Vibration displacement signal distribution characteristic analysis method for milling and application thereof

Abstract

The invention discloses a vibration displacement signal distribution characteristic analysis method for milling processing and application thereof, based on milling vibration signals, characteristic parameters of distribution characteristics of the vibration signals are solved by utilizing the distribution characteristics of the vibration signals, and the distribution characteristics of the vibration signals are solved; in the efficient and intermittent cutting process of the end mill, the instantaneous contact relation between milling cutter teeth and a workpiece is continuously changed under the influence of milling vibration factors, and the milling surface appearance is unstable and non-uniform. By adopting the method, the influence of vibration on the milling surface can be accurately analyzed.

Description

[0001] This application is a divisional application of application number 202411094826.9, filed on August 10, 2024, with the invention title "Method for Analyzing the Distribution Characteristics of Milled Surface Morphology of Aircraft Structural Components". Technical Field

[0002] This invention relates to the technical field of correlation analysis methods for surface morphology in milling, specifically to a method for analyzing the distribution characteristics of vibration displacement signals in milling and its application. Background Technology

[0003] Vibration signals during milling reflect crucial information about the machining state and influence the formation of the milled surface. Vibration during milling is dynamic and time-varying. Even minute changes in cutting conditions (such as tool wear and workpiece material variations) can lead to changes in vibration signals. This dynamic and time-varying nature makes real-time and accurate monitoring and analysis of vibration signals difficult, thus hindering a deeper understanding of the formation of the machined surface morphology. Existing methods lack sufficient quantitative correlation analysis between milled surface morphology characteristics and machining process parameters, failing to accurately reveal the influence of vibration signals on the formation of the machined surface morphology.

[0004] Due to the complexity, variability, dynamism, and time-varying nature of the milling process, as well as the limitations of experimental conditions and data, quantitative correlation analysis between vibration signals and the morphology of the machined surface becomes difficult. Existing quantitative correlation analysis between milled surface morphology features and machining process parameters is insufficient, limiting its guiding role in optimizing the machining process. Summary of the Invention

[0005] The purpose of this invention is to provide a method for analyzing the distribution characteristics of vibration displacement signals in milling processes and its application, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for analyzing the surface morphology distribution characteristics of milled aerospace structural components, comprising the following steps:

[0007] Step 1: Analyze the vibration displacement signal distribution characteristics. Collect vibration displacement signals during the end milling process, establish the distribution sequences of the dominant frequency, kurtosis, and root mean square value of the vibration signals in three directions, and determine the milling vibration distribution characteristics. The formula is as follows:

[0008] , , ;

[0009] in: K This represents the kurtosis sequence in any direction corresponding to the vibration time period. k 1. k2 represents the kurtosis of the first and second periods in any direction, respectively. k p It represents the kurtosis of the morphology for any period of time. k m Represents the kurtosis of the last cycle in the selected time period. F This represents the dominant frequency sequence in any direction during the corresponding time period of vibration. f 1. f 2 represents the main frequency of the first and second cycles in any direction, respectively. f p The dominant frequency of any period corresponding to the morphology. f m This represents the main frequency of the last cycle in the selected time period. Rms This represents the sequence of root mean square values ​​in any direction corresponding to the vibration time period. rms 1. rms 2 represents the root mean square value of the first and second periods in any direction, respectively. rms p This represents the root mean square value of the morphology for any period within the corresponding time period. rms m Represents the root mean square value of the last period in the selected time period;

[0010] The collected vibration displacement signal data is subjected to Fourier transform to obtain the signal spectrum. The frequency point with the largest amplitude is analyzed and identified as the dominant frequency.

[0011] Step 2: Acquisition of milled surface morphology and construction of milled surface morphology feature distribution curve. Based on white light interferometer, morphology detection is performed on end mills at different cutting strokes. Surface morphology is captured using a 512µm×512µm pixel array with an 800x800um field of view. The measurement area is scanned to obtain the detection results. Image noise in the detection results is processed and morphology data is extracted.

[0012] Step 3: Analyze the correlation between the distribution characteristics of vibration displacement signal and the distribution characteristics of surface morphology features. 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, analyze the correlation of dominant frequency, kurtosis, and root mean square value.

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

[0014] ;

[0015] in: Kurtosis Indicates kurtosis, m Indicates the upper bound. j Indicates the lower bound. x iIndicates the vibration signal value. This represents the average value.

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

[0017] ;

[0018] in: rms This represents the root mean square value of the vibration displacement signal. m Indicates the upper bound. j Indicates the lower bound.

[0019] Furthermore, in step two, the workpiece being milled is a 100mm long TC4 titanium alloy, and a sequence of milling cutter tooth position angles is established. C θ ,and C θ Corresponding blade sequence C ,and C Corresponding axial error sequence of the cutter teeth C zd ;

[0020] ,

[0021] in, , , To measure changes in viewfinder height using a white light interferometer;

[0022] 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. 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.

[0023] Furthermore, in step three, the time-frequency of the milled surface topography curve is used as a reference sequence, and the time-frequency of the vibration displacement signal is used as a comparison sequence. This method can effectively reveal the degree of correlation between vibration and the milled surface.

[0024] Through frequency correlation, a high correlation between the dominant frequency of the vibration displacement signal and the dominant frequency of the periodic structure in the surface morphology characteristic curve indicates that the main vibration frequency during processing directly affects the surface morphology, potentially leading to irregular ripples or textures. Through kurtosis correlation, increased kurtosis of the vibration signal signifies more peaks in the vibration displacement signal, reflecting possible atypical vibration events during processing. Changes in the kurtosis of the workpiece surface morphology characteristic curve may indirectly reflect abnormal vibration events during processing, thus assessing the processing status. Through root mean square (RMS) correlation, a high RMS value of the vibration displacement signal indicates greater vibration energy, leading to increased instability in the contact between the tool and workpiece during processing, 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.

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

[0026] (1) During the high-efficiency, intermittent cutting process of end mills, the instantaneous contact relationship between the milling cutter teeth and the workpiece is constantly changing due to the influence of milling vibration. The morphology of the milled surface is unstable and non-uniform. This method can accurately analyze the influence of vibration on the milled surface, and the correlation between vibration and the morphology characteristics of the machined surface is clear.

[0027] (2) This method is based on milling vibration signal. It uses the distribution characteristics of vibration signal to solve the characteristic parameters of its distribution characteristics, thus solving the distribution characteristics of vibration signal. White light is used to detect morphology, establish characteristic curves, and analyze its characteristic parameters. The correlation analysis between milling vibration displacement signal and surface morphology characteristics is used, and the grey relational analysis method is used to evaluate the degree of influence of milling vibration on the surface morphology. The results are accurate and the method is reliable.

[0028] (3) This method can reflect abnormal vibration events during the processing, thereby judging the processing status and understanding the reasons for the rough surface morphology of the workpiece, which helps to optimize the processing of the workpiece and improve the processing quality of the workpiece. Attached Figure Description

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

[0030] Figure 2 This is a front view of the milling cutter of the present invention;

[0031] Figure 3 This is a schematic diagram of the cutting tip of the milling cutter of the present invention.

[0032] Figure 4 This is a partial schematic diagram of the cutter body of the milling cutter of the present invention;

[0033] Figure 5 This is a schematic diagram of the correlation analysis method of the present invention;

[0034] Figure 6 This is a schematic diagram of the vibration displacement signal of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Example 1

[0037] Please see Figure 1-6 The present invention provides a technical solution: a method for analyzing the distribution characteristics of milled surface morphology of aerospace structural components.

[0038] 1. Analysis Method for Vibration Displacement Signal Distribution Characteristics

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

[0040] (1)

[0041] (2)

[0042] (3)

[0043] In the formula: K This represents the kurtosis sequence in any direction corresponding to the vibration time period. k 1. k 2 represents the kurtosis of the first and second periods in any direction, respectively. k p It represents the kurtosis of the morphology for any period of time. k m This represents the kurtosis of the last cycle in the selected time period. F This represents the dominant frequency sequence in any direction during the corresponding time period of vibration. f 1. f 2 represents the main frequency of the first and second cycles in any direction, respectively.f p The dominant frequency of any period corresponding to the morphology. f m This represents the main frequency of the last cycle in the selected time period. Rms This represents the sequence of root mean square values ​​in any direction corresponding to the vibration time period. rms 1 , rms 2 These represent the root mean square values ​​of the first and second periods in any direction, respectively. rms p This represents the root mean square value of the morphology for any period within the corresponding time period. rms m This represents the root mean square value of the last period in the selected time period.

[0044] The kurtosis of the vibration signal is:

[0045] (4)

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

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

[0048] (5)

[0049] in: rms This 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 signal spectrum. The frequency point with the largest amplitude is analyzed and identified as the dominant frequency.

[0051] 2. Methods for obtaining surface morphology of milled surfaces and constructing characteristic distribution curves of milled surfaces

[0052] The surface topography of the milled parts was measured using a Taylor Hobson CCI·MP non-contact interferometer. Topography measurements were performed for different cutting strokes, and a 512µm × 512µm pixel array was used for surface topography imaging.

[0053] The detailed measurement steps for a white light interferometer are as follows:

[0054] 1) Select a framing range of 800x800um.

[0055] 2) Place the workpiece on the stage, use the joystick to move the workpiece under the lens, and adjust the lens height until the image is in focus.

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

[0057] 4) Post-process the detection results to remove image noise and extract morphological data.

[0058] Figure 1 In this milling operation, the workpiece material was a TC4 titanium alloy, and the total length of the workpiece was 100mm. o - xz The plane is the milling plane in the workpiece coordinate system. x 0 represents the distance from the end mill's idle cutting point to the starting point of the workpiece milling operation. x 1 represents the distance from the first sampling point to the starting point of the workpiece milling, Δ x 1 represents the sampling distance between points in the topography detection area; Δ x To measure the viewfinder length using a white light interferometer, Δ z To measure the viewfinder height using a white light interferometer; t 0 corresponds to the time it takes for the milling cutter to rotate from idle to cutting. t 1 Corresponding to the topography detection location area Δ x 1. Milling stroke time, Δ t The milling cutter cutting time corresponds to the viewfinder frame of the corresponding topography detection area. Based on the milling stroke, the topography detected by the white light interferometer is sequentially A. 11 A 12 A 13 A 14 and A 15 .

[0059] in: D The diameter of the milling cutter shank is 1. β The helix angle of the milling cutter. l 1 represents the axial length of the milling cutter's cutting edge. l 2 represents the overhang of the milling cutter. l 3 represents the total length of the milling cutter. r i Let Δ be the radius of rotation of the cutting tooth i. z di For blade teeth i Axial error, θ i It is the included angle between two adjacent cutting teeth.

[0060] in: o d - x d y d z dFor the milling cutter structure coordinate system, o d It is the center of rotation of the lowest tip point in the axial direction of the milling cutter, and is coplanar with the lowest tip point in the axial direction of the milling cutter; y d The axis is parallel to the direction of the main motion velocity at the tool tip point with the largest radius of rotation. x d The axis is parallel to the radial direction of the tool tip point with the largest radius of rotation. z d The axis is the rotation axis of the milling cutter and points in the direction of the tool holder.

[0061] Depend on Figure 2 , Figure 3 , Figure 4 The milling cutter structure is used to establish the milling cutter tooth position angle sequence. C θ for:

[0062] (6)

[0063] Based on the cutter tooth position angle sequence in equation (6), construct the corresponding cutter tooth sequence. C for:

[0064] (7)

[0065] Based on the cutter tooth sequence in equation (7), construct the corresponding cutter tooth axial error sequence. C zd :

[0066] (8)

[0067] It can be determined from equations (6) to (8) Figure 2 White light interferometer detection of morphological division and morphological layer boundaries 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. Determined by the boundary of the surface morphology of the milled machine.

[0068] 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 / 2b 4- b 3) To avoid randomness a 5= b 4+ b 1.

[0069] 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.

[0070] 3. Correlation Analysis between Vibration Displacement Signal Distribution Characteristics and Surface Morphology Feature Distribution Characteristics

[0071] By establishing a correlation analysis between the time-frequency characteristics of the vibration displacement signal in three directions and the time-frequency characteristics at different positions of the surface topography curve, using the time-frequency of the milled surface topography 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 4 As shown.

[0072] like Figure 4 As shown, through dominant frequency correlation, 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 processing directly affects the surface morphology, potentially leading to irregular ripples or textures. Through kurtosis correlation, an increase in the kurtosis of the vibration signal means the presence of more peaks in the signal, reflecting possible atypical vibration events during processing. Changes in the kurtosis of the surface morphology characteristic curve may indirectly reflect abnormal vibration events during processing, thus assessing the processing status. Through root mean square (RMS) value correlation, 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 processing, which is reflected in the RMS value of the surface morphology characteristic curve. The positive correlation between the two RMS values ​​demonstrates the direct relationship between vibration intensity and surface roughness; that is, the greater the vibration, the rougher the surface.

[0073] Correlation analysis reveals how vibration affects machining quality, providing direction for optimizing process parameters. By adjusting cutting speed, feed rate, and tool type, the dominant frequency matching of vibration displacement signals can be reduced, as well as the root mean square value and kurtosis can be decreased, thereby effectively improving surface morphology and enhancing the quality and reliability of machined products.

[0074] Differences from publicly available technologies:

[0075] Most existing analyses of the influence of milling vibration signals on the surface morphology of milled surfaces directly use milling vibration signals to analyze the influence of milling vibration signals on the surface morphology of milled surfaces. These analytical methods cannot truly reflect the influence of milling vibration signals on the formation of the surface morphology of milled surfaces.

[0076] This embodiment proposes a method for correlation analysis between vibration displacement signals and the surface morphology of milled surfaces. During the milling process, the method extracts vibration displacement signals and analyzes their distribution characteristics. Through high-efficiency milling experiments, the surface morphology of the milled surfaces is obtained, and the residual characteristic curves of the transition surfaces are extracted and analyzed for their time-frequency characteristics. Using the correlation analysis between milling vibration displacement signals and surface morphology features, a grey relational analysis method is employed to assess the degree of influence of milling vibration on the surface morphology.

[0077] Example 2

[0078] 1. Analysis Method for Vibration Displacement Signal Distribution Characteristics

[0079] This experiment used a Walter solid carbide end mill with 5 teeth, a diameter of 20 mm, and a helix angle of 45 degrees. Climb milling and dry milling were employed. Acceleration signals were acquired using a Kistler Dynoware and DHDAS 5922 transient signal testing and analysis system to detect milling vibrations. Specific cutting parameters are shown in Table 1.

[0080] Table 1 Milling Scheme

[0081]

[0082] During the experiment, an accelerometer was used to collect vibration acceleration signals, which were then converted into vibration displacement signals. These vibration displacement signals were then divided into segments, such as... Figure 6 As shown.

[0083] like Figure 6 As shown, t 0 corresponds to the time it takes for the milling cutter to rotate from idle to cutting. t 1 Corresponding to the topography detection location area Δ x 1. Milling stroke time, Δ t The cutting time of the milling cutter corresponding to the viewfinder frame of the topography detection area.

[0084] Considering the impact of vibration on the formation process of milled surface morphology, a sequence of dominant frequency, kurtosis, and root mean square value of vibration signals in three directions is established to determine the degree of influence of milling vibration on the morphology curve.

[0085] (9)

[0086] (10)

[0087] (11)

[0088] In the formula: K This represents the kurtosis sequence in any direction corresponding to the vibration time period. k 1. k 2 represents the kurtosis of the first and second periods in any direction, respectively. k p It represents the kurtosis of the morphology for any period of time. k m This represents the kurtosis of the last cycle in the selected time period. F This represents the dominant frequency sequence in any direction during the corresponding time period of vibration. f 1. f 2 represents the main frequency of the first and second cycles in any direction, respectively. f p The dominant frequency of any period corresponding to the morphology. f m This represents the main frequency of the last cycle in the selected time period. Rms This represents the sequence of root mean square values ​​in any direction corresponding to the vibration time period. rms 1. rms 2 represents the root mean square value of the first and second periods in any direction, respectively. rms p This represents the root mean square value of the morphology for any period within the corresponding time period. rms m This represents the root mean square value of the last period in the selected time period.

[0089] The kurtosis of the vibration signal is:

[0090] (12)

[0091] in: Kurtosis Indicates kurtosis, m Indicates the upper bound. j Indicates the lower bound. x i Indicates the vibration signal value. This represents the average value.

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

[0093] (13)

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

[0095] Extract the vibration signal corresponding to the detected morphology, extract the characteristic parameters of the vibration signal, and solve...x , y , z The mean, kurtosis, and dominant frequency of the vibration signals in the three directions are shown in Table 2.

[0096] Table 2. Mean, kurtosis, and dominant frequency of vibration signals at the detection location.

[0097]

[0098] As shown in the table, the kurtosis of the vibration signal during milling is generally below 3, indicating that the probability density distribution of the vibration signal is close to a normal distribution, and the milling process is relatively stable. The dominant frequency and kurtosis of the vibration signal tend to stabilize, indicating a relatively stable milling process. Under the influence of tool tooth error and milling vibration, the formation of the milled surface and its geometric errors is not a stable process; its characteristic points change instantaneously, resulting in a variable distribution of errors on the milled surface.

[0099] Example 3

[0100] 2. Methods for obtaining surface morphology of milled surfaces and constructing characteristic distribution curves of milled surfaces

[0101] The surface topography of the milled parts was measured using a Taylor Hobson CCI·MP non-contact interferometer. Topography measurements were performed for different cutting strokes, and a 512µm × 512µm pixel array was used for surface topography imaging.

[0102] The detailed measurement steps for a white light interferometer are as follows:

[0103] 1) Select a framing range of 800x800um.

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

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

[0106] 4) Post-process the detection results to remove image noise and extract morphological data.

[0107] Figure 1 In this milling operation, the workpiece material was a TC4 titanium alloy, and the total length of the workpiece was 100mm. o - xz The plane is the milling plane in the workpiece coordinate system. x 0 represents the distance from the end mill's idle cutting point to the starting point of the workpiece milling operation. x 1 represents the distance from the first sampling point to the starting point of the workpiece milling, Δ x 1 represents the sampling distance between points in the topography detection area; Δx To measure the viewfinder length using a white light interferometer, Δ z To measure the viewfinder height using a white light interferometer; t 0 corresponds to the time it takes for the milling cutter to rotate from idle to cutting. t 1 Corresponding to the topography detection location area Δ x 1. Milling stroke time, Δ t The milling cutter cutting time corresponds to the viewfinder frame of the corresponding topography detection area. Based on the milling stroke, the topography detected by the white light interferometer is sequentially A. 11 A 12 A 13 A 14 and A 15 .

[0108] in x 1=31.6mm, Δ x 1=31.6mm, t1=3.3194s, Δ t =0.2513s, Δ x =2.4mm, Δ z =0.1mm.

[0109] End mill structure as follows Figure 2 , Figure 3 , Figure 4 As shown.

[0110] in: D The diameter of the milling cutter shank is 1. β The helix angle of the milling cutter. l 1 represents the axial length of the milling cutter's cutting edge. l 2 represents the overhang of the milling cutter. l 3 represents the total length of the milling cutter. r i Let Δ be the radius of rotation of the cutting tooth i. z di For blade teeth i Axial error, θ i It is the included angle between two adjacent cutting teeth.

[0111] in: o d - x d y d z d For the milling cutter structure coordinate system, o d It is the center of rotation of the lowest tip point in the axial direction of the milling cutter, and is coplanar with the lowest tip point in the axial direction of the milling cutter; y d The axis is parallel to the direction of the main motion velocity at the tool tip point with the largest radius of rotation. xd The axis is parallel to the radial direction of the tool tip point with the largest radius of rotation. z d The axis is the rotation axis of the milling cutter and points in the direction of the tool holder.

[0112] Depend on Figure 1 Milling cutter structure, establishing the milling cutter tooth position angle sequence C θ for:

[0113] (14)

[0114] Based on the tooth position angle sequence in equation (14), construct the corresponding tooth sequence. C for:

[0115] (15)

[0116] Based on the tooth sequence in equation (15), construct the corresponding tooth axial error sequence. C zd :

[0117] (16)

[0118] Equations (14)-(16) can be used to determine the morphological division boundaries detected by the 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. Determined by the boundary of the surface morphology of the milled machine.

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

[0120] 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) To avoid randomnessa 5= b 4+ b 1.

[0121] in a 1 = 10.005 mm a 2 = 10.014 mm a 3 = 10.0235 mm a 4 = 10.034 mm a 5 = 10.044 mm.

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

[0123] Table 3 End Mill Tooth Error

[0124]

[0125] Where Δ r i This represents the radial error of the end mill.

[0126] 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 curves are calculated. The distribution characteristics of the morphological feature curves of the processed surface are shown in Table 4.

[0127] Table 4 Distribution characteristics of the morphology curves of the machined surface

[0128]

[0129] As shown in the table, the kurtosis of the milled surfaces is mostly below 3, indicating that the distribution of the characteristic curves of the milled surface morphology is close to a normal distribution, and the morphology distribution of the milled surfaces is relatively uniform. The dominant frequency and kurtosis trends of the vibration signal tend to be stable, indicating that the formation process of the milled surface morphology is relatively stable.

[0130] Example 4

[0131] 3. Correlation Analysis between Vibration Displacement Signal Distribution Characteristics and Surface Morphology Feature 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 can be seen from the table, x direction and z The vibration displacement signals in all three directions show a high correlation with the milled surface morphology characteristic curves, and these signals have a significant impact on the formation of the milled surface morphology. Specifically, the dominant frequency of the vibration displacement signal is highly correlated with the dominant frequency of the periodic structure in the surface morphology characteristic curves, indicating that the main vibration frequencies during machining directly affect the surface morphology. Through kurtosis correlation, the kurtosis of the vibration signal along the feed direction shows a high correlation with the machined surface morphology, implying the presence of more peaks in the displacement signal along the feed direction. Changes in the kurtosis of the surface morphology characteristic curves may indirectly reflect abnormal vibration events during machining, thus aiding in the assessment of the machining status. Through root mean square (RMS) value correlation, the RMS value of the vibration displacement signal shows a high correlation with the machined surface morphology characteristic curves, indicating a direct link between the y-direction vibration signal and the formation of the machined surface morphology.

[0138] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing the distribution characteristics of vibration displacement signals in milling operations, characterized in that, To analyze the distribution characteristics of vibration displacement signals, vibration displacement signals were collected during the end milling process. Distribution sequences of the dominant frequency, kurtosis, and root mean square value of the vibration signals in three directions were established to determine the milling vibration distribution characteristics. The formula is as follows: 、 、 ； in: K This represents the kurtosis sequence in any direction corresponding to the vibration time period. k 1. k 2 represents the kurtosis of the first and second periods in any direction, respectively. k p It represents the kurtosis of the morphology for any period of time. k m Represents the kurtosis of the last cycle in the selected time period. F This represents the dominant frequency sequence in any direction during the corresponding time period of vibration. f 1. f 2 represents the main frequency of the first and second cycles in any direction, respectively. f p The dominant frequency of any period corresponding to the morphology. f m This represents the main frequency of the last cycle in the selected time period. Rms This represents the sequence of root mean square values ​​in any direction corresponding to the vibration time period. rms 1 , rms 2 These represent the root mean square values ​​of the first and second periods in any direction, respectively. rms p This represents the root mean square value of the morphology for any period within the corresponding time period. rms m Represents the root mean square value of the last period in the selected time period; The kurtosis of the vibration signal is: ； in: Kurtosis Indicates kurtosis, m Indicates the upper bound. j Indicates the lower bound. x i Indicates the vibration signal value. This represents the average value; The root mean square of the vibration displacement signal is: ； in: rms This represents the root mean square value of the vibration displacement signal. m Indicates the upper bound. j Indicates the lower bound; The collected vibration displacement signal data is subjected to Fourier transform to obtain the signal spectrum. The frequency point with the largest amplitude is analyzed and identified as the dominant frequency.

2. The method for analyzing the distribution characteristics of vibration displacement signals in milling operations according to claim 1, characterized in that, The milling method used is climb milling and dry milling.

3. The application of the vibration displacement signal distribution characteristic analysis method for milling as described in claim 1 in the correlation analysis of surface morphology in milling.

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