Face milling cutter cutting vibration energy change characteristic identification method

Abstract

The invention discloses a face milling cutter cutting vibration energy change characteristic identification method. Comprising the following steps: a high-efficiency face milling cutter cutting vibration experiment scheme, a cutting vibration experiment result and cutting time period division method, vibration acceleration time-frequency characteristics of different cutting time periods, a cutting vibration energy calculation method, a cutting vibration energy distribution identification method and a vibration energy dynamic characteristic evaluation method. By comprehensively analyzing and identifying the vibration energy in the milling process, the stability of the efficient milling cutter in the cutting process and the distribution characteristic of the vibration energy can be effectively revealed, and by resolving the vibration energy, the time-frequency characteristic parameters of the vibration energy in different cutting time periods in the process from the milling cutter to the process from cutting in to cutting out of a workpiece are extracted; therefore, the change rule of the milling vibration energy is accurately identified, the change characteristic of the vibration energy in the cutting process can be effectively identified, the cutting stability is improved, the negative influence of vibration on the machining quality is reduced, and the machining efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of milling cutters, and in particular to a method for identifying cutting vibration energy variation characteristics of a face milling cutter. Background Art

[0002] As a tool designed to improve milling efficiency, high-efficiency face milling cutters are widely used in the processing of high-hardness materials, the manufacture of complex workpieces, and processing occasions requiring a large material removal rate. Compared with existing face milling cutters, high-efficiency face milling cutters can complete more processing tasks in a short time and effectively extend the service life of the tool by optimizing the tool geometry, material properties, and coating design.

[0003] However, in the process of efficient milling, the processing quality is often significantly affected by factors such as the vibration of the machine tool process system itself, the centrifugal force vibration caused by the error of the milling cutter teeth, and the vibration caused by the cutting force excitation. Cutting vibration not only accelerates the wear of the tool and shortens the tool life, but also leads to the instability of the cutting force, thereby causing the geometric error of the workpiece and the decrease of the processing accuracy. Vibration energy is an important parameter reflecting the relationship between vibration intensity and time. It is not only a reflection of the energy conversion in the vibration process, but also an important basis for evaluating the stability of milling and the processing quality. Therefore, in-depth research and identification of the vibration energy change characteristics of efficient 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 the processing quality, and improving the utilization efficiency of cutting energy.

[0004] Vibration energy refers to the energy generated by various vibration sources (such as vibration of machine tool structure, centrifugal force vibration caused by milling cutter error, cutting force fluctuation, etc.) during the cutting process. Cutting vibration energy affects the workpiece processing quality, tool life and cutting force stability by affecting the amplitude and frequency of vibration. The change pattern of vibration energy reflects the dynamic behavior of the entire milling process. If the vibration energy is too large, it may cause excessive wear of the tool or cause unstable fluctuations in the processing process, thus affecting the processing accuracy. Therefore, in-depth research on vibration energy can reveal the interaction between vibration sources and provide a scientific basis for optimizing cutting conditions and improving processing quality.

[0005] Existing research on cutting vibration has mainly focused on the law of the amplitude and frequency of vibration acceleration changing with time, and has failed to effectively separate different vibration sources, especially the interaction between the vibration caused by the machine tool process system, the centrifugal force vibration caused by the milling cutter tooth error, and the vibration caused by the cutting force excitation, lacking in-depth analysis. In addition, most existing studies are limited to the stable cutting stage, and fail to fully reveal the dynamic behavior of vibration in the entire milling process, especially the failure to identify and analyze the vibration energy separately, ignoring the important role of vibration energy in improving the stability of the cutting process. Summary of the invention

[0006] The purpose of the present invention is to provide a method for identifying the characteristics of cutting vibration energy changes of a face milling cutter, so as to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for identifying the characteristics of cutting vibration energy changes of a face milling cutter, characterized in that it comprises the following steps:

[0008] Step 1: Develop a face milling cutter cutting vibration experiment plan, use dry, down-milling cutting methods to process the workpiece, and continuously sample the dynamic data of the face milling cutter;

[0009] Step 2: Cutting time division: the milling process is divided according to the different milling states during the milling process of the face milling cutter. i are different moments, where i = 0, 1, 2, 3, 4, 5, 6, 7, x ( t i) The relationship between different cutting states of the milling cutter and its position, time and cutting parameters is:

[0010]

[0011] Among them, v f is the feed speed, t i Different cutting moments for the milling cutter;

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

[0013] The kurtosis formula is:

[0014] Among them, x i is the sample vibration acceleration value at the i-th moment; n is the total number of sampling points, is the sample mean; σ is the sample standard deviation, which measures the degree of dispersion of the data in the sample.

[0015] Step 4: Cutting vibration energy solution method and cutting vibration energy distribution identification method, establish o-xyz as the workpiece coordinate system, x, y, z are the feed speed direction of the milling cutter, the milling width direction, and the milling depth direction respectively; m, w, l, h are the total mass of the milling cutter and the tool holder, the workpiece width, the workpiece length, and the workpiece height respectively; n is the milling cutter rotation direction; v f is the feed direction of the milling cutter; o c -x c y c z c is the milling cutter coordinate system; o v -x v y v z v is the milling cutter velocity coordinate system under vibration; o d -x d y d z d For the vibration acceleration detection coordinate system, the milling vibration energy in the x-direction, the milling vibration energy in the y-direction, and the milling vibration energy in the z-direction are solved. The formulas for solving the milling vibration energy in the x-direction, the milling vibration energy in the y-direction, and the milling vibration energy in the z-direction are:

[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) are the vibration force in the feed speed direction, the vibration force in the milling width direction, and the vibration force in the milling depth direction, m is the tool overhang mass, a x (t), a y (t), a z (t) are the vibration acceleration in the feed speed direction, the milling width direction, and the milling depth direction, respectively, and v x (t), v y (t), vz (t) are the vibration speed in the feed speed direction, the vibration speed in the milling width direction, and the vibration speed in the milling depth direction, respectively.

[0019] Based on the vibration energy solution method, the vibration energy time domain signal before and after the face milling cutter is separated 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 in the cutting process is evaluated to characterize the vibration energy distribution. The formula is:

[0020] Among them, η is the vibration energy ratio, P is the cutting force excitation vibration energy, and P0 is the milling vibration energy.

[0021] Step 5: Based on the root mean square value and the coefficient of variation, a vibration energy dynamic characteristics evaluation method is proposed. The formula is:

[0022]

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

[0024] Furthermore, in step 2, the vibration acceleration signal of the milling cutter during the idling period before milling is subjected to frequency domain analysis, the idling signal before milling is intercepted, spectrum analysis is performed, irrelevant machining speed frequency and its multiple frequency components are filtered, and the vibration signal generated only by the milling force excitation is obtained, and frequency domain analysis is performed.

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

[0026] (1) Comprehensively analyze and identify the vibration energy characteristics during the milling process. By extracting the time-frequency characteristic parameters of the machine tool vibration, the centrifugal force vibration of the milling cutter, and the vibration energy caused by the cutting force excitation at different cutting periods during the process of the milling cutter cutting into and out of the workpiece, the accurate identification of the vibration energy change characteristics of the efficient milling cutter can be achieved. This can not only judge the stability of the cutting process, but also provide 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 conditions; using the vibration acceleration data of the idling period to separate the collected vibration acceleration signal, obtain the vibration acceleration signal generated only by the cutting force excitation and reveal the time-frequency characteristics of the vibration acceleration in different cutting periods; a method for calculating the vibration energy of high-efficiency face milling cutter is proposed. This method uses the vibration acceleration signal and the vibration velocity signal to characterize the vibration energy signal, extracts the time-frequency characteristic parameters of the vibration energy signal, identifies the change characteristics of the vibration energy of high-efficiency face milling cutter cutting, and reveals the dynamic characteristics of the vibration energy distribution of high-efficiency face milling cutter cutting; and evaluates the vibration energy distribution based on the vibration energy dynamic characteristics evaluation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a time domain signal diagram of the vibration acceleration of the present invention; Figure 2 It is a diagram of different milling states during the milling process of the face milling cutter of the present invention; Figure 3 This is a time domain signal diagram of milling vibration of the present invention; Figure 4 This is a spectrum diagram of the vibration acceleration signal during the idling period before cutting of the present invention; Figure 5 This is a time domain signal diagram of vibration acceleration during the idling period before cutting according to the present invention; Figure 6 This is a milling vibration spectrum diagram of the present invention during the idling period before cutting with cutting force excitation; Figure 7 It is a time domain signal diagram of the cutting force excitation vibration during the idling period before cutting of the present invention; Figure 8 This is a time domain signal diagram of the milling vibration acceleration of the present invention; Fig. 9 This is a time domain signal diagram of the milling vibration acceleration excited by the cutting force of the present invention; Fig.10 This is a time domain signal diagram of the milling vibration speed of the present invention; Fig.11 This is a time domain signal diagram of the milling vibration velocity excited by the cutting force of the present invention; Fig.12 This is a time domain signal diagram of milling vibration displacement of the present invention; Fig.13 This is a time domain signal diagram of milling vibration displacement excited by cutting force of the present invention; Fig.14 This is a diagram of the root mean square value of the milling vibration acceleration at each time period of the present invention; Fig.15 It is the kurtosis value diagram of milling vibration acceleration in each time period of the present invention; Fig.16 This is the main frequency diagram of the milling vibration acceleration in each period of the present invention; Fig.17 This is a diagram of the root mean square value of the cutting force excitation vibration acceleration at each time period of the present invention; Fig.18 It is a graph of kurtosis values ​​of cutting force excitation vibration acceleration at each time period of the present invention; Fig.19 This is the main frequency diagram of the cutting force excitation vibration acceleration in each period of the present invention; Fig. 20 This is the instantaneous vibration velocity diagram of the high-efficiency face milling cutter of the present invention when milling 45 steel; Fig.21 This is a time domain signal diagram of milling vibration energy of the present invention; Fig. 22 This is a time domain signal diagram of the milling vibration energy excited by the cutting force of the present invention; Fig.23This is a diagram of the root mean square value of the milling vibration energy in each time period of the present invention; Fig.24 It is a graph of kurtosis values ​​of milling vibration energy at each time period of the present invention; Fig.25 This is the main frequency diagram of the milling vibration energy in each time period of the present invention; Fig.26 The root mean square value diagram of the milling vibration energy excited by the cutting force of the present invention at each time period; Fig. 27 It is a graph of kurtosis values ​​of milling vibration energy excited by cutting force in each period of the present invention; Fig.28 This is the main frequency diagram of each period of the milling vibration energy excited by the cutting force of the present invention; Fig.29 This is a graph showing the proportion of cutting force excitation vibration energy according to the present invention; Fig.30 This is the RMS value diagram of milling vibration energy in each time period of Experimental Scheme 2 of the present invention; Fig.31 This is the RMS value diagram of the milling vibration energy in each period of the cutting force excitation of the experimental scheme 2 of the present invention; Fig.32 This is a graph showing the proportion of cutting force excitation vibration energy in Experimental Scheme 2 of the present invention; Fig.33 This is a flow chart of the method for identifying the vibration energy variation characteristics of the face milling cutter of the present invention. DETAILED DESCRIPTION

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

[0030] Example:

[0031] See also Figure 1-33 ,The present invention provides a technical solution: a method for identifying the characteristics of cutting vibration energy changes of a face milling cutter;

[0032] 1: Experimental scheme for cutting vibration of high-efficiency face milling cutter

[0033] The CNC milling machine XK7124 three-axis milling machining center was used to conduct the face milling cutter milling 45 steel experiment. The milling cutter is the M4003-050-B22-04-6.5 face milling cutter produced by Walter Company, the blade is SDMT1204AZN-D57WKP35G, the number of teeth is 4, the clamping length of the milling cutter is 45mm, and the mass of the tool and the handle obtained by the balance is 486.32g and 900.40g respectively, a total of 1386.72g. The detailed parameters of the machine tool and the milling cutter are shown in Table 1.

[0034] Table 1 Machine tool and milling cutter parameters

[0035]

[0036] The experiment adopts dry and down-milling cutting methods. During processing, the YE7600 dynamic data acquisition and analysis system and HD-YD-211IEPE piezoelectric accelerometer are used to collect vibration acceleration signals. The sensor is installed on the workpiece and its sensitivity is 100mV / g. The sampling frequency of the vibration acceleration signal is 12kHz, the sampling mode is continuous sampling, and the trigger mode is signal triggering. The cutting parameters are shown in Table 2. Among them, n is the spindle speed, f is the feed per tooth, a is the rotation speed of the spindle, and f is the rotation speed of the spindle. p is the cutting depth, a e is the cutting width. The workpiece used in the experiment is 45# steel, and the specific material composition is shown in Table 3.

[0037] Table 2 Cutting parameters (I)

[0038]

[0039] Table 3 Material 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: Cutting vibration test results and cutting period division method

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

[0043] The milling process is divided according to the different milling states during the face milling process. The division results are as follows: Figure 2 As shown, t i is a different time, where i = 0, 1, 2, 3, 4, 5, 6, 7. The time interval from the no-load time t0 before the milling cutter cuts into the workpiece to the time t1 when the milling cutter initially cuts into the workpiece is Δt1; the time interval from the time t1 when the milling cutter initially cuts into the workpiece to the time t2 when the milling cutter radius cuts into the workpiece is Δt2; the time interval from the time t2 when the milling cutter radius cuts into the workpiece to the time t3 when the milling cutter completely cuts in is Δt3; the time interval from the time t3 when the milling cutter completely cuts in to the time t4 when the milling cutter is just about to cut out is Δt4; the time interval from the start time t4 when the milling cutter cuts out to the time t5 when the milling cutter radius cuts out the workpiece is Δt5; the time interval from the time t5 when the milling cutter radius cuts out the workpiece to the time t6 when the milling cutter completely cuts out is Δt6; the time interval from the time t6 when the milling cutter completely cuts out to the time t7 when the milling cutter retracts is Δt7. x ( t i) The relationship between different cutting states of the milling cutter and its position, time and cutting parameters is shown in formula (1).

[0044]

[0045] In the formula, v f is the feed speed, t iDifferent cutting times for the milling cutter.

[0046] According to the principle of the greatest common factor, the stable milling period is evenly divided, that is, Δt4 is evenly divided into Δt 41 ,Δt 42 ...Δt 4m There are m segments in total. The division results of the complete vibration acceleration signal are as follows: Figure 3 And as shown in Table 4.

[0047] Table 4 Node division results of milling vibration time domain signal

[0048]

[0049] The vibration in the milling process includes the vibration caused by the machine tool process system itself, the centrifugal force vibration caused by the milling cutter error, and the vibration caused by the cutting force excitation. In order to separate the vibration acceleration signal caused by the cutting force excitation, it is necessary to perform frequency domain analysis on the vibration acceleration signal during the idling period before milling, such as Figure 4 , Figure 5 shown.

[0050] The idling signal before milling is intercepted and analyzed by spectrum. The irrelevant machining speed frequency and its multiple frequency components are filtered to obtain the vibration signal generated only by the milling force. The frequency domain analysis is also performed. The results are as follows: Figure 6 , Figure 7 shown.

[0051] It can be found that the amplitude corresponding to the first main frequency of the milling vibration acceleration excited by the cutting force during the idling period before cutting changes significantly. The amplitudes of the vibration acceleration time domain signals in the three directions are all reduced and the signal fluctuations are smaller, indicating that the vibration signal frequency not related to cutting has been filtered out.

[0052] 3: Time-frequency characteristics of vibration acceleration at different cutting periods

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

[0054] In order to objectively evaluate the stability of the cutting process, the root mean square value, kurtosis and main frequency are selected as evaluation indicators. The root mean square value reflects the energy or fluctuation degree of the signal, and the formula is as follows:

[0055]

[0056] In the formula, x i is the sample vibration acceleration value at the i-th moment; n is the total number of sampling points (the number of vibration acceleration sampling data points in the time period), and kurtosis describes the sharpness of the data distribution. The higher the kurtosis value, the more shocks there are in the signal. The formula is as follows:

[0057]

[0058] Where n is the total number of sampling points; x i is the sample vibration acceleration value at the i-th moment; is the sample mean (average); σ is the sample standard deviation, which measures the degree of dispersion of the data in the sample.

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

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

[0061]

[0062] Depend on Fig.14 It can be seen that the vibration acceleration signal in the feed speed direction fluctuates less than that in the other two directions, followed by the vibration acceleration in the milling depth direction, and the vibration acceleration signal in the milling width direction fluctuates most violently. The reason may be that the cutting force during the milling process of the face milling cutter is concentrated in the milling width direction, resulting in violent vibration during the processing. As the cutting process proceeds, the closer to the milling cutter cutting period, the more violent the vibration. The reason may be that the system rigidity cannot meet the requirements of stable cutting, resulting in the occurrence of chatter. Fig.15 It can be seen that the kurtosis shows the characteristics of large cutting-in and cutting-out periods and small stable cutting periods in the three directions during the milling process; Fig.16 It can be seen that during the cutting process, the main frequency changes in the milling width direction and the milling depth direction are small, while the main frequency in the feed speed direction changes suddenly in the second half of the milling stable stage, and chatter may occur, causing the main frequency to suddenly increase. It is also found that the main frequency in the milling width direction and the milling depth direction of idling before and after milling changes, indicating that the inherent vibration characteristics of the machine tool process system have changed due to milling of the workpiece.

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

[0064] Table 6 Time-frequency characteristic parameters of vibration acceleration caused by cutting force excitation at different cutting periods

[0065]

[0066] Depend on Fig.17 It can be seen that Fig.14 In comparison, except for the decrease in the values ​​in the feed speed direction and the milling depth direction, the change trend has hardly changed, while in the milling width direction, the rising trend of the vibration acceleration slows down in the second half of the stable cutting period. The reason may be that the main frequency component of the original vibration acceleration signal in the idling stage before cutting in the milling width direction is more than that in the other two directions; Fig.18 It can be seen that the kurtosis still presents the characteristics of large cutting-in and cutting-out periods and small stable cutting periods in the three directions during the milling process; Fig.19 It can be seen that the main frequency in the feed speed direction does not change much compared with that before separation, indicating that the vibration brought by the machine tool process system in the feed speed direction has little effect on the vibration caused by cutting force excitation, while it has a greater impact in the milling width and milling depth directions, especially in the milling width direction.

[0067] 4: Cutting vibration energy calculation method and cutting vibration energy distribution identification method

[0068] Fig. 20 In the figure, o-xyz is the workpiece coordinate system, where x, y, and z are the feed speed direction, milling width direction, and milling depth direction of the milling cutter, respectively; m, w, l, and h are the total mass of the milling cutter and tool holder, workpiece width, workpiece length, and workpiece height, respectively; n is the rotation direction of the milling cutter; v is the rotation direction of the milling cutter; f is the feed direction of the milling cutter; o c -x c y c z c is the milling cutter coordinate system; o v -x v y v z v is the milling cutter velocity coordinate system under vibration; o d -x d y d z d It is the vibration acceleration detection coordinate system.

[0069] The milling vibration energy in the x-direction, the milling vibration energy in the y-direction, and the milling vibration energy in the z-direction are calculated as shown in equations (4 to 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) are the vibration force in the feed speed direction, the vibration force in the milling width direction, and the vibration force in the milling depth direction, m is the tool overhang mass, a x (t), a y (t), a z (t) are the vibration acceleration in the feed speed direction, the milling width direction, and the milling depth direction, respectively, and v x (t), v y (t), v z (t) are the vibration speed in the feed speed direction, the vibration speed in the milling width direction, and the vibration speed in the milling depth direction, respectively.

[0074] Combined with the vibration energy solution method Figure 8-12 Get as Fig.21 , Fig. 22 Time domain signals of vibration energy before and after separation.

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

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

[0077]

[0078] Depend on Fig.23 It can be seen that the milling vibration energy is mainly concentrated in the cutting width direction. The reason may be that the impact is mainly concentrated in

[0079] In the milling width direction, it can be found that the closer to the second half of the milling process, the milling vibration energy in all directions tends to increase, which also reflects that chatter may occur in the second half of the milling process; Fig.24It can be seen that the vibration energy kurtosis in the feed speed direction and the milling width direction shows the characteristics of large cutting in and out and small stable stage. The difference is that in the milling depth direction, the vibration energy kurtosis increases suddenly in the second half of milling. Fig.25 It can be seen that during the cutting process, the main frequency of the vibration energy in the milling depth and cutting width directions is relatively stable and is mostly the rotational speed frequency and its multiples, while the main frequency of the vibration energy in the feed speed direction fluctuates greatly and more high-frequency components appear.

[0080] The divided cutting force excitation milling vibration energy time domain signal is processed in the time-frequency domain, and the results are shown in Table 8 and Figure 26-28 The time-frequency characteristic parameters of different cutting periods.

[0081] Table 8 Time-frequency characteristic parameters of cutting force excitation vibration energy at different cutting periods

[0082]

[0083] Depend on Fig.26 It can be seen that Fig.23 In comparison, except for the decrease in the values ​​in the feed speed direction and the milling depth direction, the change trend has hardly changed, while in the milling width direction, the rising trend of the vibration acceleration slows down in the second half of the stable cutting period. The reason may be that the main frequency component of the original vibration energy in the pre-cutting idling stage in the milling width direction is more than that in the other two directions; Fig. 27 It can be seen that the kurtosis still presents the characteristics of large cutting-in and cutting-out periods and small stable cutting periods in the three directions during the milling process; Fig.28 It can be seen that the main frequency of the vibration energy generated by the milling force excitation is a high-frequency component in the cutting period, indicating that the impact is relatively large. When entering the stable cutting period, the main frequency of the vibration energy in the milling depth direction is relatively stable, and is twice the frequency of the rotational speed. In the feed speed direction and the milling width direction, there are more high-frequency components in the stable cutting period. In the cutting-out period, the main frequencies of the vibration energy in the three directions are the rotational speed frequency or the rotational speed multiple.

[0084] In order to evaluate the cutting efficiency during the cutting process, the vibration energy distribution is characterized as shown in Equation 7.

[0085]

[0086] Where η is the proportion of vibration energy, P is the cutting force excitation vibration energy, P0 is the milling vibration energy, and the distribution of cutting force excitation vibration energy to vibration energy is obtained as follows: Fig.16 shown.

[0087] Depend on Fig.29It can be seen that the cutting force excitation vibration energy accounts for the largest proportion in the feed speed direction at different milling periods, followed by the milling depth direction, and the smallest proportion in the milling width direction, indicating that the vibration of the machine tool process system accounts for a larger proportion of the total vibration in the milling width direction than in the other two directions, indicating that the dynamic balance of the machine tool in the milling width direction is poorer than in the other two directions, and it is found that the closer to the end of the milling process, the worse the stability of the machine tool. Therefore, it can be judged based on the vibration energy proportion whether to optimize the stability of the machine tool or optimize the milling parameters to reduce the impact of milling vibration on processing quality.

[0088] In order to verify whether different milling parameters affect the dynamic characteristics of cutting vibration energy distribution, experiments were carried out using the same experimental equipment with different spindle speeds, feed per tooth and milling depth. The milling experimental parameters are shown in Table 9.

[0089] Table 9 Experimental scheme 2

[0090]

[0091] The root mean square value of the milling vibration energy at each time period under the milling parameters of experimental scheme 2 and the root mean square value of the milling vibration energy at each time period excited by the cutting force are extracted. The results are as follows Fig.30 , Fig.31 shown.

[0092] The proportion of cutting force excitation vibration energy of experimental scheme 2 is calculated, and the results are as follows: Fig.32 shown.

[0093] Depend on Fig.18 It can be seen that at different milling periods, the cutting force excitation vibration energy in the milling width direction accounts for the largest proportion, followed by the feed speed direction, and the cutting force excitation vibration energy in the milling depth direction accounts for the smallest proportion, indicating that the vibration of the machine tool process system accounts for a larger proportion of the total vibration in the milling depth direction than in the other two directions, indicating that the dynamic balance of the machine tool in the milling depth direction is poorer than that in the other two directions, and it is found that the closer to the later stage of the milling process, the worse the stability of the machine tool.

[0094] 5: Evaluation method of dynamic characteristics of vibration energy

[0095] In order to further prove 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 truly reflect the level of vibration energy ratio and the coefficient of variation can reflect the discrete degree of vibration energy ratio in different cutting periods, a vibration energy dynamic characteristic evaluation method is proposed based on the root mean square value and the coefficient of variation, as shown in formula (8-9).

[0096]

[0097] Where N is the number of cutting periods, η rms is the root mean square value of vibration energy proportion, η i is the vibration energy ratio of different cutting periods, CV is the coefficient of variation of the vibration energy ratio of the cutting period, σ is the standard deviation of the vibration energy ratio of different cutting periods, and μ is the average value of the vibration energy ratio of different cutting periods. The evaluation results of the vibration energy ratio of the two schemes are shown in Table 10.

[0098] Table 10 Evaluation results of vibration energy proportion during cutting period of different schemes

[0099]

[0100] It can be found that in the feed speed and cutting depth directions, the RMS value and coefficient of variation of the cutting force excitation vibration energy ratio under the cutting parameters of Scheme 1 are better than those of Scheme 2. In the feed speed direction, the RMS value of the cutting force excitation vibration energy ratio of Scheme 1 is 35.04% higher than that of Scheme 2, and the coefficient of variation is 8.53% lower than that of Scheme 2; in the cutting depth direction, the RMS value of the cutting force excitation vibration energy ratio of Scheme 1 is 55.92% higher than that of Scheme 2, and the coefficient of variation is 0.61% lower than that of Scheme 2. Although the RMS value of the cutting force excitation vibration energy ratio of Scheme 1 is 33.08% lower than that of Scheme 2 in the cutting width direction, and the coefficient of variation is 11.47% higher than that of Scheme 2, Scheme 1 is better than Scheme 2 overall. Therefore, it can be proved that the vibration energy ratio and discreteness can be optimized by changing the cutting parameters, thereby improving the stability of the cutting process, and verifying the feasibility of the identification method of the vibration energy change characteristics of the efficient face milling cutter.

[0101] This embodiment realizes accurate identification of the variation characteristics of high-efficiency milling cutter cutting vibration energy 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 periods during the process of milling cutter cutting into and out of the workpiece. This method can not only judge the stability of the cutting process, but also provide important technical support and theoretical basis for optimizing cutting conditions, improving processing efficiency and extending tool life.

[0102] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying the characteristics of cutting vibration energy changes of a face milling cutter, characterized in that: The steps include: Step 1: Develop a face milling cutter cutting vibration experiment plan, use dry, down-milling cutting methods to process the workpiece, and continuously sample the dynamic data of the face milling cutter; Step 2: Cutting time division: the milling process is divided according to the different milling states during the milling process of the face milling cutter. i are different moments, where i = 0, 1, 2, 3, 4, 5, 6, 7, x ( t i) The relationship between different cutting states of the milling cutter and its position, time and cutting parameters is: Among them, v f is the feed speed, t i Different cutting moments for the milling cutter; Step 3: The vibration acceleration time-frequency characteristics at different cutting periods are integrated to obtain the milling vibration velocity and displacement time domain signals, and the root mean square value, kurtosis and main frequency are used as evaluation indicators; Step 4: Cutting vibration energy solution method and cutting vibration energy distribution identification method, establish o-xyz as the workpiece coordinate system, x, y, z are the feed speed direction of the milling cutter, the milling width direction, and the milling depth direction respectively; m, w, l, h are the total mass of the milling cutter and the tool holder, the workpiece width, the workpiece length, and the workpiece height respectively; n is the milling cutter rotation direction; v f is the feed direction of the milling cutter; o c -x c y c z c is the milling cutter coordinate system; o v -x v y v z v is the milling cutter velocity coordinate system under vibration; o d -x d y d z d For the vibration acceleration detection coordinate system, solve the milling vibration energy in the x direction, the milling vibration energy in the y direction, and the milling vibration energy in the z direction; Based on the vibration energy solution method, the vibration energy time domain signal before and after the face milling cutter is separated 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, and the cutting efficiency in the cutting process is evaluated to characterize the vibration energy distribution. Step five, a vibration energy dynamic characteristics evaluation method is proposed based on the root mean square value and the coefficient of variation.

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

3. The method for identifying the variation characteristics of cutting vibration energy of a face milling cutter according to claim 1, characterized in that: In step 2, the vibration acceleration signal of the milling cutter during the idling period before milling is analyzed in the frequency domain. The idling signal before milling is intercepted and spectrum analysis is performed. The irrelevant machining speed frequency and its multiple frequency components are filtered to obtain the vibration signal generated only by the milling force excitation, and the frequency domain analysis is performed.

4. The method for identifying the variation characteristics of cutting vibration energy of a face milling cutter according to claim 1, characterized in that: In step 3, the RMS value formula is: Among them, x i is the sample vibration acceleration value at the i-th moment; n is the total number of sampling points, and the kurtosis is the sharpness of the data distribution. The kurtosis formula is: Where n is the total number of sampling points; x i is the sample vibration acceleration value at the i-th moment; is the sample mean; σ is the sample standard deviation, which measures the degree of dispersion of the data in the sample.

5. The method for identifying the variation characteristics of cutting vibration energy of a face milling cutter according to claim 1, characterized in that: In step 4, the formulas for calculating the milling vibration energy in the x-direction, the milling vibration energy in the y-direction, and the milling vibration energy in the z-direction are: 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) are the vibration force in the feed speed direction, the vibration force in the milling width direction, and the vibration force in the milling depth direction, m is the tool overhang mass, a x (t), a y (t), a z (t) are the vibration acceleration in the feed speed direction, the milling width direction, and the milling depth direction, respectively, and v x (t), v y (t), v z (t) are the vibration speed in the feed speed direction, the vibration speed in the milling width direction, and the vibration speed in the milling depth direction, respectively.

6. The method for identifying the variation characteristics of cutting vibration energy of a face milling cutter according to claim 1, characterized in that: In step 4, the characterization formula is: Among them, η is the vibration energy ratio, P is the cutting force excitation vibration energy, and P0 is the milling vibration energy.

7. The method for identifying the variation characteristics of cutting vibration energy of a face milling cutter according to claim 1, characterized in that: In step 5, the formula for evaluating the dynamic characteristics of vibration energy is: Where, N is the number of cutting periods, η rms is the root mean square value of vibration energy proportion, η i is the proportion of vibration energy in different cutting periods, CV is the coefficient of variation of the proportion of vibration energy in cutting periods, σ is the standard deviation of the proportion of vibration energy in different cutting periods, and μ is the average value of the proportion of vibration energy in different cutting periods.

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