Method for analyzing surface topography distribution characteristics of aerostructure milling

Abstract

The application discloses a kind of milling surface topography distribution characteristics analysis methods of aviation structural parts, analysis vibration displacement signal distribution characteristics;Milling surface topography is obtained and the construction of milling surface topography feature distribution curve;Analysis vibration displacement signal distribution characteristics and the relevance of surface topography feature distribution characteristics, with milling surface topography curve time frequency as reference sequence.Overefficient milling experiment, obtain milling surface topography, extract milling transition surface residual feature curve, analyze the time-frequency characteristics of milling transition surface feature distribution curve.Using the relevance analysis between milling vibration displacement signal and surface topography feature, using grey correlation analysis method, assess the close degree of the influence of milling vibration on processing surface topography, and the correlation characteristics of vibration and processing surface topography characteristics are clear.

Description

Technical Field

[0001] This invention relates to the technical field of correlation analysis methods for milled surface morphology, specifically a method for analyzing the distribution characteristics of milled surface morphology of aerospace structural components. Background Technology

[0002] 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, 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.

[0003] 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

[0004] The purpose of this invention is to provide a method for analyzing the surface morphology distribution characteristics of milled aerospace structural components, so as to solve the problems mentioned in the background art.

[0005] 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:

[0006] 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:

[0007] K = (k1, k2, ..., k p ,…k m F = (f1, f2, ..., f p ,…f m Rms = (rms1, rms2, ..., rms) p ,…rms m );

[0008] Where: K represents the kurtosis sequence in any direction during the vibration period, k1 and k2 represent the kurtosis of the first and second periods in any direction, respectively, k pk represents the kurtosis of the morphology for any period of time. m F represents the kurtosis of the last cycle of the selected time period, F represents the dominant frequency sequence in any direction of the vibration during that time period, and f1 and f2 represent the dominant frequencies of the first and second cycles in any direction, respectively. p f represents the dominant frequency of any period corresponding to the morphology. m Rms represents the dominant frequency of the last cycle in the selected time period, Rms represents the root mean square value sequence in any direction during the corresponding time period, rms1 and rms2 represent the root mean square values ​​of the first and second cycles in any direction, respectively. p The root mean square (RMS) value represents the morphology for any period of time. m Represents the root mean square value of the last period in the selected time period;

[0009] 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 main frequency.

[0010] 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.

[0011] 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.

[0012] Furthermore, in step one, the kurtosis of the vibration signal is:

[0013]

[0014] Where: Kurtosis represents kurtosis, m represents the upper bound, j represents the lower bound, and x i Indicates the vibration signal value. This represents the average value.

[0015] Furthermore, in step one, the root mean square of the vibration displacement signal is:

[0016]

[0017] Where: rms represents the root mean square value of the vibration displacement signal, m represents the upper bound, and j represents the lower bound.

[0018] Furthermore, in step two, the workpiece being milled is a 100mm long TC4 titanium alloy, and a sequence of milling cutter tooth position angles C is established. θ 、and C θ The corresponding cutter tooth sequence C, and the cutter tooth axial error sequence C corresponding to C. zd ;

[0019] C zd =(Δz) d1 ,…,Δz di ,…,Δz dm ),

[0020] in, To measure changes in viewfinder height using a white light interferometer;

[0021] Based on C θ C, C zd The topography is divided into topography stratification boundaries b1, b2, b3 and b4 as detected by white light interferometer. The topography curves are extracted at positions a1, a2, a3, a4 and a5. Based on the stratification boundaries, the topography curve data of a1, a2, a3, a4 and a5 are extracted. The topography feature curve y(x) is fitted. The feature parameters of the topography feature curve are calculated according to the kurtosis, dominant frequency and root mean square value calculation methods.

[0022] 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.

[0023] 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 indicates 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.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] (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.

[0026] (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. 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.

[0027] (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 can help optimize the processing of the workpiece and improve the processing quality of the workpiece. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the vibration displacement signal and morphology sampling method of the present invention;

[0029] Figure 2 This is a front view of the milling cutter of the present invention;

[0030] Figure 3 This is a schematic diagram of the cutting tip of the milling cutter of the present invention.

[0031] Figure 4 This is a partial schematic diagram of the cutter body of the milling cutter of the present invention;

[0032] Figure 5 This is a schematic diagram of the correlation analysis method of the present invention;

[0033] Figure 6 This is a schematic diagram of the vibration displacement signal of the present invention. Detailed Implementation

[0034] 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.

[0035] Example 1

[0036] Please see Figure 1-6The present invention provides a technical solution: a method for analyzing the distribution characteristics of milled surface morphology of aerospace structural components.

[0037] 1. Analysis Method for Vibration Displacement Signal Distribution Characteristics

[0038] 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.

[0039] K = (k1, k2, ..., k p ,…k m (1)

[0040] F = (f1, f2, ..., f p ,…f m (2)

[0041] Rms = (rms1, rms2, ..., rms p ,…rms m (3)

[0042] In the formula: K represents the kurtosis sequence in any direction during the vibration period, k1 and k2 represent the kurtosis of the first and second periods in any direction, respectively, and k p k represents the kurtosis of the morphology for any period of time. m This represents the kurtosis of the last cycle of the selected time period. F represents the dominant frequency sequence in any direction during the corresponding time period, and f1 and f2 represent the dominant frequencies of the first and second cycles in any direction, respectively. p f represents the dominant frequency of any period corresponding to the morphology. m This represents the dominant frequency of the last cycle in the selected time period. Rms represents the root mean square (RMS) value sequence in any direction for the corresponding time period, while rms1 and rms2 represent the RMS values ​​of the first and second cycles in any direction, respectively. p The root mean square (RMS) value represents the morphology for any period of time. m This represents the root mean square value of the last period in the selected time period.

[0043] The kurtosis of the vibration signal is:

[0044]

[0045] Where: Kurtosis represents kurtosis, m represents the upper bound, j represents the lower bound, and x i Indicates the vibration signal value. This represents the average value.

[0046] The root mean square of the vibration displacement signal is:

[0047]

[0048] Where: rms represents the root mean square value of the vibration displacement signal.

[0049] 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.

[0050] 2. Methods for obtaining surface morphology of milled surfaces and constructing characteristic distribution curves of milled surfaces

[0051] 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 surface topography images were captured using a 512μm × 512μm pixel array.

[0052] The detailed measurement steps for a white light interferometer are as follows:

[0053] 1) Select a framing range of 800x800um.

[0054] 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.

[0055] 3) Scan the measurement area to obtain the detection results.

[0056] 4) Post-process the detection results to remove image noise and extract morphological data.

[0057] Figure 1 In this milling operation, the workpiece material is TC4 titanium alloy, and the total length of the workpiece is 100mm. The o-xz plane is the milling plane in the workpiece coordinate system; x0 is the distance from the cutter's idle rotation to the workpiece milling start point; x1 is the distance from the first sampling point to the workpiece milling start point; Δx1 is the sampling point spacing in the topography detection area; Δx is the length of the white light interferometer's measurement frame; Δz is the height of the white light interferometer's measurement frame; t0 corresponds to the time from the cutter's idle rotation to its entry point; t1 corresponds to the milling travel time of the topography detection area Δx1; and Δt corresponds to the cutter's cutting time within the topography detection area's measurement frame. Based on the milling travel, the topography detected by the white light interferometer is sequentially A... 11 A 12 A 13 A 14 and A 15 .

[0058] Where: D is the diameter of the milling cutter shank, β is the helix angle of the milling cutter, l1 is the axial length of the milling cutter cutting edge, l2 is the overhang of the milling cutter, and l3 is the total length of the milling cutter. i Let Δz be the radius of rotation of the cutting tooth i.di Let θ be the axial error of the cutter tooth i. i It is the included angle between two adjacent cutting teeth.

[0059] Among them: o d -x d y d z d Let o be the coordinate system of the milling cutter structure. d The center of rotation of the lowest point of the end mill's axial cutter tip is coplanar with the lowest point of the end mill's axial cutter tip; y d The axis is parallel to the direction of the main motion velocity at the tool tip point with the maximum radius of rotation; x d The axis is radially parallel to the tip point of maximum gyration; z d The axis is the rotation axis of the milling cutter and points in the direction of the tool holder.

[0060] 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:

[0061] C θ =(θ1,…,θ i ,…,θ m (6)

[0062] Based on the tooth position angle sequence in equation (6), the corresponding tooth sequence C is constructed as follows:

[0063] C = (C1, ..., C i ,…,C m (7)

[0064] Based on the tooth sequence in equation (7), construct the corresponding tooth axial error sequence C. zd :

[0065] C zd =(Δz) d1 ,…,Δz di ,…,Δz dm (8)

[0066] It can be determined from equations (6) to (8) Figure 2 The morphology detected by the white light interferometer is divided into morphology layer boundaries b1, b2, b3 and b4. The morphology curve extraction positions a1, a2, a3, a4 and a5 are determined by the morphology boundaries of the milled surface.

[0067] a1 = 1 / 2(b1 - lower boundary of morphology), a2 = 1 / 2(b2 - b1), a3 = 1 / 2(b3 - b2), a4 = 1 / 2(b4 - b3), and to avoid randomness, a5 = b4 + b1.

[0068] Based on the hierarchical boundary, the topographic curve data of a1, a2, a3, a4 and a5 are extracted, the topographic feature curve y(x) is fitted, and the feature parameters of the topographic feature curve are calculated according to the kurtosis, dominant frequency and root mean square value calculation method.

[0069] 3. Correlation Analysis between Vibration Displacement Signal Distribution Characteristics and Surface Morphology Feature Distribution Characteristics

[0070] 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.

[0071] 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.

[0072] 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.

[0073] Differences from publicly available technologies:

[0074] 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.

[0075] 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.

[0076] Example 2

[0077] 1. Analysis Method for Vibration Displacement Signal Distribution Characteristics

[0078] 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.

[0079] Table 1 Milling Scheme

[0080]

[0081] 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.

[0082] like Figure 6 As shown, t0 corresponds to the time when the milling cutter rotates from idle to cutting, t1 corresponds to the milling stroke time of the Δx1 area in the topography detection position region, and Δt corresponds to the milling cutter cutting time of the topography detection area viewfinder.

[0083] 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.

[0084] K = (k1, k2, ..., k p ,…k m (9)

[0085] F = (f1, f2, ..., f p ,…f m (10)

[0086] Rms = (rms1, rms2, ..., rms p ,…rms m (11)

[0087] In the formula: K represents the kurtosis sequence in any direction during the vibration period, k1 and k2 represent the kurtosis of the first and second periods in any direction, respectively, and k p k represents the kurtosis of the morphology for any period of time. m This represents the kurtosis of the last cycle of the selected time period. F represents the dominant frequency sequence in any direction during the corresponding time period, and f1 and f2 represent the dominant frequencies of the first and second cycles in any direction, respectively. p f represents the dominant frequency of any period corresponding to the morphology. m This represents the dominant frequency of the last cycle in the selected time period. Rms represents the root mean square (RMS) value sequence in any direction for the corresponding time period, while rms1 and rms2 represent the RMS values ​​of the first and second cycles in any direction, respectively. p The root mean square (RMS) value represents the morphology for any period of time. m This represents the root mean square value of the last period in the selected time period.

[0088] The kurtosis of the vibration signal is:

[0089]

[0090] Where: Kurtosis represents kurtosis, m represents the upper bound, j represents the lower bound, and x i Indicates the vibration signal value. This represents the average value.

[0091] The root mean square of the vibration displacement signal is:

[0092]

[0093] Where: rms represents the root mean square value of the vibration displacement signal.

[0094] The vibration signal corresponding to the detected morphology was extracted, the characteristic parameters of the vibration signal were extracted, and the mean, kurtosis and dominant frequency of the vibration signal in the x, y and z directions were calculated, as shown in Table 2.

[0095] Table 2. Mean, kurtosis, and dominant frequency of vibration signals at the detection location.

[0096]

[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 surface topography images were captured using a 512μm × 512μm pixel array.

[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 is TC4 titanium alloy, and the total length of the workpiece is 100mm. The o-xz plane is the milling plane in the workpiece coordinate system; x0 is the distance from the cutter's idle rotation to the workpiece milling start point; x1 is the distance from the first sampling point to the workpiece milling start point; Δx1 is the sampling point spacing in the topography detection area; Δx is the length of the white light interferometer's measurement frame; Δz is the height of the white light interferometer's measurement frame; t0 corresponds to the time from the cutter's idle rotation to its entry point; t1 corresponds to the milling travel time of the topography detection area Δx1; and Δt corresponds to the cutter's cutting time within the topography detection area's measurement frame. Based on the milling travel, the topography detected by the white light interferometer is sequentially A... 11 A 12 A 13 A 14 and A 15 .

[0108] Where x1 = 31.6 mm, Δx1 = 31.6 mm, t1 = 3.3194 s, Δt = 0.2513 s, Δx = 2.4 mm, and Δz = 0.1 mm.

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

[0110] Where: D is the diameter of the milling cutter shank, β is the helix angle of the milling cutter, l1 is the axial length of the milling cutter cutting edge, l2 is the overhang of the milling cutter, and l3 is the total length of the milling cutter. i Let Δz be the radius of rotation of the cutting tooth i. di Let θ be the axial error of the cutter tooth i. i It is the included angle between two adjacent cutting teeth.

[0111] Among them: o d -x d y d z d Let o be the coordinate system of the milling cutter structure. d The center of rotation of the lowest point of the end mill's axial cutter tip is coplanar with the lowest point of the end mill's axial cutter tip; y d The axis is parallel to the direction of the main motion velocity at the tool tip point with the maximum radius of rotation; x d The axis is radially parallel to the tip point of maximum gyration; 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] C θ =(θ1,…,θ i ,…,θ m (14)

[0114] Based on the tooth position angle sequence of equation (14), the corresponding tooth sequence C is constructed as follows:

[0115] C = (C1, ..., C i ,…,C m (15)

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

[0117] C zd =(Δz) d1 ,…,Δz di ,…,Δz dm (16)

[0118] Equations (14)-(16) can be used to determine the topographic division boundaries b1, b2, b3 and b4 detected by the white light interferometer. The topographic curve extraction positions a1, a2, a3, a4 and a5 are determined by the topographic boundaries of the milled surface.

[0119] Where b1 = 10.01 mm, b2 = 10.018 mm, b3 = 10.029 mm, and b4 = 10.039 mm.

[0120] a1 = 1 / 2(b1 - lower boundary of morphology), a2 = 1 / 2(b2 - b1), a3 = 1 / 2(b3 - b2), a4 = 1 / 2(b4 - b3), and to avoid randomness, a5 = b4 + b1.

[0121] Where a1 = 10.005mm, a2 = 10.014mm, a3 = 10.0235mm, a4 = 10.034mm, and a5 = 10.044mm.

[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 Δr i This represents the radial error of the end mill.

[0126] Based on the layer boundaries, the topographic curve data of a1, a2, a3, a4, and a5 are extracted, and the topographic feature curve y(x) is fitted. The feature parameters of the topographic feature curve are calculated according to the kurtosis, dominant frequency, and root mean square value calculation methods. The distribution characteristics of the topographic 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]

[0130] 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.

[0131] Example 4

[0132] 3. Correlation Analysis between Vibration Displacement Signal Distribution Characteristics and Surface Morphology Feature Distribution Characteristics

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Table 5. Correlation between vibration and milled surface morphology

[0137]

[0138]

[0139] 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.

[0140] 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 surface topography distribution characteristics of a milled aeronautical structure, characterized in that, Comprising the following steps: Step one, analyze the vibration displacement signal distribution characteristics, collect the vibration displacement signal in the milling process of the end mill, establish the main frequency, kurtosis and root mean square value distribution sequence of three direction vibration signal, determine the milling vibration distribution characteristics, the formula is as follows: K=(k1,k2,…,k p ,…k m )、F=(f1,f2,…,f p ,…f m )、Rms=(rms1,rms2,…,rms p ,…rms m ); Wherein: K represents the kurtosis sequence of any direction corresponding to the vibration period, k1 and k2 represent the kurtosis of the first period and the second period of any direction respectively, k p represents the kurtosis of any period corresponding to the morphology period, k m represents the kurtosis of the last period of the selected period, F represents the frequency sequence of any direction corresponding to the vibration period, f1 and f2 represent the frequency of the first period and the second period of any direction respectively, f p represents the frequency of any period corresponding to the morphology period, f m represents the frequency of the last period of the selected period, Rms represents the root mean square value sequence of any direction corresponding to the vibration period, rms1 and rms2 represent the root mean square value of the first period and the second period of any direction respectively, rms p represents the root mean square value of any period corresponding to the morphology period, rms m represents the root mean square value of the last period of the selected period; The collected vibration displacement signal data, Fourier transform of the vibration displacement signal, get the frequency spectrum of the signal, analyze the spectrum, the frequency point with the largest amplitude, the frequency is the main frequency; Step two, milling surface topography acquisition and milling surface topography feature distribution curve construction, based on the white light interferometer, the topography of different cutting stroke of end mill is detected, the surface topography is shot using 512 μm x 512 μm pixel array, the range of 800 x 800 um is selected, the measurement area is scanned, the detection result is obtained, the image noise of the detection result is processed, and the topography data is extracted; In step two, the milled workpiece is a 100mm long TC4 titanium alloy, and the tool tooth position angle sequence C is established θ , and C θ The corresponding tool tooth sequence C, the tool tooth axial error sequence C corresponding to C zd ; C zd = (Δz d1 ,…,Δz di ,…,Δz dm ), wherein, measure the change in height of the field of view for the white light interferometer; C θ C, C zd Determine the topography division topography layer boundary b1, b2, b3 and b4 detected by white light interferometer, topography curve extraction position a1, a2, a3, a4 and a5, according to the layer boundary, extract a1, a2, a3, a4 and a5 topography curve data, fit the topography characteristic curve y(x), according to the kurtosis, main frequency and root mean square value calculation method, solve the topography characteristic curve characteristic parameter; 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 vibration displacement signal time frequency as the comparison sequence, analyze the main frequency correlation, kurtosis correlation and root mean square value correlation; Through kurtosis correlation, the kurtosis of vibration signal increases, which represents that there are more sharp peaks in the vibration displacement signal, reflecting the existence of atypical vibration events in the processing process. The kurtosis change of workpiece surface topography feature curve indirectly reflects the abnormal vibration events in the processing process, so as to judge the processing state.

2. The method of claim 1, wherein the method further comprises: determining the milling surface profile distribution characteristics of the aircraft structural component. In step one, the kurtosis of vibration signal is: wherein: Kurtosis represents kurtosis, m represents upper bound, j represents lower bound, x i represents a vibration signal value, represents an average value.

3. The method of claim 1, wherein the method further comprises: determining the milling surface topography distribution characteristics of the aircraft structure member. In step one, the root mean square of vibration displacement signal is: Wherein: rms represents the root mean square value of vibration displacement signal, m represents upper limit, j represents lower limit.

4. The method of claim 1, wherein: In step three, the main frequency of end mill vibration displacement signal is highly correlated with the main frequency of periodic structure in surface topography feature curve, which shows that the vibration frequency in the processing process directly affects the workpiece surface topography.

5. The method of claim 1, wherein: In step three, the root mean square value of vibration displacement signal is high, which represents that the vibration energy is large, the larger the vibration is, the rougher the workpiece surface is.

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