Method for Identifying the Variation Characteristics of Vibration Energy in Face Milling Cutters

Abstract

This invention discloses a method for identifying the characteristics of vibration energy variation in face milling cutters, comprising the following steps: a high-efficiency face milling cutter cutting vibration experiment scheme, a method for dividing cutting vibration experiment results and cutting time periods, time-frequency characteristics of vibration acceleration in different cutting time periods, a method for calculating cutting vibration energy and identifying cutting vibration energy distribution, and a method for evaluating the dynamic characteristics of vibration energy. By comprehensively analyzing and identifying the vibration energy in the milling process, the method can effectively reveal the stability of the high-efficiency face milling cutter cutting process and the distribution characteristics of vibration energy. By calculating the vibration energy, the method extracts the time-frequency characteristic parameters of vibration energy in different cutting time periods during the process from the milling cutter's entry into the workpiece to its exit, thereby accurately identifying the variation law of milling vibration energy. This method can effectively identify the characteristics of vibration energy variation in the cutting process, improve cutting stability, reduce the negative impact of vibration on machining quality, and improve machining efficiency.

Description

Technical Field

[0001] This invention relates to the field of milling cutter technology, specifically to a method for identifying the characteristics of vibration energy variation in face milling cutter cutting. Background Technology

[0002] High-efficiency face milling cutters, designed specifically to improve milling efficiency, are widely used in machining high-hardness materials, manufacturing complex workpieces, and machining applications requiring high material removal rates. Compared to existing face milling cutters, high-efficiency face milling cutters, through optimized tool geometry, material properties, and coating design, can complete more machining tasks in a shorter time and effectively extend tool life.

[0003] However, in high-efficiency milling, machining quality is often significantly affected by factors such as the vibration of the machine tool system itself, centrifugal vibration caused by milling cutter tooth errors, and vibration generated by cutting force excitation. Cutting vibration not only accelerates tool wear and shortens tool life but also leads to unstable cutting forces, resulting in decreased workpiece geometric errors and machining accuracy. Vibration energy is an important parameter reflecting the relationship between vibration intensity and time. It not only reflects the energy conversion during vibration but also serves as a crucial basis for evaluating milling stability and machining quality. Therefore, in-depth research and identification of the vibration energy variation characteristics of high-efficiency face milling cutters during the cutting process are of great significance for improving the stability of the milling process by optimizing cutting parameters, reducing the negative impact of vibration on machining quality, and enhancing the utilization efficiency of cutting energy.

[0004] Vibration energy refers to the energy generated during the cutting process due to various vibration sources (such as machine tool structure vibration, centrifugal force vibration caused by milling cutter errors, and cutting force fluctuations). Cutting vibration energy affects workpiece machining quality, tool life, and the stability of cutting forces by influencing the amplitude and frequency of vibration. The variation law of vibration energy reflects the dynamic behavior throughout the milling process. Excessive vibration energy may lead to excessive tool wear or cause unstable fluctuations in the machining process, thus affecting machining accuracy. Therefore, in-depth research on vibration energy can reveal the interaction relationships between vibration sources and provide a scientific basis for optimizing cutting conditions and improving machining quality.

[0005] Existing research on cutting vibration mainly focuses on the time-varying patterns of vibration acceleration amplitude and frequency, failing to effectively separate different vibration sources. In particular, it lacks in-depth analysis of the interactions between vibrations caused by machine tool processes, centrifugal force vibrations caused by milling cutter tooth errors, and vibrations induced by cutting force excitation. Furthermore, most existing studies are limited to the stable cutting phase, failing to comprehensively reveal the dynamic behavior of vibration throughout the entire milling process. Specifically, they do not separately identify and analyze vibration energy, neglecting the crucial role of vibration energy in improving the stability of the cutting process. Summary of the Invention

[0006] The purpose of this invention is to provide a method for identifying the characteristics of vibration energy variation in face milling cutters, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying the characteristics of vibration energy variation in face milling cutters, characterized by comprising the following steps:

[0008] Step 1: Develop a test plan for the vibration of face milling cutter cutting, and use dry milling and climb milling to process the workpiece, continuously sampling the dynamic data of the face milling cutter;

[0009] Step two, dividing the cutting time period, distinguishing the milling process according to different milling states during face milling, t i Let x represent different times, where i = 0, 1, 2, 3, 4, 5, 6, 7. ( t i) The relationship between different cutting states of the milling cutter, its position, time, and cutting parameters is as follows:

[0010]

[0011] Among them, v f For the feed rate, t i For different cutting times of the milling cutter;

[0012] Step 3 reveals the time-frequency characteristics of vibration acceleration during different cutting periods. The experimentally measured vibration acceleration signals are integrated to obtain the milling vibration velocity and displacement time-domain signals. The root mean square value, kurtosis, and dominant frequency are used as evaluation indicators. The formula for the root mean square value is:

[0013] The kurtosis formula is:

[0014] Where, x i Let be the sample vibration acceleration value at time i; n is the total number of sampling points. σ is the sample mean; σ is the sample standard deviation, which measures the dispersion of the data in the sample.

[0015] Step 4: The method for calculating cutting vibration energy and identifying its distribution is as follows: Establish an o-xyz coordinate system for the workpiece, where x, y, and z represent the feed rate direction, milling width direction, and milling depth direction of the milling cutter, respectively; m, w, l, and h represent the total mass of the milling cutter and tool holder, the workpiece width, the workpiece length, and the workpiece height, respectively; n represents the direction of milling cutter rotation; v f This refers to the feed direction of the milling cutter; o c -x c y c z c For the milling cutter coordinate system; o v -x v y v z v The coordinate system for the milling cutter velocity under vibration; o d -x d y d z d For the vibration acceleration detection coordinate system, the milling vibration energy in the x-axis, y-axis, and z-axis is calculated using the following formulas:

[0016] P x (t)=F ax (t)·v x (t)=m·a x (t)·v x (t), P y (t)=F ay (t)·v y (t)=m·a y (t)·v y (t),

[0017] P z (t)=F az (t)·v z (t)=m·a z (t)·v z (t);

[0018] Among them, F ax (t), F ay (t), F az (t) represents the vibration force in the feed rate direction, the vibration force in the milling width direction, and the vibration force in the milling depth direction, respectively; m is the tool overhang mass; a x (t), a y (t), a z (t) represents the vibration acceleration in the feed rate direction, the vibration acceleration in the milling width direction, and the vibration acceleration in the milling depth direction, respectively. x (t), v y (t), vz (t) represents the vibration velocity in the feed speed direction, the vibration velocity in the milling width direction, and the vibration velocity in the milling depth direction, respectively.

[0019] Based on the vibration energy calculation method, the time-domain signal of vibration energy before and after the face milling cutter separation is obtained. The divided milling vibration energy time-domain signal is processed in the time-frequency domain to obtain the time-frequency characteristic parameters of different cutting periods. The vibration energy distribution is characterized by evaluating the cutting efficiency during the cutting process. The formula is: η=P / P0.

[0020] Where η is the proportion of vibration energy, P is the vibration energy excited by the cutting force, and P0 is the milling vibration energy.

[0021] Step 5: Based on the root mean square value and coefficient of variation, a method for evaluating the dynamic characteristics of vibration energy is proposed, with the following formula:

[0022]

[0023] Where N is the number of cutting time periods, η rms η is the root mean square value of the proportion of vibrational energy. i Let ν represent the proportion of vibration energy during different cutting periods, CV represent the coefficient of variation of the proportion of vibration energy during different cutting periods, σ represent the standard deviation of the proportion of vibration energy during different cutting periods, and μ represent the average proportion of vibration energy during different cutting periods.

[0024] Furthermore, in step two, the vibration acceleration signal during the idle period before milling is analyzed in the frequency domain. The idle signal before milling is extracted and subjected to spectrum analysis. The irrelevant machining speed frequency and its harmonic frequency components are filtered to obtain the vibration signal generated only by the milling force excitation, and then frequency domain analysis is performed.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] (1) Comprehensive analysis and identification of vibration energy characteristics in the milling process. By extracting the time-frequency characteristic parameters of machine tool vibration, milling cutter centrifugal force vibration and vibration energy caused by cutting force excitation at different cutting stages from milling cutter entry to cutting out of workpiece, the precise identification of vibration energy change characteristics of high-efficiency milling cutter cutting can be achieved. This not only evaluates the stability of the cutting process, but also provides important technical support and theoretical basis for optimizing cutting conditions, improving processing efficiency and extending tool life.

[0027] (2) A time period division method based on different cutting states; using vibration acceleration data during idle periods to separate and collect vibration acceleration signals, obtain vibration acceleration signals generated only by cutting force excitation and reveal the time-frequency characteristics of vibration acceleration in different cutting periods; propose a method for calculating the vibration energy of high-efficiency face milling cutters. This method uses vibration acceleration signals and vibration velocity signals to characterize the vibration energy signals, extracts the time-frequency characteristic parameters of the vibration energy signals, identifies the characteristics of vibration energy changes in high-efficiency face milling cutters, and reveals the dynamic characteristics of vibration energy distribution in high-efficiency face milling cutters; evaluate the vibration energy distribution based on the vibration energy dynamic characteristic evaluation method. Attached Figure Description

[0028] Figure 1 This is a time-domain signal diagram of vibration acceleration according to the present invention; Figure 2 These are diagrams illustrating different milling states during the milling process using the face milling cutter of this invention. Figure 3 This is a time-domain signal diagram of milling vibration according to the present invention; Figure 4 This is a spectrum diagram of the vibration acceleration signal during the idling period before cutting in this invention; Figure 5 This is a time-domain signal diagram of vibration acceleration during the idling period before cutting, as presented in this invention. Figure 6 This is a spectrum diagram of milling vibration caused by cutting force excitation during the idling period before cutting, as presented in this invention. Figure 7 This is a time-domain signal diagram of the vibration caused by the cutting force during the idling period before cutting, as presented in this invention. Figure 8 This is a time-domain signal diagram of milling vibration acceleration according to the present invention; Figure 9 This is a time-domain signal diagram of milling vibration acceleration excited by cutting force according to the present invention; Figure 10 This is a time-domain signal diagram of milling vibration velocity according to the present invention; Figure 11 This is a time-domain signal diagram of milling vibration velocity excited by cutting force according to the present invention; Figure 12 This is a time-domain signal diagram of milling vibration displacement according to the present invention; Figure 13 This is a time-domain signal diagram of milling vibration displacement excited by the cutting force according to the present invention; Figure 14 This is a graph showing the root mean square values ​​of milling vibration acceleration at different time points in this invention. Figure 15 This is a graph showing the kurtosis values ​​of the milling vibration acceleration at different time points in this invention. Figure 16 This is a diagram showing the dominant frequency of milling vibration acceleration at different time periods according to the present invention; Figure 17 This is a graph showing the root mean square values ​​of the vibration acceleration under cutting force excitation at different time intervals according to the present invention. Figure 18 This is a graph showing the kurtosis values ​​of the vibration acceleration excited by the cutting force at different time points in this invention. Figure 19 This is a diagram showing the dominant frequency of the vibration acceleration at different time intervals during the cutting force excitation of this invention. Figure 20 The instantaneous vibration velocity diagram is shown for the high-efficiency face milling cutter of this invention when milling 45 steel. Figure 21 This is a time-domain signal diagram of milling vibration energy according to the present invention; Figure 22 This is a time-domain signal diagram of the milling vibration energy excited by the cutting force according to the present invention; Figure 23This is a graph showing the root mean square values ​​of milling vibration energy at different time periods in this invention. Figure 24 This is a graph showing the kurtosis values ​​of the milling vibration energy at different time periods in this invention. Figure 25 This is a diagram showing the dominant frequency of milling vibration energy at different time periods in this invention; Figure 26 This is a graph showing the root mean square values ​​of the milling vibration energy excited by the cutting force in this invention at different time periods; Figure 27 This is a graph showing the kurtosis values ​​of the milling vibration energy excited by the cutting force in this invention at different time periods; Figure 28 This is a diagram showing the dominant frequency of the milling vibration energy excited by the cutting force in this invention at different time intervals. Figure 29 This is a diagram showing the percentage of vibration energy generated by the cutting force in this invention. Figure 30 This is a graph showing the root mean square values ​​of milling vibration energy at different time points in experimental scheme 2 of this invention. Figure 31 This is a graph showing the root mean square values ​​of milling vibration energy at different time points during experimental scheme 2 of the present invention, which is a cutting force excitation method. Figure 32 This is a diagram showing the percentage of vibration energy generated by the cutting force in experimental scheme 2 of the present invention. Figure 33 This is a flowchart of the method for identifying the vibration energy variation characteristics of a face milling cutter according to the present invention. Detailed Implementation

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] Example:

[0031] Please see Figure 1-33 The present invention provides a technical solution: a method for identifying the characteristics of vibration energy variation in face milling cutters;

[0032] 1: Experimental Scheme for Vibration in High-Efficiency Face Milling Cutters

[0033] An experiment was conducted on a CNC milling machine, an XK7124 three-axis milling machining center, to mill 45# steel using a face milling cutter. The milling cutter was a Walter M4003-050-B22-04-6.5 face milling cutter, with SDMT1204AZN-D57WKP35G inserts, 4 teeth, and a clamping length of 45mm. The weights of the cutter and cutter holder were measured using a balance: 486.32g and 900.40g respectively, for a total of 1386.72g. Detailed parameters of the machine tool and milling cutter are shown in Table 1.

[0034] Table 1 Machine tool and milling cutter parameters

[0035]

[0036] The experiment employed dry milling with climb milling as the cutting method. During machining, a YE7600 dynamic data acquisition and analysis system and an HD-YD-211IEPE piezoelectric accelerometer were used to acquire vibration acceleration signals. The sensor was mounted on the workpiece, with a sensitivity of 100 mV / g. The vibration acceleration signal sampling frequency was 12 kHz, the sampling method was continuous sampling, and the triggering method was signal triggering. Cutting parameters are shown in Table 2. Where n is the spindle speed, f is the feed per tooth, and a... p For the depth of cut, a e The cutting width is shown in Table 3. The workpiece used in the experiment was made of 45 steel, and its specific material composition is shown in Table 3.

[0037] Table 2 Cutting Parameters (I)

[0038]

[0039] Table 3 Composition of 45# Steel

[0040] C Si Mn P S Cu Cr Fe 0.47％ 0.20％ 0.52％ 0.018％ 0.007％ 0.02％ 0.02％ 98.745％

[0041] 2: Results of cutting vibration experiments and methods for dividing cutting time periods

[0042] The vibration acceleration time-domain signal measured using the cutting parameters in Table 2 is as follows: Figure 1 As shown.

[0043] The milling process is differentiated based on different milling states during face milling, and the results are as follows: Figure 2 As shown, t i Let i represent different times, where i = 0, 1, 2, 3, 4, 5, 6, 7. The time interval required from the no-load time t0 before the milling cutter enters the workpiece to the initial time t1 when the milling cutter enters the workpiece is Δt1; the time interval required from the initial time t1 when the milling cutter enters the workpiece to the time t2 when the milling cutter radius enters the workpiece is Δt2; the time interval required from the time t2 when the milling cutter radius enters the workpiece to the time t3 when the milling cutter is fully engaged is Δt3; the time interval required from the time t3 when the milling cutter is fully engaged to the time t4 when the milling cutter is about to exit is Δt4; the time interval required from the start of the milling cutter exit period t4 to the time t5 when the milling cutter radius exits the workpiece is Δt5; the time interval required from the time t5 when the milling cutter radius exits the workpiece to the time t6 when the milling cutter is fully exited is Δt6; and the time interval required from the time t6 when the milling cutter is fully exited to the time t7 when the milling cutter retracts is Δt7. ( t i) The relationship between different cutting states of the milling cutter, its position, time, and cutting parameters is given by equation (1).

[0044]

[0045] Where, v f For the feed rate, t iFor different cutting times of the milling cutter.

[0046] Based on the principle of the greatest common factor, the stable milling time period is uniformly divided, that is, Δt4 is uniformly divided into Δt... 41 ,Δt 42 ……Δt 4m The complete vibration acceleration signal is divided into m segments as follows: Figure 3 And as shown in Table 4.

[0047] Table 4. Results of Time-Domain Signal Node Division for Milling Vibration

[0048]

[0049] Vibrations during milling include vibrations caused by the machine tool's own machining system, centrifugal force vibrations caused by milling cutter errors, and vibrations caused by cutting force excitation. To separate the vibration acceleration signal caused by cutting force excitation, frequency domain analysis of the vibration acceleration signal during the idling period before milling is required, such as... Figure 4 , Figure 5 As shown.

[0050] The idling signal before milling was extracted and subjected to spectral analysis. The irrelevant machining speed frequencies and their harmonics were filtered to obtain the vibration signal generated solely by the milling force. Frequency domain analysis was then performed, and the results are as follows: Figure 6 , Figure 7 As shown.

[0051] It can be observed that the amplitude of the first dominant frequency of the milling vibration acceleration excited by the cutting force during the idling period before cutting changes significantly. The amplitude of the vibration acceleration time domain signal in all three directions is reduced and the signal fluctuation is smaller, indicating that the vibration signal frequency unrelated to cutting has been filtered out.

[0052] 3: Revealing the time-frequency characteristics of vibration acceleration during different cutting stages

[0053] The vibration acceleration signals measured in the experiment were integrated to obtain the following results: Figure 10 , Figure 12 The milling vibration velocity and displacement time-domain signals are shown. The filtering method in Implementation Example 2 is used to... Figure 8 , Figure 10 , Figure 12 The signal is filtered to obtain, as shown below. Figure 9 , Figure 11 , Figure 13 The cutting force excites the milling vibration acceleration, velocity, and displacement time-domain signals shown.

[0054] To objectively assess the stability of the cutting process, root mean square (RMS) value, kurtosis, and dominant frequency were selected as evaluation indicators. The RMS value reflects the energy or fluctuation level of the signal, and the formula is as follows:

[0055]

[0056] Where x i Let be the sample vibration acceleration value at time i; n is the total number of sampling points (the number of vibration acceleration sampling data points within the time period). Kurtosis describes the sharpness of the data distribution; a higher kurtosis value indicates more impact in the signal. The formula is as follows:

[0057]

[0058] In the formula, n is the total number of sampling points; x i Let be the sample vibration acceleration value at time i; σ is the sample mean; σ is the sample standard deviation, which measures the dispersion of the data in the sample.

[0059] The divided milling vibration acceleration time-domain signals were processed in the time-frequency domain, and the results are shown in Table 5. Figure 14-16 The time-frequency characteristic parameters of different cutting periods.

[0060] Table 5 Time-frequency characteristic parameters for different cutting periods

[0061]

[0062] Depend on Figure 14 It can be seen that the vibration acceleration signal in the feed speed direction fluctuates less compared to the other two directions, followed by the vibration acceleration in the milling depth direction, while the vibration acceleration signal in the milling width direction fluctuates the most violently. This may be because the cutting force is concentrated in the milling width direction during face milling, leading to severe vibration during machining. Furthermore, the vibration intensifies closer to the cutter exit point as the cutting process progresses, possibly due to the system rigidity failing to meet the requirements for stable cutting, resulting in chatter. Figure 15 It can be seen that kurtosis exhibits a large entry and exit time and a small stable cutting time in the three directions during milling; from Figure 16 It can be seen that during the cutting process, the main frequencies in the milling width and depth directions change relatively little, while the main frequency in the feed rate direction undergoes a sudden change in the latter half of the milling stabilization phase, which may be due to chatter causing a sudden increase in the main frequency. Furthermore, changes in the main frequencies in the milling width and depth directions during idling before and after milling indicate that the inherent vibration characteristics of the machine tool's process system are altered due to the milling of the workpiece.

[0063] The time-domain signals of the cutting force excitation vibration acceleration were processed in the time-frequency domain, and the results are shown in Table 6. Figure 17-19 The time-frequency characteristic parameters of different cutting periods.

[0064] Table 6. Time-frequency characteristic parameters of cutting force-excited vibration acceleration during different cutting periods.

[0065]

[0066] Depend on Figure 17 It can be seen that, with Figure 14 In comparison, apart from the decrease in values ​​in the feed rate and milling depth directions, the trend of change remained almost unchanged. However, in the milling width direction, the upward trend of vibration acceleration slowed down in the latter half of the stable cutting period. This may be because the dominant frequency component of the original vibration acceleration signal in the milling width direction during the pre-cutting idling phase is more abundant compared to the other two directions. Figure 18 It can be seen that kurtosis still exhibits the characteristics of a large entry and exit time and a small stable cutting time in the three directions during milling; from Figure 19 It can be seen that the change in the main frequency in the feed speed direction is not significant compared to before separation, indicating that the vibration brought by the machine tool process system in the feed speed direction has little impact on the vibration caused by the cutting force excitation, while the impact is greater in the milling width and milling depth directions, especially in the milling width direction.

[0067] 4: Methods for calculating cutting vibration energy and identifying cutting vibration energy distribution

[0068] Figure 20 In the coordinate system, o-xyz represents the workpiece coordinate system, where x, y, and z are the feed rate direction, width direction, and depth direction of the milling cutter, respectively; m, w, l, and h are the total mass of the milling cutter and tool holder, the workpiece width, the workpiece length, and the workpiece height, respectively; n is the direction of rotation of the milling cutter; v f This refers to the feed direction of the milling cutter; o c -x c y c z c For the milling cutter coordinate system; o v -x v y v z v The coordinate system for the milling cutter velocity under vibration; o d -x d y d z d This is the coordinate system for vibration acceleration detection.

[0069] The milling vibration energy in the x-axis, y-axis, and z-axis directions is calculated as shown in equations (4-6):

[0070] P x (t)=F ax (t)·v x (t)=m·a x (t)·v x (t) (4)

[0071] P y (t)=F ay(t)·v y (t)=m·a y (t)·v y (t) (5)

[0072] P z (t)=F az (t)·v z (t)=m·a z (t)·v z (t) (6)

[0073] In the formula, F ax (t), F ay (t), F az (t) represents the vibration force in the feed rate direction, the vibration force in the milling width direction, and the vibration force in the milling depth direction, respectively; m is the tool overhang mass; a x (t), a y (t), a z (t) represents the vibration acceleration in the feed rate direction, the vibration acceleration in the milling width direction, and the vibration acceleration in the milling depth direction, respectively. x (t), v y (t), v z (t) represents the vibration velocity in the feed speed direction, the vibration velocity in the milling width direction, and the vibration velocity in the milling depth direction, respectively.

[0074] According to the vibration energy calculation method combined with Figure 8-12 Get as Figure 21 , Figure 22 The time-domain signal of vibration energy before and after separation.

[0075] The time-frequency domain processing of the divided milling vibration energy signal is shown in Table 7. Figure 23-25 The time-frequency characteristic parameters of different cutting periods.

[0076] Table 7 Time-frequency characteristic parameters of milling vibration energy during different cutting periods

[0077]

[0078] Depend on Figure 23 It can be seen that the milling vibration energy is mainly concentrated in the cutting width direction. This may be because the impact is primarily concentrated in the milling width direction. It can be observed that the milling vibration energy increases in all directions towards the latter half of the milling process, reflecting the possibility of chatter occurring in the latter half of the milling process. Figure 24 It can be seen that the vibration energy kurtosis in both the feed rate direction and the milling width direction exhibits a large kurtosis during the entry and exit phases and a small kurtosis during the steady-state phase. The difference lies in the milling depth direction, where the vibration energy kurtosis experiences a sudden increase in the latter half of the milling process. Figure 25It can be seen that during the cutting process, the main frequency of vibration energy in the milling depth and cutting width directions is relatively stable and mostly consists of the rotational speed frequency and its harmonics, while the main frequency of vibration energy in the feed speed direction fluctuates greatly and contains more high-frequency components.

[0079] The time-domain signal of the milling vibration energy excited by the cutting force was processed in the time-frequency domain, and the results are shown in Table 8. Figure 26-28 The time-frequency characteristic parameters of different cutting periods.

[0080] Table 8. Time-frequency characteristic parameters of cutting force excitation vibration energy during different cutting periods.

[0081]

[0082] Depend on Figure 26 It can be seen that, with Figure 23 In comparison, apart from the decrease in values ​​in the feed rate and depth of cut directions, the trend of change remained almost unchanged. However, in the milling width direction, the upward trend of vibration acceleration slowed down in the latter half of the stable cutting period. This may be because the dominant frequency component of the original vibration energy in the milling width direction during the pre-cutting idling phase is more abundant than in the other two directions. Figure 27 It can be seen that kurtosis still exhibits the characteristics of a large entry and exit time and a small stable cutting time in the three directions during milling; from Figure 28 It can be seen that the dominant frequency of the vibration energy generated by the milling force excitation is a high-frequency component during the entry period, indicating that it is subjected to a large impact. During the stable cutting period, the dominant frequency of the vibration energy in the milling depth direction is relatively stable, and all of them are twice the rotational speed frequency. In the stable cutting period in the feed speed direction and the milling width direction, more high-frequency components appear. During the exit period, the dominant frequency of the vibration energy in the three directions is the rotational speed frequency or a multiple of the rotational speed frequency.

[0083] To evaluate the cutting efficiency during the cutting process, the vibration energy distribution is characterized as shown in Equation 7.

[0084] η=P / P0 (7)

[0085] In the formula, η represents the proportion of vibration energy, P represents the vibration energy excited by the cutting force, and P0 represents the milling vibration energy. The distribution of vibration energy excited by the cutting force to the total vibration energy is shown below. Figure 16 As shown.

[0086] Depend on Figure 29It can be seen that the proportion of vibration energy excited by the cutting force is largest in the feed rate direction at different milling stages, followed by the milling depth direction, and smallest in the milling width direction. This indicates that the vibration of the machine tool process system accounts for a larger proportion of the total vibration in the milling width direction compared to the other two directions. This suggests that the dynamic balance of the machine tool in the milling width direction is relatively poor compared to the other two directions. Furthermore, it was found that the machine tool stability deteriorates further towards the later stages of the milling process. Therefore, the proportion of vibration energy can be used to determine whether to optimize machine tool stability or optimize milling parameters to reduce the impact of milling vibration on machining quality.

[0087] To verify whether different milling parameters affect the dynamic characteristics of cutting vibration energy distribution, experiments were conducted using the same experimental equipment with different spindle speeds, feed per tooth, and milling depths. The milling experimental parameters are shown in Table 9.

[0088] Table 9 Experimental Scheme 2

[0089]

[0090] The root mean square (RMS) values ​​of milling vibration energy and cutting force-excited milling vibration energy at each time interval were extracted under the milling parameters of Experiment Scheme 2. The results are as follows: Figure 30 , Figure 31 As shown.

[0091] The proportion of vibration energy excited by the cutting force in experimental scheme 2 was calculated, and the results are as follows: Figure 32 As shown.

[0092] Depend on Figure 18 It can be seen that the proportion of vibration energy excited by the cutting force in the milling width direction is the largest at different times of milling, followed by the feed speed direction, and the proportion of vibration energy excited by the cutting force in the milling depth direction is the smallest. This indicates that the vibration of the machine tool process system accounts for a larger proportion of the total vibration in the milling depth direction compared to the other two directions. This suggests that the dynamic balance of the machine tool in the milling depth direction is relatively poor compared to the other two directions. Furthermore, it was found that the stability of the machine tool is worse as the milling process progresses.

[0093] 5: Evaluation Method for Dynamic Characteristics of Vibration Energy

[0094] To further demonstrate that changing cutting parameters can optimize the vibration energy ratio and thus improve the stability of the cutting process, it is necessary to evaluate the vibration energy ratio under the two schemes. Since the root mean square value can accurately reflect the vibration energy ratio level and the coefficient of variation can reflect the dispersion of the vibration energy ratio at different cutting stages, a method for evaluating the dynamic characteristics of vibration energy is proposed based on the root mean square value and the coefficient of variation, as shown in formula (8-9).

[0095]

[0096] In the formula, N is the number of cutting time periods, and η rms η is the root mean square value of the proportion of vibrational energy. i The values ​​represent the proportion of vibration energy during different cutting stages, CV is the coefficient of variation of the proportion of vibration energy during different cutting stages, σ is the standard deviation of the proportion of vibration energy during different cutting stages, and μ is the average proportion of vibration energy during different cutting stages. The evaluation results of the vibration energy proportion of the two schemes are shown in Table 10.

[0097] Table 10 Evaluation results of vibration energy proportion during cutting time for different schemes

[0098]

[0099] It can be observed that in both the feed rate and depth of cut directions, the root mean square (RMS) value and coefficient of variation of the cutting force-excited vibration energy proportion under Scheme 1 are superior to those of Scheme 2. Specifically, in the feed rate direction, the RMS value of the cutting force-excited vibration energy proportion under Scheme 1 is 35.04% higher than that under Scheme 2, while the coefficient of variation is 8.53% lower. In the depth of cut direction, the RMS value of the cutting force-excited vibration energy proportion under Scheme 1 is 55.92% higher than that under Scheme 2, while the coefficient of variation is 0.61% lower. Although in the width of cut direction, the RMS value of the cutting force-excited vibration energy proportion under Scheme 1 is 33.08% lower than that under Scheme 2, while the coefficient of variation is 11.47% higher. Overall, Scheme 1 is superior to Scheme 2. Therefore, it can be demonstrated that by changing the cutting parameters, the vibration energy proportion and dispersion can be optimized, thereby improving the stability of the cutting process and verifying the feasibility of the method for identifying the vibration energy variation characteristics of high-efficiency face milling cutters.

[0100] This embodiment achieves accurate identification of the vibration energy variation characteristics of high-efficiency milling cutters by extracting time-frequency characteristic parameters of machine tool vibration, milling cutter centrifugal force vibration, and vibration energy induced by cutting force excitation during different cutting stages from the entry to the exit of the milling cutter from the workpiece. This method not only assesses the stability of the cutting process but also provides important technical support and theoretical basis for optimizing cutting conditions, improving machining efficiency, and extending tool life.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for identifying the energy variation characteristics of face milling cutter cutting vibration, characterized in that, Includes the following steps: Step 1: Develop a test plan for the vibration of face milling cutter cutting, and use dry milling and climb milling to process the workpiece, continuously sampling the dynamic data of the face milling cutter; Step two, dividing the cutting time period, distinguishing the milling process according to different milling states during face milling, t i Let x(t) represent different times, where i = 0, 1, 2, 3, 4, 5, 6, 7. i The following is a formula relating the different cutting states of the milling cutter, its position, time, and cutting parameters: Among them, v f For the feed rate, t i For different cutting times of the milling cutter; Step 3: Reveal the time-frequency characteristics of vibration acceleration during different cutting periods. The vibration acceleration signal measured in the experiment is integrated to obtain the milling vibration velocity and displacement time-domain signals. The root mean square value, kurtosis and dominant frequency are used as evaluation indicators. Step 4: Calculation and identification methods for cutting vibration energy distribution. Establish an o-xyz workpiece coordinate system, where x, y, and z represent the feed rate direction, milling width direction, and milling depth direction of the milling cutter, respectively. Calculate the milling vibration energy in the x-direction, y-direction, and z-direction using the following formulas: P x (t)=F ax (t)·v x (t)=m·a x (t)·v x (t)、P y (t)=F ay (t)·v y (t)=m·a y (t)·v y (t)、 P z (t)=F az (t)·v z (t)=m·a z (t)·v z (t); Among them, F ax (t), F ay (t), F az (t) represents the vibration force in the feed rate direction, the vibration force in the milling width direction, and the vibration force in the milling depth direction, respectively; m is the tool overhang mass; a x (t), a y (t), a z (t) represents the vibration acceleration in the feed rate direction, the vibration acceleration in the milling width direction, and the vibration acceleration in the milling depth direction, respectively. x (t), v y (t), v z (t) represents the vibration velocity in the feed speed direction, the vibration velocity in the milling width direction, and the vibration velocity in the milling depth direction, respectively. Based on the vibration energy calculation method, the time-domain signal of vibration energy before and after the face milling cutter separation is obtained. The divided milling vibration energy time-domain signal is processed in the time-frequency domain to obtain the time-frequency characteristic parameters of different cutting periods. The cutting efficiency during the cutting process is evaluated to characterize the vibration energy distribution. Step 5: Based on the root mean square value and coefficient of variation, a method for evaluating the dynamic characteristics of vibration energy is proposed, with the following formula: Where N is the number of cutting time periods, η rms η is the root mean square value of the proportion of vibrational energy. i Let ν represent the proportion of vibration energy during different cutting periods, CV represent the coefficient of variation of the proportion of vibration energy during different cutting periods, σ represent the standard deviation of the proportion of vibration energy during different cutting periods, and μ represent the average proportion of vibration energy during different cutting periods.

2. The method for identifying the energy variation characteristics of face milling cutter cutting according to claim 1, characterized in that: In step one, a three-axis CNC milling machine is used. The workpiece is 45 steel. The mass of the face milling cutter and the tool holder are 486.32g and 900.40g, respectively.

3. The method for identifying the characteristics of vibration changes in face milling cutter cutting according to claim 1, characterized in that: In step two, the vibration acceleration signal during the idle period before milling is analyzed in the frequency domain. The idle signal before milling is extracted and subjected to spectrum analysis. The irrelevant machining speed frequency and its harmonic frequency components are filtered to obtain the vibration signal generated only by the milling force excitation, and then frequency domain analysis is performed.

4. The method for identifying the energy variation characteristics of face milling cutter cutting according to claim 1, characterized in that: In step three, the formula for the root mean square value is: Where, x i Let be the sample vibration acceleration value at time i; n be the total number of sampling points; kurtosis describes the sharpness of the data distribution, and the kurtosis formula is: Where n is the total number of sampling points; x i Let be the sample vibration acceleration value at time i; σ is the sample mean; σ is the sample standard deviation, which measures the dispersion of the data in the sample.

5. The method for identifying the energy variation characteristics of face milling cutter cutting according to claim 1, characterized in that: In step four, the characterization formula is: η = P / P0; Where η is the proportion of vibration energy, P is the vibration energy excited by the cutting force, and P0 is the milling vibration energy.

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