Method for identifying dynamic cutting force variation characteristics of high efficiency milling cutter

Abstract

The application discloses a kind of high-efficiency milling cutter dynamic cutting force variation characteristics identification method, including high-efficiency milling cutter dynamic cutting force experimental method and experimental result, milling processing dynamic cutting force time division method, different cutting time period dynamic cutting force time-frequency characteristic analysis, milling vibration and the correlation characteristics of dynamic milling force;By analyzing the cutting force variation characteristics of the overall carbide high-efficiency milling cutter when cutting workpiece, it can be more comprehensive to reflect the cutting state and its change under different cutting parameters and tool tooth error distribution conditions, which is beneficial to realize more accurate monitoring;Improved grey correlation analysis method is used, the response characteristics of dynamic cutting force and milling vibration to milling process scheme are effectively verified, which provides a theoretical basis for improving the stability of cutting process, and helps to reasonably control the cutting force in practical application, thereby prolonging the service life of the tool and ensuring the safety of machining.

Description

Technical Field

[0001] This invention relates to the field of milling cutter technology, specifically to a method for identifying the dynamic cutting force variation characteristics of high-efficiency milling cutters. Background Technology

[0002] Face milling cutters possess excellent cutting performance and machining accuracy, making them suitable for machining various flat surfaces, especially large and complex workpieces. Their high machining efficiency, high precision, simple structure, and wide applicability have led to their widespread use in the machining field. During face milling, cutting force is a crucial factor determining cutting heat generation, influencing tool wear, breakage, service life, machining quality, and causing tool vibration and workpiece deformation. Cutting force is also an essential basis for calculating cutting power, determining cutting parameters, monitoring cutting conditions, and designing and using machine tools, cutting tools, and fixtures.

[0003] During high-efficiency milling cutter cutting, the cutting force fluctuates frequently due to the intermittent cutting impact of the cutter teeth. This leads to unstable cutting and deterioration of machining quality, making it impossible to effectively guarantee the cutting efficiency and reliability of the milling cutter process. Revealing the dynamic cutting force variation characteristics of high-efficiency milling cutters is a prerequisite for solving these problems. The research on identification methods for the dynamic cutting force variation characteristics of high-efficiency milling cutters aims to reveal the dynamic characteristics and variation laws of cutting force during milling, monitor cutting force fluctuations in real time during machining, and adjust cutting parameters in a timely manner to optimize machining quality and efficiency.

[0004] Cutting force is the force generated by the interaction between the cutting tool and the workpiece material during milling. It is generally divided into three parts: the feed direction, the cutting width direction, and the cutting depth direction. The dynamic characteristics of cutting force are mainly reflected in the changes of cutting force at different machining stages or under different cutting parameters, such as the influence of feed rate, cutting depth, and tool geometry on the cutting force. Understanding the dynamic characteristics of cutting force can reveal the real-time interaction between the tool and the machined material, thereby better grasping the energy conversion and cutting efficiency during milling, and providing important basis for tool design and machining process optimization.

[0005] Currently, there are two main types of methods for addressing the dynamic characteristics of cutting force variation: one is the experimental-based empirical method, which uses transient cutting forces obtained experimentally to reflect the dynamic characteristics of cutting force variation during actual cutting. However, this method is limited by experimental conditions and lacks universal applicability. The other is the analytical method, which obtains cutting forces based on theoretical calculations. This method has universal applicability, but the variation of dynamic cutting forces deviates significantly from the actual variation. In actual cutting processes, the dynamic characteristics of cutting forces are affected by various factors, and the cutting forces differ under different cutting conditions. Existing methods typically only provide the average value or trend of cutting forces, lacking in-depth analysis of dynamic characteristics. Summary of the Invention

[0006] The purpose of the present application is to provide a high-efficiency milling cutter dynamic cutting force variation characteristic identification method to solve the problems raised in the background art.

[0007] (1) High-efficiency milling cutter dynamic cutting force experimental method and experimental results;

[0008] (2) Milling processing dynamic cutting force time period division method;

[0009] (3) Dynamic cutting force time-frequency characteristic analysis of different cutting periods;

[0010] (4) Milling vibration and dynamic milling force correlation characteristics.

[0011] To achieve the above purpose, the present application provides the following technical solutions: a high-efficiency milling cutter dynamic cutting force variation characteristic identification method, comprising the following steps:

[0012] Step one, develop a milling cutter dynamic cutting force experimental method, collect 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 two, divide the dynamic cutting force time period in milling processing, divide the entire cutting period of the milling cutter into 12 segments, and the relationship between time and cutting parameters is:

[0014]

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

[0016] Step three, dynamic cutting force time-frequency characteristic analysis of different cutting periods, using root mean square, main frequency, kurtosis, frequency spectrum value, standard deviation and variation coefficient as evaluation system cutting force signal strength evaluation index;

[0017] Step four, milling vibration and dynamic milling force correlation characteristics, using an improved grey correlation analysis method, in the milling steel scheme, the root mean square value, kurtosis and main frequency in the same direction are grey correlated.

[0018] Further, in step one, the tool maker instrument is used to measure the axial and radial errors of the milling cutter teeth used in the experiment, and each group of tools takes tooth 1 as the measurement reference, wherein Δc i is the axial error of tooth i, and Δr iThe radial error of the blade tooth i, the experiment adopts dry cutting and down milling, and cutting force testing equipment is used to collect the cutting force signals in the milling direction of the feed speed, the milling width direction and the milling depth direction.

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

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

[0021]

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

[0023] The kurtosis formula is:

[0024]

[0025] Wherein, n is the total sampling point number; 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] Wherein, 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 beneficial effects of the present application are:

[0030] (1) By analyzing the cutting force variation characteristics of the whole cemented carbide high-efficiency milling cutter when cutting the workpiece, the dynamic variation of the cutting force can be mastered under different cutting parameters and tooth error distribution conditions. This method divides the whole cutting process into different cutting periods, extracts the time-frequency characteristic parameters of the cutting force in different periods, and more comprehensively reflects the cutting state and its change, which is beneficial to realize more accurate monitoring and find out abnormality in advance to reduce the occurrence of faults and accidents.

[0031] (2) The improved grey correlation analysis method is adopted to effectively verify the response characteristics of dynamic cutting force and milling vibration to the milling process scheme. Theoretical basis is provided for improving the stability of the cutting process, which is helpful to reasonably control the cutting force in practical application, thereby prolonging the service life of the cutter and ensuring the machining safety. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The flow chart of the high-efficiency milling cutter dynamic cutting force variation characteristic identification method of the application;

[0033] Figure 2 The cutting route position map of the face milling cutter of the application;

[0034] Figure 3 The cutting force time domain signal diagram of the application;

[0035] Figure 4 The cutting force experiment idle frequency domain cutting idle schematic diagram of the application;

[0036] Figure 5 The cutting force experiment idle frequency domain cutting idle schematic diagram of the application;

[0037] Figure 6 The cutting force signal time period division parameter scheme 1 schematic diagram of the application;

[0038] Figure 7 The cutting force signal time period division parameter scheme 2 schematic diagram of the application;

[0039] Figure 8 The cutting force experiment idle frequency domain filtering schematic diagram of the application;

[0040] Figure 9 The untreated stable stage frequency domain diagram of the application;

[0041] Figure 10 The filtering stable stage frequency domain diagram of the application;

[0042] Figure 11 The parameter scheme 1 cutting force root mean square value diagram of the application;

[0043] Figure 12The figure of the root mean square value of each time period of the cutting force of the parameter scheme 2 of the application;

[0044] Figure 13 The figure of the cutting force time domain signal of the application;

[0045] Figure 14 The figure of the kurtosis value of each time period of the cutting force of the parameter scheme 1 of the application;

[0046] Figure 15 The figure of the kurtosis value of each time period of the cutting force of the parameter scheme 2 of the application;

[0047] Figure 16 The figure of the main frequency of each time period of the cutting force of the parameter scheme 1 of the application;

[0048] Figure 17 The figure of the main frequency of each time period of the cutting force of the parameter scheme 2 of the application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0050] Embodiment:

[0051] Please refer to Figures 1-17 The application provides a technical solution: a high-efficiency milling cutter dynamic cutting force change characteristic identification method.

[0052] The dynamic cutting force may have instantaneous sharp fluctuations due to material inhomogeneity, tool wear and other factors in the milling process. If the cutting force in the whole time period is to be analyzed, the key is to divide the cutting time period according to the milling state, so as to analyze the time-frequency characteristics in different time periods. The different cutting time periods of the milling cutter from cutting into the workpiece to cutting out are disclosed, and the dynamic cutting force change characteristics of the milling cutter are analyzed.

[0053] The embodiment discloses the dynamic characteristics of the cutting force, and proposes a dynamic cutting force change characteristic identification method. The method divides the whole cutting process into a cutting-in time period, an intermediate time period and a cutting-out time period, obtains the cutting force curve of each stage, and analyzes the change characteristics of the cutting force in different cutting time periods. The improved grey correlation analysis method is used to realize the characterization of the dynamic cutting force on the milling vibration response characteristics.

[0054] High-efficiency milling cutter dynamic cutting force experimental method and experimental results

[0055] In this embodiment, different experimental schemes are set up to carry out high-efficiency face milling cutter cutting of 45 steel, and the same milling cutter, workpiece, mounting method, cutting method and detection method are used. Before the experiment, the cutter tooth error is measured. 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] During the production and preparation of the milling cutter, the milling cutter will inevitably produce machining errors due to the influence of many factors, so it has a certain error distribution state before being 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 tool setting instrument is used to measure the axial and radial errors of the cutter teeth of the milling cutter used in the experiment. Each group of cutters takes tooth 1 as the measurement reference, wherein 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 scheme and cutter tooth error

[0058]

[0059] A numerical control milling machine XK7124 three-axis milling machining center is used to carry out face milling cutter milling experiment of 45 steel. The milling cutter is 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 cutter and the shank obtained by the balance is 486.32g and 900.40g respectively, and the total is 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 method. During processing, KISTLER9257B cutting force test equipment is used to collect cutting force, and the cutting force signals of the milling cutter along the feed speed direction, the milling width direction and the milling depth direction in the milling process are measured. 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 period in milling process

[0067] There are different impact processes in the whole cutting period of the milling cutter from cutting into the workpiece to cutting out the workpiece, which inevitably causes the change of cutting force. Therefore, according to the characteristic time corresponding to the mutation of the cutting force time domain characteristic curve, the whole cutting period of the milling cutter is divided into 12 segments, specifically, idle (Δt1), cutting-in period 2 segments (Δt2, Δt3), cutting-mid period (Δt4) is divided into 6 segments, and the stable milling period is uniformly divided according to the greatest common divisor principle, specifically, Δt 41 , Δt 42 , Δt 43 , Δt 44 , Δt 45 , Δt 46 , cutting-out period 2 segments (Δt5, Δt6) and idle (Δt7), Δt i =t i -t i-1 , wherein i=1, 2, 3, 4, 5, 6, 7. Using the above milling cutter cutting period division method, the milling cutter cutting route position map is shown in Figure 2 , and the relationship between time and cutting parameters is formula (1).

[0068]

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

[0070] Table 4 Milling vibration and cutting force time domain signal node division results of experimental scheme 1

[0071]

[0072] Table 5 Milling vibration and cutting force time domain signal node division results of experimental scheme 2

[0073]

[0074] In the milling process, due to the influence of many external factors such as environment, there will be noise in the cutting force signal obtained in the experiment. The noise covers 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 carry out low-pass filtering processing on the original waveform. After processing, the clarity of the waveform signal is significantly improved.

[0075] The cutting force waveform obtained in the experiment is analyzed in the frequency domain at the idle time, as shown in Figure 4 、 Figure 5 .

[0076] As can be seen from Figure 4 、 Figure 5 , the frequency spectrum features recorded in the idle state are not integer multiples of the spindle speed of the machine tool system. It shows 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 cutting force changes in the cutting process, the original signal is filtered to remove unnecessary interference components. The filtering process first deals with the interference of alternating current signals and the influence of idle stage noise. After filtering, the cutting force signal is obtained as shown in Figure 6 、 Figure 7 .

[0077] As can be seen from Figure 6 、 Figure 7 , the cutting force experiment frequency domain of the idle stage and the stable stage frequency domain is not affected by other noise.

[0078] Analysis of the time-frequency characteristics of dynamic cutting force in different cutting periods

[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, root mean square value, etc. 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 represents the concentration trend 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 represents the difference between the highest and lowest values in a periodic signal, i.e. the change range of the signal, so it is also easily affected by extreme data; the root mean square value represents the signal transmission power and can also represent the energy of the signal. In this paper, root mean square, main frequency, kurtosis, spectral value, standard deviation and coefficient of variation are used as evaluation indexes to evaluate the strength of the cutting force signal.

[0081] The root mean square value of the cutting force is the effective value of the signal, which is usually used to represent the strength of the cutting force signal, and 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 in the time period), and there are 5000 data points per second.

[0084] Kurtosis is a statistical quantity that describes the peak state of a signal, reflecting the impact degree of the cutting force signal, and 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; sample mean (average); σ is the sample standard deviation, which measures the degree of dispersion of data in the sample.

[0087] The dominant frequency refers to the frequency with the maximum amplitude in the frequency spectrum of the cutting force signal, and the formula is as follows:

[0088]

[0089] where 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 points of FFT (Fast Fourier Transform).

[0090] The time-frequency domain processing is performed on all cutting force periods to obtain the time-frequency characteristic parameters of different cutting periods as shown in Tables 6, 7 and Figures 11-17

[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] From the root mean square values of the cutting forces in each period, Figure 10 , Figure 11 it can be seen that the root mean square values in the x and y directions under the parameter scheme 1 are significantly lower than that in the z direction, the root mean square value in the y direction fluctuates less and is concentrated between 30-50 N, showing a relatively uniform stress characteristic, while the root mean square value in the z direction maintains a relatively high value, and the stable cutting period can reach more than 100 N. Compared with scheme 1, the root mean square values in scheme 2 are generally lower, all below 60 N, and the overall change trend is more stable.

[0098] The kurtosis of the cutting force can be used to measure the degree of impact. High kurtosis means that the cutting force signal has high peak values and more violent fluctuations in the time series, which usually indicates the existence of strong instantaneous force or impact events in the cutting process. In scheme 1, the kurtosis values of the cutting force in Δt 12 and Δt 31 ​The value of y direction has a mutation in the period, that is, the second stage of cutting in and the first stage of cutting out, and the impact is relatively large. It may be caused by the sudden change when the tool contacts the workpiece, such as the sudden cutting in or cutting out of the tool.

[0099] The main frequency of cutting force reflects the dynamic characteristics and stability in the cutting process. In the cutting process, the main frequency under parameter scheme 1 is about 35 Hz, and the main frequency under parameter scheme 2 is about 40 Hz, which shows the characteristics corresponding to the spindle speed. The main frequency in three directions under parameter scheme 2 is relatively stable, which indicates 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 frequency spectrum value of scheme 2 is relatively stable, the fluctuation range is small, and the cutting state is more stable.

[0101] The overall time-frequency domain of the stable period, such as root mean square, kurtosis, main frequency, frequency spectrum value, and the standard deviation and coefficient of variation of the 6 segments of the stable period, are selected as evaluation indexes for evaluating the strength of the cutting force signal of the system. Compared with scheme 1, the improved values of each index of scheme 2 are shown in table 8.

[0102] Table 8 Improved values of each index of scheme 2

[0103]

[0104]

[0105] Compared with scheme 1, the cutting force in the feed direction of scheme 2 is significantly reduced, which is increased by 75.92%, and the kurtosis is increased by 20.59%. The cutting depth of scheme 2 is increased by 33.33%, and the feed per tooth is increased by 14.78%, which leads to a decrease of 98.66% in the root mean square value of the cutting force in the width direction. Compared with scheme 1, the kurtosis in the width direction is increased by 38.46%, the root mean square value of the cutting force in the depth direction is increased by 72.9%, and the kurtosis is increased by 36.49%.

[0106] The standard deviation of the root mean square value of the cutting force in the feed direction of scheme 2 is increased by 88.74%, and the coefficient of variation is increased by 6.19%; in the width direction and the depth direction, the standard deviation and the coefficient of variation are increased. Except that the standard deviation and the coefficient of variation in the width direction are equal to those of scheme 1, the remaining parameter values are increased. Among them, the kurtosis in the width direction, the frequency spectrum value and the root mean square value of the cutting force in the depth direction are increased by 0% to 10%, and the remaining parameter values are increased by 10.36% to 100%.

[0107] From the standard deviation and coefficient of variation of the cutting intermediate period, 24 indicators, scheme 2 compared with scheme 1 index increased 17, unchanged 2, decreased 5, the promotion rate was 70.83%. Scheme 2 shows better performance in root mean square, standard deviation, coefficient of variation, reflecting its advantages in stability, consistency and performance effect.

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

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

[0110] Table 9 Correlation degree of different cutting schemes

[0111] feed direction cutting width direction cutting depth direction root mean square value 0.5716 0.8056 0.5858 kurtosis 0.8384 0.8034 0.6853 dominant frequency 0.9371 0.5002 0.5003

[0112] From Table 9, the correlation degree of each is greater than 0.5. The correlation degrees in the three directions are different under different milling parameters, and the change of milling force has directionality. Milling speed, tool tooth error distribution, and feed per tooth have significant influence on the dynamic cutting behavior of the milling tool 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 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 degree of the main frequency in the feed speed direction is 0.9371, indicating that the change characteristics are similar under parameter changes.

[0113] Taking the milling force as the reference sequence and the milling vibration as the comparison sequence, the correlation analysis is carried out, and the correlation degree results of the milling force characteristic parameters and the milling vibration characteristic parameters along the milling feed speed direction, the cutting width direction and the cutting depth direction are shown in Table 10.

[0114] Table 10 Correlation degree of milling force and milling vibration

[0115]

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

[0117] In the process of face milling, the milling cutter experiences five different states: little cutting in, most cutting in, full cutting in, little cutting out, and most cutting out. In this process, the cutting force shows different characteristics. The existing method usually focuses on the cutting force characteristics in the full cutting in, i.e., the stable cutting state, ignores the transient impact force generated in the cutting in and cutting out stages, or analyzes the cutting force as a whole without focusing on the different sizes of the milling cutter in the cutting process.

[0118] The embodiment considers the nonlinear effects of cutting in and cutting out and the actual situation of the milling cutter in different cutting stages, and divides it into seven different cutting states: idle, little cutting in, most cutting in, stable cutting state, little cutting out, most cutting out, and idle. By revealing the seven different processes, the dynamic characteristics of the cutting force in the whole cutting state are reflected.

[0119] In the processing of the cutting force signal, the existing method mainly relies on simple statistics such as maximum value, minimum value, and mean value for analysis, and does not comprehensively reflect the dynamic changes and complex characteristics in the cutting process. In the processing of the cutting force signal, the embodiment adopts more complex and accurate statistical characteristics such as root mean square value, kurtosis, main frequency, standard deviation, and coefficient of variation. By using more comprehensive feature parameters, the method of the 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 such simulation process often cannot accurately reproduce the complexity in the actual experimental process. In order to solve this problem, the embodiment calculates the correlation degree between the milling vibration and the dynamic cutting force based on two groups of milling experimental results using the improved grey correlation analysis method. By comparing the correlation degrees under two different experimental conditions, the response characteristics of the dynamic cutting force and the milling vibration to the milling process scheme are effectively verified.

[0121] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements, and variations can be made to these embodiments without departing from the principles and spirits of the present application, and the scope of the present application 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, Includes the following steps: Step 1: Develop an experimental method for the dynamic cutting force of the milling cutter, and collect milling vibration and dynamic cutting force signals of the milling cutter along the feed speed direction, cutting depth direction, and cutting width direction; The experiment used dry, climb milling cutting methods and employed cutting force testing equipment to collect cutting forces, 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. Step 2: Divide the dynamic cutting force time period in the milling process into 12 segments. The relationship between time and cutting parameters is as follows: ； in, x ( t i ) is in time t i The position at that time; v f It is the feed rate of the object; t i It is the cutting time; Step 3: Dynamic cutting force time-frequency characteristics analysis at different cutting time periods, using root mean square, dominant frequency, kurtosis, spectral value, standard deviation and coefficient of variation as evaluation indicators for the strength of the system cutting force signal; The kurtosis formula is: ； in, n This represents the total number of sampling points; x i For the first i The values ​​of each data sample; Sample mean; σ The standard deviation is the sample standard deviation. Step 4: Correlation characteristics between milling vibration and dynamic milling force. An improved grey relational analysis method is used to perform grey relational analysis on the root mean square value, kurtosis, and dominant frequency in the same direction for the steel to be milled. In step two, segment 12 specifically refers to the idling Δ t 1. Cut-in time period 2 segments Δ t 2. Δ t 3. Timing Δ t The 4-segment is divided into 6 segments, and the stable milling time period is evenly divided according to the principle of the greatest common factor, specifically Δ t 41 Δ t 42 Δ t 43 Δ t 44 Δ t 45 Δ t 46 Cut out two time periods Δ t 5. Δ t 6 and idling Δ t 7, Δ t i Δ represents the i-th time interval during the cutting process. t i = t i - t i-1 For different time periods, among which 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.

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 one, the axial and radial errors of the milling cutter teeth used in the experiment are measured using a tool setting device. For each set of tools, tooth 1 is used as the measurement reference, where Δ... c i For blade teeth i axial error, Δ r i For blade teeth i Radial error.

3. 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 three, the root mean square formula for the cutting force is: ； in, x i For the first i Cutting force at any given moment; n Total number of sampling points: 5000 data points per second.

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

Images