A tool status monitoring method for high-speed milling process

Abstract

The invention discloses a method for monitoring tool status in a high-speed milling process, which relates to the technical field of intelligent manufacturing and mechanical processing. A plurality of groups of sensors are deployed to monitor the operating status of a tool head and a tool body of a tool in real time, and after processing, fatigue parameters of the tool head operating status and stability parameters of the tool body operating status are obtained, and then a tool head status fatigue index Zph and a tool body operating status imbalance index Zwd are respectively obtained, and then compared with a set corresponding matching table, and the surface flatness and dimensional error reference values ​​of a workpiece formed after the comparison are compared with the parameters of a target workpiece, so as to judge whether the current tool operating status meets the accuracy requirements of the target workpiece. The method judges the qualified status of the tool through multi-source data fusion and dynamic analysis, so as to improve the processing accuracy and efficiency of high-speed milling.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing and mechanical processing technology, and in particular to a tool state monitoring method for a high-speed milling process. Background Art

[0002] The field of intelligent manufacturing and mechanical processing mainly focuses on efficient and precise manufacturing technology and intelligent management of equipment. In intelligent manufacturing, the status monitoring and optimization of CNC machine tools are important research directions, especially in the typical processing method of high-speed milling. The operating status of the tool directly affects the surface quality and dimensional accuracy of the workpiece. High-speed milling is widely used in high-end manufacturing fields such as aerospace and automobile manufacturing due to its high processing efficiency and good surface finish. As the core component of processing, the status monitoring of the tool has become the main link to improve the processing quality and efficiency. Therefore, real-time monitoring of the tool status has become a key requirement in high-speed milling technology.

[0003] In the high-speed milling process, tool state monitoring faces many challenges, such as crack propagation, tool tip bending and tool body vibration problems. The accumulation of these conditions may lead to a decrease in the surface flatness of the workpiece and excessive dimensional errors. In addition, traditional tool monitoring methods often rely on a single signal source or intermittent detection, making it difficult to obtain tool state information in real time. This method has the problems of insufficient data coverage and low parameter correlation, which can easily lead to tool wear or failure not being discovered in time. In addition, the existing monitoring system has limited analysis capabilities for tool state parameters, especially in the analysis of complex parameters related to crack distribution, tool tip bending and tool body vibration stability. There is a lack of effective fusion processing and evaluation methods, which affects the surface flatness and dimensional accuracy of the workpiece and makes it difficult to meet the accuracy requirements of the target workpiece. Therefore, a multi-sensor fusion real-time monitoring method is urgently needed. By comprehensively collecting and comprehensively analyzing the operating state data of the tool head and the tool body, it is judged that the tool operating state in the current high-speed milling process meets the accuracy requirements of the target workpiece, so as to improve the accuracy and efficiency of high-speed milling. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for monitoring tool status in a high-speed milling process, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for monitoring tool status during high-speed milling process, comprising the following steps;

[0006] S1. Based on the deployed multiple sets of sensors, the tool head operation status and tool body operation status of the tool during the high-speed milling process are monitored to generate a tool head operation status data information set and a tool body operation status data information set;

[0007] S2, respectively processing the generated cutter head operation state data information set and cutter body operation state data information set to obtain the cutter head operation state fatigue parameter and cutter body operation state stability parameter during high-speed milling;

[0008] S3, analyzing the fatigue parameters of the cutter head operation state and the stability parameters of the cutter body operation state during the high-speed milling process, and obtaining the cutter head state fatigue index Zph and the cutter body operation state imbalance index Zwd during the high-speed milling process respectively;

[0009] S4. The cutter head fatigue index Zph and the cutter body operation state imbalance index Zwd in the high-speed milling process are compared with the set corresponding matching tables respectively, and the surface flatness of the workpiece and the maximum allowable value of the workpiece size error formed after the comparison are compared with the target workpiece parameters to determine the qualified state of the tool in the current high-speed milling process.

[0010] Preferably, the specific steps of S1 include:

[0011] S11, generating a set of data information on the operation status of the cutter head and a set of data information on the operation status of the cutter body, specifically in the following manner:

[0012] S111, deploying multiple sets of sensors around the tool in advance, wherein the multiple sets of sensors include microscopic imaging sensors, laser displacement sensors, three-dimensional laser scanners, high-precision rotary encoders, acceleration sensors, and torque sensors;

[0013] S112, monitoring the operating state of the cutter head of the tool during the high-speed milling process by deploying a microscopic imaging sensor and a laser displacement sensor, and then collecting data information on the operating state of the cutter head during the high-speed milling process, and generating a set of cutter head operating state data information after data preprocessing;

[0014] S113. The tool body operation status of the tool during high-speed milling is monitored in real time by deploying a three-dimensional laser scanner, a high-precision rotary encoder, an acceleration sensor and a torque sensor. After feature extraction, the tool body operation status data information during the high-speed milling process is collected. After data preprocessing, a tool body operation status data information set is generated.

[0015] Preferably, the specific steps of S2 include:

[0016] S21, processing the cutter head operation status data information set generated in S113 to obtain the cutter head operation status fatigue parameter during the high-speed milling process, specifically in the following manner:

[0017] S211. The crack lengths of each crack in the cutter head operation status data information set are summed up to obtain the total crack length Lcd of the cutter head during high-speed milling. The maximum crack position width of each crack in the cutter head operation status data information set is used as the crack width of each crack. The crack widths of each crack are summed up to obtain the total crack width Dkd of the cutter head during high-speed milling. At the same time, the number of cracks in the cutter head operation status data information set is counted to obtain the total number Nzs of cracks in the cutter head during high-speed milling.

[0018] Preferably, the specific step S2 also includes:

[0019] S212, according to the distance from the center point of the tail of the tool body to the tool tip and the original specification size of the factory tool, the actual length of the tool tip and the comparison length of the tool tip are obtained, the actual length of the tool tip and the comparison length of the tool tip are compared to obtain the ratio of the actual length of the tool tip to the comparison length of the tool tip, and the ratio of the actual length of the tool tip to the comparison length of the tool tip is compared with a preset curvature mapping table to obtain the tool tip curvature Qdj of the tool head during the high-speed milling process;

[0020] The fatigue parameters of the tool head operating state during high-speed milling are constructed based on the total crack length Lcd, total crack width Dkd, total number of cracks Nzs and tool tip curvature Qdj of the tool head during high-speed milling.

[0021] Preferably, the specific step S2 also includes:

[0022] S22, processing the generated cutter body operation status data information set to obtain the cutter body operation status imbalance parameter during the high-speed milling process, specifically in the following manner:

[0023] S221. Obtain the vibration amplitude of each monitoring period from the tool body operation status data information set, and then calculate the average of the vibration amplitude of each monitoring period to obtain the vibration frequency Pzd of the tool body during the high-speed milling process, and screen out the maximum vibration amplitude and the minimum vibration amplitude in the tool body operation status data information set, and then numerically sum and average the maximum vibration amplitude and the minimum vibration amplitude to obtain the vibration amplitude Pzf of the tool body during the high-speed milling process.

[0024] Preferably, the specific step S2 also includes:

[0025] S222, extracting the actual rotation axis position of the cutter body in each monitoring period from the cutter body operation status data information set, and combining it with the original rotation axis position of the cutter body obtained by geometric measurement and calibration when the cutter body is in a stationary state, by comparing the offset distance between the actual rotation axis of the cutter body and the original rotation axis of the cutter body, to determine the axis offset value of the cutter body in each monitoring period, and after averaging calculation, obtain the axis offset value Lpy of the cutter body in the high-speed milling process;

[0026] S223, constructing the imbalance parameter of the cutter body operation state during the high-speed milling process according to the vibration frequency Pzd, vibration amplitude Pzf and axis offset value Lpy of the cutter body during the high-speed milling process.

[0027] Preferably, the specific steps of S3 include:

[0028] S31, analyzing the fatigue parameters of the cutter head operation state during the high-speed milling process to obtain the cutter head state fatigue index Zph during the high-speed milling process;

[0029] S311, analyzing the fatigue parameters of the tool head operation state during the high-speed milling process, and obtaining the tool head state fatigue index Zph through fitting after dimensionless processing, wherein the tool head state fatigue index Zph is obtained by the following formula;

[0030] Wherein, Lcd represents the total crack length, Dkd represents the total crack width, Nzs represents the total number of cracks, Qdj represents the tool tip curvature, α1, α2, α3 and α4 represent the weight values ​​of the total crack length Lcd, the total crack width Dkd, the total number of cracks Nzs and the tool tip curvature Qdj, respectively, and A represents the first correction constant.

[0031] Preferably, the specific step S3 also includes:

[0032] S32, analyzing the stable parameters of the cutter body running state during the high-speed milling process to obtain the cutter body running state imbalance index Zwd during the high-speed milling process;

[0033] S321, analyzing the imbalance parameters of the cutter body operation state during the high-speed milling process, and obtaining the cutter body operation state imbalance index Zwd by fitting after dimensionless processing, wherein the cutter body operation state imbalance index Zwd is obtained by the following formula;

[0034]

[0035] In the formula, Pzd represents the vibration frequency, Pzf represents the vibration amplitude, and Lpy represents the center axis offset distance. It is expressed as the normal vibration frequency of the cutter body during high-speed milling. It represents the normal vibration amplitude of the tool body during high-speed milling. β1, β2 and β3 are all weight values. B represents the second correction constant.

[0036] Preferably, the specific step of S4 includes:

[0037] S41, acquiring parameters of the target workpiece to obtain the surface flatness of the target workpiece and the maximum allowable value of the size error of the target workpiece;

[0038] S42, comparing the cutter head fatigue index Zph during high-speed milling with the set corresponding matching table, and comparing the surface flatness of the workpiece formed after the comparison with the target workpiece parameters, specifically in the following manner:

[0039] S421, using historical data, setting a flatness matching table, and comparing the cutter head fatigue index Zph during the high-speed milling process with the flatness matching table to determine the surface flatness of the workpiece formed during the current high-speed milling process;

[0040] S422. Based on the surface flatness of the workpiece formed, compare it with the surface flatness of the target workpiece. If the surface flatness of the workpiece currently formed is greater than the surface flatness of the target workpiece, it is determined that the surface flatness of the workpiece currently formed meets the precision requirements of the target workpiece. Otherwise, it is determined that the surface flatness of the workpiece currently formed does not meet the precision requirements of the target workpiece.

[0041] Preferably, the specific step S4 also includes:

[0042] S43, comparing the tool body operation state imbalance index Zwd during high-speed milling with the set corresponding matching table, and comparing the maximum allowable value of the workpiece size error formed after the comparison with the target workpiece parameters, specifically in the following manner:

[0043] S431, using historical data, setting an error matching table, comparing the tool body operation state imbalance index Zwd during the high-speed milling process with the error matching table to determine the maximum allowable value of the workpiece size error formed during the current high-speed milling process;

[0044] S432, based on the formed maximum allowable value of the workpiece dimensional error, compare it with the maximum allowable value of the target workpiece dimensional error. If the formed maximum allowable value of the workpiece dimensional error is smaller than the maximum allowable value of the target workpiece dimensional error, it is determined that the currently formed maximum allowable value of the workpiece dimensional error meets the precision requirement of the target workpiece. Otherwise, it is determined that the currently formed maximum allowable value of the workpiece dimensional error does not meet the precision requirement of the target workpiece.

[0045] S44. Mark the tools corresponding to the workpiece surface flatness and the maximum allowable value of the workpiece dimensional error that meet the target workpiece precision requirements as qualified tools, and collect statistics on the tool head operating state fatigue parameters and tool body operating state imbalance parameters corresponding to the qualified tools to form tool operating state parameters of the qualified tools, and transmit the tool operating state parameters of the qualified tools to the cloud platform for visual display.

[0046] The present invention provides a method for monitoring tool status in a high-speed milling process, which has the following beneficial effects:

[0047] (1) Through multi-step, all-round data collection and analysis, it is determined whether the tool operation status during the current high-speed milling process meets the precision requirements of the target workpiece, so as to improve the precision and efficiency of high-speed milling processing; by collecting and comprehensively analyzing the operation status data of the cutter head and the cutter body, the shortcomings of the traditional monitoring method in terms of accuracy and real-time performance are solved. Microscopic imaging sensors, laser displacement sensors, three-dimensional laser scanners and high-precision rotary encoders are used to monitor the tool operation parameters in real time, including the total crack length Lcd, total crack width Dkd, total number of cracks Nzs of the cutter head, and the vibration frequency Pzd, vibration amplitude Pzf and axis offset value Lpy of the cutter body. Through multi-source data fusion technology, the fatigue parameters of the cutter head operation status and the stability parameters of the cutter body operation status are integrated and analyzed, and quantified into the cutter head state fatigue index Zph and the cutter body operation status index Zph. The dynamic imbalance index Zwd is used to reflect the overall state of the tool during the machining process; the core advantage of this method is that through comprehensive processing analysis and dynamic matching, the tool operation state is directly associated with the surface flatness and dimensional accuracy requirements of the formed workpiece. With the help of the matching table constructed with historical data, it is possible to judge in real time whether the current tool operation state meets the accuracy requirements of the target workpiece, and provide clear operation state feedback and decision-making basis. This method ensures the real-time and comprehensiveness of tool state monitoring, effectively improves the stability of the machining process, and reduces errors caused by tool imbalance and fatigue. Through real-time monitoring and comprehensive analysis, this method makes the surface flatness and dimensional accuracy of the workpiece reliably guaranteed, which not only reduces the risk of downtime, but also reduces the scrap rate and material waste, and improves the level of intelligent production as well as the accuracy and efficiency of high-speed milling.

[0048] (2) Multiple sets of sensors are used to comprehensively collect the operating status data of the tool head and tool body. Combined with feature extraction, a tool operating status parameter system is constructed to achieve real-time monitoring and scientific evaluation of the tool status. The total length Lcd, total width Dkd, total number of cracks Nzs and tool tip curvature Qdj of the tool head are obtained through microscopic imaging sensors and laser displacement sensors, which can carefully characterize the fatigue state of the tool head. At the same time, the vibration frequency Pzd, vibration amplitude Pzf and axis offset value Lpy of the tool body are collected through a three-dimensional laser scanner, a high-precision rotary encoder and an acceleration sensor to quantify the operating stability of the tool body. The beneficial effect of this method is that it breaks through the limitations of traditional reliance on a single monitoring signal and intermittent detection, and through multi-dimensional data fusion and dimensionless processing. , generating tool head fatigue parameters and tool body imbalance parameters, not only improves the monitoring accuracy, but also provides a complete data basis for subsequent comprehensive status analysis, dynamically combines the tool head and tool body operating status parameters, constructs the overall status of the tool operation status, and realizes a more comprehensive quantitative description of the tool status from fatigue characteristics to stability. Compared with traditional empirical judgment or single indicator analysis, this method has higher flexibility and accuracy, and can timely capture potential problems such as tool cracks, tool tip bending and tool body vibration changes, and effectively prevent the excessive surface flatness and dimensional errors of the workpiece caused by tool abnormalities. On the whole, this method improves the scientificity and intelligence level of high-speed milling tool monitoring, and provides strong technical support for ensuring machining accuracy, extending tool life and optimizing machining efficiency.

[0049] (3) Use historical data to establish a matching table of tool head fatigue index Zph and tool body imbalance index Zwd, and directly associate the tool operation status parameters with the surface flatness and dimensional error of the workpiece. By comparing the real-time monitoring data with the matching table, it is possible to quickly determine whether the current workpiece meets the target accuracy requirements. The beneficial effect of this method is reflected in the dynamic control of the quality of the workpiece, and it can monitor the deviation trend of the processing accuracy in real time. Compared with the traditional method that relies on post-detection, this method improves the efficiency and accuracy of processing quality management through the preset and real-time comparison of the matching table. In addition, this method can quickly identify processing quality problems caused by tool fatigue or imbalance, so as to take adjustment measures in advance, effectively reduce the processing scrap rate and rework rate, provide intelligent guarantee for precision processing in the field of high-end manufacturing, and improve the accuracy and efficiency of high-speed milling. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is a schematic flow chart of a method for monitoring tool status in a high-speed milling process. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example 1, please refer to Figure 1 ,The present invention provides a method for monitoring tool status in a high-speed milling process, comprising the following steps;

[0053] S1. Based on the deployed multiple sets of sensors, the tool head operation status and tool body operation status of the tool during the high-speed milling process are monitored to generate a tool head operation status data information set and a tool body operation status data information set;

[0054] S2, respectively processing the generated cutter head operation state data information set and cutter body operation state data information set to obtain the cutter head operation state fatigue parameter and cutter body operation state stability parameter during high-speed milling;

[0055] S3, analyzing the fatigue parameters of the cutter head operation state and the stability parameters of the cutter body operation state during the high-speed milling process, and obtaining the cutter head state fatigue index Zph and the cutter body operation state imbalance index Zwd during the high-speed milling process respectively;

[0056] S4. The cutter head fatigue index Zph and the cutter body operation state imbalance index Zwd in the high-speed milling process are compared with the set corresponding matching tables respectively, and the surface flatness of the workpiece and the maximum allowable value of the workpiece size error formed after the comparison are compared with the target workpiece parameters to determine the qualified state of the tool in the current high-speed milling process.

[0057] In this embodiment, the tool state monitoring method for the high-speed milling process is based on multiple groups of sensors to realize the monitoring of the operating state of the tool head and the tool body, and generates the tool head operating state fatigue parameters and the tool body operating state stability parameters through data processing to construct the tool operating state parameters. This method can not only quantify the tool operating state through the fatigue index Zph and the imbalance index Zwd, but also can combine the historical matching table to evaluate and control the surface flatness and dimensional error of the workpiece in real time. Compared with the traditional method that relies on a single signal source or intermittent detection, this method effectively solves the problems of insufficient data coverage, low parameter correlation, and untimely tool wear prediction through multi-dimensional data fusion and real-time analysis. It can directly associate the complex tool state with the precision requirements of the workpiece to ensure that the processing process is always in an efficient and high-precision state. The unique advantage of this technology is reflected in the combination of intelligence and precise control capabilities, which not only reduces the risk of tool failure, but also improves the surface quality and dimensional accuracy of the workpiece, meets the stringent requirements of high-end manufacturing fields such as aerospace and automobile manufacturing, provides a solution for tool state management in the field of intelligent manufacturing, and improves the overall efficiency and reliability of the processing process.

[0058] Example 2, please refer to Figure 1 , specifically: S1 specific steps include:

[0059] S11, generating a set of tool head operation status data information and a set of tool body operation status data information, specifically in the following manner:

[0060] S111, deploying multiple sets of sensors around the tool in advance, wherein the multiple sets of sensors include microscopic imaging sensors, laser displacement sensors, three-dimensional laser scanners, high-precision rotary encoders, acceleration sensors, and torque sensors;

[0061] S112, monitoring the operating state of the cutter head of the tool during the high-speed milling process by deploying a microscopic imaging sensor and a laser displacement sensor, and then collecting data information on the operating state of the cutter head during the high-speed milling process, and generating a set of cutter head operating state data information after data preprocessing;

[0062] S113. The tool body operation status of the tool during high-speed milling is monitored in real time by deploying a three-dimensional laser scanner, a high-precision rotary encoder, an acceleration sensor and a torque sensor. After feature extraction, the tool body operation status data information during the high-speed milling process is collected. After data preprocessing, a tool body operation status data information set is generated.

[0063] In this embodiment, the operating status of the tool is monitored by deploying multiple groups of sensors. Microscopic imaging sensors and laser displacement sensors are used to monitor the operating status of the tool head, while three-dimensional laser scanners, high-precision rotary encoders, acceleration sensors and torque sensors jointly monitor the operating status of the tool body. Through real-time monitoring and data preprocessing of these sensors, data information sets of the operating status of the tool head and tool body are generated respectively, providing comprehensive and accurate basic data for subsequent status analysis. The unique advantage of this method is that it breaks through the limitations of traditional single signal acquisition through the efficient fusion of multi-source sensors and improves the diversity and integrity of data. At the same time, it can capture the microscopic changes and overall dynamic response of the tool in real time, and realize accurate characterization of the tool operating status. This method lays the foundation for intelligent monitoring of high-speed milling, which not only reduces the quality risks caused by tool failure, but also improves the stability and production efficiency of the machining process, providing important technical support for precision machining in the field of high-end manufacturing.

[0064] Example 3, please refer to Figure 1 , specifically: S2 specific steps include:

[0065] S21, processing the cutter head operation status data information set generated in S113 to obtain the cutter head operation status fatigue parameter during the high-speed milling process, specifically in the following manner:

[0066] S211. The crack lengths of each crack in the cutter head operation status data information set are summed up to obtain the total crack length Lcd of the cutter head during high-speed milling. The maximum crack position width of each crack in the cutter head operation status data information set is used as the crack width of each crack. The crack widths of each crack are summed up to obtain the total crack width Dkd of the cutter head during high-speed milling. At the same time, the number of cracks in the cutter head operation status data information set is counted to obtain the total number Nzs of cracks in the cutter head during high-speed milling.

[0067] Specifically, the specific steps of S2 also include:

[0068] S212, according to the distance from the center point of the tail of the tool body to the tool tip and the original specification size of the factory tool, the actual length of the tool tip and the comparison length of the tool tip are obtained, the actual length of the tool tip and the comparison length of the tool tip are compared to obtain the ratio of the actual length of the tool tip to the comparison length of the tool tip, and the ratio of the actual length of the tool tip to the comparison length of the tool tip is compared with a preset curvature mapping table to obtain the tool tip curvature Qdj of the tool head during the high-speed milling process;

[0069] It should be noted that the ratio of the actual length of the tool tip to the comparative length of the tool tip is matched with the tool tip curvature corresponding to each preset ratio. Specifically, the actual length of the tool tip and the comparative length of the tool tip of multiple groups of tool samples in different bending states are measured, the ratio is calculated, and the tool tip curvature in the corresponding state is accurately measured by a three-dimensional laser scanner to construct a calibration data set of tool tip curvature. Subsequently, the relationship between the ratio and the curvature is modeled by using a data polynomial fitting method to generate a curvature mapping table corresponding to the ratio and the curvature. In actual application, the ratio of the actual length of the tool tip to the comparative length of the tool tip is calculated in real time and substituted into the curvature mapping table to accurately match the corresponding tool tip curvature.

[0070] The fatigue parameters of the tool head operating state during high-speed milling are constructed based on the total crack length Lcd, total crack width Dkd, total number of cracks Nzs and tool tip curvature Qdj of the tool head during high-speed milling.

[0071] It should be noted that the total crack length Lcd, total crack width Dkd, total number of cracks Nzs and tool tip curvature Qdj in the fatigue parameters of the tool head operating state during high-speed milling mainly affect the tool head contact stability and cutting performance during the cutting process. The increase in the length, width and number of tool head cracks will lead to a decrease in tool head strength, fluctuations in cutting force and local micro-chipping, leaving scratches and ripples on the machined surface. In addition, changes in the curvature of the tool head will cause uneven cutting depth, resulting in depressions and ridges on the machined surface, reducing the surface flatness of the workpiece. The fatigue accumulation effect of the tool head operating state weakens the tool's ability to maintain stable cutting during high-speed milling, thereby affecting the surface finish and overall flatness of the workpiece.

[0072] In this embodiment, through in-depth processing of the tool head operation status data information set, the fatigue parameters of the tool head are extracted, including the total crack length Lcd, the total crack width Dkd, the total number of cracks Nzs and the tool tip curvature Qdj. The data processing technology is used to quantitatively analyze the microscopic characteristics of cracks and curvature, and the fatigue parameters of the tool head operation status are constructed in combination with the original specification parameters of the tool and the actual processing data. The special advantage of this method is that the complex crack distribution, width fluctuation and tool tip bending characteristics are converted into quantifiable fatigue indicators, thereby achieving accurate characterization of the tool head health status. Compared with the traditional method that relies on manual inspection or indirect speculation, this method not only significantly improves the accuracy and real-time performance of fatigue status analysis, but also can capture the potential fatigue failure trend of the tool head, thereby providing strong support for the timely maintenance of the tool and the improvement of the quality of the processed parts. This method based on multi-dimensional parameter construction has opened up a new direction for the intelligent monitoring of the high-speed milling tool head status, while improving the stability and efficiency of the processing process and reducing the impact of tool failure on production.

[0073] Example 4, please refer to Figure 1 Specifically: S2 includes the following specific steps:

[0074] S22, processing the generated cutter body operation status data information set to obtain the cutter body operation status imbalance parameter during the high-speed milling process, specifically in the following manner:

[0075] S221. Obtain the vibration amplitude of each monitoring period from the tool body operation status data information set, and then calculate the average of the vibration amplitude of each monitoring period to obtain the vibration frequency Pzd of the tool body during the high-speed milling process, and screen out the maximum vibration amplitude and the minimum vibration amplitude in the tool body operation status data information set, and then numerically sum and average the maximum vibration amplitude and the minimum vibration amplitude to obtain the vibration amplitude Pzf of the tool body during the high-speed milling process.

[0076] Specifically, the specific steps of S2 also include:

[0077] S222, extracting the actual rotation axis position of the cutter body in each monitoring period from the cutter body operation status data information set, and combining it with the original rotation axis position of the cutter body obtained by geometric measurement and calibration when the cutter body is in a stationary state, by comparing the offset distance between the actual rotation axis of the cutter body and the original rotation axis of the cutter body, to determine the axis offset value of the cutter body in each monitoring period, and after averaging calculation, obtain the axis offset value Lpy of the cutter body in the high-speed milling process;

[0078] S223, constructing the imbalance parameter of the cutter body operation state during the high-speed milling process according to the vibration frequency Pzd, vibration amplitude Pzf and axis offset value Lpy of the cutter body during the high-speed milling process.

[0079] It should be noted that the vibration frequency Pzd, vibration amplitude Pzf and axis offset value Lpy in the imbalance parameters of the tool body operation state mainly affect the dimensional error of the workpiece due to the dynamic instability during the machining process. The fluctuations in the vibration frequency and amplitude will lead to uneven distribution of cutting force, resulting in changes in the local size of the workpiece, such as overcutting and undercutting. At the same time, the tool body axis offset value Lpy reflects the position change of the tool during operation. Excessive offset will directly affect the accuracy of the machining path, resulting in the accumulation of dimensional deviations and destroying the stability and accuracy of the machining. Especially in high-speed milling, the amplification effect is more significant.

[0080] In this embodiment, by processing the tool body operation status data information set, the method extracts the tool body operation status imbalance parameters, including vibration frequency Pzd, vibration amplitude Pzf and axis offset value Lpy. By comprehensively analyzing the vibration data of each monitoring period, comprehensive information on the vibration characteristics of the tool body is obtained. At the same time, combined with the offset value between the actual axis of the tool body and the original axis, the influence of the axis offset on the stability of the tool body is quantified. The special advantage of this method is that it uses multi-dimensional vibration and offset data to achieve a comprehensive characterization of the tool body imbalance state, directly linking the vibration and geometric characteristics with the tool body operation stability, which makes up for the shortcomings of traditional monitoring methods in analyzing vibration and geometric imbalance problems. Its unique real-time and refined analysis capabilities improve the tool body monitoring level during high-speed milling and ensure the stability and high-precision requirements of the machining process.

[0081] Example 5, please refer to Figure 1 , specifically: S3 specific steps include:

[0082] S31, analyzing the fatigue parameters of the cutter head operation state during the high-speed milling process to obtain the cutter head state fatigue index Zph during the high-speed milling process;

[0083] S311, analyzing the fatigue parameters of the tool head operation state during the high-speed milling process, and obtaining the tool head state fatigue index Zph through fitting after dimensionless processing, wherein the tool head state fatigue index Zph is obtained by the following formula;

[0084]

[0085] Wherein, Lcd represents the total crack length, Dkd represents the total crack width, Nzs represents the total number of cracks, Qdj represents the tool tip curvature, α1, α2, α3 and α4 represent the weight values ​​of the total crack length Lcd, the total crack width Dkd, the total number of cracks Nzs and the tool tip curvature Qdj, respectively, and A represents the first correction constant.

[0086] Specifically, the S3 specific steps also include:

[0087] S32, analyzing the stable parameters of the cutter body running state during the high-speed milling process to obtain the cutter body running state imbalance index Zwd during the high-speed milling process;

[0088] S321, by analyzing the imbalance parameters of the cutter body running state during the high-speed milling process, after dimensionless processing, the cutter body running state imbalance index Zwd is obtained by fitting, and the cutter body running state imbalance index Zwd is obtained by the following formula:

[0089]

[0090] In the formula, Pzd represents the vibration frequency, Pzf represents the vibration amplitude, and Lpy represents the center axis offset distance. It is expressed as the normal vibration frequency of the cutter body during high-speed milling. It represents the normal vibration amplitude of the tool body during high-speed milling. β1, β2 and β3 are all weight values. B represents the second correction constant.

[0091] In this embodiment, through in-depth analysis of the fatigue parameters of the tool head and the stability parameters of the tool body, the method calculates the tool head state fatigue index Zph and the tool body operation state imbalance index Zwd respectively, and through dimensionless processing and the use of fitting formulas, the total crack length Lcd, the total crack width Dkd, the total number of cracks Nzs, the tool tip curvature Qdj micro-fatigue characteristics, and the vibration frequency PzdP, the vibration amplitude Pzf and the center axis offset value Lpy macro-imbalance characteristics are accurately quantified into two independent comprehensive state indicators. The special advantage of this method is that through weight allocation and correction constant adjustment, the calculation model can be dynamically optimized according to actual processing requirements, providing flexible and accurate evaluation of the tool state. Compared with the traditional single parameter or intuitive empirical analysis method, this method is based on mathematical models and significantly improves the scientificity and operability of tool state monitoring, as well as the ability of accurate quantification.

[0092] Example 6, please refer to Figure 1 , specifically: S4 specific steps include:

[0093] S41, acquiring parameters of the target workpiece to obtain the surface flatness of the target workpiece and the maximum allowable value of the size error of the target workpiece;

[0094] S42, comparing the cutter head fatigue index Zph during high-speed milling with the set corresponding matching table, and comparing the surface flatness of the workpiece formed after the comparison with the target workpiece parameters, specifically in the following manner:

[0095] S421, using historical data, setting a flatness matching table, and comparing the cutter head fatigue index Zph during the high-speed milling process with the flatness matching table to determine the surface flatness of the workpiece formed during the current high-speed milling process;

[0096] S422. Based on the surface flatness of the workpiece formed, compare it with the surface flatness of the target workpiece. If the surface flatness of the workpiece currently formed is greater than the surface flatness of the target workpiece, it is determined that the surface flatness of the workpiece currently formed meets the precision requirements of the target workpiece. Otherwise, it is determined that the surface flatness of the workpiece currently formed does not meet the precision requirements of the target workpiece.

[0097] Among them, the setting method of the flatness matching table is as follows: based on historical data, the tool head state fatigue index Zph in each historical period is obtained, and the surface flatness of the workpiece formed in each historical period is monitored, and the tool head state fatigue index Zph in each historical period is matched one by one with the surface flatness of the workpiece formed in the corresponding historical period to construct a flatness matching table.

[0098] In this embodiment, by constructing a flatness matching table and correlating the tool head fatigue index Zph with the workpiece surface flatness, the method realizes real-time evaluation and precise control of the workpiece surface quality. The matching table established by historical data is used to directly link the tool fatigue state with the workpiece surface flatness, thereby providing a reliable reference benchmark for the current machining process. By comparing the current workpiece surface flatness with the target workpiece precision requirements, the method can quickly determine whether the tool state meets the machining precision requirements. The special advantage of this method is that it quantifies the complex tool fatigue state into a key indicator that directly reflects the workpiece surface flatness, breaking the limitations of traditional methods that rely on single experience and post-detection. Its dynamic matching capability not only improves the real-time monitoring efficiency of the machining process, but also effectively ensures the stability and consistency of the workpiece surface quality, especially in high-precision machining scenarios of high-speed milling, providing strong technical support for the realization of lean production and intelligent manufacturing.

[0099] Example 7, please refer to Figure 1 Specifically: S4 includes the following specific steps:

[0100] S43, comparing the tool body operation state imbalance index Zwd during high-speed milling with the set corresponding matching table, and comparing the maximum allowable value of the workpiece size error formed after the comparison with the target workpiece parameters, specifically in the following manner:

[0101] S431, using historical data, setting an error matching table, comparing the tool body operation state imbalance index Zwd during the high-speed milling process with the error matching table to determine the maximum allowable value of the workpiece size error formed during the current high-speed milling process;

[0102] S432, based on the formed maximum allowable value of the workpiece dimensional error, compare it with the maximum allowable value of the target workpiece dimensional error. If the formed maximum allowable value of the workpiece dimensional error is smaller than the maximum allowable value of the target workpiece dimensional error, it is determined that the currently formed maximum allowable value of the workpiece dimensional error meets the precision requirement of the target workpiece. Otherwise, it is determined that the currently formed maximum allowable value of the workpiece dimensional error does not meet the precision requirement of the target workpiece.

[0103] S44. Mark the tools corresponding to the workpiece surface flatness and the maximum allowable value of the workpiece dimensional error that meet the target workpiece precision requirements as qualified tools, and collect statistics on the tool head operating state fatigue parameters and tool body operating state imbalance parameters corresponding to the qualified tools to form tool operating state parameters of the qualified tools, and transmit the tool operating state parameters of the qualified tools to the cloud platform for visual display.

[0104] Among them, the error matching table is set as follows: based on historical data, the tool body operation state imbalance index Zwd in each historical period is obtained, and the maximum allowable value of the workpiece size error formed in each historical period is monitored, and the tool body operation state imbalance index Zwd in each historical period is matched one by one with the maximum allowable value of the workpiece size error formed in the corresponding historical period to construct an error matching table.

[0105] In this embodiment, by constructing an error matching table and correlating the tool body operation state imbalance index Zwd with the maximum allowable value of the workpiece dimensional error, the method realizes dynamic monitoring and effective control of the processing dimensional accuracy. The error matching table constructed by historical data is used to quantify the tool body imbalance state as a core indicator directly related to the processing dimensional error, thereby realizing the precise correspondence between the tool body operation state and the workpiece dimensional accuracy. By comparing the maximum allowable reference value of the current workpiece dimensional error with the target parameter, it can be quickly determined whether the tool operation state meets the dimensional accuracy requirements. The unique advantage of this method is that it integrates the influence of the tool body imbalance characteristics on the processing size, and overcomes the limitations of the traditional detection method with strong lag and insufficient accuracy through dynamic comparison of the matching table. This scheme provides a strong technical guarantee for precision control in the high-speed milling process, can improve the consistency of processing size and the accuracy compliance rate, and at the same time reduce the processing error and material waste caused by abnormal tool status, and provides a systematic and intelligent solution for precision processing in high-end manufacturing.

[0106] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring tool status in a high-speed milling process, characterized in that: The steps include: S1. Based on the deployed multiple sets of sensors, the tool head operation status and tool body operation status of the tool during the high-speed milling process are monitored to generate a tool head operation status data information set and a tool body operation status data information set; the specific steps of S1 include: S11, generating a set of data information on the operation status of the cutter head and a set of data information on the operation status of the cutter body, specifically in the following manner: S111, deploying multiple sets of sensors around the tool in advance, wherein the multiple sets of sensors include microscopic imaging sensors, laser displacement sensors, three-dimensional laser scanners, high-precision rotary encoders, acceleration sensors, and torque sensors; S112, monitoring the operating state of the cutter head of the tool during the high-speed milling process by deploying a microscopic imaging sensor and a laser displacement sensor, and then collecting data information on the operating state of the cutter head during the high-speed milling process, and generating a set of cutter head operating state data information after data preprocessing; S113, using the deployed three-dimensional laser scanner, high-precision rotary encoder, acceleration sensor and torque sensor to monitor the operation status of the tool body during the high-speed milling process in real time, and after feature extraction, collect the data information of the tool body operation status during the high-speed milling process, and after data preprocessing, generate a set of tool body operation status data information; S2, respectively processing the generated cutter head operation status data information set and cutter body operation status data information set to obtain the cutter head operation status fatigue parameter and cutter body operation status stability parameter during high-speed milling; S2 specifically comprises the following steps: S21, processing the cutter head operation status data information set generated in S113 to obtain the cutter head operation status fatigue parameter during the high-speed milling process, specifically in the following manner: S211, summing up the crack lengths of each crack in the tool head operation status data information set to obtain the total crack length Lcd of the tool head during the high-speed milling process, and taking the maximum crack position width of each crack in the tool head operation status data information set as the crack width of each crack, summing up the crack widths of each crack to obtain the total crack width Dkd of the tool head during the high-speed milling process, and at the same time counting the number of cracks in the tool head operation status data information set to obtain the total number Nzs of cracks in the tool head during the high-speed milling process; S212, according to the distance from the center point of the tail of the tool body to the tool tip and the original specification size of the factory tool, the actual length of the tool tip and the comparison length of the tool tip are obtained, the actual length of the tool tip and the comparison length of the tool tip are compared to obtain the ratio of the actual length of the tool tip to the comparison length of the tool tip, and the ratio of the actual length of the tool tip to the comparison length of the tool tip is compared with a preset curvature mapping table to obtain the tool tip curvature Qdj of the tool head during the high-speed milling process; The fatigue parameters of the tool head operation state during high-speed milling are constructed based on the total crack length Lcd, total crack width Dkd, total number of cracks Nzs and tool tip curvature Qdj of the tool head during high-speed milling. S222, extracting the actual rotation axis position of the cutter body in each monitoring period from the cutter body operation status data information set, and combining it with the original rotation axis position of the cutter body obtained by geometric measurement and calibration when the cutter body is in a stationary state, by comparing the offset distance between the actual rotation axis of the cutter body and the original rotation axis of the cutter body, to determine the axis offset value of the cutter body in each monitoring period, and after averaging calculation, obtain the axis offset value Lpy of the cutter body in the high-speed milling process; S223, constructing the imbalance parameter of the cutter body operation state during the high-speed milling process according to the vibration frequency Pzd, vibration amplitude Pzf and axis offset value Lpy of the cutter body during the high-speed milling process; S3, analyzing the fatigue parameters of the cutter head operation state and the stability parameters of the cutter body operation state during the high-speed milling process, and obtaining the cutter head state fatigue index Zph and the cutter body operation state imbalance index Zwd during the high-speed milling process respectively; the specific steps of S3 include: S31, analyzing the fatigue parameters of the cutter head operation state during the high-speed milling process to obtain the cutter head state fatigue index Zph during the high-speed milling process; S311, analyzing the fatigue parameters of the tool head operation state during the high-speed milling process, and obtaining the tool head state fatigue index Zph through fitting after dimensionless processing, wherein the tool head state fatigue index Zph is obtained by the following formula; ； Where Lcd represents the total length of the crack, Dkd represents the total width of the crack, Nzs represents the total number of cracks, and Qdj represents the curvature of the tool tip. and They are respectively represented as the weight values ​​of the total crack length Lcd, the total crack width Dkd, the total number of cracks Nzs and the tool tip curvature Qdj, and A is represented as the first correction constant; S32, analyzing the stable parameters of the cutter body running state during the high-speed milling process to obtain the cutter body running state imbalance index Zwd during the high-speed milling process; S321, analyzing the imbalance parameters of the cutter body operation state during the high-speed milling process, and obtaining the cutter body operation state imbalance index Zwd by fitting after dimensionless processing, wherein the cutter body operation state imbalance index Zwd is obtained by the following formula; ； In the formula, Pzd represents the vibration frequency, Pzf represents the vibration amplitude, and Lpy represents the center axis offset distance. It is expressed as the normal vibration frequency of the cutter body during high-speed milling. It is expressed as the normal vibration amplitude of the cutter body during high-speed milling. and All are expressed as weight values, and B is expressed as the second correction constant; S4. The cutter head fatigue index Zph and the cutter body operation state imbalance index Zwd in the high-speed milling process are compared with the set corresponding matching tables respectively, and the surface flatness of the workpiece and the maximum allowable value of the workpiece size error formed after the comparison are compared with the target workpiece parameters to determine the qualified state of the tool in the current high-speed milling process.

2. The method for monitoring tool status during high-speed milling process according to claim 1, characterized in that: The specific steps of S2 also include: S22, processing the generated cutter body operation status data information set to obtain the cutter body operation status imbalance parameter during the high-speed milling process, specifically in the following manner: S221. Obtain the vibration amplitude of each monitoring period from the tool body operation status data information set, and then calculate the average of the vibration amplitude of each monitoring period to obtain the vibration frequency Pzd of the tool body during the high-speed milling process, and screen out the maximum vibration amplitude and the minimum vibration amplitude in the tool body operation status data information set, and then numerically sum and average the maximum vibration amplitude and the minimum vibration amplitude to obtain the vibration amplitude Pzf of the tool body during the high-speed milling process.

3. The method for monitoring tool status during high-speed milling process according to claim 1, characterized in that: The specific steps of S4 include: S41, acquiring parameters of the target workpiece to obtain the surface flatness of the target workpiece and the maximum allowable value of the size error of the target workpiece; S42, comparing the cutter head fatigue index Zph during high-speed milling with the set corresponding matching table, and comparing the surface flatness of the workpiece formed after the comparison with the target workpiece parameters, specifically in the following manner: S421, using historical data, setting a flatness matching table, and comparing the cutter head fatigue index Zph during the high-speed milling process with the flatness matching table to determine the surface flatness of the workpiece formed during the current high-speed milling process; S422. Based on the surface flatness of the workpiece formed, compare it with the surface flatness of the target workpiece. If the surface flatness of the workpiece currently formed is greater than the surface flatness of the target workpiece, it is determined that the surface flatness of the workpiece currently formed meets the precision requirements of the target workpiece. Otherwise, it is determined that the surface flatness of the workpiece currently formed does not meet the precision requirements of the target workpiece.

4. The method for monitoring tool status during high-speed milling process according to claim 1, characterized in that: The specific steps of S4 also include: S43, comparing the tool body operation state imbalance index Zwd during high-speed milling with the set corresponding matching table, and comparing the maximum allowable value of the workpiece size error formed after the comparison with the target workpiece parameters, specifically in the following manner: S431, using historical data, setting an error matching table, comparing the tool body operation state imbalance index Zwd during the high-speed milling process with the error matching table to determine the maximum allowable value of the workpiece size error formed during the current high-speed milling process; S432, based on the formed maximum allowable value of the workpiece dimensional error, compare it with the maximum allowable value of the target workpiece dimensional error. If the formed maximum allowable value of the workpiece dimensional error is smaller than the maximum allowable value of the target workpiece dimensional error, it is determined that the currently formed maximum allowable value of the workpiece dimensional error meets the precision requirement of the target workpiece. Otherwise, it is determined that the currently formed maximum allowable value of the workpiece dimensional error does not meet the precision requirement of the target workpiece. S44. Mark the tools corresponding to the workpiece surface flatness and the maximum allowable value of the workpiece dimensional error that meet the target workpiece precision requirements as qualified tools, and collect statistics on the tool head operating state fatigue parameters and tool body operating state imbalance parameters corresponding to the qualified tools to form tool operating state parameters of the qualified tools, and transmit the tool operating state parameters of the qualified tools to the cloud platform for visual display.

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