Dynamic Monitoring Method and System for the State of a Five-Axis Machine Tool Spindle Based on Machining Feedback

Abstract

This application belongs to the technical field of numerical control machine tool manufacturing, and particularly relates to a method and system for dynamically monitoring the spindle state of a five-axis machine tool based on machining feedback. The method includes: collecting key data during the machining process in real time through various sensors installed on the five-axis machine tool; preprocessing the key data; performing machining feedback analysis based on the key data to obtain a machining feedback analysis result; evaluating the spindle state based on the key data to obtain a spindle state evaluation result; and dynamically adjusting the monitoring strategy according to the machining feedback analysis result and the spindle state evaluation result. This application realizes the dynamic monitoring and accurate evaluation of the spindle state by real-time monitoring of key parameters during the machining process and in-depth analysis and evaluation in combination with the machining characteristics of the five-axis machine tool, improves the accuracy and real-time performance of monitoring, effectively copes with various challenges that may occur in the complex machining environment of the five-axis machine tool, and ensures the safety and stability of the machining process.

Description

Technical Field

[0001] This application belongs to the technical field of numerically controlled machine tool manufacturing, and particularly relates to a method and system for dynamically monitoring the spindle state of a five-axis machine tool based on machining feedback. Background Art

[0002] In the prior art, real-time spindle detection is the core link to ensure machining accuracy and operation stability of machinery, covering a variety of technical means. On the one hand, detection methods based on vibration and displacement, such as laser interferometry and accelerometer measurement, can accurately capture the vibration and displacement of the spindle during high-speed rotation, providing strong support for the evaluation of dynamic performance. At the same time, temperature and thermal imaging detection are also an indispensable part. Through the real-time monitoring of temperature sensors and thermal imagers, the temperature distribution and thermal deformation state of the spindle can be visually displayed, providing important basis for the evaluation of cooling effect and thermal management.

[0003] On the other hand, dynamic balance detection also plays an important role in real-time spindle detection. The application of single-plane and double-plane dynamic balance methods and field dynamic balance technology can effectively reduce the unbalance of the spindle, significantly improving its smoothness and machining accuracy. In addition, static performance testing methods, including geometric accuracy measurement, surface finish inspection, material quality analysis, and static mechanical property evaluation, are also important measures to ensure the quality of the spindle and extend its service life. In summary, according to the specific type and working condition requirements of the spindle, selecting the appropriate real-time detection method and combining with regular maintenance are the keys to ensuring the stable performance of the spindle and improving the machining efficiency of machinery.

[0004] Although these methods have improved the level of spindle state monitoring to a certain extent, there are still some limitations in terms of the accuracy of real-time monitoring, the combination of machining feedback, and the adaptability to the complex machining environment of five-axis machine tools.

[0005] Therefore, it is desirable to have a technical solution to overcome or at least mitigate at least one of the above defects of the prior art. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for dynamically monitoring the spindle state of a five-axis machine tool based on machining feedback to solve at least one problem existing in the prior art.

[0007] The technical solution of this application is as follows:

[0008] The first aspect of this application provides a method for dynamically monitoring the spindle state of a five-axis machine tool based on machining feedback, including:

[0009] S10. Real-time collect key data during the machining process through a variety of sensors installed on the five-axis machine tool;

[0010] S20. Preprocess the key data;

[0011] S30. Perform machining feedback analysis based on the key data to obtain a machining feedback analysis result;

[0012] S40. Evaluate the spindle status based on the key data to obtain a spindle status evaluation result;

[0013] S50. Dynamically adjust the monitoring strategy according to the machining feedback analysis result and the spindle status evaluation result.

[0014] In at least one embodiment of the present application, in S10, key data during the machining process is collected in real time through a variety of sensors installed on a five-axis machine tool, including:

[0015] Cutting force data during the machining process is collected in real time through a cutting force sensor installed on a five-axis machine tool at a first collection frequency;

[0016] Vibration data during the machining process is collected in real time through a three-axis vibration sensor installed on a five-axis machine tool at a second collection frequency;

[0017] Temperature data during the machining process is collected in real time through a temperature sensor installed on a five-axis machine tool at a third collection frequency.

[0018] In at least one embodiment of the present application, in S20, the preprocessing methods for the key data at least include cleaning, denoising, and filtering.

[0019] In at least one embodiment of the present application, in S30, performing machining feedback analysis based on the key data to obtain a machining feedback analysis result includes:

[0020] Determine a cutting force threshold, compare the cutting force data with the cutting force threshold, and obtain a first machining feedback analysis result;

[0021] Determine a vibration spectrum feature threshold, compare the vibration data with the vibration spectrum feature threshold, and obtain a second machining feedback analysis result;

[0022] Determine a temperature threshold, compare the temperature data with the temperature threshold, and obtain a third machining feedback analysis result;

[0023] Perform comprehensive machining feedback analysis on the cutting force data, the vibration data, and the temperature data to obtain a comprehensive machining feedback analysis result, including:

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] Among them, F is the comprehensive processing feedback evaluation value, C is the cutting force stability index, is the threshold value of the cutting force stability index, V is the vibration intensity index, is the threshold value of the vibration intensity index, T is the temperature deviation index, is the threshold value of the temperature deviation index, α is the cutting force weight coefficient, β is the vibration weight coefficient, γ is the temperature weight coefficient, is the cutting force sensitivity coefficient, is the vibration sensitivity coefficient, is the temperature sensitivity coefficient.

[0029] In at least one embodiment of the present application, in S40, the spindle state is evaluated according to the key data to obtain a spindle state evaluation result, including:

[0030] S41. Determine the spindle state evaluation index, and the spindle state evaluation index includes a cutting force stability index, a vibration intensity index, and a temperature deviation index;

[0031] S42. Construct a spindle state evaluation model according to the spindle state evaluation index, and the spindle state evaluation model is:

[0032] ;

[0033] ;

[0034] Among them, S is the spindle state score, is the theoretical stable value of the cutting force, is the cutting force power weight, is the vibration power weight, is the temperature power weight;

[0035] S43. Quantitatively evaluate the spindle state according to the spindle state evaluation model to obtain a spindle state evaluation result;

[0036] S44. Determine the spindle state abnormality level according to the spindle state evaluation result, and give an early warning according to the spindle state abnormality level and a preset early warning mechanism.

[0037] In at least one embodiment of the present application, in S50, dynamically adjusting the monitoring strategy according to the processing feedback analysis result and the spindle state evaluation result includes:

[0038] Determining the abnormal type of the spindle state according to the processing feedback analysis result and the spindle state evaluation result;

[0039] Dynamically adjusting the monitoring strategy according to the abnormal type of the spindle state, including:

[0040] If the abnormal type of the spindle state is abnormal cutting force, increasing the monitoring frequency of the vibration data and the temperature data;

[0041] If the abnormal type of the spindle state is abnormal vibration spectrum characteristics, increasing the monitoring frequency of the cutting force data and the temperature data;

[0042] If the abnormal type of the spindle state is abnormal temperature, increasing the monitoring frequency of the cutting force data and the vibration data.

[0043] In at least one embodiment of the present application, in S50, it further includes recording the reasons, times, and effects of each monitoring strategy adjustment.

[0044] The second aspect of the present application provides a five-axis machine tool spindle state dynamic monitoring system based on processing feedback. Based on the above-mentioned five-axis machine tool spindle state dynamic monitoring method based on processing feedback, it includes:

[0045] A sensor module for real-time collecting key data during the processing through a variety of sensors installed on the five-axis machine tool;

[0046] A data processing module for preprocessing the key data;

[0047] A processing feedback analysis module for performing processing feedback analysis based on the key data to obtain a processing feedback analysis result;

[0048] A state evaluation and warning module for performing spindle state evaluation based on the key data to obtain a spindle state evaluation result;

[0049] A dynamic monitoring strategy adjustment module for dynamically adjusting the monitoring strategy according to the processing feedback analysis result and the spindle state evaluation result.

[0050] In at least one embodiment of the present application, the processing feedback analysis module includes:

[0051] A first processing feedback analysis unit for determining a cutting force threshold, comparing and analyzing the cutting force data with the cutting force threshold to obtain a first processing feedback analysis result;

[0052] The second machining feedback analysis unit is configured to determine the vibration spectrum feature threshold, compare and analyze the vibration data with the vibration spectrum feature threshold, and obtain the second machining feedback analysis result;

[0053] The third machining feedback analysis unit is configured to determine the temperature threshold, compare and analyze the temperature data with the temperature threshold, and obtain the third machining feedback analysis result;

[0054] The comprehensive machining feedback analysis unit is configured to perform comprehensive machining feedback analysis on the cutting force data, the vibration data, and the temperature data, and obtain the comprehensive machining feedback analysis result, including:

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] wherein, F is the comprehensive machining feedback evaluation value, C is the cutting force stability index, is the cutting force stability index threshold, V is the vibration intensity index, is the vibration intensity index threshold, T is the temperature deviation index, is the temperature deviation index threshold, α is the cutting force weight coefficient, β is the vibration weight coefficient, γ is the temperature weight coefficient, is the cutting force sensitivity coefficient, is the vibration sensitivity coefficient, is the temperature sensitivity coefficient.

[0060] The invention has at least the following beneficial technical effects:

[0061] The five-axis machine tool spindle state dynamic monitoring method based on machining feedback of the present application realizes dynamic monitoring and accurate evaluation of the spindle state by real-time monitoring of key parameters during the machining process and in-depth analysis and evaluation in combination with the machining characteristics of the five-axis machine tool, improves the accuracy and real-time performance of the monitoring, effectively copes with various challenges that may occur in the complex machining environment of the five-axis machine tool, and ensures the safety and stability of the machining process. Description of the Drawings

[0062] Figure 1It is a flowchart of a method for dynamically monitoring the spindle status of a five-axis machine tool based on machining feedback according to an embodiment of the present application;

[0063] Figure 2 It is a schematic diagram of a system for dynamically monitoring the spindle status of a five-axis machine tool based on machining feedback according to an embodiment of the present application. Detailed implementation manners

[0064] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present application. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application. The embodiments of the present application will be described in detail below with reference to the drawings.

[0065] In the description of the present application, it should be understood that the orientation or positional relationships indicated by the terms "center", "longitudinal", "transverse", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present application 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 thus should not be construed as limiting the protection scope of the present application.

[0066] The following combines the attached Figures 1 to 2 to further elaborate on the present application.

[0067] The present application provides a method for dynamically monitoring the spindle status of a five-axis machine tool based on machining feedback, as Figure 1 shown, including the following steps:

[0068] S10. Real-time collect key data during the machining process through various sensors installed on the five-axis machine tool;

[0069] S20. Preprocess the key data;

[0070] S30. Conduct machining feedback analysis based on the key data to obtain a machining feedback analysis result;

[0071] S40. Evaluate the spindle status based on the key data to obtain a spindle status evaluation result;

[0072] S50. Dynamically adjust the monitoring strategy according to the machining feedback analysis result and the spindle status evaluation result.

[0073] In the method for dynamically monitoring the spindle status of a five-axis machine tool based on machining feedback of the present application, in S10, the process of data acquisition specifically includes:

[0074] Real-time collect the cutting force data during the machining process through a cutting force sensor installed on the five-axis machine tool at the first acquisition frequency;

[0075] Real-time collect the vibration data during the machining process through a three-axis vibration sensor installed on the five-axis machine tool at the second acquisition frequency;

[0076] Real-time collect the temperature data during the machining process through a temperature sensor installed on the five-axis machine tool at the third acquisition frequency.

[0077] In the application scenario, according to the structural characteristics and machining requirements of the five-axis machine tool, the sensors that need to be arranged generally include cutting force sensors, three-axis vibration sensors, temperature sensors, etc. Each sensor is accurately installed at key parts of the machine tool, such as the spindle, tool, workpiece support, etc. The type and quantity of sensors need to be optimized according to the specific machining scenario to ensure that the key parameter changes during the machining process can be comprehensively and accurately captured. According to the real-time and accuracy requirements of the machining process, set the data acquisition frequency and accuracy of the sensors. For parameters such as cutting force and vibration that change rapidly, high-frequency sampling is adopted. For parameters with slow temperature changes, the sampling frequency can be appropriately reduced. At the same time, ensure that the sensors have high accuracy and stability to reduce errors and noise.

[0078] In the method for dynamically monitoring the spindle status of a five-axis machine tool based on machining feedback of the present application, in S20, preprocess the collected key data, including cleaning, denoising, filtering, etc. of the key data. Through the preprocessing process, remove the invalid values, outliers and duplicate values in the key data to ensure the accuracy, reliability and consistency of the key data. Select appropriate algorithms and parameters according to the data characteristics and application scenarios during the cleaning process; digital filtering technology can be used to remove the noise interference in the data and improve the signal-to-noise ratio of the data. The type and parameters of the filter are set according to the spectral characteristics and noise characteristics of the data. According to the machining characteristics of the five-axis machine tool, develop a data preprocessing algorithm applicable to the spindle status monitoring of the five-axis machine tool to automatically identify and process the abnormal fluctuations and periodic changes in the data, providing a reliable data basis for subsequent analysis and evaluation.

[0079] In the method for dynamically monitoring the spindle status of a five-axis machine tool based on machining feedback of the present application, in S30, according to the machining characteristics of the five-axis machine tool, perform machining feedback analysis on the key data to obtain the machining feedback analysis result. Specifically:

[0080] Determine the cutting force threshold, compare the cutting force data with the cutting force threshold for analysis, and obtain the first machining feedback analysis result;

[0081] Determine the vibration spectrum feature threshold, compare the vibration data with the vibration spectrum feature threshold for analysis, and obtain the second machining feedback analysis result;

[0082] Determine the temperature threshold, compare the temperature data with the temperature threshold for analysis, and obtain the third machining feedback analysis result;

[0083] Conduct comprehensive machining feedback analysis on the cutting force data, vibration data, and temperature data to obtain the comprehensive machining feedback analysis result.

[0084] In this embodiment, when conducting comprehensive machining feedback analysis, calculate the comprehensive machining feedback evaluation value through a comprehensive evaluation formula, the cutting force stability index C , the vibration intensity index V , the temperature deviation index T Adopt dynamic weight non-linear combination to obtain the comprehensive machining feedback evaluation value F :

[0085] ;

[0086] Among them, F is the comprehensive machining feedback evaluation value, C is the cutting force stability index, is the cutting force stability index threshold, V is the vibration intensity index, is the vibration intensity index threshold, T is the temperature deviation index, is the temperature deviation index threshold, α is the cutting force weight coefficient, β is the vibration weight coefficient, γ is the temperature weight coefficient, is the cutting force sensitivity coefficient, is the vibration sensitivity coefficient, is the temperature sensitivity coefficient.

[0087] Each weight coefficient , , is dynamically adjusted through real-time data:

[0088] ;

[0089] ;

[0090] ;

[0091] Each sensitivity coefficient , , Optimized based on historical data.

[0092] In this embodiment, the machining feedback analysis includes cutting force variation analysis, vibration spectrum feature analysis, temperature variation analysis, and comprehensive analysis. Through the cutting force variation analysis, the variation of the cutting force is monitored in real time, and the relationship between it and the spindle state is analyzed. By setting the cutting force threshold and algorithm, it is judged whether the cutting force is within the normal range. If it exceeds the normal range, it indicates that there may be problems such as abnormal wear of the spindle or tool breakage. The vibration spectrum feature analysis is to perform spectrum analysis on the collected vibration data, extract the main frequency and secondary frequency characteristic parameters, and judge whether there is abnormal vibration of the spindle caused by problems such as imbalance and looseness by comparing with the spectrum characteristics in the normal state. The temperature variation analysis is to analyze the relationship between the temperature and the machining process by monitoring the temperature variation of the spindle and the environment around the spindle. If the temperature variation is abnormal, it indicates that there may be problems such as poor lubrication and overheating of the spindle. The comprehensive analysis is to perform comprehensive analysis by combining the cutting force data, vibration data, and temperature data, and obtain the quantified comprehensive machining feedback analysis result through the comprehensive evaluation formula.

[0093] In the method for dynamically monitoring the spindle state of a five-axis machine tool based on machining feedback of the present application, in S40, the spindle state is evaluated according to the key data to obtain the spindle state evaluation result, including:

[0094] S41. Determine the spindle state evaluation index, and the spindle state evaluation index includes the cutting force stability index, vibration intensity index, and temperature deviation index;

[0095] S42. Construct a spindle state evaluation model according to the spindle state evaluation index;

[0096] In this embodiment, the spindle state evaluation model is specifically:

[0097] Based on the fuzzy comprehensive evaluation method, define the spindle state score S :

[0098] ;

[0099] Wherein, S is the spindle state score, is the theoretical stable value of the cutting force, is the cutting force power weight, is the vibration power weight, is the temperature power weight;

[0100] The theoretical stable value of the cutting force is obtained by statistical analysis of historical machining data or calibration by finite element simulation; each power weight , , Meet , which is dynamically allocated by the entropy weight method;

[0101] S43. Quantitatively evaluate the spindle status according to the spindle status evaluation model to obtain the spindle status evaluation result;

[0102] S44. Determine the spindle status abnormality level according to the spindle status evaluation result, and issue a warning according to the spindle status abnormality level and the preset warning mechanism.

[0103] When the spindle status score S exceeds the critical threshold , the spindle status evaluation result shows that the spindle status is abnormal, and a warning is immediately triggered. Different warning levels are defined in the warning mechanism, and corresponding warning strategies are configured for each warning level. Determine the spindle status abnormality level according to the degree and urgency of the spindle status abnormality, judge the warning level to which the spindle status abnormality level belongs, and thus issue a warning according to the corresponding warning strategy. For serious spindle status abnormalities, stop the machine immediately for inspection; for minor spindle status abnormalities, the machine can be temporarily not stopped, and monitoring and observation can be strengthened.

[0104] In the dynamic monitoring method of the five-axis machine tool spindle based on processing feedback of the present application, in S50, the monitoring strategy is dynamically adjusted according to the processing feedback analysis result and the spindle status evaluation result, including:

[0105] Determine the spindle status abnormality type according to the processing feedback analysis result and the spindle status evaluation result;

[0106] Dynamically adjust the monitoring strategy according to the spindle status abnormality type, including:

[0107] If the spindle status abnormality type is cutting force abnormality, increase the monitoring frequency of vibration data and temperature data;

[0108] If the spindle status abnormality type is vibration spectrum feature abnormality, increase the monitoring frequency of cutting force data and temperature data;

[0109] If the spindle status abnormality type is temperature abnormality, increase the monitoring frequency of cutting force data and vibration data.

[0110] Through the above method, the monitoring parameters are automatically adjusted according to the real-time feedback during the five-axis machine tool processing, and the dynamic monitoring of the spindle status is realized. Each monitoring frequency can be determined according to the quantitative characterization results of the processing feedback analysis and the spindle status evaluation.

[0111] In the preferred embodiment of the present application, in S50, it also includes recording information such as the reason, time, and effect of each monitoring strategy adjustment, providing a reference for subsequent optimization and improvement.

[0112] Based on the above-mentioned five-axis machine tool spindle state dynamic monitoring method based on machining feedback, the second aspect of the present application provides a five-axis machine tool spindle state dynamic monitoring system based on machining feedback, as Figure 2 shown, including:

[0113] A sensor module for collecting key data during the machining process in real time through various sensors installed on the five-axis machine tool;

[0114] A data processing module for preprocessing the key data;

[0115] A machining feedback analysis module for performing machining feedback analysis based on the key data to obtain a machining feedback analysis result;

[0116] A state evaluation and warning module for evaluating the spindle state based on the key data to obtain a spindle state evaluation result;

[0117] A dynamic monitoring strategy adjustment module for dynamically adjusting the monitoring strategy according to the machining feedback analysis result and the spindle state evaluation result.

[0118] For the five-axis machine tool spindle state dynamic monitoring system based on machining feedback of the present application, sensors such as cutting force sensors, three-axis vibration sensors, temperature sensors, etc. are selected and arranged, and accurately installed at key parts of the five-axis machine tool such as the spindle, tool, workpiece support, etc.; the sensor module is used to set the data acquisition frequency and accuracy of the sensors to ensure that key parameter changes during the machining process can be comprehensively and accurately captured, and the key data collected by the sensors is transmitted to the data processing module in real time by wired or wireless means and stored for backup. Encryption technology is used during the data transmission process to ensure data security and integrity, and the stored data needs to be traceable for subsequent analysis and processing. The data processing module cleans, denoises, and filters the collected key data, removes invalid values, outliers, and duplicate values, and removes noise interference in the key data to improve the signal-to-noise ratio of the data. In addition, this data processing module is also used to automatically identify and process abnormal fluctuations and periodic changes in the data through a data preprocessing algorithm applicable to the spindle state monitoring of five-axis machine tools, providing a reliable basis for subsequent analysis and evaluation.

[0119] In this embodiment, the machining feedback analysis module includes:

[0120] A first machining feedback analysis unit for determining a cutting force threshold and comparing and analyzing the cutting force data with the cutting force threshold to obtain a first machining feedback analysis result;

[0121] A second machining feedback analysis unit for determining a vibration spectrum feature threshold and comparing and analyzing the vibration data with the vibration spectrum feature threshold to obtain a second machining feedback analysis result;

[0122] The third machining feedback analysis unit is configured to determine a temperature threshold, compare and analyze the temperature data with the temperature threshold, and obtain a third machining feedback analysis result;

[0123] The comprehensive machining feedback analysis unit is configured to perform a comprehensive machining feedback analysis on the cutting force data, vibration data, and temperature data to obtain a comprehensive machining feedback analysis result, including:

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] wherein, F is the comprehensive machining feedback evaluation value, C is the cutting force stability index, is the cutting force stability index threshold, V is the vibration intensity index, is the vibration intensity index threshold, T is the temperature deviation index, is the temperature deviation index threshold, α is the cutting force weight coefficient, β is the vibration weight coefficient, γ is the temperature weight coefficient, is the cutting force sensitivity coefficient, is the vibration sensitivity coefficient, is the temperature sensitivity coefficient.

[0129] For the five-axis machine tool spindle state dynamic monitoring system based on machining feedback of the present application, the machining feedback analysis module is used to monitor the changes of parameters such as cutting force, vibration, and temperature in real time and perform analysis; by setting thresholds and algorithms, it determines whether the cutting force is within the normal range, analyzes the spectral characteristics of the vibration data, extracts characteristic parameters such as the main frequency and sub-frequency, and compares them with the spectral characteristics in the normal state, and monitors the temperature changes of the spindle and the environment around the spindle; this module is also responsible for performing a comprehensive analysis by combining data from multiple aspects.

[0130] The status evaluation and warning module is used to determine the spindle status evaluation indicators according to the processing feedback analysis results, such as cutting force stability index, vibration intensity index, temperature deviation index, etc., and establish a spindle status evaluation model based on these indicators to quantitatively evaluate the spindle status; when the evaluation result shows that the spindle status is abnormal, this module is responsible for immediately triggering warnings, including audible and visual alarms, SMS notifications, email reminders, etc., to ensure that the operator can detect and handle the abnormal spindle status in a timely manner. At the same time, according to the degree and urgency of the abnormal spindle status, corresponding warning strategies are executed.

[0131] The dynamic monitoring strategy adjustment module is used to dynamically adjust the monitoring strategy according to the processing feedback analysis results and the spindle status evaluation results; for example, when it is detected that the cutting force increases abnormally, the monitoring frequency of the spindle vibration and temperature can be increased; when it is detected that the vibration spectrum characteristics are abnormal, the monitoring of the cutting force and spindle speed can be strengthened. This module also automatically adjusts the monitoring parameters according to the real-time feedback during the five-axis machining process through an adaptive algorithm, and records information such as the reason, time, and effect of each monitoring strategy adjustment, providing a reference for subsequent optimization and improvement.

[0132] The dynamic monitoring system for the spindle status of a five-axis machine tool based on processing feedback in this application, through the collaborative work of each module, real-time monitors the key parameters during the machining process, and combines the machining characteristics of the five-axis machine tool for analysis and evaluation, realizing the dynamic monitoring and accurate evaluation of the spindle status of the five-axis machine tool. The system has high accuracy and real-time performance, can effectively handle various situations that may occur in the complex machining environment of the five-axis machine tool, ensures the safety and stability of the five-axis machine tool machining process, and optimizes the maintenance cost.

[0133] Compared with the prior art, the dynamic monitoring method and system for the spindle status of a five-axis machine tool based on processing feedback in this application have at least the following beneficial effects:

[0134] By introducing a processing feedback mechanism and dynamically adjusting the monitoring strategy, it is possible to more accurately evaluate the spindle status of a five-axis machine tool, reduce the possibility of false alarms and missed alarms, and improve the monitoring accuracy. A processing feedback mechanism is introduced. By real-time monitoring the key parameters during the machining process and combining the machining characteristics of the five-axis machine tool for analysis, the dynamic monitoring and accurate evaluation of the spindle status are realized. Compared with the prior art method that only relies on sensor data for monitoring, it has higher accuracy and real-time performance.

[0135] By real-time monitoring and evaluating the spindle status, it helps to timely detect and correct the deviations during the five-axis machine tool machining process, thereby improving the machining accuracy and product quality, and enhancing the machining accuracy.

[0136] Through the comprehensive evaluation and early warning mechanism, the abnormal state of the spindle can be detected and processed in a timely manner, avoiding safety problems such as machine tool shutdown or damage caused by spindle failures. By comprehensively evaluating data from multiple aspects such as cutting force, vibration, and temperature, the spindle state is comprehensively evaluated. When the evaluation result shows that the spindle is abnormal, the early warning mechanism can be immediately triggered to remind the operator to take corresponding measures, thus ensuring the safety and stability of the machining process. And it can dynamically adjust the monitoring strategy according to the machining feedback analysis result and the spindle state evaluation result, which helps to reasonably arrange the maintenance time and maintenance content, reduces the maintenance cost, and improves the utilization rate of the five-axis machine tool.

[0137] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A dynamic monitoring method for the spindle state of a five-axis machine tool based on processing feedback, characterized in that Including: S10. Real-time collect key data during the machining process through a variety of sensors installed on a five-axis machine tool; S20. Preprocess the key data; S30. Conduct machining feedback analysis based on the key data to obtain a machining feedback analysis result; S40. Evaluate the spindle state based on the key data to obtain a spindle state evaluation result; S50. Dynamically adjust the monitoring strategy according to the machining feedback analysis result and the spindle state evaluation result; In S10, real-time collect key data during the machining process through a variety of sensors installed on a five-axis machine tool, including: Real-time collect cutting force data during the machining process through a cutting force sensor installed on a five-axis machine tool at a first collection frequency; Real-time collect vibration data during the machining process through a three-axis vibration sensor installed on a five-axis machine tool at a second collection frequency; Real-time collect temperature data during the machining process through a temperature sensor installed on a five-axis machine tool at a third collection frequency; In S30, conduct machining feedback analysis based on the key data to obtain a machining feedback analysis result, including: Determine a cutting force threshold, compare and analyze the cutting force data with the cutting force threshold to obtain a first machining feedback analysis result; Determine a vibration spectrum feature threshold, compare and analyze the vibration data with the vibration spectrum feature threshold to obtain a second machining feedback analysis result; Determine a temperature threshold, compare and analyze the temperature data with the temperature threshold to obtain a third machining feedback analysis result; Conduct comprehensive machining feedback analysis on the cutting force data, the vibration data, and the temperature data to obtain a comprehensive machining feedback analysis result, including: Among them, F is the comprehensive processing feedback evaluation value, C is the cutting force stability index, is the threshold value of the cutting force stability index, V is the vibration intensity index, is the threshold value of the vibration intensity index, T is the temperature deviation index, is the threshold value of the temperature deviation index, α is the cutting force weight coefficient, β is the vibration weight coefficient, γ is the temperature weight coefficient, is the cutting force sensitivity coefficient, is the vibration sensitivity coefficient, is the temperature sensitivity coefficient; In S40, evaluate the spindle state based on the key data to obtain a spindle state evaluation result, including: S41. Determine spindle state evaluation indicators, where the spindle state evaluation indicators include a cutting force stability index, a vibration intensity index, and a temperature deviation index; S42. Construct a spindle state evaluation model according to the spindle state evaluation indicators, and the spindle state evaluation model is: Among them, S is the spindle state score, is the theoretical stable value of the cutting force, is the cutting force power weight, is the vibration power weight, is the temperature power weight; S43. Quantitatively evaluate the spindle state according to the spindle state evaluation model to obtain a spindle state evaluation result; S44. Determine the abnormal level of the spindle state according to the spindle state evaluation result, and issue a warning according to the abnormal level of the spindle state and a preset warning mechanism.

2. The dynamic monitoring method for the spindle state of a five-axis machine tool based on processing feedback according to claim 1, characterized in that, In S20, the preprocessing methods for the key data at least include cleaning, denoising, and filtering.

3. The dynamic monitoring method for the spindle state of a five-axis machine tool based on processing feedback according to claim 2, wherein In S50, dynamically adjust the monitoring strategy according to the machining feedback analysis result and the spindle state evaluation result, including: Determine the abnormal type of the spindle state according to the machining feedback analysis result and the spindle state evaluation result; Dynamically adjust the monitoring strategy according to the abnormal type of the spindle state, including: If the abnormal type of the spindle state is cutting force abnormality, increase the monitoring frequency of the vibration data and the temperature data; If the abnormal type of the spindle state is vibration spectrum feature abnormality, increase the monitoring frequency of the cutting force data and the temperature data; If the abnormal type of the main shaft state is temperature abnormality, the monitoring frequencies of the cutting force data and the vibration data are increased.

4. The dynamic monitoring method for the spindle state of a five-axis machine tool based on machining feedback according to claim 3, characterized in that, In S50, it also includes recording the reasons, times, and effects of each monitoring strategy adjustment.

5. A dynamic monitoring system for the spindle state of a five-axis machine tool based on machining feedback, based on the method for dynamic monitoring of the spindle state of a five-axis machine tool based on machining feedback according to claim 4, characterized in that, It includes: A sensor module for real-time collecting key data during the machining process through a variety of sensors installed on the five-axis machine tool; A data processing module for preprocessing the key data; A machining feedback analysis module for performing machining feedback analysis based on the key data to obtain a machining feedback analysis result; A state evaluation and warning module for performing main shaft state evaluation based on the key data to obtain a main shaft state evaluation result; A dynamic monitoring strategy adjustment module for dynamically adjusting the monitoring strategy according to the machining feedback analysis result and the main shaft state evaluation result.

6. The dynamic monitoring system for the spindle state of a five-axis machine tool based on machining feedback according to claim 5, characterized in that, The machining feedback analysis module includes: A first machining feedback analysis unit for determining a cutting force threshold, comparing the cutting force data with the cutting force threshold for comparative analysis to obtain a first machining feedback analysis result; A second machining feedback analysis unit for determining a vibration spectrum feature threshold, comparing the vibration data with the vibration spectrum feature threshold for comparative analysis to obtain a second machining feedback analysis result; A third machining feedback analysis unit for determining a temperature threshold, comparing the temperature data with the temperature threshold for comparative analysis to obtain a third machining feedback analysis result; A comprehensive machining feedback analysis unit for performing comprehensive machining feedback analysis on the cutting force data, the vibration data, and the temperature data to obtain a comprehensive machining feedback analysis result, including: Among them, F is the comprehensive processing feedback evaluation value, C is the cutting force stability index, is the threshold value of the cutting force stability index, V is the vibration intensity index, is the threshold value of the vibration intensity index, T is the temperature deviation index, is the threshold value of the temperature deviation index, α is the cutting force weight coefficient, β is the vibration weight coefficient, γ is the temperature weight coefficient, is the cutting force sensitivity coefficient, is the vibration sensitivity coefficient, is the temperature sensitivity coefficient.

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