Method for identifying dynamic cutting force change characteristics of efficient milling cutter

Abstract

The invention discloses a method for identifying dynamic cutting force change characteristics of an efficient milling cutter. The method comprises an efficient milling cutter dynamic cutting force experiment method and experiment results, a dynamic cutting force time period division method in milling machining, dynamic cutting force time-frequency characteristic analysis of different cutting time periods and associated characteristics of milling vibration and dynamic milling force. By analyzing the cutting force change characteristics of the integral hard alloy efficient milling cutter during workpiece cutting, the cutting state and the change thereof can be reflected more comprehensively under the condition of different cutting parameters and cutter tooth error distribution, and more accurate monitoring can be realized; an improved grey correlation analysis method is adopted, the response characteristics of dynamic cutting force and milling vibration to a milling process scheme are effectively verified, a theoretical basis is provided for improving the stability of the cutting process, the cutting force can be reasonably controlled in practical application, and therefore the service life of a cutter is prolonged, and machining safety is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of milling cutters, and in particular to a method for identifying dynamic cutting force variation characteristics of a high-efficiency milling cutter. Background Art

[0002] Face milling cutters have good cutting performance and machining accuracy, and are suitable for machining various planes, especially large and complex workpieces. Their advantages of high machining efficiency, high machining accuracy, simple structure, and wide application range make them widely used in the field of mechanical machining. In the milling process of face milling cutters, cutting force is an important factor that determines the generation of cutting heat, affects tool wear, breakage, service life, machining quality, and causes tool vibration and workpiece machining deformation. Cutting force is also a necessary basis for calculating cutting power, formulating cutting parameters, monitoring cutting status, and designing and using machine tools, tools, and fixtures.

[0003] During the cutting process of high-efficiency milling cutters, the cutting force changes frequently due to the impact of intermittent cutting of the cutter teeth. The resulting unstable cutting and deterioration of processing quality lead to the inability to effectively guarantee the cutting efficiency and reliability of the cutting process of the milling cutter. Revealing the dynamic cutting force change characteristics of high-efficiency milling cutters is the premise for solving the above problems. The research on the identification method of the dynamic cutting force change characteristics of high-efficiency milling cutters aims to reveal the dynamic characteristics of the cutting force and its changing laws during the milling process, and to monitor the cutting force fluctuations during the processing process in real time, so as to adjust the cutting parameters in time and optimize the processing quality and efficiency.

[0004] Cutting force is the force generated by the interaction between the tool and the workpiece material during the milling process, which is usually divided into three parts: feed direction, cutting width direction and cutting depth direction. The dynamic cutting force characteristics are mainly reflected in the changes of cutting force at different processing stages or different cutting parameters, such as the influence of feed speed, cutting depth and tool geometric parameters on cutting force. Understanding the dynamic characteristics of cutting force can reveal the real-time interaction between the tool and the material being processed, so as to better understand the energy conversion and cutting efficiency in the milling process, and provide an important basis for tool design and processing technology optimization.

[0005] There are currently two methods for solving the changing characteristics of dynamic cutting force: one is an empirical method based on experiments, which uses the transient cutting force obtained from the experiment to reflect the changing characteristics of the dynamic cutting force in the actual cutting process, but is limited by experimental conditions and lacks universal applicability; the other is an analytical method, which obtains the cutting force based on theoretical calculations. This method has universal applicability, but there is a large deviation between the change in dynamic cutting force and the actual change. In the actual cutting process, the dynamic characteristics of the cutting force are affected by many factors, and the cutting force in different cutting states is also different. Existing methods can usually only provide the average value or change trend of the cutting force, and lack an in-depth analysis of the dynamic characteristics. Summary of the invention

[0006] The purpose of the present invention is to provide a method for identifying the dynamic cutting force variation characteristics of an efficient milling cutter to solve the problems raised in the above background technology. The method comprises:

[0007] (1) Experimental methods and results of dynamic cutting force of high-efficiency milling cutters;

[0008] (2) Dynamic cutting force time division method in milling process;

[0009] (3) Analysis of time-frequency characteristics of dynamic cutting force at different cutting stages;

[0010] (4) Correlation characteristics between milling vibration and dynamic milling force.

[0011] To achieve the above object, the present invention provides the following technical solution: a method for identifying the dynamic cutting force variation characteristics of a high-efficiency milling cutter, comprising the following steps:

[0012] Step 1: formulate a dynamic cutting force experimental method for the milling cutter, and collect the milling vibration and dynamic cutting force signals of the milling cutter along the feed speed direction, cutting depth direction, and cutting width direction;

[0013] Step 2: Divide the dynamic cutting force period in milling processing, and divide the entire cutting period of the milling cutter into 12 sections. The relationship between time and cutting parameters is:

[0014]

[0015] Among them, x(t i ) is at time t i The position at the time; v f is the feed speed of the object; t i is the cutting time;

[0016] Step 3: Analyze the time-frequency characteristics of dynamic cutting force at different cutting periods, using root mean square, main frequency, kurtosis, spectrum value, standard deviation and coefficient of variation as evaluation indicators for evaluating the strength of the system cutting force signal;

[0017] Step 4: The correlation characteristics between milling vibration and dynamic milling force are analyzed by using an improved grey correlation analysis method. In the scheme of steel to be milled, the root mean square value, kurtosis, and main frequency in the same direction are subjected to grey correlation.

[0018] Furthermore, in step 1, the axial and radial errors of the milling cutter teeth used in the experiment were measured using a tool setting instrument. Each set of tools took tooth 1 as the measurement reference, where Δc i is the axial error of tooth i, Δr iis the radial error of tooth i. The experiment adopts dry and down milling cutting method. The cutting force testing equipment is used to collect the cutting force. The cutting force signals of the milling cutter along the feed speed direction, milling width direction and milling depth direction during the milling process are measured.

[0019] Furthermore, in step 2, the 12 segments are specifically divided into 6 segments: idling Δt1, cutting period 2 Δt2, Δt3, and cutting period Δt4. The stable milling period is evenly divided according to the principle of the greatest common factor, specifically Δt 41 , Δt 42 , Δt 43 , Δt 44 , Δt 45 , Δt 46 , cut out the 2 periods Δt5, Δt6 and idling Δt7, Δt i represents the i-th time interval in the cutting process, Δt i =t i -t i-1 For different time periods, i = 1, 2, 3, 4, 5, 6, 7. During the milling process, the cutting force signal is filtered and the original waveform is low-pass filtered.

[0020] Furthermore, in step 3, the root mean square value formula of the cutting force is:

[0021]

[0022] Among them, x i is the cutting force at the i-th moment; n is the total number of sampling points, and there are 5000 data points per second;

[0023] The kurtosis formula is:

[0024]

[0025] Where n is the total number of sampling points; x i is the value of the i-th data sample; The sample mean; σ is the sample standard deviation;

[0026] The main frequency formula is:

[0027]

[0028] Among them, k max is the index of the maximum value in the amplitude spectrum; F s is the sampling frequency; n is the number of fast Fourier transform points.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] (1) By analyzing the cutting force variation characteristics of the solid carbide high-efficiency milling cutter when cutting the workpiece, it is possible to grasp the dynamic changes of the cutting force under different cutting parameters and tooth error distribution conditions. This method divides the entire cutting process into different cutting periods, and by extracting the cutting force time-frequency characteristic parameters in different time periods, it can more comprehensively reflect the cutting state and its changes, which is conducive to more accurate monitoring and early detection of abnormalities to reduce the occurrence of failures and accidents.

[0031] (2) The improved grey correlation analysis method is used to effectively verify the response characteristics of dynamic cutting force and milling vibration to the milling process scheme. This provides a theoretical basis for improving the stability of the cutting process and helps to reasonably control the cutting force in practical applications, thereby extending the service life of the tool and ensuring processing safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of the identification method of the dynamic cutting force variation characteristics of the high-efficiency milling cutter of the present invention;

[0033] Figure 2 It is the cutting route position diagram of the face milling cutter of the present invention;

[0034] Figure 3 It is the cutting force time domain signal diagram of the present invention;

[0035] Figure 4 It is a schematic diagram of cutting into idling in the idling frequency domain of the cutting force experiment of the present invention;

[0036] Figure 5 It is a schematic diagram of cutting out idling in the idling frequency domain of the cutting force experiment of the present invention;

[0037] Figure 6 A schematic diagram of a parameter scheme 1 for dividing a cutting force signal into time periods according to the present invention;

[0038] Figure 7 A schematic diagram of a parameter scheme 2 for dividing a cutting force signal into time periods according to the present invention;

[0039] Figure 8 It is a schematic diagram of the cutting force experiment after idling frequency domain filtering of the present invention;

[0040] Fig. 9 This is the frequency domain diagram of the unprocessed stable phase of the present invention;

[0041] Fig.10 This is a frequency domain diagram of the present invention in the post-filtering stable stage;

[0042] Fig.11 This is the RMS value diagram of cutting force at each time period of parameter scheme 1 of the present invention;

[0043] Fig.12This is the RMS value diagram of cutting force at each time period of parameter scheme 2 of the present invention;

[0044] Fig.13 It is the cutting force time domain signal diagram of the present invention;

[0045] Fig.14 This is the kurtosis value diagram of the cutting force at each time period of parameter scheme 1 of the present invention;

[0046] Fig.15 This is the kurtosis value diagram of the cutting force at each time period of parameter scheme 2 of the present invention;

[0047] Fig.16 This is the main frequency diagram of the cutting force in each period of parameter scheme 1 of the present invention;

[0048] Fig.17 This is the main frequency diagram of the cutting force in each time period of parameter scheme 2 of the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] Example:

[0051] See also Figure 1-17 ,The present invention provides a technical solution: a method for identifying the dynamic cutting force variation characteristics of an efficient milling cutter;

[0052] The dynamic cutting force may fluctuate violently during the milling process due to factors such as material unevenness and tool wear. If you want to analyze the cutting force in the entire period, the key is to divide the cutting period according to the milling state, and then analyze the time-frequency characteristics of different periods. Reveal the different cutting periods from the milling cutter cutting into the workpiece to cutting out the workpiece, and analyze the dynamic cutting force change characteristics of the milling cutter.

[0053] This embodiment reveals the dynamic characteristics of cutting force and proposes a method for identifying the dynamic cutting force change characteristics. This method divides the entire cutting process into the cutting-in period, the middle period and the cutting-out period, obtains the cutting force curve of each stage, and analyzes the changing characteristics of the cutting force in different cutting periods. The improved grey correlation analysis method is used to characterize the dynamic cutting force response characteristics to milling vibration.

[0054] Experimental method and results of dynamic cutting force of high-efficiency milling cutter

[0055] This embodiment conducts an experiment of cutting 45 steel with a high-efficiency face milling cutter by setting different experimental schemes, using the same milling cutter, workpiece, installation method, cutting method and detection method. The tooth error is measured before the experiment. The milling vibration and dynamic cutting force signals of the milling cutter along the feed speed direction, cutting depth direction and cutting width direction are collected.

[0056] In the process of milling cutter production and preparation, affected by many factors, the milling cutter will inevitably produce processing errors, so that it has a certain error distribution state before it is put into use. In order to more accurately reveal the dynamic characteristics of milling vibration and milling cutter cutting force, the influence of milling cutter error should be considered. The axial and radial errors of the milling cutter teeth used in the experiment were measured using a tool setting instrument. Each set of tools took tooth 1 as the measurement reference, where Δc i is the axial error of tooth i, Δr i is the radial error of tooth i; n is the spindle speed, a p is the cutting depth, a e is the cutting width, v f is the feed speed. The milling experiment scheme and cutter tooth error are shown in Table 1.

[0057] Table 1 Milling experiment plan and cutter tooth error

[0058]

[0059] 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 2.

[0060] Table 2 Machine tool and milling cutter parameters

[0061]

[0062]

[0063] The experiment adopts dry and down-milling cutting methods. During processing, the KISTLER9257B cutting force test equipment is used to collect cutting force and measure the cutting force signals of the milling cutter along the feed speed direction, milling width direction and milling depth direction during milling. The chemical composition of 45 steel is shown in Table 3.

[0064] Table 3 Material composition of 45# steel

[0065] C Si Mn P S Cu Cr Fe 0.47％ 0.20％ 0.52％ 0.018％ 0.007％ 0.02％ 0.02％ 98.745％

[0066] Method for dividing dynamic cutting force time periods in milling

[0067] During the entire cutting period from when the milling cutter cuts into the workpiece to when it cuts out of the workpiece, there are different impact processes, which will inevitably cause changes in the cutting force. Therefore, according to the characteristic time corresponding to the sudden change of the cutting force time domain characteristic curve, the entire cutting period of the milling cutter is divided into 12 sections, specifically idling (Δt1), 2 cutting periods (Δt2, Δt3), and the cutting period (Δt4) is divided into 6 sections. According to the principle of the greatest common factor, the stable milling period is evenly divided, specifically Δt 41 , Δt 42 , Δt 43 , Δt 44 , Δt 45 , Δt 46 , cut out two periods (Δt5, Δt6) and idling (Δt7), Δt i =t i -t i-1 are different time periods, where i = 1, 2, 3, 4, 5, 6, 7. Using the above-mentioned milling cutter cutting time period division method, the milling cutter cutting route position diagram is determined as follows: Figure 2 As shown in Figure 1, the relationship between time and cutting parameters is expressed as formula (1).

[0068]

[0069] In the formula, x(t i ) is at time t i The position at the time; v f is the feed speed of the object; t i is the cutting time.

[0070] Table 4 Node division results of milling vibration and cutting force time domain signals in experimental scheme 1

[0071]

[0072] Table 5 Node division results of milling vibration and cutting force time domain signals in experimental scheme 2

[0073]

[0074] In the milling process, affected by many external factors such as the environment, there will be noise in the cutting force signal obtained in the experiment. The noise masks the characteristics reflected by the milling cutting force signal itself, and the cutting force signal needs to be filtered. In order to improve the quality of the signal and accurately reflect the actual mechanical behavior in the milling process, it is necessary to perform low-pass filtering on the original waveform. After processing, the clarity of the waveform signal is significantly improved.

[0075] The cutting force waveform obtained from the experiment is analyzed in the frequency domain at the idling moment, such as Figure 4 , Figure 5 shown.

[0076] Depend on Figure 4 , Figure 5 It can be seen that the frequency spectrum characteristics recorded in the no-load state are not integer multiples of the spindle speed of the machine tool system. This indicates that there are non-periodic noise or other interference components in the signal, which may come from the inherent vibration characteristics of the mechanical system or other external factors. In order to accurately analyze the changes in cutting force during the cutting process, the original signal is filtered to remove unnecessary interference components. The filtering process first deals with the interference of the AC signal and the influence of the clutter in the idling stage. After filtering, the cutting force signal obtained is as follows Figure 6 , Figure 7 shown.

[0077] Depend on Figure 6 , Figure 7 It can be seen that after filtering, there is no influence of other clutter in the idling frequency domain and the stable stage frequency domain of the cutting force experiment.

[0078] Analysis of time-frequency characteristics of dynamic cutting force at different cutting stages

[0079] When analyzing vibration signals and cutting force signals, there are usually evaluation methods such as maximum value, absolute value average, peak-to-peak value, and root mean square value. The maximum value can express the highest point of a signal, but it cannot reflect the overall level of the signal;

[0080] The absolute value average value indicates the central tendency of the signal and can reflect the average level of the signal, but it is easily affected by extreme data; the peak-to-peak value indicates the difference between the highest and lowest values ​​in the periodic signal, that is, the range of signal variation, and is therefore also easily affected by extreme data; the root mean square value indicates the ability of the signal to transmit power and can also indicate the energy of the signal. This paper uses the root mean square, main frequency, kurtosis, spectrum value, standard deviation and coefficient of variation as evaluation indicators for evaluating the strength of the system cutting force signal.

[0081] The root mean square value of the cutting force is the effective value of the signal and is usually used to indicate the intensity of the cutting force signal. The formula is as follows.

[0082]

[0083] In the formula, x i is the cutting force at the i-th moment; n is the total number of sampling points (the number of cutting force sampling data points within the time period), and there are 5000 data points per second.

[0084] Kurtosis is a statistic that describes the peak state of the signal and reflects the impact degree of the cutting force signal. The formula is as follows.

[0085]

[0086] Where n is the total number of sampling points; x i is the value of the i-th data sample; The sample mean (average); σ is the sample standard deviation, which measures the degree of dispersion of the data in the sample.

[0087] The main frequency refers to the frequency with the largest amplitude in the spectrum of the cutting force signal. The formula is as follows:

[0088]

[0089] In the formula, k max is the index of the maximum value in the amplitude spectrum; F s is the sampling frequency (Hz); n is the number of FFT (Fast Fourier Transform) points.

[0090] All cutting force periods were processed in the time-frequency domain and the results were shown in Tables 6, 7 and Figure 11-17 Time-frequency characteristic parameters of different cutting periods.

[0091] Table 6 Time-frequency characteristic parameters of different cutting periods in parameter scheme 1

[0092]

[0093]

[0094] Table 7 Time-frequency characteristic parameters of different cutting periods in parameter scheme 2

[0095]

[0096]

[0097] Depend on Fig.10 , Fig.11 From the RMS values ​​of cutting force at different time periods, it can be seen that the RMS values ​​in the x and y directions under parameter scheme 1 are significantly lower than those in the z direction. The RMS value in the y direction fluctuates slightly, concentrated between 30-50N, showing a relatively uniform force characteristic, while the Z direction maintains a higher RMS value, and the stable cutting period can reach more than 100N. Compared with scheme 1, the RMS values ​​of scheme 2 are lower overall, all below 60N, and the overall change trend is more stable.

[0098] The kurtosis of the cutting force can be used to measure the impact degree. A high kurtosis means that the cutting force signal has a higher peak value and more violent fluctuations in the time series, which usually indicates that there are strong instantaneous forces or impact events in the cutting process. 12 and Δt 31During the period, i.e., the second stage of cutting in and the first stage of cutting out, the value in the y direction changes suddenly, and the impact is relatively large. This may be due to the sudden changes when the tool contacts the workpiece, such as the sudden cutting in or cutting out of the tool.

[0099] The main frequency of the cutting force reflects the dynamic characteristics and stability of the cutting process. During the cutting process, the main frequency under parameter scheme 1 is around 35Hz, and the main frequency under parameter scheme 2 is around 40Hz, showing characteristics corresponding to the spindle speed. The main frequencies in the three directions of parameter scheme 2 are relatively stable, indicating that the cutting process is relatively uniform, the contact between the tool and the workpiece is good, and stable cutting conditions can be maintained.

[0100] Compared with Scheme 1, the spectrum value of Scheme 2 is relatively stable, with a small fluctuation range and a more stable cutting state.

[0101] The overall time-frequency domain of the stable period, such as root mean square, kurtosis, main frequency, spectrum value, and standard deviation and coefficient of variation of the six sections of the stable period are selected as evaluation indicators for evaluating the cutting force signal strength of the system. Compared with Scheme 1, the improvement values ​​of various indicators of Scheme 2 are shown in Table 8.

[0102] Table 8 Improvement values ​​of various indicators in Scheme 2

[0103]

[0104]

[0105] Compared with Scheme 1, the cutting force in the feed speed direction of Scheme 2 is significantly reduced, increasing by 75.92%, and the kurtosis increases by 20.59%. Scheme 2 increases the cutting depth by 33.33%, and the feed per tooth by 14.78%, resulting in a 98.66% reduction in the root mean square value of the cutting force in the cutting width direction. Compared with Scheme 1, the kurtosis in the cutting width direction increases by 38.46%, the root mean square value of the cutting force in the cutting depth direction increases by 72.9%, and the kurtosis increases by 36.49%.

[0106] The standard deviation of the RMS value of cutting force in the feed speed direction of Scheme 2 increased by 88.74%, and the coefficient of variation increased by 6.19%. In the cutting width direction and cutting depth direction, the standard deviation and coefficient of variation were equal to those of Scheme 1, and the other parameter values ​​were improved. Among them, the kurtosis, spectrum value in the cutting width direction and the RMS value of cutting force in the cutting depth direction increased by 0% to 10%, and the other parameter values ​​increased by 10.36% to 100%.

[0107] From the standard deviation and coefficient of variation of the middle period of cutting, there are 24 indicators in total. Compared with Scheme 1, Scheme 2 has improved 17 indicators, unchanged 2 indicators, and reduced 5 indicators, with an improvement rate of 70.83%. Scheme 2 shows better performance in terms of root mean square, standard deviation, and coefficient of variation, reflecting its advantages in stability, consistency, and performance effect.

[0108] Correlation characteristics between milling vibration and dynamic milling force

[0109] Grey correlation analysis is a statistical analysis method that uses grey correlation to describe the strength of the relationship between related factors. It is suitable for dynamic process analysis of data. It can not only characterize the similarity of the change characteristics of the comparison sequence and the reference sequence, but also reflect the influence of the comparison sequence on the reference sequence. Therefore, in order to further study the influence of milling vibration on the cutting force of the milling cutter, the improved grey correlation analysis method is used to perform grey correlation on the root mean square value, kurtosis, and main frequency of the two schemes for milling 45 steel in the same direction, and the results are shown in Table 9.

[0110] Table 9 Correlation of different cutting schemes

[0111] Feed speed direction Cutting width direction Cutting depth direction RMS value 0.5716 0.8056 0.5858 Kurtosis 0.8384 0.8034 0.6853 Frequency 0.9371 0.5002 0.5003

[0112] From Table 9, all correlations are greater than 0.5. The correlations in the three directions are different under different milling parameters, and the change of milling force has directionality. Process variables such as milling cutter speed, tooth error distribution, and feed per tooth have a significant effect on the dynamic cutting behavior of the milling cutter and its correlation characteristics. The root mean square value in the cutting width direction is higher than that in the feed speed direction and the cutting depth direction, indicating that with the change of milling parameters, the change characteristics in the cutting width direction are more similar than those in the feed speed direction and the cutting depth direction; the correlation of the main frequency in the feed speed direction is 0.9371, indicating that the change characteristics are similar when the parameters change.

[0113] Taking the milling cutter cutting force as the reference sequence and the milling vibration as the comparison sequence, a correlation analysis was performed. The correlation results of the milling cutter cutting force characteristic parameters and the milling vibration characteristic parameters along the milling cutter feed speed direction, cutting width direction and cutting depth direction are shown in Table 10.

[0114] Table 10 Correlation between milling cutter cutting force and milling vibration

[0115]

[0116] In the table, the calculated results of the correlation between the characteristic parameters of the milling vibration acceleration signal and the characteristic parameters of the milling cutter cutting force along the milling cutter feed speed direction, cutting width direction and cutting depth direction are all greater than 0.5, indicating that the dynamic cutting force distribution is closely related to the milling vibration change, but the correlation in the three directions is different, indicating that the relationship between milling vibration and dynamic cutting force is directional.

[0117] During the milling process of a face milling cutter, the cutter experiences five different states when cutting in and out of the workpiece: a small cut-in, a large cut-in, a full cut-in, a small cut-out, and a large cut-out. The cutting force exhibits different characteristics during this process. Existing methods usually focus on the cutting force characteristics under full cut-in, that is, the stable cutting state, ignoring the transient impact force generated during the cut-in and cut-out stages, or analyzing the cutting force by considering the cut-in and cut-out stages as a whole, without paying attention to the different cutting sizes of the milling cutter in this process.

[0118] This embodiment takes into account the nonlinear effects of cutting in and cutting out and the actual situation of the milling cutter participating in cutting in different cutting stages, and is divided into 7 different cutting states, namely, idling, a small part of cutting in, a large part of cutting in, a stable cutting state, a small part of cutting out, a large part of cutting out, and idling. By revealing 7 different processes, the dynamic characteristics of the cutting force in the entire cutting state are reflected.

[0119] In the existing methods, the processing of cutting force signals mainly relies on simple statistics such as maximum value, minimum value and mean value for analysis, which does not fully reflect the dynamic changes and complex characteristics in the cutting process. This embodiment uses more complex and accurate statistical features such as root mean square value, kurtosis, main frequency, standard deviation and coefficient of variation in the processing of cutting force signals. By adopting more comprehensive characteristic parameters, the method of this embodiment has more depth and accuracy in dynamic cutting force analysis.

[0120] The existing verification method of dynamic cutting force response characteristics mainly relies on the comparison of simulation results under different conditions, but this simulation process often cannot accurately reproduce the complexity of the actual experimental process. In order to solve this problem, this embodiment uses an improved grey correlation analysis method based on two sets of milling experimental results to calculate the correlation between milling vibration and dynamic cutting force. By comparing the correlation under two different experimental conditions, the response characteristics of dynamic cutting force and milling vibration to the milling process scheme are effectively verified.

[0121] 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 dynamic cutting force variation characteristics of a high-efficiency milling cutter, characterized in that: The steps include: Step 1: formulate a dynamic cutting force experimental method for the milling cutter, and collect the milling vibration and dynamic cutting force signals of the milling cutter along the feed speed direction, cutting depth direction, and cutting width direction; Step 2: Divide the dynamic cutting force period in milling processing, and divide the entire cutting period of the milling cutter into 12 sections. The relationship between time and cutting parameters is: Among them, x(t i ) is at time t i The position at the time; v f is the feed speed of the object; t i is the cutting time; Step 3: The time-frequency characteristics of dynamic cutting force at different cutting periods are analyzed, and the root mean square, main frequency, kurtosis, spectrum value, standard deviation and coefficient of variation are used as evaluation indicators to evaluate the strength of the cutting force signal of the system; Step 4: The correlation characteristics between milling vibration and dynamic milling force are analyzed by using an improved grey correlation analysis method. In the scheme of steel to be milled, the root mean square value, kurtosis, and main frequency in the same direction are subjected to grey correlation.

2. The method for identifying the dynamic cutting force variation characteristics of a high-efficiency milling cutter according to claim 1, characterized in that: In step 1, the axial and radial errors of the milling cutter teeth used in the experiment were measured using a tool setting instrument. Each set of tools took tooth 1 as the measurement reference, where Δc i is the axial error of tooth i, Δr i is the radial error of tooth i.

3. The method for identifying the dynamic cutting force variation characteristics of a high-efficiency milling cutter according to claim 2, characterized in that: In step one, the experiment adopted a dry, down-milling cutting method, and used a cutting force testing device to collect the cutting force, measuring the cutting force signals of the milling cutter along the feed speed direction, milling width direction, and milling depth direction during the milling process.

4. The method for identifying the dynamic cutting force variation characteristics of a high-efficiency milling cutter according to claim 1, characterized in that: In step 2, the 12 segments are divided into 6 segments, namely, idling Δt1, cutting period 2 Δt2, Δt3, and cutting period Δt4. The stable milling period is evenly divided according to the principle of the greatest common factor, specifically Δt 41 , Δt 42 , Δt 43 , Δt 44 , Δt 45 , Δt 46 , cut out the 2 periods Δt5, Δt6 and idling Δt7, Δt i represents the i-th time interval in the cutting process, Δt i =t i -t i-1 are different time periods, where i=1,2,3,4,5,6,7.

5. The method for identifying the dynamic cutting force variation characteristics of a high-efficiency milling cutter according to claim 4, characterized in that: During the milling process, the cutting force signal is filtered and the original waveform is low-pass filtered.

6. The method for identifying the dynamic cutting force variation characteristics of a high-efficiency milling cutter according to claim 1, characterized in that: In step 3, the root mean square value formula of the cutting force is: Among them, x i is the cutting force at the i-th moment; n is the total number of sampling points, and there are 5000 data points per second.

7. The method for identifying the dynamic cutting force variation characteristics of a high-efficiency milling cutter according to claim 6, characterized in that: In step 3, the kurtosis formula is: Where n is the total number of sampling points; x i is the value of the ith data sample; x is the sample mean; σ is the sample standard deviation.

8. The method for identifying the dynamic cutting force variation characteristics of a high-efficiency milling cutter according to claim 7, characterized in that: In step 3, the main frequency formula is: Among them, k max is the index of the maximum value in the amplitude spectrum; F s is the sampling frequency; n is the number of fast Fourier transform points.

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