Milling surface topography feature distribution curve construction method

Abstract

The invention discloses a milling surface topography feature distribution curve construction method, which comprises the following steps of: detecting topography by adopting white light, establishing a feature curve, and analyzing feature parameters of the feature curve; according to the method, correlation analysis between milling vibration displacement signals and surface topography characteristics is utilized, a grey correlation degree analysis method is adopted, the tightness degree of the influence of milling vibration on the machined surface topography is evaluated, the result is accurate, and the method is reliable.

Description

[0001] This application is a divisional application of application No. 202411094826.9, filed on August 10, 2024, with the title of "Analysis method for surface topography distribution characteristics of aerostructure milling". TECHNICAL FIELD

[0002] The present application relates to the technical field of correlation analysis methods for milling surface topography, in particular to a method for constructing a milling surface topography feature distribution curve. BACKGROUND

[0003] Vibration signals in the milling process reflect key information about the processing state and affect the formation of the milling surface. The vibration in the milling process is dynamic and time-varying. Small changes in cutting conditions (such as tool wear, changes in workpiece material, etc.) can cause changes in vibration signals. This dynamic and time-varying nature makes it difficult to monitor and analyze vibration signals in real time and accurately, which in turn affects the in-depth understanding of the formation of the milling surface topography. The existing methods lack quantitative correlation analysis between the milling surface topography features and the processing parameters, and cannot correctly reveal the influence characteristics of the vibration signals on the formation of the milling surface topography.

[0004] Due to the complexity, multivariable, dynamic and time-varying nature of the milling process, as well as the limitations of experimental conditions and data, it is difficult to quantitatively correlate the vibration signals and the milling surface topography. The existing quantitative correlation analysis between the milling surface topography features and the processing parameters is insufficient, which limits the guidance of the optimization of the processing process. SUMMARY

[0005] The purpose of the present application is to provide a method for constructing a milling surface topography feature distribution curve to solve the problems raised in the background.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: an analysis method for the surface topography distribution characteristics of an aerostructure milling, comprising the following steps:

[0007] Step one, analyze the vibration displacement signal distribution characteristics, collect the vibration displacement signals in the milling process of the end mill, establish the main frequency, kurtosis and root mean square value distribution sequence of the vibration signals in three directions, determine the milling vibration distribution characteristics, and the formula is as follows:

[0008] , , ;

[0009] Wherein: K represents the kurtosis sequence of the vibration in any direction of the corresponding period, k 1、 k2 represents the kurtosis of the first period, the second period in any direction, respectively, k p representing the kurtosis of any period corresponding to the topography of the selected period, k m representing the kurtosis of the last period of the selected period, F representing the sequence of the main frequency of any direction corresponding to the vibration of the selected period, f 1、 f 2 represents the main frequency of the first period, the second period in any direction, respectively, f p representing the main frequency of any period corresponding to the topography of the selected period, f m representing the main frequency of the last period of the selected period, Rms representing the sequence of the root mean square value of any direction corresponding to the vibration of the selected period, rms 1、 rms 2 represents the root mean square value of the first period, the second period in any direction, respectively, rms p representing the root mean square value of any period corresponding to the topography of the selected period, rms m representing the root mean square value of the last period of the selected period;

[0010] The collected vibration displacement signal data is subjected to Fourier transform to obtain the frequency spectrum of the signal, the frequency point with the maximum amplitude in the spectrum is analyzed, and the frequency is the main frequency;

[0011] Step two, milling surface topography acquisition and milling surface topography feature distribution curve construction, based on white light interferometer, different cutting strokes of end mill are detected for topography, 512µm×512µm pixel array is used for surface topography shooting, 800x800um viewfinder range is selected, measurement area is scanned, detection result is obtained, image noise of detection result is processed, and topography data is extracted;

[0012] Step three, analyze the correlation between vibration displacement signal distribution characteristics and surface topography feature distribution characteristics, take the milling surface topography curve time frequency as the reference sequence, and take the time frequency of the vibration displacement signal as the comparison sequence, and analyze the main frequency correlation, the kurtosis correlation and the root mean square value correlation.

[0013] Further, in step one, the kurtosis of the vibration signal is:

[0014] ;

[0015] wherein: Kurtosis kurtosis, m upper limit, j lower limit, x irepresents the vibration signal value, represents the average value.

[0016] Further, in step one, the root mean square of the vibration displacement signal is:

[0017] ;

[0018] wherein: rms represents the root mean square value of the vibration displacement signal, m represents the upper limit, j represents the lower limit.

[0019] Further, in step two, the milled workpiece is a 100mm long TC4 titanium alloy, and the tooth position angle sequence of the milling cutter is established C θ , and C θ the corresponding tooth sequence C , and C the corresponding tooth axial error sequence C zd ;

[0020] ,

[0021] wherein, , , is the change of the height of the measurement frame of the white light interferometer;

[0022] Based on C θ , C , C zd determine the morphology division morphology layer boundary detected by the white light interferometer b 1、 b 2、 b 3 and b 4, the morphology curve extraction position a 1、 a 2、 a 3、 a 4 and a 5, according to the layer boundary, extract a 1、 a 2、 a 3、 a 4 and a 5 morphology curve data, fit the morphology characteristic curve y ( x ), according to the kurtosis, main frequency and root mean square value calculation method, the characteristic parameters of the morphology characteristic curve are solved.

[0023] Further, in step three, the milling surface morphology curve is taken as the reference sequence, and the time-frequency of the vibration displacement signal is taken as the comparison sequence, so that the correlation degree between the vibration and the milling surface can be effectively revealed.

[0024] Through the main frequency correlation, if the main frequency of the vibration displacement signal is highly correlated with the main frequency of the periodic structure in the surface morphology characteristic curve, it indicates that the main vibration frequency in the machining process directly affects the surface morphology, which may cause irregular ripples or textures. Through the kurtosis correlation, the kurtosis of the vibration signal increases, which means that there are more spikes in the vibration displacement signal, reflecting that there may be atypical vibration events in the machining process. The change of the kurtosis of the workpiece surface morphology characteristic curve may indirectly reflect the abnormal vibration events in the machining process, so as to judge the machining state. Through the root mean square value correlation, the root mean square value of the vibration displacement signal is high, which means that the vibration energy is large, which increases the instability of the contact between the tool and the workpiece in the machining process, and then reflects on the root mean square value of the surface morphology characteristic curve. The positive correlation between the root mean square values of the two indicates the direct connection between the vibration intensity and the surface roughness, that is, the greater the vibration, the rougher the surface.

[0025] Compared with the prior art, the beneficial effects of the present application are:

[0026] (1) In the efficient and intermittent cutting process of the end mill, the instantaneous contact relationship between the cutter teeth and the workpiece is constantly changing due to the influence of milling vibration factors, and the milling machining surface morphology forms shows instability and unevenness. The present method can accurately analyze the influence of vibration on the milling machining surface, and the correlation between vibration and the characteristics of the machining surface morphology is clear.

[0027] (2) The present method is based on the milling vibration signal, uses the distribution characteristics of the vibration signal, solves the distribution characteristics of the vibration signal, uses white light to detect the morphology, establishes the characteristic curve, analyzes the characteristic parameters, uses the correlation analysis method of grey correlation degree, and evaluates the close degree of the influence of the milling vibration on the machining surface morphology. The result is accurate and the method is reliable.

[0028] (3) The present method can reflect the abnormal vibration events in the machining process, so as to judge the machining state, and can also understand the reasons for the roughness of the workpiece surface morphology, which is convenient for optimizing the machining of the workpiece and improving the machining quality of the workpiece. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 Fig. 1 is a schematic diagram of the vibration displacement signal and the morphology sampling method of the present application;

[0030] Figure 2 Fig. 4 is a front view of the milling cutter of the present application;

[0031] Figure 3 Schematic diagram of the tool tip of the milling cutter of the present application

[0032] Figure 4 Schematic diagram of the tool body of the milling cutter of the present application

[0033] Figure 5 Schematic diagram of the correlation analysis method of the present application

[0034] Figure 6 Schematic diagram of the vibration displacement signal of the present application DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0036] Embodiment one

[0037] Please refer to Figures 1-6 The present application provides a technical solution: a method for analyzing the surface topography distribution characteristics of an aeronautical structural part.

[0038] 1. A vibration displacement signal distribution characteristic analysis method

[0039] To realize the distribution characteristics of the vibration displacement signal, the main frequency, kurtosis and root mean square value distribution sequences of the vibration signals in three directions are established to determine the milling vibration distribution characteristics.

[0040] (1)

[0041] (2)

[0042] (3)

[0043] In the formula: K represents the kurtosis sequence of the vibration in any direction corresponding to the time period, k 1, k 2 respectively represent the kurtosis of the first cycle and the second cycle in any direction, k p represents the kurtosis of the topography in any cycle corresponding to the time period, k m represents the kurtosis of the last cycle in the selected time period. F represents the main frequency sequence of the vibration in any direction corresponding to the time period, f 1, f 2 respectively represent the main frequency of the first cycle and the second cycle in any direction,f p representing the dominant frequency of the selected period, f m representing the dominant frequency of the last cycle of the selected period. Rms representing the sequence of the root mean square value of the vibration in any direction of the corresponding period, rms 1、 rms 2representing the root mean square value of the first cycle and the second cycle in any direction respectively, rms p representing the root mean square value of the corresponding period of the topography, rms m representing the root mean square value of the last cycle of the selected period.

[0044] where the kurtosis of the vibration signal is:

[0045] (4)

[0046] where: Kurtosis represents the kurtosis, m represents the upper bound, j represents the lower bound, x i represents the value of the vibration signal, represents the average value.

[0047] The root mean square of the vibration displacement signal is:

[0048] (5)

[0049] where: rms represents the root mean square value of the vibration displacement signal.

[0050] The collected vibration displacement signal data is subjected to Fourier transform to obtain the frequency spectrum of the signal, the frequency point with the largest amplitude in the spectrum is analyzed, and the frequency is the dominant frequency.

[0051] 2. Milling surface topography acquisition and milling surface topography feature distribution curve construction method

[0052] The instrument used for this milling surface topography detection is Taylor Hobson's non-contact interferometer CCI·MP for measurement. The topography is detected for different cutting strokes, and the surface topography is shot using a 512µm×512µm pixel array.

[0053] The detailed measurement steps of the white light interferometer are:

[0054] 1). Select the view range of 800x800um.

[0055] 2). Put the workpiece on the stage, use the joystick to move the workpiece under the lens, adjust the height of the lens to focus the image.

[0056] 3). Scan the measurement area to obtain the detection result.

[0057] 4). Post-process the detection result, process image noise, and extract topography data.

[0058] Figure 1 In this milling, the workpiece material is TC4 titanium alloy, and the total length of the workpiece is 100mm. Among them o - xz The milling plane is the milling plane under the workpiece coordinate system, x 0 is the distance from the milling cutter to the workpiece milling start point, x 1 is the distance from the first point position to the workpiece milling start point, Δ x 1 is the topography detection area point spacing; Δ x is the white light interferometer measurement view frame length, Δ z is the white light interferometer measurement view frame height; t 0 corresponds to the time when the milling cutter is idling to cut in, t 1 corresponds to the topography detection position area Δ x 1 is the time of milling travel, Δ t corresponds to the milling cutter cutting time of the topography detection area view frame. According to the milling travel white light interferometer detection topography in turn A 11 , A 12 , A 13 , A 14 and A 15 .

[0059] Among them: D is the diameter of the milling cutter handle, β is the helix angle of the milling cutter, l 1 is the axial length of the milling cutter cutting edge, l 2 is the overhang of the milling cutter, l 3 is the total length of the milling cutter. r i is the rotation radius of the tooth i, Δ z di is the axial error of the tooth i, θ i is the tooth spacing angle between adjacent two teeth.

[0060] Among them: o d - x d y d z dA coordinate system of the milling cutter structure, o d A rotation center of the axial lowest tool tip point of the milling cutter, coplanar with the axial lowest tool tip point of the milling cutter; y d The axis is parallel to the direction of the main motion velocity of the maximum tool tip point of the rotation radius; x d The axis is radially parallel to the maximum tool tip point of the rotation radius; z d The axis is the rotation axis of the milling cutter and points to the direction of the tool shank.

[0061] The milling cutter structure of Figure 2 , Figure 3 , Figure 4 establishes a sequence of tool position angles of the milling cutter teeth C θ as follows:

[0062] (6)

[0063] According to the sequence of tool position angles of the tool teeth according to formula (6), a sequence of tool teeth C corresponding thereto is constructed as follows:

[0064] (7)

[0065] According to the sequence of tool teeth according to formula (7), a sequence of axial error of tool teeth C corresponding thereto is constructed as follows: zd

[0066] (8)

[0067] The sequence of tool position angles of the tool teeth according to formula (6) to formula (8) can determine Figure 2 the profile division layer boundary detected by the white light interferometer b 1, b 2, b 3 and b 4, the profile curve extraction positions a 1, a 2, a 3, a 4 and a 5 are determined by the profile division of the machined surface.

[0068] a 1=1 / 2( b 1- the lower boundary of the profile), a 2=1 / 2( b 2- b 1), a 3=1 / 2( b 3- b 2), a 4=1 / 2( b 4- b ​3), to avoid contingency a 5= b 4+ b 1.

[0069] According to the hierarchical boundary, the morphology curve data of a 1、 a 2、 a 3、 a 4 and a 5 are extracted, and the morphology feature curve is fitted y ( x ), and the feature parameters of the morphology feature curve are calculated according to the kurtosis, dominant frequency and root mean square value calculation method.

[0070] 3. Correlation analysis of vibration displacement signal distribution characteristics and surface morphology feature distribution characteristics

[0071] The time-frequency characteristics of the vibration displacement signal in three directions are correlated with the time-frequency characteristics of the surface morphology curve at different positions. The time-frequency of the milling surface morphology curve is taken as the reference sequence, and the time-frequency of the vibration displacement signal is taken as the comparison sequence. This method can effectively reveal the correlation degree between vibration and the milling surface. The correlation analysis method is shown in Figure 4 .

[0072] As shown in Figure 4 , through the correlation of the dominant frequency, if the correlation degree of the dominant frequency of the vibration displacement signal and the periodic structure in the surface morphology feature curve is high, it indicates that the main vibration frequency in the machining process directly affects the surface morphology, which may lead to irregular ripples or textures. Through the kurtosis correlation, the increase of the kurtosis of the vibration signal means that there are more sharp peaks in the signal, which reflects the possible existence of atypical vibration events in the machining process. The change of the kurtosis of the surface morphology feature curve may indirectly reflect the abnormal vibration events in the machining process, so as to judge the machining state. Through the root mean square value correlation, the high root mean square value of the vibration displacement signal means that there is a large vibration energy, which leads to the increase of the instability of the contact between the tool and the workpiece in the machining process, and then reflects on the root mean square value of the surface morphology feature curve. The positive correlation between the root mean square values of the two indicates the direct connection between the vibration intensity and the surface roughness, that is, the greater the vibration, the rougher the surface.

[0073] The correlation degree analysis reveals how vibration affects the machining quality, which provides a direction for the optimization of process parameters. By adjusting the cutting speed, feed rate, tool type, etc., the dominant frequency matching of the vibration displacement signal is reduced, the root mean square value and kurtosis are reduced, so as to effectively improve the surface morphology and improve the quality and reliability of the machining products.

[0074] The difference from the already disclosed technology is:

[0075] Most of the existing studies on the influence of milling surface topography directly use milling vibration signals to analyze the influence of milling surface topography, and these analysis methods cannot truly reflect the formation influence of milling surface topography.

[0076] The embodiment proposes a correlation analysis method of vibration displacement signals and milling surface topography. In the milling process, the vibration displacement signals are extracted, and the distribution characteristics of the vibration displacement signals are analyzed. Through efficient milling experiments, the milling surface topography is obtained, the residual feature curve of the milling transition surface is extracted, and the time-frequency characteristics of the milling transition surface feature distribution curve are analyzed. The correlation analysis between the milling vibration displacement signals and the surface topography features is used, the grey correlation degree analysis method is adopted, and the close degree of the influence of the milling vibration on the machining surface topography is evaluated.

[0077] Embodiment two

[0078] 1. Vibration displacement signal distribution characteristic analysis method

[0079] In this experiment, the whole carbide end mill of Walter Company is used, the number of teeth is 5, the diameter is 20 mm, and the helix angle is 45 degrees. The milling method adopts down milling and dry milling. The acceleration signal acquisition is carried out by using Kistler Dynoware and DHDAS 5922 transient signal test analysis system for the milling vibration generated in the milling process. The specific cutting parameters are shown in Table 1.

[0080] Table 1 Milling scheme

[0081]

[0082] During the experiment, the acceleration sensor is used to collect the vibration acceleration signal, which is converted into a vibration displacement signal, and the vibration displacement signal is divided, as shown in Figure 6 .

[0083] As shown in Figure 6 , t 0 corresponds to the time from the idle rotation of the milling cutter to the cutting in, t 1 corresponds to the morphology detection position area Δ x 1 milling stroke time, Δ t corresponds to the milling cutter cutting time of the morphology detection area viewfinder.

[0084] Considering the influence of vibration on the formation process of the milling surface topography, the main frequency, kurtosis and root mean square value sequences of the vibration signals in three directions are established, and the influence degree of the milling vibration on the morphology curve is determined.

[0085] (9)

[0086] (10)

[0087] (11)

[0088] wherein: K represents the kurtosis sequence of the vibration in any direction in the corresponding period, k 1, k 2 represent the kurtosis of the first period and the second period in any direction respectively, k p the kurtosis of the vibration in any period in the corresponding period, k m the kurtosis of the last period in the selected period. F the dominant frequency sequence of the vibration in any direction in the corresponding period, f 1, f 2 represent the dominant frequency of the first period and the second period in any direction respectively, f p the dominant frequency of the vibration in any period in the corresponding period, f m the dominant frequency of the last period in the selected period. Rms the root mean square value sequence of the vibration in any direction in the corresponding period, rms 1, rms 2 represent the root mean square value of the first period and the second period in any direction respectively, rms p the root mean square value of the vibration in any period in the corresponding period, rms m the root mean square value of the last period in the selected period.

[0089] wherein the kurtosis of the vibration signal is:

[0090] (12)

[0091] wherein: Kurtosis represents the kurtosis, m represents the upper limit, j represents the lower limit, x i represents the vibration signal value, represents the average value.

[0092] the root mean square of the vibration displacement signal is:

[0093] (13)

[0094] wherein: rms represents the root mean square value of the vibration displacement signal.

[0095] extract the vibration signal corresponding to the detected topography, extract the characteristic parameters of the vibration signal, and solvex 、 y 、 z The mean value, kurtosis and dominant frequency of the vibration signals in three directions are shown in Table 2.

[0096] Table 2 Mean value, kurtosis and dominant frequency of vibration signals at detection positions

[0097]

[0098] From the table, it can be seen that during the milling process, the kurtosis of the vibration signal is basically below 3, indicating that the probability density distribution of the vibration signal is close to the normal distribution, and the milling process is relatively stable. The dominant frequency and kurtosis of the vibration signal tend to be stable, and the milling process is relatively stable. Under the action of tool tooth error and milling vibration, the formation of milling surface and its geometric error is not a stable process, and the characteristic points change instantaneously, resulting in the variability of the milling surface error distribution.

[0099] Example Three

[0100] 2. Milling surface topography acquisition and milling surface topography feature distribution curve construction method

[0101] The instrument used for this milling surface topography detection is a Taylor Hobson non-contact interferometer CCI·MP for measurement. The topography is detected for different cutting strokes, and the surface topography is shot using a 512µm×512µm pixel array.

[0102] The detailed measurement steps of the white light interferometer are as follows:

[0103] 1). Select an 800x800um view range.

[0104] 2). Place the workpiece on the stage, use the joystick to move the workpiece under the lens, and adjust the lens height to focus the image.

[0105] 3). Scan the measurement area to obtain the detection results.

[0106] 4). Post-process the detection results, process image noise, and extract topography data.

[0107] Figure 1 In this milling, the workpiece material is TC4 titanium alloy, and the total length of the cutting workpiece is 100mm. Among them o - xz The plane is the milling plane under the workpiece coordinate system, x 0 is the distance from the idle rotation of the milling cutter to the start point of the workpiece milling, x 1 is the distance from the first point position to the start point of the workpiece milling, Δ x1 is the interval of the profile detection area; Δ x is the length of the viewfinder for the white light interferometer measurement, Δ z is the height of the viewfinder for the white light interferometer measurement; t 0 corresponds to the time from the idle milling cutter to the cutting in, t 1 corresponds to the profile detection position area Δ x 1 is the time of the milling pass, Δ t corresponds to the milling cutter cutting time of the profile detection area viewfinder. According to the milling pass, the white light interferometer detects the profile in turn A 11 , A 12 , A 13 , A 14 , and A 15 .

[0108] wherein x 1=31.6mm, Δ x 1=31.6mm, t 1=3.3194s, Δ t =0.2513s, Δ x =2.4mm, Δ z =0.1mm.

[0109] The end mill structure is shown in Figure 2 , Figure 3 , Figure 4 .

[0110] wherein: D is the shank diameter of the milling cutter, β is the helix angle of the milling cutter, l 1 is the axial length of the milling cutter cutting edge, l 2 is the overhang of the milling cutter, l 3 is the total length of the milling cutter. r i is the radius of rotation of the tooth i, Δ z di is the axial error of the tooth i , θ i is the tooth gap angle between the adjacent two teeth.

[0111] wherein: o d - x d y d z d is the milling cutter structure coordinate system, o d is the center of rotation of the axial lowest tool tip point, which is coplanar with the axial lowest tool tip point of the milling cutter; y d the axis is parallel to the direction of the main motion velocity of the maximum tool tip point of the radius of rotation;x d The axis is radially parallel to the maximum tool tip point of the turning radius; z d The axis is the milling cutter rotation axis and points to the direction of the tool holder.

[0112] By Figure 1 The milling cutter structure establishes the sequence of the milling cutter tooth position angle C θ For:

[0113] (14)

[0114] According to the sequence of the milling cutter tooth position angle of formula (14), the corresponding sequence of the milling cutter tooth is constructed C For:

[0115] (15)

[0116] According to the sequence of the milling cutter tooth of formula (15), the corresponding sequence of the milling cutter tooth axial error C is constructed zd :

[0117] (16)

[0118] The appearance boundary of the appearance curve extraction position 1, b 1、 b 2、 b 3 and b 4, the appearance curve extraction position a 1、 a 2、 a 3、 a 4 and a 5 is determined by the milling surface topography boundary.

[0119] Wherein b 1=10.01mm、 b 2=10.018mm、 b 3=10.029mm、 b 4=10.039mm.

[0120] a 1=1 / 2( b 1- the lower boundary of the appearance), a 2=1 / 2( b 2- b 1), a 3=1 / 2( b 3- b 2), a 4=1 / 2( b 4- b 3), in order to avoid contingencya 5= b 4+ b 1。

[0121] wherein a 1=10.005mm、 a 2=10.014mm、 a 3=10.0235mm、 a 4=10.034mm、 a 5=10.044mm。

[0122] The specific data of the end mill tooth error is shown in Table 3.

[0123] Table 3 End mill tooth error

[0124]

[0125] wherein Δ r i is the radial error of the end mill.

[0126] According to the layered boundary, the profile curve data of a 1、 a 2、 a 3、 a 4 and a 5 are extracted, the profile characteristic curve y ( x ) is fitted, and the profile characteristic curve characteristic parameters are solved according to the kurtosis, the main frequency and the root mean square value calculation method. The machining surface profile characteristic curve distribution characteristics are shown in Table 4.

[0127] Table 4 Machining surface profile characteristic curve distribution characteristics

[0128]

[0129] It can be seen from the table that the kurtosis of the milled surface is basically below 3, indicating that the distribution of the milled surface profile characteristic curve is close to the normal distribution, and the milled surface profile distribution is relatively uniform. The main frequency and kurtosis trend of the vibration signal tend to be stable, and the milled surface profile formation process is relatively stable.

[0130] Example Four

[0131] 3. Correlation analysis of vibration displacement signal distribution characteristics and surface profile characteristic distribution characteristics

[0132] To verify the influence of vibration on the morphology of milled surfaces, a correlation analysis was established between the time-frequency characteristics of the vibration displacement signal in three directions and the time-frequency characteristics at different positions of the surface morphology curve. Using the time-frequency of the milled surface morphology curve as the reference sequence and the time-frequency of the vibration displacement signal as the comparison sequence, this method can effectively reveal the degree of correlation between vibration and the milled surface. The correlation analysis method is as follows: Figure 6 As shown.

[0133] like Figure 5 As shown, through the correlation of dominant frequency, if the dominant frequency of the vibration displacement signal has a high correlation with the dominant frequency of the periodic structure in the surface morphology characteristic curve, it indicates that the main vibration frequency during the machining process directly affects the surface morphology, potentially leading to irregular ripples or textures. Through the correlation of kurtosis, an increase in the kurtosis of the vibration signal means the presence of more peaks in the vibration displacement signal, reflecting possible atypical vibration events during machining. Changes in the kurtosis of the workpiece surface morphology characteristic curve may indirectly reflect abnormal vibration events during machining, thus assessing the machining status. Through the correlation of root mean square (RMS) values, a high RMS value of the vibration displacement signal indicates greater vibration energy, leading to increased instability in the contact between the tool and the workpiece during machining, which is reflected in the RMS value of the surface morphology characteristic curve. The positive correlation between the RMS values ​​of both indicates a direct link between vibration intensity and surface roughness; that is, the greater the vibration, the rougher the surface.

[0134] The correlation analysis results between the vibration displacement signal distribution characteristics and the surface morphology feature distribution characteristics are shown in Table 5, obtained by using the above method.

[0135] Table 5. Correlation between vibration and milled surface morphology

[0136]

[0137] As shown in the table, the vibration displacement signals in the x and z directions have a high correlation with the milled surface morphology characteristic curves, and the vibration displacement signals in all three directions have a significant impact on the formation of the milled surface morphology. Specifically, the dominant frequency of the vibration displacement signal has a high correlation with the dominant frequency of the periodic structure in the surface morphology characteristic curve, indicating that the main vibration frequency during machining directly affects the surface morphology. Through kurtosis correlation, the kurtosis of the vibration signal along the feed direction has a high correlation with the machined surface morphology, meaning that there are more peaks in the displacement signal along the feed direction. Changes in the kurtosis of the surface morphology characteristic curve may indirectly reflect abnormal vibration events during machining, thus assessing the machining state. Through root mean square (RMS) value correlation, the RMS value of the vibration displacement signal has a high correlation with the machined surface morphology characteristic curve, indicating a direct connection between the y-direction vibration signal and the formation of the machined surface morphology.

[0138] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for constructing the surface morphology feature distribution curve of a milled surface, characterized in that, The surface topography of the end mill was detected by a white light interferometer for different cutting strokes. The surface topography was captured using a 512µm×512µm pixel array with an 800×800um field of view. The measurement area was scanned to obtain the detection results. The image noise of the detection results was processed and the topography data was extracted. Establish the milling cutter tooth position angle sequence C θ ,and C θ Corresponding blade sequence C ,and C The corresponding axial error sequence C of the cutter teeth zd ; ； in, , , To measure changes in viewfinder height using a white light interferometer; based on C θ , C C zd Determine the topography and delineate topography layer boundaries detected by white light interferometer. b 1. b 2. b 3 and b 4. Location for extracting topography curves a 1. a 2. a 3. a 4 and a 5; a 1 = 1 / 2 ( b 1-Lower boundary of morphology), a 2 = 1 / 2 ( b 2- b 1), a 3 = 1 / 2 b 3- b 2), a 4 = 1 / 2 b 4- b 3), a 5= b 4+ b 1; Extract based on layer boundaries a 1. a 2. a 3. a 4 and a 5. Morphological curve data, fitting morphological characteristic curves y ( x Based on the calculation methods of kurtosis, dominant frequency and root mean square value, the characteristic parameters of the morphological feature curve are solved.

2. The method for constructing the surface morphology feature distribution curve of a milled surface according to claim 1, characterized in that, The workpiece being milled is a 100mm long TC4 titanium alloy.

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