting parameters and path information into the operating system of the CNC machine tool. It is necessary to ensure that the data format is compatible with the programming interface of the machine tool. The dynamic path planning data includes real-time adjustment information of cutting depth, speed and feed rate. The data is automatically adjusted based on real-time monitoring of vibration feedback and preset performance standards. The implementation of these dynamic path planning ensures that the machine tool automatically adapts to various changes during the processing process, reduces downtime, and improves production efficiency and product quality.

[0137] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A CNC machine tool motion control system, characterized in that: The system comprises: The cutting force feedback monitoring module installs a cutting force sensor at the axle position of the CNC machine tool to obtain the cutting force signal of the axle in real time, record the load change of each axle, analyze the cutting force fluctuation of the axle, and obtain the cutting force feedback data of the machine tool; The multi-axis load coordination module calculates the load of the axle in real time based on the cutting force feedback data of the machine tool, compares the axle load with the set load value, determines the load imbalance according to the comparison result, adjusts the feed speed of each axis according to the current load ratio of the axle, performs load balancing, and generates a load distribution result; The spindle vibration suppression module monitors the vibration data of the machine tool spindle in real time through a vibration sensor according to the load distribution result, calculates the vibration amplitude of the machine tool spindle, adjusts the machine tool spindle speed in real time according to the vibration amplitude calculation result, adjusts the cutting force distribution according to the vibration characteristics, adjusts the spindle operation state to suppress the resonance phenomenon, and generates spindle vibration adjustment data; The dynamic path optimization module performs dynamic modal analysis based on the spindle vibration adjustment data, evaluates the dynamic response of the machine tool in the differential processing stage, adjusts the processing path of each process in real time according to the evaluation results, optimizes the cutting direction and feed speed, and obtains dynamic path planning data.

2. The CNC machine tool motion control system according to claim 1, characterized in that: The steps for obtaining the cutting force feedback data of the machine tool are as follows: Select cutting force sensors compatible with CNC machine tool axles, install them at key locations on the axles, conduct initial functional verification of the cutting force sensors, and output the sensor configuration after the test is completed; According to the sensor configuration completed in the test, the standard operation process of the machine tool is run to collect the axle cutting force data in real time, perform data cleaning and formatting, and generate processed axle cutting force data; According to the processed axle cutting force data, the formula is used: Calculated standard axle cutting force data F adjusted , and obtain the machine tool cutting force feedback data, where F raw Represents the axle cutting force data after processing.

3. The CNC machine tool motion control system according to claim 2, characterized in that: The steps of comparing the axle load with the set load value are: Calling the cutting force feedback data of the machine tool, recording the cutting force of each axle during the machining process, and forming a real-time cutting force data set; Based on the real-time cutting force data set, the formula is adopted: Calculate the actual load ratio L for each axle i , get the axle load ratio data, where F i Represents the real-time cutting force data value, F set Set the load value for the target axle; The axle load ratio data is analyzed, the actual load of each axle is compared with the set load, overloaded and underloaded axles are identified and marked, and an axle load status report is generated.

4. The CNC machine tool motion control system according to claim 3, characterized in that: The steps for obtaining the load distribution result are: Analyze the axle load status report, mark the axles with uneven load, record the current load ratio of the corresponding axles, analyze the deviation from the standard load value, and generate uneven load marking data; According to the uneven load marking data, the feed speed of the marked axle is adjusted using the formula: Calculate the adjusted feed rate V adj , generate the adjusted feed speed data, where V orig is the original feed speed, ΔL is the difference between the load ratio and the set load, L set is the set load standard; The adjusted feed speed data is applied to perform load distribution adjustment on all axles, dynamically balance the workload of the axles, and generate a load distribution result.

5. The CNC machine tool motion control system according to claim 4, characterized in that: The calculation steps of the vibration amplitude of the machine tool spindle are: In combination with the load distribution result, a vibration sensor is installed at the position of the machine tool spindle to capture the spindle vibration signal in real time, extract time series data, and obtain time series vibration data; Performing spectrum analysis on the time series vibration data, extracting key frequency components, analyzing energy distribution under difference frequencies, and obtaining spectrum energy distribution data; According to the spectrum energy distribution data, the formula is adopted: Calculate the root mean square amplitude A of the vibration and generate the vibration amplitude data of the machine tool spindle, where s i represents a single vibration sample value, represents the average of the sample values, and N represents the total number of samples.

6. The CNC machine tool motion control system according to claim 5, characterized in that: The steps for obtaining the spindle vibration adjustment data are as follows: According to the vibration amplitude data of the machine tool spindle, the current mechanical operation state is evaluated, and whether the spindle speed needs to be adjusted is analyzed in real time, and speed adjustment decision data is generated through preset judgment criteria; According to the speed adjustment decision data, the spindle speed is adjusted, the cutting force distribution is adjusted synchronously, the current vibration characteristics are matched, and the adjusted spindle operating parameters are obtained; According to the adjusted spindle operating parameters, the formula is used: Calculate the adjusted spindle responsiveness value R, suppress the resonance phenomenon, and generate the spindle vibration adjustment data. Δv is the change in vibration amplitude, which represents the difference in vibration amplitude measured before and after, and Δt is the time interval for the adjustment operation.

7. The CNC machine tool motion control system according to claim 6, characterized in that: The evaluation steps of the dynamic response of the machine tool during the differential processing phase are: According to the spindle vibration adjustment data, Fourier transform is performed to convert the time domain signal into a frequency domain signal to obtain a spectrum distribution result; Using the frequency spectrum distribution result, a modal analysis is performed to analyze the natural frequency and vibration mode of the machine tool spindle to obtain a modal analysis result; Based on the modal analysis results, combined with the operating parameters of the processing stage, the dynamic response evaluation is carried out using the formula: The dynamic response value DRE(f,m,p) of the machine tool in the differential processing stage is calculated to obtain the dynamic response evaluation data, where f represents the frequency, m represents the vibration mode value, and p represents the parameter value of the processing stage.

8. The CNC machine tool motion control system according to claim 7, characterized in that: The steps for acquiring the dynamic path planning data are as follows: Analyze the dynamic response evaluation data, identify the vibration sensitive area of ​​the machine tool in the different processing stages, and generate the vibration sensitive area analysis result by combining the natural frequency of the machine tool and the vibration mode during actual operation; According to the analysis result of the vibration sensitive area, the machining path is adjusted, the cutting direction and the feed speed are changed, and an optimized machining path is obtained; Based on the optimized machining path, the operating parameters of the machine tool are adjusted by dynamic planning, the cutting depth and speed are optimized, and dynamic path planning data is generated.

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