A high-voltage switch circumferential part milling method avoiding angular movement

Abstract

The application relates to the technical field of milling processing, and proposes a high-pressure switch circumferential part milling processing method capable of avoiding angular movement, which comprises the following steps: collecting spindle current, three-axis vibration data and a feed rate, and extracting historical processing data; calculating a cutting load index at a collection time; calculating a vibration comprehensive index of a processing feature section, screening characteristic frequencies in a chatter monitoring frequency band, and calculating a chatter risk degree at the collection time; calculating a system load index at the collection time; and according to the feed rate, the chatter risk degree and the system load index at the collection time, feed rate control of high-pressure switch circumferential part milling is realized. The application aims to realize the feed rate control of high-pressure switch circumferential part milling.

Description

Technical Field

[0001] This application relates to the field of milling technology, specifically to a milling method for high-voltage switch circumferential parts that avoids angular movement. Background Technology

[0002] The milling accuracy of circumferential parts in high-voltage switches, such as flanges and insulator bases, directly affects the sealing performance, mechanical strength, and long-term operational reliability of the equipment. In the milling of circumferential parts of high-voltage switches, avoiding angular movement is a core requirement for ensuring part accuracy, assembly compatibility, switch function reliability, and production stability.

[0003] Generally, a fixed feed rate is preset for the CNC program by those skilled in the art based on experience or recommendations from the tool manufacturer. However, during milling, when encountering sharp corners or small-radius arc transitions, the tool path direction changes drastically. Since the machine tool servo drive system and mechanical structure have physical inertia, the actual tool path deviates from the theoretical path, causing overcutting or undercutting at corners, i.e., the "angular movement" problem. This severely affects the dimensions and geometric tolerances of circumferential parts in high-voltage switches. Summary of the Invention

[0004] This application provides a milling method for high-voltage switch circumferential parts that avoids angular movement, in order to solve the problem that a constant feed rate cannot adapt to the rapid changes in the tool path direction during the milling of high-voltage switch circumferential parts, resulting in a decrease in machining accuracy. The specific technical solution adopted is as follows:

[0005] One embodiment of this application provides a milling method for high-voltage switch circumferential parts to avoid angular movement. The method includes the following steps:

[0006] Collect spindle current, triaxial vibration data and feed rate for each machining feature segment during the milling process of high-voltage switch circumferential parts, and extract historical machining data of parts of the same model.

[0007] For the same machining feature segment, the cutting load index at the acquisition time is calculated based on the difference in spindle current at adjacent acquisition times. The cutting load index is used to characterize the cutting load borne by the spindle motor and the cutting tool.

[0008] A target frequency is preset, and the vibration comprehensive index of the processing feature segment is calculated by combining the triaxial vibration data of all acquisition times within the same processing feature segment. The vibration comprehensive index is used to characterize the overall vibration energy level of the processing. A flutter monitoring frequency band is preset, and the characteristic frequencies within the flutter monitoring frequency band are selected based on the result of the power spectral density integration within the flutter monitoring frequency band. The flutter risk degree at the acquisition time is calculated based on the frequency of the characteristic frequencies occurring within the preset time period before the acquisition time and the energy difference between two adjacent characteristic frequencies. The flutter risk degree is used to characterize the possibility of flutter occurring.

[0009] Based on the cutting load index and vibration comprehensive index at the acquisition time, the system load index at the acquisition time is calculated. Based on the feed rate, chatter risk, and system load index at the acquisition time, the feed rate control for milling high-voltage switch circumferential parts is realized.

[0010] Furthermore, the specific method for obtaining the cutting load index at the acquisition time is as follows:

[0011] Calculate the change in spindle current at each acquisition time based on the difference in spindle current between adjacent acquisition times;

[0012] When the spindle current change value at the acquisition time is greater than 0, the normalized value of the ratio of the spindle current change value at the acquisition time to the preset warning spindle current is recorded as the load current change rate at the acquisition time; the ratio of the spindle current at the acquisition time to the maximum value of the spindle current in all historical machining data is recorded as the relative spindle current at the acquisition time; the positive correlation result between the load current change rate at the acquisition time and the relative spindle current is recorded as the cutting load index at the acquisition time.

[0013] When the spindle current change value at the acquisition time is less than or equal to zero, the cutting load index of the previous adjacent acquisition time is used as the cutting load index at the acquisition time.

[0014] Furthermore, the specific method for determining the spindle current change value at the acquisition time is as follows:

[0015] The absolute value of the difference between the spindle current at the acquisition time and the previous adjacent acquisition time is recorded as the spindle current change value at the acquisition time.

[0016] Furthermore, the method for preset the target frequency is as follows:

[0017] The ratio of the spindle speed of the milling machine tool to the number 60 is recorded as the spindle rotation fundamental frequency. The product of the spindle rotation fundamental frequency and the number of teeth of the cutting tool is recorded as the tooth passing frequency. The product of the tooth passing frequency with the numbers 1, 2, 3, 4, 5, and 6 is recorded as the target frequency.

[0018] Furthermore, the specific calculation method for the comprehensive vibration index of the processing feature segment is as follows:

[0019] Vector summation is performed on the three-axis vibration data at the acquisition time to obtain the vibration data at the acquisition time. Based on the vibration data at all acquisition times within the same processing feature segment, the vibration energy of the processing feature segment is calculated. The normalized value of the vibration energy of the processing feature segment is recorded as the vibration comprehensive index of the corresponding processing feature segment.

[0020] Furthermore, the method for presetting the flutter monitoring frequency band is as follows:

[0021] The product of 1.5 and the frequency through which the blade passes is used as the lower limit, and the ratio of the sampling frequency to 2.56 is used as the upper limit to establish the flutter monitoring frequency band.

[0022] The frequency band to be removed is defined as the frequency range centered on each target frequency and with a bandwidth equal to twice the absolute value of the frequency difference between two adjacent spectral lines in the spectrum.

[0023] Remove all frequency bands to be removed from the flutter monitoring frequency band to achieve the preset flutter monitoring frequency band.

[0024] Furthermore, the method for screening characteristic frequencies within the flutter monitoring frequency band is as follows:

[0025] The average energy of the flutter monitoring frequency band is calculated by integrating the power spectral density over the flutter monitoring frequency band.

[0026] Within the flutter monitoring frequency band, a frequency band interval is established with the frequency point as the center. When the energy of the upper and lower limits of the frequency band interval is greater than or equal to the energy threshold determined by the average energy, the frequency corresponding to the center of the frequency band interval is recorded as the characteristic frequency.

[0027] Furthermore, the specific calculation method for the flutter risk at the acquisition time is as follows:

[0028] Select a preset number of time periods with a preset duration closest to the acquisition time, count the frequency of the characteristic frequency within the selected time period, and record the ratio of the absolute value of the energy difference between two adjacent characteristic frequencies to the maximum value of the energy between two adjacent characteristic frequencies as the energy variation degree between two adjacent characteristic frequencies.

[0029] The sum of the energy variations of all two adjacent characteristic frequencies within the selected time period and the product of the frequency of occurrence of the characteristic frequency within the selected time period are recorded as the flutter risk at the time of acquisition.

[0030] Furthermore, the specific calculation method for the system load index at the time of data acquisition is as follows:

[0031] Calculate the cutting load risk level at the time of data acquisition based on the cutting load index; calculate the vibration risk level at the time of data acquisition based on the comprehensive vibration index; record the sum of the cutting load risk level and the vibration risk level at the time of data acquisition as the first sum value at the time of data acquisition; record the ratio of the cutting load risk level to the first sum value at the time of data acquisition as the first weighting factor at the time of data acquisition; record the ratio of the vibration risk level to the first sum value at the time of data acquisition as the second weighting factor at the time of data acquisition.

[0032] Based on the first and second weighting factors at the time of data acquisition, the cutting load risk and vibration risk at the time of data acquisition are weighted and summed, and the weighted sum is recorded as the system load index at the time of data acquisition.

[0033] Furthermore, the specific method for controlling the feed rate of milling circumferential parts of high-voltage switches based on the feed rate, chatter risk, and system load index at the time of data acquisition includes:

[0034] Based on the feed rate, chatter risk, and system load index at the acquisition time, the adjusted feed rate at the acquisition time is calculated. The adjusted feed rate at the acquisition time that has passed the upper and lower limit constraints is used as the feed rate of the next adjacent acquisition time.

[0035] The beneficial effects of this application are:

[0036] This application evaluates the cutting load on the spindle motor and tool by considering that the magnitude of the spindle current reflects the strength of the instantaneous cutting force and that changes in the spindle current reflect the rate of change of the cutting load. It obtains the cutting load index at the acquisition time. The stability of the machining process is analyzed. First, the overall vibration energy level of the machining is quantified to obtain the comprehensive vibration index of the machining characteristic segment. Then, characteristic frequencies within the chatter monitoring band are selected. These characteristic frequencies represent frequencies where chatter may occur. Since the typical sign of regenerative chatter is a peak with rapidly increasing energy and a fixed frequency in the spectrum, the probability of each characteristic frequency corresponding to a chatter frequency is evaluated to obtain the chatter risk level at the acquisition time. Finally, based on the cutting load index and chatter risk level at the acquisition time, the relative load condition of the system is evaluated, and the system load index at the acquisition time is calculated. Based on the feed rate, chatter risk level, and system load index at the acquisition time, feed rate control is implemented for milling high-voltage switch circumferential parts. This solves the problem that a constant feed rate cannot adapt to rapid changes in the tool path direction during the milling of high-voltage switch circumferential parts, leading to a decrease in machining accuracy. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic flowchart of a milling method for avoiding angular movement of a high-voltage switch circumferential part, provided as an embodiment of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. 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.

[0040] Please see Figure 1 The diagram illustrates a milling method for avoiding angular movement of a high-voltage switch circumferential part according to an embodiment of this application. The method includes the following steps:

[0041] Step S001: Collect spindle current, triaxial vibration data and feed rate for each machining feature segment during the milling process of high-voltage switch circumferential parts, and extract historical machining data of parts of the same model.

[0042] Milling of circumferential parts for high-voltage switches is a periodic batch process. A complete milling process for these parts needs to cover the entire flow from preparation to completion and machine tool reset / standby. The starting boundary is the instant the machine tool starts the part machining program from its idle standby state, and the ending boundary is when the machine tool's axes return to a safe position or program zero point after machining, the spindle stops, and the machine enters the state ready to process the next part. For each complete machining process, it can be further divided based on the specific machining feature segments of the CNC machine tool part program. Specifically, by analyzing the G-code line numbers of the CNC program or monitoring the curvature of the tool position coordinate changes, the start and end times of feature segments such as the G01 straight line segment and the G02 / G03 constant radius arc segment can be identified, and these serve as the boundaries for dividing the machining feature segments.

[0043] In the milling process of high-voltage switch circumferential parts, at each machining feature segment, the spindle current driving the spindle motor is collected by the spindle load current sensor; the high-frequency vibration acceleration signals in the X, Y, and Z directions are collected by the triaxial vibration acceleration sensor installed at the spindle nose to obtain triaxial vibration data; and the feed rate of the program instructions is collected by the CNC numerical control system.

[0044] In this embodiment, the acquisition time and time interval of spindle current, triaxial vibration data and feed rate are the same, and the sampling frequency is set to 12.8 kHz.

[0045] Since the high-voltage switch circumferential parts were milled using the same part model, historical machining data of the same part model was extracted. The historical machining data included spindle current, triaxial vibration data, and feed rate.

[0046] Thus, the spindle current, triaxial vibration data, and feed rate of each machining feature segment during the milling process of high-voltage switch circumferential parts were obtained, along with historical machining data of parts of the same model.

[0047] Step S002: For the same machining feature segment, calculate the cutting load index at the acquisition time based on the difference in spindle current at adjacent acquisition times. The cutting load index is used to characterize the cutting load borne by the spindle motor and the cutting tool.

[0048] In the milling of circumferential parts for high-voltage switches, the feed rate is typically set by process engineers or programmers based on part drawings, material type, tool manual recommendations, and personal experience. When milling parts with a constant feed rate, if the machining path direction changes abruptly, the servo system cannot respond instantaneously due to the machine tool's physical inertia. This can cause the tool path to deviate from the programmed trajectory, resulting in over- or under-cutting at corners. Furthermore, sudden directional changes can trigger strong vibrations in the machine tool and tooling system, producing chatter marks on the workpiece surface, severely impacting surface finish and dimensional accuracy. Therefore, adaptive feed rate control is necessary.

[0049] The core of adaptive feed rate control is accurate identification of the cutting load, typically analyzed through spindle current. Specifically, the magnitude of the spindle current reflects the strength of the instantaneous cutting force, and its changes reflect the rate of change of the cutting load; that is, the larger the spindle current, the greater the instantaneous cutting force and the higher the cutting load index. However, relying solely on spindle current to identify the cutting load has limitations. If the cutting force is low at the time of data acquisition, but suddenly increases in the next moment, it will cause a lag in cutting load identification, a slower system response, and consequently affect machining accuracy and efficiency.

[0050] For the same machining feature segment, the difference between the spindle current at the acquisition time and the previous adjacent acquisition time is recorded as the spindle current change value at the acquisition time. When the spindle current change value at the acquisition time is greater than 0, the normalized value of the ratio of the spindle current change value at the acquisition time to the preset warning spindle current is recorded as the load current change rate at the acquisition time. The ratio of the spindle current at the acquisition time to the maximum value of the spindle current in all historical machining data is recorded as the relative spindle current at the acquisition time. The positive correlation result between the load current change rate at the acquisition time and the relative spindle current is recorded as the cutting load index at the acquisition time. When the spindle current change value at the acquisition time is less than or equal to zero, the acquisition time is determined to be in a load stabilization or unloading stage, and the cutting load index should not be increased. The cutting load index of the previous adjacent acquisition time is used as the cutting load index at the acquisition time.

[0051] It is understood that a positive correlation is applied to the load current change rate and the relative spindle current at the acquisition time, ensuring that both the load current change rate and the relative spindle current at the acquisition time are positively correlated with the cutting load index at the acquisition time. It is understood that the positive correlation in this application refers to the relationship between the independent and dependent variables. The independent variables are the load current change rate and the relative spindle current at the acquisition time, and the dependent variable is the cutting load index at the acquisition time. The positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), and can be an additive or multiplicative relationship.

[0052] Preferably, as an embodiment of this application, a preset first weighting factor and a second weighting factor are used as the weights of the load current change rate and the relative spindle current at the acquisition time, respectively. The weighted sum of the load current change rate and the relative spindle current at the acquisition time is recorded as the cutting load index at the acquisition time. The sum of the first weighting factor and the second weighting factor is 1. In this embodiment, the values ​​of the first weighting factor and the second weighting factor are 0.8 and 0.2, respectively.

[0053] It should be noted that this embodiment uses the maximum-minimum normalization method to calculate the normalized value. In practical applications, implementers may use other methods of existing technology, such as the tanh function or the sigmoid function, to calculate the normalized value, which is not limited here.

[0054] It should be noted that the first acquisition time has no preceding adjacent acquisition time, and feed rate control is not applied to the first acquisition time.

[0055] The greater the rate of change of load current and the greater the spindle current at the time of acquisition, the more drastic the change of cutting load at the time of acquisition corresponding to the rate of change of load current. The greater the cutting load index, the greater the cutting force borne by the spindle motor and tool at the time of acquisition during the machining process, and the more work is required per unit time to overcome material deformation and cut off chips.

[0056] At this point, the cutting load index at the time of data acquisition is obtained.

[0057] Step S003: Preset target frequency, combine triaxial vibration data from all acquisition times within the same processing feature segment, calculate the comprehensive vibration index of the processing feature segment. The comprehensive vibration index is used to characterize the overall vibration energy level of the processing. Preset flutter monitoring frequency band, and select characteristic frequencies within the flutter monitoring frequency band based on the result of integrating the power spectral density within the flutter monitoring frequency band. Calculate the flutter risk level at the acquisition time based on the frequency of occurrence of characteristic frequencies within the pre-defined time interval before acquisition and the energy difference between two adjacent characteristic frequencies. The flutter risk level is used to characterize the probability of flutter occurring.

[0058] Furthermore, the stability during the processing was analyzed.

[0059] The ratio of the spindle speed of the milling machine tool to the number 60 is recorded as the spindle rotation fundamental frequency. The product of the spindle rotation fundamental frequency and the number of teeth of the cutting tool is recorded as the tooth passage frequency. The products of the tooth passage frequency with the numbers 1, 2, 3, 4, 5, and 6 are recorded as target frequencies. Vector summation is performed on the three-axis vibration data at the acquisition time to obtain the vibration data at the acquisition time. Short-time Fourier transform and fast Fourier transform are used to process the vibration data at all acquisition times within the same machining feature segment to obtain the amplitude spectrum and power spectral density of all time intervals within the same machining feature segment and for all preset time lengths. A preset bandwidth frequency band is established centered on each target frequency. The power spectral density (PSD) of all discrete frequency points within the frequency band is integrated. The sum of the integration results corresponding to all frequency bands is recorded as the vibration energy of the corresponding machining feature segment. The normalized value of the vibration energy of the machining feature segment is recorded as the comprehensive vibration index of the corresponding machining feature segment.

[0060] The preset time length for dividing the time period should be greater than or equal to 0.1 seconds and less than or equal to 0.5 seconds. In this embodiment, the preset time length is set to 0.3 seconds and the preset bandwidth is set to 2Hz. The normalized value of the vibration energy of the processing feature segment is the ratio of the vibration energy of the processing feature segment to the maximum value of the vibration energy of all processing feature segments corresponding to the historical processing data.

[0061] The comprehensive vibration index of the machining feature segment is used to characterize the overall vibration energy level during the machining process and is an important indicator for evaluating the stability of machine tool and cutting tool systems.

[0062] In the milling process of high-voltage switch circumferential parts, there exists a specific and highly dangerous vibration phenomenon—chatter—that requires special attention and differentiation. Chatter is a self-excited vibration; its frequency is independent of the harmonics of the spindle speed and the cutting tooth passing frequency. It can drastically worsen the cutting process, causing not only chatter marks on the workpiece surface but also potential damage to the cutting tool. Relying solely on a comprehensive vibration index that reflects the total energy over a wide frequency band is insufficient for accurately predicting this specific risk. To achieve early identification and quantitative assessment of this critical instability factor, it is necessary to construct an index that specifically characterizes the probability of chatter occurrence, based on in-depth analysis of the vibration signal spectrum.

[0063] Flutter is usually more pronounced in frequency bands above the target frequency, manifesting as an isolated, high-energy band or a wideband energy spike.

[0064] The product of 1.5 and the frequency through which the blade passes is used as the lower limit, and the ratio of the sampling frequency to 2.56 is used as the upper limit to establish a preset, initial flutter monitoring frequency band. The frequency band interval centered on each target frequency and with a bandwidth of twice the absolute value of the frequency difference between two adjacent spectral lines in the spectrum is denoted as the frequency band interval to be removed. All frequency band intervals to be removed are removed from the flutter monitoring frequency band to achieve the preset flutter monitoring frequency band.

[0065] Understandably, the flutter monitoring frequency band is a relatively wide band that excludes normal cutting characteristic frequencies; setting the bandwidth of the frequency band to be removed can prevent the sidelobe energy of forced vibration from being misjudged as flutter.

[0066] The total flutter energy is obtained by integrating the power spectral density within the flutter monitoring frequency band, and the average energy of the flutter monitoring frequency band is also obtained. Three times the average energy is taken as the energy threshold. Within the flutter monitoring frequency band, a frequency band interval is established with the frequency point as the center and twice the absolute value of the frequency difference between two adjacent spectral lines in the spectrum as the bandwidth. The energy of the frequency band interval is compared with the energy threshold. When the energy of the frequency band interval is greater than or equal to the energy threshold, the frequency corresponding to the center of the frequency band interval is recorded as the characteristic frequency.

[0067] The average energy of the flutter monitoring frequency band is the average power spectral density of all frequency points within the flutter monitoring frequency band; the energy of the frequency band interval is obtained by integrating the power spectral density.

[0068] Understandably, the characteristic frequency is the frequency at which flutter may occur.

[0069] The typical characteristic of regenerative flutter is the appearance of a peak in the spectrum with rapidly increasing energy and a fixed frequency. Therefore, the probability of flutter frequency corresponding to each characteristic frequency is evaluated.

[0070] Select a preset number of time periods closest to the acquisition time, and count the frequency of the characteristic frequency within the selected time periods. For any characteristic frequency, the ratio of the absolute value of the difference between the energy corresponding to the current time period and the previous adjacent time period to the maximum value of the energy corresponding to the characteristic frequency in these two time periods is recorded as the temporal energy variability of the characteristic frequency. The sum of the temporal energy variability of all characteristic frequencies within the selected time periods is positively correlated with the frequency of the characteristic frequency within the selected time periods, and the result is recorded as the flutter risk at the acquisition time.

[0071] Preferably, as an embodiment of this application, the sum of the temporal energy variation of all characteristic frequencies within the selected time period and the product of the frequency of occurrence of the characteristic frequencies within the selected time period are recorded as the flutter risk at the acquisition time.

[0072] The greater the risk of flutter, the more likely typical signs of flutter will appear in the spectrum graph corresponding to the nearest preset number of time periods before the acquisition time. That is, there is a peak in the spectrum with rapidly increasing energy and a fixed frequency. The more likely flutter will occur at the acquisition time, the smaller the value of the feed rate should be at the acquisition time.

[0073] At this point, the flutter risk level at the time of data acquisition is obtained.

[0074] Step S004: Calculate the system load index at the time of data acquisition based on the cutting load index and vibration comprehensive index at the time of data acquisition. Based on the feed rate, chatter risk, and system load index at the time of data acquisition, realize the feed rate control for milling high-voltage switch circumferential parts.

[0075] Based on the cutting load index at the time of data acquisition, the cutting load risk level at that time is calculated. The formula for calculating the cutting load risk level is:

[0076]

[0077] in, Indicates the cutting load risk level at the time of data acquisition; This represents the normalized value of the cutting load index at the time of data acquisition. This represents the third quartile of the normalized value of the cutting load index at all acquisition times in the historical machining data; , These represent the preset first index and the second index, respectively. The value of the first index should be greater than or equal to 1, and the value of the second index should be greater than 1. In this embodiment, the values ​​of the first index and the second index are 1 and 2, respectively.

[0078] It should be noted that this embodiment uses the maximum-minimum normalization method to calculate the normalized value. In practical applications, implementers may use other methods of existing technology, such as the tanh function or the sigmoid function, to calculate the normalized value, which is not limited here.

[0079] The calculation of the third and quartiles is a well-known technique in statistics and will not be elaborated further.

[0080] Based on the comprehensive vibration index at the time of data acquisition, the vibration risk level at that time is calculated. The formula for calculating the vibration risk level is as follows:

[0081]

[0082] in, Indicates the vibration risk level at the time of data collection; The normalized value of the comprehensive vibration index at the time of data acquisition; This represents the third quartile of the normalized value of the comprehensive vibration index at all acquisition times in the historical processing data.

[0083] The sum of the cutting load risk and the vibration risk at the acquisition time is recorded as the first sum at the acquisition time. The ratio of the cutting load risk at the acquisition time to the first sum is recorded as the first weighting factor at the acquisition time. The ratio of the vibration risk at the acquisition time to the first sum is recorded as the second weighting factor at the acquisition time.

[0084] In this embodiment of the application, to avoid the denominator being zero during the ratio calculation process, a preset value needs to be added to the denominator. The preset value in this embodiment is [value to be filled in]. .

[0085] The first and second weighting factors at the acquisition time are used as the weights of the cutting load risk and vibration risk at the acquisition time, respectively. The cutting load risk and vibration risk at the acquisition time are weighted and summed, and the weighted sum is recorded as the system load index at the acquisition time.

[0086] The adjusted feed rate at the time of data acquisition is calculated based on the feed rate, chatter risk, and system load index at the time of data acquisition.

[0087]

[0088] in, The adjusted feed rate at the time of data acquisition; Indicates the feed rate at the time of data acquisition; , , These represent the preset first gain coefficient, second gain coefficient, and third gain coefficient, respectively. The values ​​of the first gain coefficient, second gain coefficient, and third gain coefficient should all be greater than or equal to 0 and less than or equal to 1. In this embodiment, the values ​​of the first gain coefficient, second gain coefficient, and third gain coefficient are 0.3, 0.3, and 0.2, respectively. Indicates the flutter risk level at the moment of data acquisition; The second tertiary value representing the flutter risk at all acquisition moments in the historical processed data; The system load index at the time of data acquisition; The third quartile of the system load index at all acquisition times in the historical processed data; , These represent the preset third and fourth indices, respectively. The values ​​of the third and fourth indices should both be greater than 1. In this embodiment, the values ​​of the third and fourth indices are both 2. This represents the minimum value function, which calculates the minimum value among all the comma-separated values ​​in parentheses. This indicates a preset value parameter. In this embodiment, the value of the parameter is... .

[0089] The feed rate at the acquisition time is subject to upper and lower limit constraints. The upper limit is 1.5 times the feed rate preset by the CNC program, and the lower limit is 0.2 times the feed rate preset by the CNC program. This ensures that the feed rate at the acquisition time is greater than or equal to 0.2 times the feed rate preset by the CNC program and less than or equal to 1.5 times the feed rate preset by the CNC program.

[0090] The feed rate adjusted at the acquisition time subject to upper and lower limit constraints is used as the feed rate for the next adjacent acquisition time, thereby achieving feed rate control for milling circular parts of high-voltage switches.

[0091] This completes the feed rate control for milling circumferential parts of high-voltage switches.

[0092] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A milling method for high-voltage switch circumferential parts to avoid angular movement, characterized in that, The method includes the following steps: Collect spindle current, triaxial vibration data and feed rate for each machining feature segment during the milling process of high-voltage switch circumferential parts, and extract historical machining data of parts of the same model. For the same machining feature segment, the cutting load index at the acquisition time is calculated based on the difference in spindle current at adjacent acquisition times. The cutting load index is used to characterize the cutting load borne by the spindle motor and the cutting tool. A target frequency is preset, and the vibration comprehensive index of the processing feature segment is calculated by combining the triaxial vibration data of all acquisition times within the same processing feature segment. The vibration comprehensive index is used to characterize the overall vibration energy level of the processing. A flutter monitoring frequency band is preset, and the characteristic frequencies within the flutter monitoring frequency band are selected based on the result of the power spectral density integration within the flutter monitoring frequency band. The flutter risk degree at the acquisition time is calculated based on the frequency of the characteristic frequencies occurring within the preset time period before the acquisition time and the energy difference between two adjacent characteristic frequencies. The flutter risk degree is used to characterize the possibility of flutter occurring. Based on the cutting load index and vibration comprehensive index at the acquisition time, the system load index at the acquisition time is calculated. Based on the feed rate, chatter risk and system load index at the acquisition time, the feed rate control of milling high-voltage switch circumferential parts is realized. The specific calculation method for the cutting load index at the acquisition time is as follows: The spindle current variation at each acquisition time is calculated based on the difference in spindle current between adjacent acquisition times. When the spindle current change value at the acquisition time is greater than 0, the normalized value of the ratio of the spindle current change value at the acquisition time to the preset warning spindle current is recorded as the load current change rate at the acquisition time; the ratio of the spindle current at the acquisition time to the maximum value of the spindle current in all historical machining data is recorded as the relative spindle current at the acquisition time; the positive correlation result between the load current change rate at the acquisition time and the relative spindle current is recorded as the cutting load index at the acquisition time. When the spindle current change value at the acquisition time is less than or equal to zero, the cutting load index of the previous adjacent acquisition time is used as the cutting load index at the acquisition time. The specific calculation method for the comprehensive vibration index of the processing feature segment is as follows: Vector summation is performed on the three-axis vibration data at the acquisition time to obtain the vibration data at the acquisition time. Based on the vibration data at all acquisition times within the same processing feature segment, the vibration energy of the processing feature segment is calculated. The normalized value of the vibration energy of the processing feature segment is recorded as the vibration comprehensive index of the corresponding processing feature segment. The specific calculation method for the flutter risk at the acquisition time is as follows: Select a preset number of time periods with a preset duration closest to the acquisition time, count the frequency of the characteristic frequency within the selected time period, and record the ratio of the absolute value of the energy difference between two adjacent characteristic frequencies to the maximum value of the energy between two adjacent characteristic frequencies as the energy variation degree between two adjacent characteristic frequencies. The sum of the energy variation of all two adjacent characteristic frequencies within the selected time period and the product of the frequency of occurrence of the characteristic frequency within the selected time period are recorded as the flutter risk at the time of acquisition. The specific calculation method for the system load index at the time of data acquisition is as follows: Calculate the cutting load risk level at the time of data acquisition based on the cutting load index; calculate the vibration risk level at the time of data acquisition based on the comprehensive vibration index; record the sum of the cutting load risk level and the vibration risk level at the time of data acquisition as the first sum value at the time of data acquisition; record the ratio of the cutting load risk level to the first sum value at the time of data acquisition as the first weighting factor at the time of data acquisition; record the ratio of the vibration risk level to the first sum value at the time of data acquisition as the second weighting factor at the time of data acquisition. Based on the first and second weighting factors at the time of data acquisition, the cutting load risk and vibration risk at the time of data acquisition are weighted and summed, and the weighted sum is recorded as the system load index at the time of data acquisition. The method for controlling the feed rate of milling circular parts of high-voltage switches based on the feed rate, chatter risk, and system load index at the time of data acquisition includes the following specific methods: Based on the feed rate, chatter risk, and system load index at the acquisition time, the adjusted feed rate at the acquisition time is calculated. The adjusted feed rate at the acquisition time that has passed the upper and lower limit constraints is used as the feed rate of the next adjacent acquisition time.

2. The milling method for high-voltage switch circumferential parts to avoid angular movement according to claim 1, characterized in that, The specific method for determining the spindle current change value at the acquisition time is as follows: The absolute value of the difference between the spindle current at the acquisition time and the previous adjacent acquisition time is recorded as the spindle current change value at the acquisition time.

3. The milling method for high-voltage switch circumferential parts to avoid angular movement according to claim 1, characterized in that, The method for presetting the target frequency is as follows: The ratio of the spindle speed of the milling machine tool to the number 60 is recorded as the spindle rotation fundamental frequency. The product of the spindle rotation fundamental frequency and the number of teeth of the cutting tool is recorded as the tooth passing frequency. The product of the tooth passing frequency with the numbers 1, 2, 3, 4, 5, and 6 is recorded as the target frequency.

4. A milling method for high-voltage switch circumferential parts to avoid angular movement according to claim 1, characterized in that, The method for presetting the flutter monitoring frequency band is as follows: The product of 1.5 and the frequency through which the blade passes is used as the lower limit, and the ratio of the sampling frequency to 2.56 is used as the upper limit to establish the flutter monitoring frequency band. The frequency band to be removed is defined as the frequency range centered on each target frequency and with a bandwidth equal to twice the absolute value of the frequency difference between two adjacent spectral lines in the spectrum. Remove all frequency bands to be removed from the flutter monitoring frequency band to achieve the preset flutter monitoring frequency band.

5. A milling method for high-voltage switch circumferential parts to avoid angular movement according to claim 1, characterized in that, The method for selecting characteristic frequencies within the flutter monitoring frequency band is as follows: The average energy of the flutter monitoring frequency band is calculated by integrating the power spectral density over the flutter monitoring frequency band. Within the flutter monitoring frequency band, a frequency band interval is established with the frequency point as the center. When the energy of the upper and lower limits of the frequency band interval is greater than or equal to the energy threshold determined by the average energy, the frequency corresponding to the center of the frequency band interval is recorded as the characteristic frequency.

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