Five-axis machine tool spindle state dynamic monitoring method and system based on machining feedback

Abstract

The invention belongs to the technical field of numerical control machine tool manufacturing, and particularly relates to a five-axis machine tool spindle state dynamic monitoring method and system based on machining feedback. The method comprises the steps that key data in the machining process are collected in real time through multiple sensors installed on the five-axis machine tool; preprocessing the key data; performing processing feedback analysis according to the key data to obtain a processing feedback analysis result; performing spindle state evaluation according to the key data to obtain a spindle state evaluation result; and dynamically adjusting a monitoring strategy according to a processing feedback analysis result and a spindle state evaluation result. According to the method, key parameters in the machining process are monitored in real time, deep analysis and evaluation are carried out in combination with the machining characteristics of the five-axis machine tool, and therefore dynamic monitoring and accurate evaluation of the state of the main shaft are achieved, the monitoring accuracy and real-time performance are improved, various challenges possibly occurring in the complex machining environment of the five-axis machine tool are effectively coped with, and the machining efficiency of the five-axis machine tool is improved. And the safety and the stability of the machining process are ensured.

Description

Technical Field

[0001] The present application belongs to the technical field of CNC machine tool manufacturing, and in particular relates to a method and system for dynamically monitoring the spindle status of a five-axis machine tool based on processing feedback. Background Art

[0002] In the existing technology, real-time spindle detection is the core link to ensure machining accuracy and operation stability, 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 real-time monitoring of temperature sensors and thermal imagers, the temperature distribution and thermal deformation state of the spindle can be intuitively displayed, providing an important basis for cooling effect evaluation and thermal management.

[0003] On the other hand, dynamic balancing detection also occupies an important position in the real-time detection of the spindle. The application of single-plane and double-plane dynamic balancing methods and on-site dynamic balancing technology can effectively reduce the imbalance of the spindle and significantly improve its stability and machining accuracy. In addition, static performance testing methods, including geometric accuracy measurement, surface finish inspection, material quality analysis and static mechanical properties evaluation, are also important measures to ensure the quality of the spindle and extend its service life. In summary, according to the specific type of spindle and working conditions, choosing a suitable real-time detection method, combined with regular maintenance, is the key to ensuring the stability of spindle performance and improving machining efficiency.

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

[0005] Therefore, it is desired to have a technical solution to overcome or at least alleviate at least one of the above-mentioned defects of the prior art. Summary of the invention

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

[0007] The technical solution of this application is: The first aspect of the present application provides a five-axis machine tool spindle state dynamic monitoring method based on processing feedback, comprising: S10, collect key data of the machining process in real time through various sensors installed on the five-axis machine tool; S20, preprocessing the key data; S30, performing processing feedback analysis according to the key data to obtain processing feedback analysis results; S40, performing spindle status assessment according to the key data to obtain a spindle status assessment result; S50, dynamically adjusting the monitoring strategy according to the machining feedback analysis result and the spindle state evaluation result.

[0008] In at least one embodiment of the present application, in S10, key data of the machining process is collected in real time by a variety of sensors installed on the five-axis machine tool, including: The cutting force sensor installed on the five-axis machine tool collects the cutting force data in the machining process in real time at the first collection frequency; The vibration data of the machining process is collected in real time at the second collection frequency by means of a three-dimensional vibration sensor installed on the five-axis machine tool; The temperature data of the machining process is collected in real time at the third collection frequency through the temperature sensor installed on the five-axis machine tool.

[0009] In at least one embodiment of the present application, in S20, the preprocessing method of the key data at least includes cleaning, denoising, and filtering.

[0010] In at least one embodiment of the present application, in S30, processing feedback analysis is performed according to the key data to obtain processing feedback analysis results, including: Determine a cutting force threshold, compare and analyze the cutting force data with the cutting force threshold, and obtain a first processing feedback analysis result; Determine a vibration spectrum characteristic threshold, compare and analyze the vibration data with the vibration spectrum characteristic threshold, and obtain a second processing feedback analysis result; Determine a temperature threshold, compare and analyze the temperature data with the temperature threshold, and obtain a third processing feedback analysis result; Performing a comprehensive processing feedback analysis on the cutting force data, the vibration data, and the temperature data to obtain a comprehensive processing feedback analysis result, including: ; ; ; ; in, F is the comprehensive processing feedback evaluation value, C is the cutting force stability index, is the cutting force stability index threshold, V is the vibration severity index, is the vibration severity index threshold, Tis 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.

[0011] In at least one embodiment of the present application, in S40, performing spindle status evaluation according to the key data to obtain a spindle status evaluation result includes: S41, determining a spindle state evaluation index, wherein the spindle state evaluation index includes a cutting force stability index, a vibration severity index, and a temperature deviation index; S42: construct a spindle state assessment model according to the spindle state assessment index, wherein the spindle state assessment model is: ; ; in, S For the main axis status score, is the theoretical stable value of cutting force, is the cutting force power weight, is the vibration power weight, is the temperature power weight; S43, performing quantitative evaluation on the spindle state according to the spindle state evaluation model to obtain a spindle state evaluation result; S44, determining the abnormality level of the spindle state according to the spindle state evaluation result, and issuing an early warning according to the abnormality level of the spindle state and a preset early warning mechanism.

[0012] In at least one embodiment of the present application, in S50, dynamically adjusting the monitoring strategy according to the machining feedback analysis result and the spindle state evaluation result includes: Determine the abnormal spindle state type according to the machining feedback analysis result and the spindle state evaluation result; Dynamically adjust the monitoring strategy according to the abnormal spindle status type, including: If the spindle state abnormality type is cutting force abnormality, increasing the monitoring frequency of the vibration data and the temperature data; If the spindle state abnormality type is abnormal vibration spectrum characteristics, increasing the monitoring frequency of the cutting force data and the temperature data; If the spindle state abnormality type is temperature abnormality, the monitoring frequency of the cutting force data and the vibration data is increased.

[0013] In at least one embodiment of the present application, S50 further includes recording the reason, time and effect of each monitoring strategy adjustment.

[0014] 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 five-axis machine tool spindle state dynamic monitoring method based on processing feedback as described above, comprising: The sensor module is used to collect key data of the machining process in real time through various sensors installed on the five-axis machine tool; A data processing module, used for preprocessing the key data; A processing feedback analysis module, used to perform processing feedback analysis based on the key data to obtain processing feedback analysis results; A status assessment and early warning module is used to perform a spindle status assessment based on the key data to obtain a spindle status assessment result; A dynamic monitoring strategy adjustment module is used to dynamically adjust the monitoring strategy according to the processing feedback analysis result and the spindle state evaluation result.

[0015] In at least one embodiment of the present application, the processing feedback analysis module includes: A first processing feedback analysis unit is used to determine a cutting force threshold, compare and analyze the cutting force data with the cutting force threshold, and obtain a first processing feedback analysis result; A second processing feedback analysis unit is used to determine a vibration spectrum characteristic threshold, compare and analyze the vibration data with the vibration spectrum characteristic threshold, and obtain a second processing feedback analysis result; A third processing feedback analysis unit is used to determine a temperature threshold, compare and analyze the temperature data with the temperature threshold, and obtain a third processing feedback analysis result; The comprehensive processing feedback analysis unit is used to perform comprehensive processing feedback analysis on the cutting force data, the vibration data and the temperature data to obtain a comprehensive processing feedback analysis result, including: ; ; ; ; in, F is the comprehensive processing feedback evaluation value, C is the cutting force stability index, is the cutting force stability index threshold, V is the vibration severity index, is the vibration severity 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.

[0016] The invention has at least the following beneficial technical effects: The five-axis machine tool spindle status dynamic monitoring method based on processing feedback of the present application realizes dynamic monitoring and precise evaluation of the spindle status by real-time monitoring of key parameters in the processing process and conducting in-depth analysis and evaluation based on the processing characteristics of the five-axis machine tool, thereby improving the accuracy and real-time performance of monitoring, effectively responding to various challenges that may arise in the five-axis machine tool in a complex processing environment, and ensuring the safety and stability of the processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a five-axis machine tool spindle state dynamic monitoring method based on processing feedback according to an embodiment of the present application; Figure 2 It is a schematic diagram of a five-axis machine tool spindle status dynamic monitoring system based on processing feedback according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the implementation of this application clearer, the technical scheme in the embodiment of this application will be described in more detail below in conjunction with the drawings in the embodiment of this application. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of this application, not all of them. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain this application, and should not be construed as limitations on this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The embodiments of this application are described in detail below in conjunction with the drawings.

[0019] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the scope of protection of the present application.

[0020] The following is combined with Figure 1 to Figure 2 This application is described in further detail.

[0021] This application provides a five-axis machine tool spindle state dynamic monitoring method based on processing feedback, such as Figure 1 As shown, the following steps are included: S10, collect key data of the machining process in real time through various sensors installed on the five-axis machine tool; S20, preprocessing key data; S30, performing processing feedback analysis according to key data to obtain processing feedback analysis results; S40, performing spindle status evaluation according to key data to obtain a spindle status evaluation result; S50, dynamically adjusting the monitoring strategy according to the processing feedback analysis results and the spindle status evaluation results.

[0022] In the five-axis machine tool spindle state dynamic monitoring method based on processing feedback of the present application, in S10, the data collection process specifically includes: The cutting force sensor installed on the five-axis machine tool collects the cutting force data in the machining process in real time at the first collection frequency; The vibration data of the machining process is collected in real time at the second collection frequency by means of a three-dimensional vibration sensor installed on the five-axis machine tool; The temperature data of the machining process is collected in real time at the third collection frequency through the temperature sensor installed on the five-axis machine tool.

[0023] In the application scenario, according to the structural characteristics and processing requirements of the five-axis machine tool, the sensors that need to be arranged generally include cutting force sensors, three-way vibration sensors, temperature sensors, etc. Each sensor is precisely installed in the key parts of the machine tool, such as the spindle, tool, workpiece support, etc. The type and number of sensors need to be optimized according to the specific processing scenario to ensure that the key parameter changes in the processing process can be fully and accurately captured. According to the real-time and accuracy requirements of the processing process, the data acquisition frequency and accuracy of the sensor are set. For parameters that change rapidly, such as cutting force and vibration, high-frequency sampling is used. For parameters with slower temperature changes, the sampling frequency can be appropriately reduced. At the same time, ensure that the sensor has high accuracy and stability to reduce errors and noise.

[0024] In the five-axis machine tool spindle status dynamic monitoring method based on processing feedback of the present application, in S20, the collected key data are preprocessed, including cleaning, denoising, filtering, etc. of the key data. Through the preprocessing process, invalid values, abnormal values ​​and duplicate values ​​in the key data are removed to ensure the accuracy, reliability and consistency of the key data. During the cleaning process, appropriate algorithms and parameters are selected according to the data characteristics and application scenarios; digital filtering technology can be used to remove 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 processing characteristics of the five-axis machine tool, a data preprocessing algorithm suitable for the five-axis machine tool spindle status monitoring is developed to automatically identify and process abnormal fluctuations and periodic changes in the data, providing a reliable data basis for subsequent analysis and evaluation.

[0025] In the five-axis machine tool spindle state dynamic monitoring method based on processing feedback of the present application, in S30, processing feedback analysis is performed on key data according to the processing characteristics of the five-axis machine tool to obtain processing feedback analysis results. Specifically: Determine a cutting force threshold, compare and analyze the cutting force data with the cutting force threshold, and obtain a first processing feedback analysis result; Determine a vibration spectrum characteristic threshold, compare and analyze the vibration data with the vibration spectrum characteristic threshold, and obtain a second processing feedback analysis result; Determine a temperature threshold, compare and analyze the temperature data with the temperature threshold, and obtain a third processing feedback analysis result; A comprehensive processing feedback analysis is performed on the cutting force data, vibration data and temperature data to obtain a comprehensive processing feedback analysis result.

[0026] In this embodiment, when performing comprehensive processing feedback analysis, the comprehensive processing feedback evaluation value is calculated by the comprehensive evaluation formula, and the cutting force stability index C , vibration severity index V , Temperature deviation index T The dynamic weight nonlinear combination is used to obtain the comprehensive processing feedback evaluation value F : ; in, F is the comprehensive processing feedback evaluation value, C is the cutting force stability index, is the cutting force stability index threshold, V is the vibration severity index, is the vibration severity 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.

[0027] Each weight coefficient , , Dynamic adjustment through real-time data: ; ; ; Each sensitivity coefficient , , Optimized based on historical data.

[0028] In this embodiment, the processing feedback analysis includes cutting force change analysis, vibration spectrum feature analysis, temperature change analysis and comprehensive analysis. Through cutting force change analysis, the cutting force change is monitored in real time, and its relationship with the spindle state is analyzed. By setting the cutting force threshold and algorithm, it is determined whether the cutting force is in the normal range. If it exceeds the normal range, it indicates that the spindle may have abnormal wear or tool breakage. Problems. Vibration spectrum feature analysis is to perform spectrum analysis on the collected vibration data, extract the main frequency and secondary frequency feature parameters, and compare them with the spectrum features under normal conditions to determine whether the spindle has abnormal vibration caused by imbalance, looseness and other problems. Temperature change analysis is to analyze the relationship between temperature and processing by monitoring the temperature changes of the spindle and the surrounding environment of the spindle. If the temperature changes abnormally, it indicates that the spindle may have problems such as poor lubrication and overheating. Comprehensive analysis is to combine cutting force data, vibration data, and temperature data for comprehensive analysis, and obtain quantitative comprehensive processing feedback analysis results through a comprehensive evaluation formula.

[0029] In the five-axis machine tool spindle state dynamic monitoring method based on processing feedback of the present application, in S40, the spindle state evaluation is performed according to the key data to obtain the spindle state evaluation result, including: S41, determining a spindle state evaluation index, where the spindle state evaluation index includes a cutting force stability index, a vibration severity index, and a temperature deviation index; S42, constructing a spindle condition assessment model according to the spindle condition assessment index; In this embodiment, the spindle state assessment model is specifically: Based on the fuzzy comprehensive evaluation method, the spindle status score is defined S : ; in, SFor the main axis status score, is the theoretical stable value of cutting force, is the cutting force power weight, is the vibration power weight, is the temperature power weight; Theoretical stable value of cutting force Obtained through historical processing data statistics or finite element simulation calibration; each power weight , , satisfy , obtained by dynamic allocation through the entropy weight method; S43, quantitatively evaluating the spindle state according to the spindle state evaluation model to obtain a spindle state evaluation result; S44. Determine the abnormality level of the spindle state according to the spindle state evaluation result, and issue an early warning according to the abnormality level of the spindle state and a preset early warning mechanism.

[0030] When the spindle status score S Exceeding critical threshold , the spindle status assessment result shows that the spindle status is abnormal, and an early warning is triggered immediately. Different early warning levels are divided in the early warning mechanism, and corresponding early warning strategies are configured for each early warning level. The spindle status abnormality level is determined according to the degree and urgency of the spindle status abnormality, and the early warning level to which the spindle status abnormality level belongs is judged, so as to issue an early warning according to the corresponding early warning strategy. For serious spindle status abnormalities, the machine should be stopped for inspection immediately; for minor spindle status abnormalities, the machine can be temporarily kept in place for enhanced monitoring and observation.

[0031] In the five-axis machine tool spindle state dynamic monitoring method 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 state evaluation result, including: Determine the abnormal spindle status type based on the processing feedback analysis results and the spindle status assessment results; Dynamically adjust the monitoring strategy according to the abnormal spindle status type, including: If the abnormal spindle status type is abnormal cutting force, increase the monitoring frequency of vibration data and temperature data; If the abnormal spindle status type is abnormal vibration spectrum characteristics, increase the monitoring frequency of cutting force data and temperature data; If the spindle status abnormality type is temperature abnormality, increase the monitoring frequency of cutting force data and vibration data.

[0032] Through the above method, the monitoring parameters are automatically adjusted according to the real-time feedback during the five-axis machine tool processing to realize the dynamic monitoring of the spindle state. Each monitoring frequency can be determined according to the quantitative characterization results of the processing feedback analysis and the spindle state evaluation.

[0033] In a preferred embodiment of the present application, S50 also includes recording information such as the reason, time, and effect of each monitoring strategy adjustment to provide a reference for subsequent optimization and improvement.

[0034] Based on the above-mentioned five-axis machine tool spindle state dynamic monitoring method based on processing feedback, the second aspect of the present application provides a five-axis machine tool spindle state dynamic monitoring system based on processing feedback, such as Figure 2 As shown, including: The sensor module is used to collect key data of the machining process in real time through various sensors installed on the five-axis machine tool; Data processing module, used for preprocessing key data; A processing feedback analysis module is used to perform processing feedback analysis based on key data to obtain processing feedback analysis results; The status assessment and early warning module is used to assess the spindle status based on key data and obtain the spindle status assessment result; 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.

[0035] The five-axis machine tool spindle state dynamic monitoring system based on processing feedback of this application selects and arranges sensors, such as cutting force sensors, three-way vibration sensors, temperature sensors, etc., and accurately installs them in key parts of the five-axis machine tool such as spindles, tools, workpiece supports, etc.; the sensor module is used to set the data acquisition frequency and accuracy of the sensor to ensure that the key parameter changes in the processing process can be fully and accurately captured, and the key data collected by the sensor is transmitted to the data processing module in real time by wired or wireless means, and stored and backed up. Encryption technology is used during data transmission to ensure data security and integrity. 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, abnormal values ​​and duplicate values, and removes noise interference in the key data to improve the signal-to-noise ratio of the data. In addition, the data processing module is also used to automatically identify and process abnormal fluctuations and periodic changes in the data through a data preprocessing algorithm suitable for five-axis machine tool spindle state monitoring, providing a reliable basis for subsequent analysis and evaluation.

[0036] In this embodiment, the processing feedback analysis module includes: A first processing feedback analysis unit is used to determine a cutting force threshold, compare and analyze the cutting force data with the cutting force threshold, and obtain a first processing feedback analysis result; A second processing feedback analysis unit is used to determine a vibration spectrum characteristic threshold, compare and analyze the vibration data with the vibration spectrum characteristic threshold, and obtain a second processing feedback analysis result; A third processing feedback analysis unit is used to determine a temperature threshold, compare and analyze the temperature data with the temperature threshold, and obtain a third processing feedback analysis result; The comprehensive processing feedback analysis unit is used to perform comprehensive processing feedback analysis on cutting force data, vibration data and temperature data to obtain comprehensive processing feedback analysis results, including: ; ; ; ; in, F is the comprehensive processing feedback evaluation value, C is the cutting force stability index, is the cutting force stability index threshold, V is the vibration severity index, is the vibration severity 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.

[0037] The five-axis machine tool spindle status dynamic monitoring system based on processing feedback of the present application, the processing feedback analysis module is used to monitor the changes of cutting force, vibration, temperature and other parameters in real time, and analyze them; by setting thresholds and algorithms, it is determined whether the cutting force is within the normal range, the spectral characteristics of the vibration data are analyzed, the characteristic parameters such as the main frequency and secondary frequency are extracted, and compared with the spectral characteristics under normal conditions, and the temperature changes of the spindle and the surrounding environment of the spindle are monitored; this module is also responsible for combining various data for comprehensive analysis.

[0038] The status assessment and early warning module is used to determine the spindle status assessment indicators, such as cutting force stability index, vibration severity index, temperature deviation index, etc., based on the processing feedback analysis results, and establish a spindle status assessment model based on these indicators to quantitatively assess the spindle status; when the assessment results show that the spindle status is abnormal, the module is responsible for immediately triggering an early warning, including sound and light alarms, SMS notifications, email reminders, etc., to ensure that the operator can promptly discover and handle the abnormal spindle status. At the same time, according to the degree and urgency of the abnormal spindle status, the corresponding early warning strategy is executed.

[0039] The dynamic monitoring strategy adjustment module is used to dynamically adjust the monitoring strategy based on the processing feedback analysis results and the spindle status evaluation results; for example, when an abnormal increase in cutting force is detected, the monitoring frequency of spindle vibration and temperature can be increased; when an abnormal vibration spectrum feature is detected, the monitoring of cutting force and spindle speed can be strengthened. This module also uses an adaptive algorithm to automatically adjust the monitoring parameters based on real-time feedback during the five-axis machine tool processing process, and records the reasons, time, and effects of each monitoring strategy adjustment, providing a reference for subsequent optimization and improvement.

[0040] The five-axis machine tool spindle status dynamic monitoring system based on processing feedback in this application monitors the key parameters in the processing process in real time through the collaborative work of various modules, and analyzes and evaluates the processing characteristics of the five-axis machine tool, thereby realizing dynamic monitoring and accurate evaluation of the five-axis machine tool spindle status. The system has high accuracy and real-time performance, and can effectively deal with various situations that may occur in the five-axis machine tool in a complex processing environment, ensuring the safety and stability of the five-axis machine tool processing process and optimizing maintenance costs.

[0041] Compared with the prior art, the five-axis machine tool spindle status dynamic monitoring method and system based on processing feedback of the present application has at least the following beneficial effects: By introducing a processing feedback mechanism and dynamically adjusting the monitoring strategy, the spindle status of the five-axis machine tool can be evaluated more accurately, reducing the possibility of false alarms and missed alarms and improving monitoring accuracy. The processing feedback mechanism was introduced to monitor the key parameters in the processing process in real time and analyze the processing characteristics of the five-axis machine tool to achieve dynamic monitoring and accurate evaluation of the spindle status. Compared with the existing method that only relies on sensor data for monitoring, it has higher accuracy and real-time performance.

[0042] Real-time monitoring and evaluation of the spindle status helps to promptly detect and correct deviations during five-axis machine tool processing, thereby improving processing accuracy and product quality and enhancing processing precision.

[0043] Through comprehensive evaluation and early warning mechanisms, abnormal spindle conditions can be discovered and handled in a timely manner, avoiding safety issues such as machine shutdown or damage caused by spindle failures. By comprehensively evaluating data on cutting force, vibration, temperature and other aspects, the spindle condition is comprehensively evaluated. When the evaluation results show that the spindle is abnormal, the early warning mechanism can be triggered immediately to remind the operator to take corresponding measures, thereby ensuring the safety and stability of the machining process. In addition, the monitoring strategy can be dynamically adjusted based on the machining feedback analysis results and the spindle condition evaluation results, which helps to reasonably arrange maintenance time and maintenance content, reduce maintenance costs, and improve the utilization rate of five-axis machine tools.

[0044] The above is only a specific implementation 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 a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A five-axis machine tool spindle state dynamic monitoring method based on processing feedback, characterized in that: include: S10, collect key data of the machining process in real time through various sensors installed on the five-axis machine tool; S20, preprocessing the key data; S30, performing processing feedback analysis according to the key data to obtain processing feedback analysis results; S40, performing spindle status assessment according to the key data to obtain a spindle status assessment result; S50, dynamically adjusting the monitoring strategy according to the machining feedback analysis result and the spindle state evaluation result.

2. The five-axis machine tool spindle state dynamic monitoring method based on processing feedback according to claim 1 is characterized in that: In S10, a variety of sensors installed on the five-axis machine tool collect key data during the machining process in real time, including: The cutting force sensor installed on the five-axis machine tool collects the cutting force data in the machining process in real time at the first collection frequency; The vibration data of the machining process is collected in real time at the second collection frequency by means of a three-dimensional vibration sensor installed on the five-axis machine tool; The temperature data of the machining process is collected in real time at the third collection frequency through the temperature sensor installed on the five-axis machine tool.

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

4. The five-axis machine tool spindle state dynamic monitoring method based on processing feedback according to claim 3 is characterized in that: In S30, processing feedback analysis is performed according to the key data to obtain processing feedback analysis results, including: Determine a cutting force threshold, compare and analyze the cutting force data with the cutting force threshold, and obtain a first processing feedback analysis result; Determine a vibration spectrum characteristic threshold, compare and analyze the vibration data with the vibration spectrum characteristic threshold, and obtain a second processing feedback analysis result; Determine a temperature threshold, compare and analyze the temperature data with the temperature threshold, and obtain a third processing feedback analysis result; Performing a comprehensive processing feedback analysis on the cutting force data, the vibration data, and the temperature data to obtain a comprehensive processing feedback analysis result, including: ； ； ； ； in, F is the comprehensive processing feedback evaluation value, C is the cutting force stability index, is the cutting force stability index threshold, V is the vibration severity index, is the vibration severity 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.

5. The five-axis machine tool spindle state dynamic monitoring method based on processing feedback according to claim 4 is characterized in that: In S40, the spindle state is evaluated according to the key data to obtain a spindle state evaluation result, including: S41, determining a spindle state evaluation index, wherein the spindle state evaluation index includes a cutting force stability index, a vibration severity index, and a temperature deviation index; S42: construct a spindle state assessment model according to the spindle state assessment index, wherein the spindle state assessment model is: ； ； in, S For the main axis status score, is the theoretical stable value of cutting force, is the cutting force power weight, is the vibration power weight, is the temperature power weight; S43, performing quantitative evaluation on the spindle state according to the spindle state evaluation model to obtain a spindle state evaluation result; S44, determining the abnormality level of the spindle state according to the spindle state evaluation result, and issuing an early warning according to the abnormality level of the spindle state and a preset early warning mechanism.

6. The five-axis machine tool spindle state dynamic monitoring method based on processing feedback according to claim 5 is characterized in that: In S50, the monitoring strategy is dynamically adjusted according to the machining feedback analysis result and the spindle state evaluation result, including: Determine the abnormal spindle state type according to the machining feedback analysis result and the spindle state evaluation result; Dynamically adjust the monitoring strategy according to the abnormal spindle status type, including: If the spindle state abnormality type is cutting force abnormality, increasing the monitoring frequency of the vibration data and the temperature data; If the spindle state abnormality type is abnormal vibration spectrum characteristics, increasing the monitoring frequency of the cutting force data and the temperature data; If the spindle state abnormality type is temperature abnormality, the monitoring frequency of the cutting force data and the vibration data is increased.

7. The five-axis machine tool spindle state dynamic monitoring method based on processing feedback according to claim 6 is characterized in that: S50 also includes recording the reason, time and effect of each monitoring strategy adjustment.

8. A five-axis machine tool spindle state dynamic monitoring system based on processing feedback, based on the five-axis machine tool spindle state dynamic monitoring method based on processing feedback according to claim 7, characterized in that: include: The sensor module is used to collect key data of the machining process in real time through various sensors installed on the five-axis machine tool; A data processing module, used for preprocessing the key data; A processing feedback analysis module, used to perform processing feedback analysis based on the key data to obtain processing feedback analysis results; A status assessment and early warning module is used to perform a spindle status assessment based on the key data to obtain a spindle status assessment result; A dynamic monitoring strategy adjustment module is used to dynamically adjust the monitoring strategy according to the processing feedback analysis result and the spindle state evaluation result.

9. The five-axis machine tool spindle state dynamic monitoring system based on processing feedback according to claim 8 is characterized in that: The processing feedback analysis module includes: A first processing feedback analysis unit is used to determine a cutting force threshold, compare and analyze the cutting force data with the cutting force threshold, and obtain a first processing feedback analysis result; A second processing feedback analysis unit is used to determine a vibration spectrum characteristic threshold, compare and analyze the vibration data with the vibration spectrum characteristic threshold, and obtain a second processing feedback analysis result; A third processing feedback analysis unit is used to determine a temperature threshold, compare and analyze the temperature data with the temperature threshold, and obtain a third processing feedback analysis result; The comprehensive processing feedback analysis unit is used to perform comprehensive processing feedback analysis on the cutting force data, the vibration data and the temperature data to obtain a comprehensive processing feedback analysis result, including: ； ； ； ； in, F is the comprehensive processing feedback evaluation value, C is the cutting force stability index, is the cutting force stability index threshold, V is the vibration severity index, is the vibration severity 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.

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