A tool service life dynamic adjustment method, device, equipment and medium

Abstract

The application discloses a tool life dynamic adjustment method, device, equipment and medium, relates to the numerical control machining technical field, and is used for solving the poor part quality technical problem caused by not replacing the severely worn tool in time in the prior art. The method comprises the following steps: for any preset moving time window, performing polar coordinate transformation on a cutting vibration signal collected in a target part machining process to obtain a plurality of polar coordinate angle value sets; performing image drawing and image processing on the plurality of polar coordinate angle value sets to obtain a monitoring characteristic value set; then, the monitoring characteristic value set is discriminated to determine whether to adopt alarm shutdown, use the stable state tool life or dynamically call the corresponding tool life parameter, and dynamically adjust the use life setting index corresponding to the current tool. Therefore, the application can avoid using the severely worn tool by "adopting the monitoring characteristic value to dynamically adjust the tool life", so as to improve the part quality.

Description

Technical Field

[0001] This application relates to the field of CNC machining technology, and provides a method, device, equipment and medium for dynamically adjusting the tool life. Background Technology

[0002] As is well known, in metal cutting, cutting tools gradually wear down and may even break or fracture over time. Since the tool is in direct contact with the workpiece, excessive wear and breakage will reduce the dimensional accuracy and surface quality of the parts, and may even lead to part scrap. In practical applications, aircraft structural components extensively use difficult-to-machine materials such as titanium alloys, and severe tool wear or breakage is common during their machining. Furthermore, the cutting condition relies heavily on the operator's experience, making it highly susceptible to human error and difficult to respond promptly to abnormal situations.

[0003] Furthermore, with the transformation of manufacturing models, automated processing on flexible production lines has become an effective way to improve the processing quality and efficiency of aircraft structural components. In existing technologies, flexible production lines are unattended. For tool management of difficult-to-machine materials, tool life application can be used, i.e., setting expected tool life parameters and replacing the tool with a new one when the set tool life indicator is reached before continuing processing. This method is suitable for normal tool wear and failure. For tool failure, a processing signal monitoring method can be used for identification. After failure is detected, the machine is stopped for inspection. If no problems are found, processing can continue. For example, patent 202310341181.3 discloses a tool monitoring method that proposes to use polar coordinate transformation of processing vibration signals for tool failure identification, thereby improving the accuracy of identifying tool failure in aircraft structural component processing. Another example is patent 202311340393.6, which discloses a method for identifying abnormal processing data. This method proposes an adaptive region segmentation processing and feature extraction method for polar coordinate images, which is beneficial to improving data processing efficiency and realizing the application of tool failure identification in processing monitoring.

[0004] However, for difficult-to-machine materials used in aircraft, although the two methods mentioned above can control tool wear and failure according to normal patterns to achieve the goal of automated machining on flexible lines, the cutting load on each tooth of the tool is not balanced due to factors such as the tool's structure and deformation. This results in some teeth wearing more severely than others. Therefore, abnormal situations of severe tool wear still exist during machining. That is, some tools, although not failing during use, have a service life significantly shorter than the expected tool life. In this case, the monitoring system does not alarm or stop the machine, nor has the expected tool life for replacement been reached. Continuing to use severely worn tools will cause problems such as poor surface quality or dimensional deviations.

[0005] Therefore, how to avoid poor part quality caused by improper monitoring and failure to replace severely worn cutting tools in a timely manner has become an urgent problem to be solved. Summary of the Invention

[0006] This application provides a method, device, equipment, and medium for dynamically adjusting the service life of cutting tools, which is used to solve the technical problem of poor part quality caused by failure to replace severely worn cutting tools in a timely manner in the prior art.

[0007] On the one hand, a method for dynamically adjusting the service life of a cutting tool is provided, the method comprising: For any preset moving time window, the cutting vibration signal collected during the processing of the target part is transformed into polar coordinates to obtain a set of multiple polar coordinate angle values ​​corresponding to the preset moving time window. The multiple polar coordinate angle value sets are plotted to obtain the polar coordinate image of the vibration signal corresponding to any preset movement time window; Image processing is performed on the polar coordinate image of the vibration signal to obtain a set of monitoring feature values; Based on the set of monitored feature values, determine whether to trigger an alarm and shut down the system; If it is determined that no alarm shutdown will be triggered, then based on the set of monitored feature values, it is determined whether the tool life index is the tool life in a stable state. If the tool life index is determined to be a tool lifespan that is not in a stable state, then the tool lifespan setting index corresponding to the current tool is dynamically adjusted according to the set of monitored feature values.

[0008] Optionally, the step of performing polar coordinate transformation on the cutting vibration signal collected during the machining of the target part for any preset moving time window to obtain a set of multiple polar coordinate angle values ​​corresponding to the preset moving time window includes: Data is acquired during the machining process of the target part to obtain the CNC program and cutting vibration signal; wherein, the NC program includes the tool drawing number and spindle speed; Based on the spindle speed and vibration sampling frequency, determine the number of vibration acceleration amplitude values ​​corresponding to each spindle revolution; The number of vibration acceleration data points within any preset moving time window is determined based on the duration of the preset moving time window and the vibration sampling frequency. The number of spindle rotations within any preset movement time window is determined based on the number of vibration acceleration amplitudes and the number of vibration acceleration data points. Based on the number of spindle rotations and the number of vibration acceleration amplitudes, the data points within any preset movement time window are segmented to obtain multiple datasets corresponding to any preset movement time window. Using a preset polar coordinate transformation formula, the vibration acceleration amplitude values ​​in the multiple datasets are transformed into polar coordinates to obtain a set of multiple polar coordinate angle values ​​corresponding to any preset moving time window.

[0009] Optionally, the step of plotting the multiple polar coordinate angle value sets to obtain the polar coordinate image of the vibration signal corresponding to any preset movement time window includes: The image is plotted on the multiple polar coordinate angle value sets to obtain the initial polar coordinate map corresponding to any preset moving time window; The initial polar coordinate image is normalized to obtain the polar coordinate image of the vibration signal corresponding to any preset moving time window.

[0010] Optionally, the step of performing image processing on the polar coordinate image of the vibration signal to obtain the set of monitoring feature values ​​includes: Connect the data points in the polar coordinate image of the vibration signal to construct a data point connectivity graph; wherein, the data point connectivity graph includes the set of data points corresponding to any preset moving time window, the set of data point connecting edges, and the data point distance metric weight; The vibration signal polar coordinate image is analyzed and processed using the k-means clustering method to obtain k sub-images; where the value of k is equal to the number of teeth of the cutting tool. Feature values ​​are calculated for the k subgraphs to obtain the initial feature values ​​corresponding to each of the k subgraphs; wherein, the initial feature values ​​are area feature values ​​or length feature values; the area feature values ​​are determined by the data point connectivity graph; The initial feature values ​​corresponding to each of the k subgraphs are normalized and sorted in descending order to obtain the set of monitoring feature values.

[0011] Optionally, the step of determining whether to initiate an alarm shutdown based on the set of monitored feature values ​​includes: Determine whether each monitoring feature value in the set of monitoring feature values ​​is greater than a preset alarm threshold; If it is determined that each monitoring feature value in the set of monitoring feature values ​​is not greater than the preset alarm threshold, then an alarm shutdown is initiated. If it is determined that all monitoring feature values ​​in the set of monitoring feature values ​​are greater than the preset alarm threshold, then it is determined that no alarm will be triggered and the system will be shut down.

[0012] Optionally, the step of determining whether the tool life index is a stable tool life based on the set of monitored feature values ​​includes: Determine whether each monitoring feature value in the set of monitoring feature values ​​is greater than a preset adjustment threshold; If it is determined that each monitoring feature value in the set of monitoring feature values ​​is greater than the preset adjustment threshold, then the tool life index is determined to be the tool life in a stable state. If it is determined that each monitoring feature value in the set of monitoring feature values ​​is not greater than a preset adjustment threshold, then the tool life index is determined to be in an unstable state.

[0013] Optionally, the step of dynamically adjusting the current tool life setting index based on the monitored feature value set includes: Determine the number of blade adjustments for each monitored feature value in the set of monitored feature values ​​that is less than a preset adjustment threshold; The number of high-load cutting teeth is determined based on the number of tooth adjustments and the number of cutting teeth; wherein, the number of cutting teeth is obtained from the cutting tool drawing number; Based on the number of high-load cutting teeth, the service life setting index corresponding to the current tool is retrieved from the candidate tool service life set; wherein, the candidate tool service life set contains multiple expected tool service life parameters, which are obtained based on experimental data and historical usage data.

[0014] On the one hand, a tool life dynamic adjustment device is provided, the device comprising: The polar coordinate transformation unit is used to perform polar coordinate transformation on the cutting vibration signal collected during the processing of the target part for any preset moving time window, so as to obtain a set of multiple polar coordinate angle values ​​corresponding to the preset moving time window. An image drawing unit is used to draw images of the multiple polar coordinate angle value sets to obtain a polar coordinate image of the vibration signal corresponding to any preset movement time window. The image processing unit is used to perform image processing on the polar coordinate image of the vibration signal to obtain a set of monitoring feature values; An alarm shutdown determination unit is used to determine whether to perform an alarm shutdown based on the set of monitored feature values. The lifespan strategy determination unit is used to determine whether the tool lifespan index is a stable tool lifespan based on the set of monitored feature values ​​if it is determined that no alarm shutdown will be performed. The tool life adjustment unit is used to dynamically adjust the tool life setting index corresponding to the current tool based on the set of monitored feature values ​​if the tool life index is determined to be in an unstable state.

[0015] On one hand, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0016] On the one hand, a storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement any of the methods described above.

[0017] Compared with the prior art, the beneficial effects of this application are as follows: In this application, when dynamically adjusting the tool life, firstly, for any preset moving time window, the cutting vibration signal collected during the machining of the target part can be transformed into polar coordinates to obtain multiple sets of polar coordinate angle values ​​corresponding to the preset moving time window; then, the multiple sets of polar coordinate angle values ​​are plotted to obtain a polar coordinate image of the vibration signal corresponding to the preset moving time window; next, the polar coordinate image of the vibration signal can be processed to obtain a set of monitoring feature values; then, based on the set of monitoring feature values, it can be determined whether to trigger an alarm shutdown; next, if it is determined that no alarm shutdown should be triggered, it can be determined whether the tool life index is a stable tool life; then, if it is determined that the tool life index is not a stable tool life, the tool life setting index corresponding to the current tool can be dynamically adjusted based on the set of monitoring feature values.

[0018] Based on this, in this application, the cutting vibration signal during the processing of the target part is transformed by polar coordinates, and a set of monitoring feature values ​​is obtained based on image rendering and image processing. Then, by judging the set of monitoring feature values, methods such as alarm shutdown, using the tool life in a stable state, and dynamically adjusting the set value of the corresponding tool life parameter are determined. Therefore, compared with the prior art (which uses a fixed tool life value, making it difficult to deal with the situation where the tool life is significantly reduced due to the influence of uneven tool load), this application can avoid the continued use of severely worn tools by matching a more accurate tool life, thereby improving the quality of the part (part surface quality or dimensions). Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application; Figure 2 A flowchart illustrating a method for dynamically adjusting the tool lifespan provided in an embodiment of this application; Figure 3 A schematic diagram of a cutting vibration signal within a preset moving time window provided in an embodiment of this application; Figure 4 A schematic diagram of a polar coordinate image of a vibration signal provided in an embodiment of this application; Figure 5 A schematic diagram illustrating feature value extraction provided in an embodiment of this application; Figure 6 A detection schematic diagram of alarm threshold and adjustment threshold provided in the embodiments of this application; Figure 7 A schematic diagram of a tool wear curve provided in an embodiment of this application; Figure 8 This is a schematic diagram of a tool life dynamic adjustment device provided in an embodiment of this application.

[0021] The diagram is labeled as follows: 10-Dynamic tool life adjustment device, 101-Processor, 102-Memory, 103-I / O interface, 104-Database, 80-Dynamic tool life adjustment device, 801-Polar coordinate transformation unit, 802-Image drawing unit, 803-Image processing unit, 804-Alarm shutdown determination unit, 805-Life strategy determination unit, 806-Tool life adjustment unit. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0023] As is well known, in metal cutting, cutting tools gradually wear down and may even break or fracture over time. Since the tool is in direct contact with the workpiece, excessive wear and breakage will reduce the dimensional accuracy and surface quality of the parts, and may even lead to part scrap. In practical applications, aircraft structural components extensively use difficult-to-machine materials such as titanium alloys, and severe tool wear or breakage is common during their machining. Furthermore, the cutting condition relies heavily on the operator's experience, making it highly susceptible to human error and difficult to respond promptly to abnormal situations.

[0024] Furthermore, with the transformation of manufacturing models, automated processing on flexible production lines has become an effective way to improve the processing quality and efficiency of aircraft structural components. In existing technologies, flexible production lines are unattended. For tool management of difficult-to-machine materials, tool life application can be used, i.e., setting expected tool life parameters and replacing the tool with a new one when the set tool life indicator is reached before continuing processing. This method is suitable for normal tool wear and failure. For tool failure, a processing signal monitoring method can be used for identification. After failure is detected, the machine is stopped for inspection. If no problems are found, processing can continue. For example, patent 202310341181.3 discloses a tool monitoring method that proposes to use polar coordinate transformation of processing vibration signals for tool failure identification, thereby improving the accuracy of identifying tool failure in aircraft structural component processing. Another example is patent 202311340393.6, which discloses a method for identifying abnormal processing data. This method proposes an adaptive region segmentation processing and feature extraction method for polar coordinate images, which is beneficial to improving data processing efficiency and realizing the application of tool failure identification in processing monitoring.

[0025] However, for difficult-to-machine materials used in aircraft, although the two methods mentioned above can control tool wear and failure according to normal patterns to achieve automated machining on flexible lines, the cutting load on each tooth is not balanced due to factors such as the tool's structure and deformation. This results in some teeth wearing more severely than others. Therefore, abnormal situations of severe tool wear still exist during machining. That is, some tools, although not failing during use, have a service life significantly shorter than the expected tool life. In this case, the monitoring system does not alarm or stop the machine, nor has the expected tool life for replacement been reached. Continuing to use severely worn tools will cause problems such as poor surface finish or dimensional inconsistencies.

[0026] Based on this, this application provides a method for dynamically adjusting tool life. In this method, firstly, for any preset moving time window, the cutting vibration signal collected during the machining of the target part can be transformed into polar coordinates to obtain multiple sets of polar coordinate angle values ​​corresponding to the preset moving time window; then, the multiple sets of polar coordinate angle values ​​are plotted to obtain a polar coordinate image of the vibration signal corresponding to the preset moving time window; next, the polar coordinate image of the vibration signal can be processed to obtain a set of monitoring feature values; then, based on the set of monitoring feature values, it can be determined whether to trigger an alarm shutdown; next, if it is determined that no alarm shutdown should be triggered, it can be determined whether the tool life index is a stable tool life; then, if it is determined that the tool life index is not a stable tool life, the tool life setting index corresponding to the current tool can be dynamically adjusted based on the set of monitoring feature values.

[0027] Based on this, in this application, the cutting vibration signal during the processing of the target part is transformed by polar coordinates, and a set of monitoring feature values ​​is obtained based on image rendering and image processing. Then, by judging the set of monitoring feature values, methods such as alarm shutdown, using the tool life in a stable state, and dynamically adjusting the set value of the corresponding tool life parameter are determined. Therefore, compared with the prior art (which uses a fixed tool life value, making it difficult to deal with the situation where the tool life is significantly reduced due to the influence of uneven tool load), this application can avoid the continued use of severely worn tools by matching a more accurate tool life, thereby improving the quality of the part (part surface quality or dimensions).

[0028] After introducing the design concept of the embodiments of this application, the following is a brief introduction to the application scenarios to which the technical solutions of the embodiments of this application can be applied. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and are not intended to limit the scope. In specific implementation, the technical solutions provided by the embodiments of this application can be flexibly applied according to actual needs.

[0029] like Figure 1 The diagram shown illustrates an application scenario provided by an embodiment of this application. This application scenario may include a tool lifespan dynamic adjustment device 10.

[0030] The tool life dynamic adjustment device 10 can be used to dynamically adjust the tool life, for example, it can be a personal computer (PC), server, or laptop. The tool life dynamic adjustment device 10 may include one or more processors 101, memory 102, I / O interfaces 103, and databases 104. Specifically, the processor 101 can be a central processing unit (CPU) or a digital processing unit, etc. The memory 102 can be volatile memory, such as random-access memory (RAM); the memory 102 can also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or the memory 102 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. The memory 102 can be a combination of the above-mentioned memories. The memory 102 can store some program instructions of the tool life dynamic adjustment method provided in the embodiments of this application. When these program instructions are executed by the processor 101, they can be used to implement the steps of the tool life dynamic adjustment method provided in the embodiments of this application, so as to solve the technical problem of poor part quality caused by failure to replace severely worn tools in time in the prior art. The database 104 can be used to store data such as cutting vibration signals, polar coordinate angle value sets, vibration signal polar coordinate images, and monitoring feature value sets involved in the solutions provided in the embodiments of this application.

[0031] In this embodiment, the tool life dynamic adjustment device 10 can obtain life adjustment instructions through the I / O interface 103. Then, the processor 101 of the tool life dynamic adjustment device 10 will solve the technical problem of poor part quality caused by the failure to replace severely worn tools in a timely manner according to the program instructions of the tool life dynamic adjustment method provided in this embodiment of the application in the memory 102. In addition, cutting vibration signals, polar coordinate angle value sets, vibration signal polar coordinate images, and monitoring feature value sets can be stored in the database 104.

[0032] Of course, the methods provided in the embodiments of this application are not limited to... Figure 1 The application scenarios shown can also be used in other possible scenarios, and this application embodiment does not impose any limitations. Figure 1The functions that the various devices in the application scenarios shown can achieve will be described in subsequent method embodiments, and will not be elaborated on here. Below, the methods of the embodiments of this application will be described in conjunction with the accompanying drawings.

[0033] like Figure 2 The diagram shown is a flowchart illustrating a method for dynamically adjusting the tool lifespan according to an embodiment of this application. This method can... Figure 1 The tool life dynamic adjustment device 10 is used to perform this operation. The specific process of this method is described below.

[0034] Step 201: For any preset moving time window, perform polar coordinate transformation on the cutting vibration signal collected during the processing of the target part to obtain the set of polar coordinate angle values ​​corresponding to any preset moving time window.

[0035] Specifically, firstly, within a preset processing time, data can be collected during the machining process of the target part to obtain the CNC program and cutting vibration signals; wherein, the NC program includes the tool drawing number and the spindle speed. In this application, the number of tool teeth Z can be obtained from the tool drawing number, for example, Z=4; and a vibration acceleration sensor can be used to analyze the collected cutting vibration signals, such as... Figure 3 The image shown is a schematic diagram of a cutting vibration signal within a preset moving time window provided in an embodiment of this application.

[0036] Then, the number of vibration acceleration amplitude values ​​per spindle revolution can be determined based on the spindle speed and vibration sampling frequency. For example, the spindle speed n=500 can be obtained by reading the speed information through the NC program, and the vibration sampling frequency can be obtained by the operator setting it. Then, the number of vibration acceleration amplitude values ​​N per spindle revolution can be calculated using the vibration acceleration amplitude calculation formula, which is as follows:

[0037] in, This refers to the vibration sampling frequency (i.e., the sampling frequency of the cutting vibration signal). Based on this, if the vibration sampling frequency is set... =2048Hz, spindle speed n=500, then according to the formula for calculating the amplitude of vibration acceleration, N=245.76, and rounding down, N is finally 245.

[0038] Next, the duration of the preset moving time window can be used. With vibration sampling frequency To determine the number of vibration acceleration data points within any preset movement time window. For example, it can be done through formulas. To determine the number of vibration acceleration data points within any preset motion time window. Based on this, if the duration of any preset movement time window is set to t=10s, =2048Hz, then, we can obtain =20480.

[0039] Then, based on the number of vibration acceleration amplitudes N and the number of vibration acceleration data points... Determine the number of spindle rotations within any preset movement time window. For example, it can be done through formulas. To determine the number of spindle rotations within any preset movement time window. Continuing with the previous example, =20480, N=245, then, we can obtain =83.

[0040] Next, we can determine the spindle rotation count. The data points within any preset movement time window are divided according to the number N of vibration acceleration amplitudes to obtain multiple datasets corresponding to any preset movement time window. For example, continuing with the previous example, That is, within any preset moving time window, there are a total of 83×245 datasets.

[0041] Finally, a preset polar coordinate transformation formula can be used to transform the multiple datasets. The vibration acceleration amplitude is transformed into polar coordinates to obtain a set of multiple polar coordinate angle values ​​corresponding to any preset movement time window. For example, it can be done through formulas. This allows us to obtain a set of multiple polar coordinate angle values ​​corresponding to any preset moving time window. .

[0042] Step 202: Plot the images of multiple polar coordinate angle value sets to obtain the polar coordinate image of the vibration signal corresponding to any preset motion time window.

[0043] Specifically, firstly, it is possible to process multiple polar coordinate angle value sets. Image rendering is performed to obtain an initial polar coordinate image corresponding to any preset motion time window. Then, this initial polar coordinate image can be normalized to obtain a vibration signal polar coordinate image corresponding to any preset motion time window. The maximum value of the radius axis of the vibration signal polar coordinate image is the maximum value of the vibration acceleration amplitude within each preset motion time window, and the minimum value of the radius axis is set to 0. Figure 4The image shown is a schematic diagram of a polar coordinate image of a vibration signal provided in an embodiment of this application.

[0044] Step 203: Perform image processing on the polar coordinate image of the vibration signal to obtain a set of monitoring feature values.

[0045] Specifically, firstly, the polar coordinate image of the vibration signal can be connected to form a weighted data point connectivity graph G=(V, E, W); wherein, the data point connectivity graph includes the data point set V (i.e., the vertex set of vibration acceleration amplitude) corresponding to any preset moving time window, the data point connection edge set E (the set formed by the connection edges between data points), and the data point distance metric weight W (the set formed by the weights of the distance metric between data points). The data point connectivity graph is used to describe the connectivity between data points.

[0046] Then, the k-means clustering method can be used to analyze and process the polar coordinate image of the vibration signal to obtain k sub-images, thereby dividing the data point set corresponding to the polar coordinate image of the vibration signal into multiple sub-images, each of which is a partition; where the value of k is equal to the number of tool teeth; for example, continuing with the previous example, the number of tool teeth Z=4 is obtained from the tool drawing number, so k=4 here.

[0047] Next, eigenvalues ​​can be calculated for these k subgraphs to obtain the initial eigenvalues ​​corresponding to each of the k subgraphs; wherein, the initial eigenvalues ​​are area eigenvalues. or length characteristic value The area characteristic value It can be determined by the connectivity graph of the data points. For example... Figure 5 The diagram shown is a schematic representation of feature value extraction provided in an embodiment of this application.

[0048] Finally, the initial feature values ​​corresponding to each of these k subgraphs can be normalized and sorted in descending order to obtain a set of monitoring feature values. For example, if the area feature value is selected as the initial feature value, the monitoring feature value sets (1, 0.8, 0.65, 0.2) and (1, 0.85, 0.5, 0.4) can be obtained under the two different cutting conditions A and B respectively.

[0049] Step 204: Determine whether to trigger an alarm and shut down the system based on the set of monitored feature values.

[0050] Specifically, firstly, it can be determined whether each monitoring feature value in the set of monitoring feature values ​​is greater than a preset alarm threshold; for example, assuming a preset alarm threshold... = 0.3. For example... Figure 6 The diagram shown is a detection schematic of the alarm threshold and adjustment threshold provided in an embodiment of this application.

[0051] Then, if it is determined that each monitoring feature value in the set of monitoring feature values ​​is not greater than the preset alarm threshold, an alarm shutdown is initiated; for example, continuing with the previous example, the preset alarm threshold... = 0.3. For tool cutting condition A, there is a monitoring feature value of 0.2 in the monitoring feature value set (1, 0.8, 0.65, 0.2) that is less than the preset alarm threshold. If so, an alarm should be triggered, the machine should be shut down, and an inspection should be conducted.

[0052] Conversely, if it is determined that all monitoring feature values ​​in the set of monitoring feature values ​​are greater than the preset alarm threshold, then it is determined not to trigger an alarm and shut down the system. For example, continuing with the previous example, the preset alarm threshold... = 0.3. For tool cutting condition B, all monitored feature values ​​in the monitored feature value set (1, 0.85, 0.5, 0.4) are greater than the alarm threshold. If so, then it is determined that no alarm will be triggered and the machine will be shut down.

[0053] Step 205: If it is determined that no alarm shutdown will be triggered, then based on the set of monitored characteristic values, determine whether the tool life index is the tool life in a stable state.

[0054] Specifically, if it is determined that no alarm shutdown will be triggered, it is necessary to first determine whether each monitoring characteristic value in the monitoring characteristic value set is greater than the preset adjustment threshold; for example, assuming a preset alarm threshold... = 0.7.

[0055] Then, if it is determined that each monitoring feature value in the monitoring feature value set is greater than the preset adjustment threshold, the tool life index is determined to be the tool life in a stable state.

[0056] Conversely, if it is determined that each monitored feature value in the set of monitored feature values ​​is not uniformly greater than a preset adjustment threshold, then the tool life index is determined to be in an unstable state. For example, continuing with the previous example, a preset adjustment threshold is set... = 0.7, for both tool cutting condition A and tool cutting condition B, the monitored characteristic values ​​in the monitored characteristic value set are not all greater than the alarm threshold. If the tool life index is not in a stable state, it is necessary to further determine the tool load state based on the characteristic value and adjust the corresponding tool life parameters accordingly.

[0057] Step 206: If the tool life index is determined to be a tool life in an unstable state, then the tool life setting index corresponding to the current tool is dynamically adjusted according to the set of monitored feature values.

[0058] Specifically, if the tool life index is determined to be a tool life that is not in a stable state, then it is necessary to first determine the number of tooth adjustments required for the monitored characteristic values ​​in the monitored characteristic value set that are less than the preset adjustment threshold. For example, continuing with the previous example, a preset adjustment threshold is used. = 0.7. For cutting condition B, in the set of monitored feature values ​​(1, 0.85, 0.5, 0.4), there are two monitored feature values, 0.5 and 0.4, that are less than 0.7. Therefore, the number of tooth adjustments... =2.

[0059] Then, the number can be adjusted according to the number of cutting teeth. Determine the number of teeth for high load based on the number of teeth Z of the cutting tool. The number of teeth on the cutting tool is obtained from the tool drawing number; for example, continuing with the previous example, according to the formula... The number of high-load cutting teeth can be determined. =4-2=2.

[0060] Finally, based on the number of teeth under high load, the service life setting index corresponding to the current tool is retrieved from the candidate tool service life set. This candidate tool service life set contains multiple expected tool service life parameters, which are obtained based on experimental data and historical usage data. For example, the expected service life value of the tool with 2 teeth retrieved from the candidate tool service life set can be set as the service life setting index for the current tool. Of course, the stable tool service life is also obtained based on experimental data and historical usage data.

[0061] In this application, during the subsequent machining of the target part, the cutting vibration signal can be continuously monitored. When the monitored characteristic value changes, the tool life determination strategy can be dynamically adjusted, and the corresponding expected tool life parameters can be called to control the tool usage time.

[0062] In one possible implementation, when obtaining the expected parameters of each tool life in the tool life set based on experimental data and historical usage data, the determination can be made in the following manner: For a cutting tool with Z teeth and its machining parameters, experimental cutting tools with 1 to Z teeth can be obtained by installing different numbers of inserts or preparing different numbers of teeth. The wear amount of cutting tools with different numbers of teeth during machining is tested and collected to form tool wear curve data. In this application, indexable insert cutting tools are used. Cutting tools with one to four inserts are used, and machining is performed using the corresponding program parameters. The wear amount is measured, and the corresponding tool wear curves are obtained through fitting multiple experimental data. Figure 7The image shown is a schematic diagram of a tool wear curve provided in an embodiment of this application.

[0063] Then, the tool wear standard can be set to 250μm. The cutting time corresponding to different numbers of teeth (one to four) can be calculated. Based on the cutting time and the corresponding number of teeth, the expected tool life parameters T1, T2, T3, and T4 can be formed. For example, the expected tool life parameter corresponding to two teeth is calculated as T2. For cutting condition B, the tool life setting index is adjusted, and the tool usage time is controlled based on the value of T2.

[0064] In summary, regarding the need for tool control in the automated machining of difficult-to-machine material structural parts for aircraft, the current approach of using fixed-value tool life and combining tool failure monitoring is insufficient to effectively control severe wear caused by uneven load. This application addresses this issue by performing polar coordinate transformation on the cutting vibration signals collected during the target part machining process, and obtaining a set of monitoring feature values ​​based on image rendering and processing. Then, by discriminating the monitoring feature value set, methods such as alarm shutdown, using a stable tool life, and dynamically adjusting the set value of the corresponding tool life parameter based on the monitoring feature value set are determined. Therefore, compared to existing technologies (which use fixed-value tool life and struggle to handle the severe wear and significant reduction in tool life caused by uneven tool load), this application can avoid the continued use of severely worn tools by matching a more accurate tool life, thereby improving part quality (part surface quality or dimensions).

[0065] Based on the same inventive concept, embodiments of this application provide a tool life dynamic adjustment device 80, such as... Figure 8 As shown, the tool life dynamic adjustment device 80 includes: The polar coordinate transformation unit 801 is used to perform polar coordinate transformation on the cutting vibration signal collected during the processing of the target part for any preset moving time window, so as to obtain a set of multiple polar coordinate angle values ​​corresponding to any preset moving time window. The image drawing unit 802 is used to draw images of multiple polar coordinate angle value sets to obtain a polar coordinate image of the vibration signal corresponding to any preset movement time window. The image processing unit 803 is used to perform image processing on the polar coordinate image of the vibration signal to obtain a set of monitoring feature values; The alarm shutdown determination unit 804 is used to determine whether to perform an alarm shutdown based on the set of monitored characteristic values. The lifespan strategy determination unit 805 is used to determine whether the tool lifespan index is a stable tool lifespan based on the set of monitored feature values ​​if it is determined that no alarm shutdown will be performed. The tool life adjustment unit 806 is used to dynamically adjust the tool life setting index corresponding to the current tool based on the set of monitored feature values ​​if the tool life index is determined to be in an unstable state.

[0066] Optionally, the polar coordinate transformation unit 801 is also used for: Data is acquired during the machining process of the target part to obtain the CNC program and cutting vibration signals; the NC program includes the tool drawing number and spindle speed. Based on the spindle speed and vibration sampling frequency, determine the number of vibration acceleration amplitude values ​​corresponding to each spindle revolution; Based on the duration of the preset motion time window and the vibration sampling frequency, determine the number of vibration acceleration data points within any preset motion time window; The number of spindle rotations within any preset movement time window is determined based on the number of vibration acceleration amplitudes and the number of vibration acceleration data points. Based on the number of spindle rotations and the number of vibration acceleration amplitudes, the data points within any preset motion time window are segmented to obtain multiple datasets corresponding to any preset motion time window. By using a preset polar coordinate transformation formula, the vibration acceleration amplitude values ​​in multiple datasets are transformed into polar coordinates to obtain a set of multiple polar coordinate angle values ​​corresponding to any preset moving time window.

[0067] Optionally, the image drawing unit 802 is also used for: Plot the image using multiple polar coordinate angle value sets to obtain the initial polar coordinate map corresponding to any preset moving time window; The initial polar coordinate image is normalized to obtain the polar coordinate image of the vibration signal corresponding to any preset moving time window.

[0068] Optionally, the image processing unit 803 is also used for: Connect the data points in the polar coordinate image of the vibration signal to construct a data point connectivity graph; the data point connectivity graph includes the set of data points corresponding to any preset moving time window, the set of data point connecting edges, and the distance metric weight of the data points; The k-means clustering method was used to analyze and process the polar coordinate image of the vibration signal to obtain k sub-images; where the value of k is equal to the number of teeth of the cutting tool. Eigenvalues ​​are calculated for each of the k subgraphs to obtain the initial eigenvalues ​​for each subgraph. The initial eigenvalues ​​are either area eigenvalues ​​or length eigenvalues. The area eigenvalues ​​are determined by the connectivity of the data points in the graph. The initial feature values ​​corresponding to each of the k subgraphs are normalized and sorted in descending order to obtain the set of monitoring feature values.

[0069] Optionally, the alarm shutdown determination unit 804 is also used for: Determine whether each monitoring feature value in the set of monitoring feature values ​​is greater than the preset alarm threshold; If it is determined that each monitoring feature value in the set of monitoring feature values ​​is not greater than the preset alarm threshold, then an alarm shutdown will be initiated. If it is determined that all monitoring feature values ​​in the set of monitoring feature values ​​are greater than the preset alarm threshold, then it is determined not to trigger an alarm and shut down the system.

[0070] Optionally, the lifetime strategy determination unit 805 is also used for: Determine whether each monitoring feature value in the monitoring feature value set is greater than the preset adjustment threshold; If it is determined that all the monitored feature values ​​in the set of monitored feature values ​​are greater than the preset adjustment threshold, then the tool life index is determined to be the tool life in a stable state. If it is determined that each monitoring feature value in the set of monitoring feature values ​​is not greater than the preset adjustment threshold, then the tool life index is determined to be in an unstable state.

[0071] Optionally, the tool life adjustment unit 806 is also used for: Determine the number of blades to adjust if the monitored feature value in the set of monitored feature values ​​is less than the preset adjustment threshold; The number of high-load cutting teeth is determined based on the number of tooth adjustments and the number of cutting teeth; the number of cutting teeth is obtained from the cutting tool drawing number. Based on the number of high-load cutting teeth, the service life setting index corresponding to the current tool is retrieved from the candidate tool service life set. The candidate tool service life set contains multiple expected tool service life parameters, which are obtained based on experimental data and historical usage data.

[0072] The tool life dynamic adjustment device 80 can be used for execution. Figure 2 The method performed in the illustrated embodiment is described above. Therefore, the functions that each functional module of the tool life dynamic adjustment device 80 can achieve can be referred to. Figure 2 The embodiments shown are described in detail below.

[0073] In some possible implementations, various aspects of the methods provided in this application can also be implemented as a program product comprising program code that, when run on a computer device, causes the computer device to perform the steps of the methods according to the various exemplary embodiments of this application described above. For example, the computer device may perform actions such as... Figure 2 The method performed in the illustrated embodiment.

[0074] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. Alternatively, if the integrated units of this application are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0075] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0076] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for dynamically adjusting the service life of a cutting tool, characterized in that, The method includes: For any preset moving time window, the cutting vibration signal collected during the processing of the target part is transformed into polar coordinates to obtain a set of multiple polar coordinate angle values ​​corresponding to the preset moving time window. The multiple polar coordinate angle value sets are plotted to obtain the polar coordinate image of the vibration signal corresponding to any preset movement time window; Image processing is performed on the polar coordinate image of the vibration signal to obtain a set of monitoring feature values; Based on the set of monitored feature values, determine whether to trigger an alarm and shut down the system; If it is determined that no alarm shutdown will be triggered, then based on the set of monitored feature values, it is determined whether the tool life index is the tool life in a stable state. If the tool life index is determined to be a tool lifespan that is not in a stable state, then the tool lifespan setting index corresponding to the current tool is dynamically adjusted according to the set of monitored feature values.

2. The method as described in claim 1, characterized in that, The step of performing polar coordinate transformation on the cutting vibration signal collected during the machining of the target part for any preset moving time window to obtain multiple polar coordinate angle value sets corresponding to the preset moving time window includes: Data is acquired during the machining of the target part to obtain the CNC program and cutting vibration signals; wherein, the NC program includes the tool drawing number and the spindle speed; Based on the spindle speed and vibration sampling frequency, determine the number of vibration acceleration amplitude values ​​corresponding to each spindle revolution; The number of vibration acceleration data points within any preset moving time window is determined based on the duration of the preset moving time window and the vibration sampling frequency. The number of spindle rotations within any preset movement time window is determined based on the number of vibration acceleration amplitudes and the number of vibration acceleration data points. Based on the number of spindle rotations and the number of vibration acceleration amplitudes, the data points within any preset movement time window are segmented to obtain multiple datasets corresponding to any preset movement time window. Using a preset polar coordinate transformation formula, the vibration acceleration amplitude values ​​in the multiple datasets are transformed into polar coordinates to obtain a set of multiple polar coordinate angle values ​​corresponding to any preset moving time window.

3. The method as described in claim 1, characterized in that, The step of plotting the multiple polar coordinate angle value sets to obtain the polar coordinate image of the vibration signal corresponding to any preset movement time window includes: The image is plotted on the multiple polar coordinate angle value sets to obtain the initial polar coordinate map corresponding to any preset moving time window; The initial polar coordinate image is normalized to obtain the polar coordinate image of the vibration signal corresponding to any preset moving time window.

4. The method as described in claim 1, characterized in that, The step of performing image processing on the polar coordinate image of the vibration signal to obtain the set of monitoring feature values ​​includes: Connect the data points in the polar coordinate image of the vibration signal to construct a data point connectivity graph; wherein, the data point connectivity graph includes the set of data points corresponding to any preset moving time window, the set of data point connecting edges, and the data point distance metric weight; The vibration signal polar coordinate image is analyzed and processed using the k-means clustering method to obtain k sub-images; where the value of k is equal to the number of teeth of the cutting tool. Feature values ​​are calculated for the k subgraphs to obtain the initial feature values ​​corresponding to each of the k subgraphs; wherein, the initial feature values ​​are area feature values ​​or length feature values; the area feature values ​​are determined by the data point connectivity graph; The initial feature values ​​corresponding to each of the k subgraphs are normalized and sorted in descending order to obtain the set of monitoring feature values.

5. The method as described in claim 1, characterized in that, The step of determining whether to trigger an alarm and shut down the system based on the set of monitored feature values ​​includes: Determine whether each monitoring feature value in the set of monitoring feature values ​​is greater than a preset alarm threshold; If it is determined that each monitoring feature value in the set of monitoring feature values ​​is not greater than the preset alarm threshold, then an alarm shutdown is initiated. If it is determined that all monitoring feature values ​​in the set of monitoring feature values ​​are greater than the preset alarm threshold, then it is determined that no alarm will be triggered and the system will be shut down.

6. The method as described in claim 1, characterized in that, The step of determining whether the tool life index is a stable tool life based on the set of monitored feature values ​​includes: Determine whether each monitoring feature value in the set of monitoring feature values ​​is greater than a preset adjustment threshold; If it is determined that each monitoring feature value in the set of monitoring feature values ​​is greater than the preset adjustment threshold, then the tool life index is determined to be the tool life in a stable state. If it is determined that each monitoring feature value in the set of monitoring feature values ​​is not greater than a preset adjustment threshold, then the tool life index is determined to be in an unstable state.

7. The method as described in claim 1, characterized in that, The step of dynamically adjusting the current tool life setting index based on the monitored feature value set includes: Determine the number of blade adjustments for each monitored feature value in the set of monitored feature values ​​that is less than a preset adjustment threshold; The number of high-load cutting teeth is determined based on the number of tooth adjustments and the number of cutting teeth; wherein, the number of cutting teeth is obtained from the cutting tool drawing number; Based on the number of high-load cutting teeth, the service life setting index corresponding to the current tool is retrieved from the candidate tool service life set; wherein, the candidate tool service life set contains multiple expected tool service life parameters, which are obtained based on experimental data and historical usage data.

8. A dynamic adjustment device for the service life of a cutting tool, characterized in that, For implementing the method as described in claim 1, the apparatus comprises: The polar coordinate transformation unit is used to perform polar coordinate transformation on the cutting vibration signal collected during the processing of the target part for any preset moving time window, so as to obtain a set of multiple polar coordinate angle values ​​corresponding to the preset moving time window. An image drawing unit is used to draw images of the multiple polar coordinate angle value sets to obtain a polar coordinate image of the vibration signal corresponding to any preset movement time window. The image processing unit is used to perform image processing on the polar coordinate image of the vibration signal to obtain a set of monitoring feature values; An alarm shutdown determination unit is used to determine whether to perform an alarm shutdown based on the set of monitored feature values. The lifespan strategy determination unit is used to determine whether the tool lifespan index is a stable tool lifespan based on the set of monitored feature values ​​if it is determined that no alarm shutdown will be performed. The tool life adjustment unit is used to dynamically adjust the tool life setting index corresponding to the current tool based on the set of monitored feature values ​​if the tool life index is determined to be in an unstable state.

9. An electronic device, characterized in that, The device includes: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method described in any one of claims 1-7 according to the obtained program instructions.

10. A storage medium, characterized in that, The storage medium stores computer-executable instructions for causing a computer to perform the method described in any one of claims 1-7.

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