A surface topography precision measurement method for reducing vibration noise interference

Abstract

The application discloses a surface topography precision measurement method for reducing vibration noise interference, and relates to the field of surface topography measurement. The method comprises the following steps: obtaining first surface topography measurement data by scanning at a first scanning speed; extracting abnormal peak value frequencies to obtain a first abnormal peak value frequency set; determining a second scanning speed; obtaining second surface topography measurement data by scanning at the second scanning speed; judging whether there is overlap in the spatial spectrum of the first surface topography measurement data and the second surface topography measurement data; extracting a vibration frequency set of the spatial spectrum; replacing amplitude data caused by vibration in the spatial spectrum of the first surface topography measurement data with corresponding amplitude data of the second surface topography measurement data to obtain third surface topography measurement data and outputting the third surface topography measurement data. The application provides a surface topography measurement denoising method for solving the problems of missing high-frequency details and poor denoising effect in the prior art, and achieves the purpose of accurately positioning and quickly and effectively removing vibration noise.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of surface topography measurement, and in particular to a surface topography precision measurement method for reducing vibration noise interference. BACKGROUND

[0002] In the process of surface topography measurement, external vibration is one of the key factors affecting the measurement accuracy. Whether it is the vibration of equipment running in the workshop environment, ground-borne vibration, or the vibration generated by the movement of the parts of the measuring instrument, it will cause the surface topography data collected by the measuring sensor to mix in vibration noise. These noises will cover up the true topography characteristics of the measured surface, making subsequent product quality evaluation, process optimization and other work based on the measurement data lose accurate basis.

[0003] The existing technology for removing vibration noise in surface topography measurement mainly includes filtering method, averaging method, adaptive noise reduction method, etc. Among them, the filtering method removes noise frequency band by designing a filter of a specific frequency, but this method is easy to cause the loss of high-frequency detail information of the surface topography, especially when the noise frequency band overlaps with the real feature frequency band of the surface, it cannot achieve effective noise reduction; the averaging method weakens the noise effect by repeatedly measuring and averaging the data, however, this method not only increases the measurement time and labor cost, and in the case of unstable vibration source, the noise reduction effect is greatly discounted; the adaptive noise reduction method relies on real-time identification and dynamic adjustment of noise characteristics, but the complex algorithm logic leads to low operation efficiency, which is difficult to meet the real-time demand in high-precision measurement scenarios.

[0004] Therefore, it has become a technical problem to be solved in the current surface topography measurement technology field to develop a vibration noise removal method that can accurately locate the vibration noise frequency band, efficiently eliminate noise interference, and not damage the real topography information of the surface. SUMMARY

[0005] The present application provides a surface topography precision measurement method for reducing vibration noise interference, to solve the problems of existing surface topography measurement denoising methods, such as loss of high-frequency details and poor noise reduction effect, and to achieve the purpose of accurate positioning and rapid and effective removal of vibration noise.

[0006] The present application is implemented by the following technical solutions:

[0007] A surface topography precision measurement method for reducing vibration noise interference, comprising the following steps:

[0008] S1, scanning the surface of the measured object at a first scanning speed to obtain first surface topography measurement data;

[0009] S2, extracting abnormal peak frequency caused by external vibration interference based on the first surface topography measurement data, to obtain a first abnormal peak frequency set;

[0010] S3, determining a second scanning speed based on the first abnormal peak frequency set;

[0011] S4, scanning the surface of the measured object at the second scanning speed to obtain second surface topography measurement data;

[0012] S5, judging whether the abnormal peak in the spatial spectrum of the second surface topography measurement data overlaps with the abnormal peak in the spatial spectrum of the first surface topography measurement data on the frequency axis:

[0013] If yes, return to step S3 to determine the second scanning speed again;

[0014] If no, enter step S6;

[0015] S6, extracting a vibration frequency set of the spatial spectrum in the first surface topography measurement data and the second surface topography measurement data;

[0016] S7, replacing the amplitude data caused by vibration in the spatial spectrum of the first surface topography measurement data with the corresponding amplitude data in the spatial spectrum of the second surface topography measurement data based on the vibration frequency set, to obtain third surface topography measurement data;

[0017] S8, outputting the third surface topography measurement data as the surface topography precision measurement result of the measured object.

[0018] The surface morphology measurement denoising method in the prior art cannot remove vibration noise efficiently while completely preserving the real detail information of the surface morphology, and has problems such as loss of high-frequency details and poor denoising effect. In order to overcome the above problems, the present application provides a surface morphology precision measurement method for reducing vibration noise interference. First, the surface of the measured object is scanned at a first scanning speed to obtain noisy surface morphology measurement data, that is, first surface morphology measurement data. The first scanning speed is determined by the limiting speed of the measuring instrument and the roughness value of the measured object determined by experience, so as to select a qualified measurement speed to ensure that the data sampling is sufficient and the measurement efficiency is low. Then, based on the first surface morphology measurement data, a first abnormal peak frequency set is obtained, and a second scanning speed is determined. Based on the determined second scanning speed, the surface of the measured object is scanned again to obtain second surface morphology measurement data. Then, according to the first surface morphology measurement data and the second surface morphology measurement data, a vibration frequency set of the spatial spectrum is obtained. Finally, according to the vibration frequency set, the spatial spectrum of the first surface morphology measurement data is selectively replaced, and the third surface morphology measurement data in which the vibration noise interference is eliminated is obtained.

[0019] The scanning of the surface of the measured object in the present application can be realized by any existing surface morphology scanning technology, preferably by a non-contact three-dimensional morphology scanner.

[0020] The present application provides a precise and adaptive vibration noise solution by scanning the measured object twice at different scanning speeds and combining the frequency replacement algorithm, which overcomes the technical problems of existing filtering methods that easily lose real morphology characteristics when denoising, high cost of hardware vibration isolation schemes and limited application scenarios. The generated denoised surface morphology data can be directly used for high-precision surface morphology analysis, product quality detection and evaluation, which not only improves the anti-interference ability of various contact or non-contact measuring instruments, but also promotes the wide application of precision measurement technology in complex industrial environments, and truly realizes the precise positioning and rapid and effective removal of vibration noise.

[0021] Further, step S2 specifically comprises:

[0022] S201, performing spatial domain Fourier transform on the first surface morphology measurement data to obtain a first spatial spectrum;

[0023] S202, converting the first surface morphology measurement data into first time domain measurement data based on the first scanning speed and the sampling frequency in the scanning process; performing time domain Fourier transform on the first time domain measurement data to obtain a first time spectrum;

[0024] S203. Perform peak detection on the first spatial spectrum and the first time spectrum respectively, and identify all abnormal peak signals in the two spectra;

[0025] S204. Based on the abnormal peak signals in the first spatial spectrum and the first time spectrum, identify the noise peaks caused by external vibration interference; extract the frequency of the noise peaks to obtain the first abnormal peak frequency set.

[0026] This scheme uses dual-domain Fourier analysis to locate noise frequencies, providing a more reliable theoretical basis for the identification and separation of vibration noise. This is beneficial for improving the automation and intelligence of surface topography measurement data processing, while also improving the accuracy of vibration noise identification.

[0027] Those skilled in the art will understand that the sampling frequency in this application refers to the sensor sampling frequency of the measuring instrument used to scan the surface topography.

[0028] Furthermore, in step S202, the method for converting the first surface topography measurement data into first time-domain measurement data includes:

[0029] S2021. Calculate the spatial distance d between adjacent sampling points. Where v1 is the first scan speed, f s This refers to the sampling frequency during the scanning process;

[0030] S2022. Calculate the total measurement time t based on the total length L of the measurement path. total , ;

[0031] S2023. Map the height values ​​of the sampling points in the first surface morphology measurement data onto the time axis in chronological order to obtain the first time domain data.

[0032] Step S2021 aims to determine the basic sampling period of the time-domain data. By calculating the ratio of the spatial distance d between adjacent sampling points to the first scanning speed v1, this step essentially defines the scaling factor for converting the spatial sampling interval into the temporal sampling interval, i.e., ∆t = 1 / f. s This parameter is the basic unit for constructing timeline sequences.

[0033] Step S2022 serves to define the total time span of the time-domain data sequence. By dividing the total spatial length L of the measurement path by the first scan speed v1, this step precisely calculates the total time t required to complete the entire measurement process. total This parameter defines the start and end points of the first time-domain data on the time axis.

[0034] In summary, the core function of steps S2021 and S2022 is to establish precise transformation parameters for the data mapping from the spatial domain to the time domain in step S2023. Steps S2021 and S2022 together provide crucial time axis definition parameters for the mapping operation in step S2023, including the sampling rate and total duration of the time series, thereby ensuring that spatial topography data can be accurately reconstructed into time series data for subsequent time-domain signal analysis and processing.

[0035] Furthermore, in step S204, the method for identifying noise peaks caused by external vibration interference includes:

[0036] Identify all abnormal peak signals in the two spectra. If an abnormal peak simultaneously satisfies both condition one and condition two, it is determined to be a noise peak caused by external vibration interference.

[0037] Condition 1: The abnormal peak value appears simultaneously in both the first spatial spectrum and the first temporal spectrum;

[0038] Condition 2: The spatial frequency and temporal frequency of this abnormal peak satisfy the following relationship: ;in, The time frequency of this abnormal peak value. v1 represents the spatial frequency of the abnormal peak value, and v1 represents the first scan speed.

[0039] This scheme first identifies all peak signals in the first spatial spectrum and the first temporal spectrum, then determines all abnormal peak signals, and finally identifies noise peaks among the abnormal peak signals. In this scheme, the identified peak signals are matched and verified: if a certain peak appears simultaneously in the first spatial spectrum and the first temporal spectrum, and its spatial frequency and temporal frequency satisfy the constraint relationship of condition two, then the peak is determined to be a noise peak caused by external vibration; collecting all peak frequencies determined to be noise peaks forms the first abnormal peak frequency set.

[0040] The judgment of abnormal peak signals can be accomplished by empirical judgment or by manually setting a threshold.

[0041] Furthermore, step S3 specifically includes:

[0042] S301. Perform significant peak analysis on the first spatial spectrum to obtain a set of significant peak frequencies A; define the first set of abnormal peak frequencies as set P.

[0043] S302. Set constraints: For any and any All satisfy ; where p i Let a represent the i-th value in set P.j δ represents the j-th value in set A, v1 is the first scan speed, v2 is the second scan speed, and δ is the preset tolerance threshold.

[0044] S303. The second scanning speed that satisfies the constraints is obtained by using the particle swarm optimization algorithm.

[0045] In this scheme, the process of performing significant peak analysis on the first spatial spectrum can be carried out using existing significant peak analysis methods, which will not be elaborated here. This significant peak analysis process can reflect the peak signals (such as the frequencies corresponding to surface textures, the frequencies corresponding to surface contours, etc.) that reflect the true morphological characteristics of the measured surface, and the resulting set of significant peak frequencies is defined as set A.

[0046] This scheme employs a particle swarm optimization algorithm to find the second scanning speed that satisfies the aforementioned constraints. The iteration process of the particle swarm optimization algorithm can be terminated early when a feasible solution is found. Of course, the second scanning speed also needs to meet the scanning speed range requirements of the measuring instrument. Furthermore, the preset tolerance threshold δ is adaptively determined based on the spectral resolution of the measuring instrument to avoid misjudgments due to peak overlap caused by frequency resolution limitations.

[0047] When using the particle swarm optimization algorithm, the optimization objective is to "find any feasible second scanning speed v2 within the constraints". Parameters such as the number of iterations and population size are set. The penalty function method is used to handle the constraints. The degree of constraint violation is incorporated into the fitness function. The reflection boundary handling strategy is adopted to maintain population diversity. When a feasible solution is found, the iteration process is terminated in advance to ensure that the algorithm can converge to a feasible solution efficiently.

[0048] Preferably, if the algorithm solves for multiple v2 values ​​that satisfy the constraints, the v2 value with the smaller difference from the first scanning speed v1 is selected first, so as to reduce the impact of changes in environmental factors (such as temperature and humidity) on the measurement data during the two measurements.

[0049] The method for calculating the second scan speed proposed in this scheme can ensure effective separation of the noise spectrum and the characteristic spectrum, significantly reducing the risk of accidentally deleting valid signals or leaving residual noise.

[0050] Furthermore, in step S5, it is determined whether there is overlap on the frequency axis using the following method:

[0051] S501. Perform a spatial domain Fourier transform on the second surface topography measurement data to obtain the second spatial spectrum;

[0052] S502. Based on the second scanning speed and the sampling frequency during the scanning process, the second surface morphology measurement data is converted into second time-domain measurement data; the second time-domain measurement data is subjected to time-domain Fourier transform to obtain the second time spectrum;

[0053] S503. Perform peak detection on the second spatial spectrum and the second time spectrum respectively, and identify all abnormal peak signals in the two spectra; based on the abnormal peak signals in the second spatial spectrum and the second time spectrum, identify noise peaks caused by external vibration interference; extract the frequency of the noise peaks to obtain the second abnormal peak frequency set;

[0054] S504. If the first abnormal peak frequency set and the second abnormal peak frequency set have the same value, then they are considered to overlap on the frequency axis.

[0055] If the first set of abnormal peak frequencies and the second set of abnormal peak frequencies overlap on the frequency axis, then, based on the original constraints, further constraints are introduced. ( Given the constraints of the currently determined second scan rate, return to step S3 to solve for the new v2.

[0056] Repeat the above verification and solution process until a second scanning speed is obtained that satisfies the condition that the first abnormal peak frequency set and the second abnormal peak frequency set do not overlap.

[0057] Furthermore, step S6 specifically includes: taking the union of the first abnormal peak frequency set and the second abnormal peak frequency set to obtain the vibration frequency set of the spatial spectrum.

[0058] This step can be equivalently understood as follows: if a new peak frequency that meets the criteria for vibration noise is found in the second spatial spectrum and the second temporal spectrum, it is included in the abnormal peak frequency set; and all identified vibration noise peak frequencies are combined to form the final vibration frequency set.

[0059] Furthermore, step S7 specifically includes:

[0060] S701. Create a copy of the first spatial spectrum, and define it as a copy of the first spatial spectrum;

[0061] S702. Extract all peak frequencies from the vibration frequency set and obtain the frequency replacement range based on the preset replacement radius;

[0062] S703. Calculate the frequency index range corresponding to the frequency replacement range;

[0063] S704. Extract the amplitude data corresponding to the frequency index range in the spatial spectrum of the second surface topography measurement data, and replace it with the same index range of the first spatial spectrum copy to obtain the copy spatial spectrum;

[0064] S705. Perform an inverse Fourier transform in the spatial domain on the replica spatial spectrum to obtain the third surface topography measurement data.

[0065] This scheme uses a copy of the first spatial spectrum as the object of modification, avoiding direct alteration of the original spectrum data and ensuring ease of subsequent data traceability. The preset replacement radius is adaptively determined based on the noise bandwidth and is not specifically limited here.

[0066] This scheme uses frequency-selective replacement instead of global filtering, which significantly reduces the amount of computation, improves the processing efficiency of surface morphology measurement, and also ensures the integrity and high fidelity of the denoised surface morphology data.

[0067] Furthermore, step S703 specifically includes:

[0068] S7031. The frequency replacement range is determined as follows: ;where: f t The frequency r represents the t-th peak frequency in the set of vibration frequencies; r is the preset replacement radius.

[0069] S7032. Determine the starting frequency f of the spectrum. start Termination frequency f end The spectrum here refers to the first spatial spectrum or a copy of the first spatial spectrum.

[0070] S7033. Calculate the frequency index range using the following formula. :

[0071] ;

[0072] ;

[0073] Where: index start This is the lower limit of the frequency index range; index end Δf represents the upper limit of the frequency index range; Δf represents the spectral resolution. This is the floor operator.

[0074] Furthermore, in step S704, during the replacement process, linear interpolation is used to smooth the amplitude at the boundary of the frequency index range to avoid abrupt changes in the spectrum after replacement, which would cause distortion in the surface topography data generated by the subsequent inverse Fourier transform.

[0075] Compared with the prior art, the present invention has at least the following advantages and beneficial effects:

[0076] 1. The present invention provides a precise surface topography measurement method to reduce vibration noise interference. By performing two scans on the object under test at two different scanning speeds and combining a frequency replacement algorithm, it provides a precise and adaptive vibration noise solution, overcoming the technical problems of existing filtering methods that easily lose true topography features during noise reduction, and the high cost and limited applicability of hardware vibration isolation solutions.

[0077] 2. The present invention provides a method for precise measurement of surface morphology to reduce vibration and noise interference. The generated denoised surface morphology data can be directly used for high-precision surface morphology analysis, product quality inspection and evaluation. It can not only improve the anti-interference ability of various contact or non-contact measuring instruments, but also promote the widespread application of precision measurement technology in complex industrial environments, and truly realize the accurate positioning and rapid and effective removal of vibration and noise.

[0078] 3. The present invention provides a method for precise measurement of surface morphology to reduce vibration and noise interference. It uses dual-domain Fourier analysis to locate the noise frequency, providing a more reliable theoretical basis for the identification and separation of vibration and noise. This method is conducive to improving the automation and intelligence of the surface morphology measurement data processing process, while also improving the accuracy of vibration and noise identification.

[0079] 4. The present invention provides a method for precise measurement of surface morphology to reduce vibration and noise interference. It creatively proposes a method for calculating the second scanning speed, which can ensure effective separation of noise spectrum and characteristic spectrum, and significantly reduce the risk of erroneous deletion of effective signals or residual noise.

[0080] 5. The present invention provides a method for precise measurement of surface morphology that reduces vibration noise interference. It adopts frequency selective replacement instead of global filtering, which significantly reduces the amount of computation, improves the processing efficiency of surface morphology measurement, and also ensures the integrity and high fidelity of the surface morphology data after noise reduction. Attached Figure Description

[0081] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0082] Figure 1 This is a flowchart illustrating a specific embodiment of the present invention;

[0083] Figure 2 This is a schematic diagram of the first spatial spectrum and the second spatial spectrum in a specific embodiment of the present invention;

[0084] Figure 3 This is a schematic diagram of the first time spectrum and the second time spectrum in a specific embodiment of the present invention;

[0085] Figure 4 This is a schematic diagram of the third surface morphology measurement data in a specific embodiment of the present invention. Detailed Implementation

[0086] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0087] Example 1:

[0088] like Figure 1 The method for precise measurement of surface topography to reduce vibration and noise interference, as shown, includes the following steps:

[0089] Step S1: Scan the surface of the object under test at a first scanning speed to obtain the first surface morphology measurement data.

[0090] The specific process is as follows:

[0091] Step S101: Set the first scanning speed v1 of the surface topography scanner. The value of v1 must meet the scanning speed range requirements of the scanner and be determined in combination with the topography characteristics of the surface being measured (such as surface roughness and texture density) to avoid insufficient data sampling due to excessively fast scanning speed or low measurement efficiency due to excessively slow scanning speed.

[0092] Step S102: Scan the surface to be measured along the preset measurement path at the first scanning speed v1, collect the height information of the surface to be measured, obtain the noisy surface morphology measurement data, namely the first surface morphology measurement data L1, and store L1 as a two-dimensional array of "position-height".

[0093] Step S2: Based on the first surface morphology measurement data, extract the abnormal peak frequencies caused by external vibration interference to obtain the first abnormal peak frequency set.

[0094] The specific process is as follows:

[0095] Step S201: Perform a spatial domain Fourier transform on the first surface topography measurement data L1 to convert the spatial domain height data into spatial frequency domain spectrum data, and obtain the first spatial spectrum K1.

[0096] Step S202: Based on the first scanning speed and the sampling frequency during the scanning process, the first surface morphology measurement data is converted into first time-domain measurement data; specifically including:

[0097] Calculate the spatial distance d between adjacent sampling points. Where v1 is the first scan speed, f s The sampling frequency of the surface topography scanner used during the scanning process;

[0098] Calculate the total measurement time t based on the total length L of the measurement path. total , ;

[0099] The height values ​​of the sampling points in the first surface morphology measurement data L1 are mapped sequentially onto the time axis according to the time sequence to obtain the first time domain data L. 11 Its data format is a two-dimensional array of "time-altitude";

[0100] For the first time domain measurement data L 11 Perform a time-domain Fourier transform to convert the height data in the time domain into spectral data in the time-frequency domain, thus obtaining the first time spectrum T1.

[0101] Step S203: Peak detection is performed on the first spatial spectrum K1 and the first time spectrum T1 respectively. The threshold method combined with the local maximum search algorithm is used to identify all abnormal peak signals in the two spectra.

[0102] In this embodiment of the threshold method, the threshold is set to 3 times the average amplitude of the spectrum in order to filter out interference signals with smaller amplitudes.

[0103] Step S204: Based on the abnormal peak signals in the first spatial spectrum K1 and the first time spectrum T1, identify the noise peaks caused by external vibration interference. Specifically:

[0104] Identify all abnormal peak signals in the first spatial spectrum K1 and the first time spectrum T1. If an abnormal peak simultaneously satisfies both condition one and condition two, it is determined to be a noise peak caused by external vibration interference:

[0105] Condition 1: The abnormal peak value appears simultaneously in the first spatial spectrum K1 and the first temporal spectrum T1;

[0106] Condition 2: The spatial frequency and temporal frequency of this abnormal peak satisfy the following relationship: ; where f time f represents the time frequency of this abnormal peak value. space v1 represents the spatial frequency of the abnormal peak value, and v1 represents the first scan speed.

[0107] Then, all peak frequencies identified as vibration noise are collected to obtain the first abnormal peak frequency set P: , where n is the number of vibration noise peaks.

[0108] Step S3: Determine the second scanning speed based on the first abnormal peak frequency set.

[0109] The specific process is as follows:

[0110] Step S301: Perform significant peak analysis on the first spatial spectrum to identify peak signals (such as frequencies corresponding to surface textures, frequencies corresponding to surface contours, etc.) that reflect the true morphological characteristics of the measured surface, excluding vibration noise peaks, and obtain a set of significant peak frequencies A. , where m is the number of significant peaks.

[0111] Step S302: To ensure that the vibration noise peak value in the spatial spectrum of the surface topography data measured at the second scanning speed v2 does not overlap with the vibration noise peak value and significant peak value in the spatial spectrum of the first noisy surface topography data L1, the following constraint condition is set: For any and any All satisfy ; where p i Let a represent the i-th value in set P. j δ represents the j-th value in set A, v1 is the first scan speed, v2 is the second scan speed, and δ is the preset tolerance threshold.

[0112] In this embodiment, the value of the preset tolerance threshold δ is determined according to the spectral resolution of the scanning instrument, and is preferably 1-2 times the minimum frequency interval of the spectrum, so as to avoid misjudgment of peak overlap due to frequency resolution limitations.

[0113] In addition, the second scanning speed v2 must also meet the scanning speed range requirements of the scanning instrument.

[0114] Step S303: Use the particle swarm optimization algorithm to solve for the second scanning speed that satisfies the constraints.

[0115] In this embodiment, the optimization objective is to "find any feasible v2 within the constraints". The population size is set at 30-100 particles and the number of iterations is 50-200. The penalty function method is used to handle the constraints, and the degree of constraint violation is incorporated into the fitness function. A reflection boundary handling strategy is adopted to maintain population diversity. When a feasible solution is found, the iteration process is terminated in advance. Finally, the second scanning speed v2 that meets the requirements is obtained.

[0116] Step S4: Scan the surface of the object under test at the second scanning speed to obtain the second surface morphology measurement data. Specifically:

[0117] Keep other parameters of the surface topography scanner (such as sensor sampling frequency, measurement path, measurement distance, etc.) consistent with step S1, only adjust the scanning speed to the second scanning speed v2; start the scanning instrument and scan the same target area of ​​the surface being measured again at the second scanning speed v2 to collect surface height information and generate the second surface topography data L2; store L2 in the same way, and the storage format of L2 is consistent with L1 to ensure compatibility with subsequent spectrum analysis and frequency replacement.

[0118] Step S5: Determine whether the abnormal peaks in the spatial spectrum of the second surface topography measurement data overlap with the abnormal peaks in the spatial spectrum of the first surface topography measurement data on the frequency axis.

[0119] If so, return to step S3 and redetermine the second scan speed;

[0120] If not, proceed to step S6.

[0121] In this embodiment, the following method is used to determine whether there is overlap on the frequency axis:

[0122] Step S501: Perform a spatial domain Fourier transform on the second surface morphology measurement data to obtain the second spatial spectrum K2;

[0123] Step S502: Based on the second scanning speed and the sampling frequency during the scanning process, the second surface morphology measurement data is converted into second time domain measurement data; the second time domain measurement data is subjected to time domain Fourier transform to obtain the second time spectrum T2.

[0124] The method for obtaining the second spatial spectrum and the second temporal spectrum in this embodiment is the same as the method for obtaining the first spatial spectrum and the first temporal spectrum, so it will not be described in detail here.

[0125] Step S503: Perform peak detection on the second spatial spectrum K2 and the second time spectrum T2 respectively to identify all abnormal peak signals in the two spectra; based on the abnormal peak signals in the second spatial spectrum and the second time spectrum, identify noise peaks caused by external vibration interference; extract the frequency of the noise peaks to obtain the second abnormal peak frequency set R2;

[0126] Step S504: If the first abnormal peak frequency set P and the second abnormal peak frequency set R2 have the same value (i.e., they overlap), then they are considered to have overlap on the frequency axis.

[0127] Step S6: Extract the set of vibration frequencies F1 of the spatial spectrum from the first surface morphology measurement data and the second surface morphology measurement data.

[0128] The specific process includes:

[0129] The set of vibration frequencies F1 in the spatial spectrum is obtained by taking the union of the first set of abnormal peak frequencies and the second set of abnormal peak frequencies.

[0130] It should be noted that the second abnormal peak frequency set in this step refers to the abnormal peak frequency set obtained after scanning according to the latest second scan speed v2 and solving according to the steps above.

[0131] Step S7: Based on the vibration frequency set, replace the amplitude data caused by vibration in the spatial spectrum of the first surface morphology measurement data with the corresponding amplitude data in the spatial spectrum of the second surface morphology measurement data to obtain the third surface morphology measurement data.

[0132] The specific process includes:

[0133] Step S701: Create a copy of the first spatial spectrum K1, defined as the first spatial spectrum copy K. 1-copy .

[0134] Step S702: Extract all peak frequencies f in the vibration frequency set F1. t Where t=1,2,…,k, and k is the number of vibration noise frequencies finally confirmed;

[0135] Based on the noise bandwidth, the preset replacement radius r is determined, resulting in the frequency replacement range as follows: .

[0136] Step S703: Calculate the frequency index range corresponding to the frequency replacement range. :

[0137] ;

[0138] ;

[0139] Where: index start This is the lower limit of the frequency index range; index end Δf represents the upper limit of the frequency index range; Δf represents the spectral resolution. f is the floor operator; start f end These are the starting and ending frequencies of the spectrum, respectively.

[0140] Step S704: Extract the amplitude data corresponding to the frequency index range from the spatial spectrum of the second surface topography measurement data, and replace it with the same index range of the first spatial spectrum copy to obtain the copy spatial spectrum; this process can be equivalently understood as:

[0141] Retrieve the spatial spectrum K2 of the second surface topography data L2, and extract the index positions in K2. The corresponding amplitude data is replaced in the first spatial spectrum copy K. 1-copy Amplitude data at the same index position.

[0142] Preferably, during the replacement process, linear interpolation is used to smooth the amplitude at the boundary of the index position to avoid abrupt changes in the spectrum after replacement, which would cause distortion in the surface morphology data generated by the subsequent inverse Fourier transform.

[0143] Step S705: Perform an inverse Fourier transform in the spatial domain on the replica spatial spectrum to convert the frequency domain spectrum data back into surface topography data in the spatial domain, thereby obtaining the surface topography data after vibration and noise removal, which is the third surface topography measurement data.

[0144] Step S8: Output the third surface morphology measurement data as the precise measurement result of the surface morphology of the object being measured.

[0145] In this embodiment, Figure 2 The diagram illustrates the first spatial spectrum K1 obtained based on the first surface topography measurement data L1 and the second spatial spectrum K2 obtained based on the second surface topography measurement data L2.

[0146] Figure 3 The diagram illustrates the first time spectrum T1 obtained based on the first surface topography measurement data L1 and the second time spectrum T2 obtained based on the second surface topography measurement data L2.

[0147] Figure 4 This is a schematic diagram of the third surface topography measurement data output after the method of this application has been replaced. Figure 4 As can be seen, all abnormal peak values ​​were removed, demonstrating the significant performance of this application in reducing vibration and noise interference. It should be noted that... Figure 4 In L 3 refers to the measurement data of the third surface morphology.

[0148] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0149] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. A method for precise measurement of surface morphology with reduced vibration and noise interference, characterized in that, Includes the following steps: S1. Scan the surface of the object under test at the first scanning speed to obtain the first surface morphology measurement data; S2. Based on the first surface morphology measurement data, extract the abnormal peak frequencies caused by external vibration interference to obtain the first abnormal peak frequency set; S3. Determine the second scanning speed based on the first set of abnormal peak frequencies; S4. Scan the surface of the object under test at the second scanning speed to obtain the second surface morphology measurement data; S5. Determine whether the abnormal peaks in the spatial spectrum of the second surface topography measurement data overlap with the abnormal peaks in the spatial spectrum of the first surface topography measurement data on the frequency axis: If so, return to step S3 and redetermine the second scan speed; If not, proceed to step S6; S6. Extract the set of vibration frequencies of the spatial spectrum from the first surface morphology measurement data and the second surface morphology measurement data; S7. Based on the set of vibration frequencies, replace the amplitude data caused by vibration in the spatial spectrum of the first surface morphology measurement data with the corresponding amplitude data in the spatial spectrum of the second surface morphology measurement data to obtain the third surface morphology measurement data. S8. Output the third surface morphology measurement data as the precise measurement result of the surface morphology of the object being measured. Step S2 specifically includes: S201. Perform a spatial domain Fourier transform on the first surface morphology measurement data to obtain the first spatial spectrum; S202. Based on the first scanning speed and the sampling frequency during the scanning process, the first surface morphology measurement data is converted into first time-domain measurement data; the first time-domain measurement data is subjected to time-domain Fourier transform to obtain the first time spectrum; S203. Perform peak detection on the first spatial spectrum and the first time spectrum respectively, and identify all abnormal peak signals in the two spectra; S204. Based on the abnormal peak signals in the first spatial spectrum and the first time spectrum, identify the noise peaks caused by external vibration interference; extract the frequencies of the noise peaks to obtain the first abnormal peak frequency set; Step S3 specifically includes: S301. Perform significant peak analysis on the first spatial spectrum to obtain a set of significant peak frequencies A; define the first set of abnormal peak frequencies as set P. S302. Set constraints: For any and any All satisfy ; where p i Let a represent the i-th value in set P. j δ represents the j-th value in set A, v1 is the first scan speed, v2 is the second scan speed, and δ is the preset tolerance threshold. S303. The second scanning speed that satisfies the constraints is obtained by using the particle swarm optimization algorithm.

2. The method for precise measurement of surface morphology to reduce vibration and noise interference according to claim 1, characterized in that, In step S202, the method for converting the first surface topography measurement data into first time-domain measurement data includes: S2021. Calculate the spatial distance d between adjacent sampling points. Where v1 is the first scan speed, f s This refers to the sampling frequency during the scanning process; S2022. Calculate the total measurement time t based on the total length L of the measurement path. total , ; S2023. Map the height values ​​of the sampling points in the first surface morphology measurement data onto the time axis in chronological order to obtain the first time domain data.

3. The method for precise measurement of surface morphology to reduce vibration and noise interference according to claim 1, characterized in that, In step S204, the method for identifying noise peaks caused by external vibration interference includes: Identify all abnormal peak signals in the two spectra. If an abnormal peak simultaneously satisfies both condition one and condition two, it is determined to be a noise peak caused by external vibration interference. Condition 1: The abnormal peak value appears simultaneously in both the first spatial spectrum and the first temporal spectrum; Condition 2: The spatial frequency and temporal frequency of this abnormal peak satisfy the following relationship: ; where f time f represents the time frequency of this abnormal peak value. space v1 represents the spatial frequency of the abnormal peak value, and v1 represents the first scan speed.

4. The method for precise measurement of surface morphology to reduce vibration and noise interference according to claim 1, characterized in that, In step S5, the following method is used to determine whether there is overlap on the frequency axis: S501. Perform a spatial domain Fourier transform on the second surface topography measurement data to obtain the second spatial spectrum; S502. Based on the second scanning speed and the sampling frequency during the scanning process, the second surface morphology measurement data is converted into second time-domain measurement data; the second time-domain measurement data is subjected to time-domain Fourier transform to obtain the second time spectrum; S503. Perform peak detection on the second spatial spectrum and the second time spectrum respectively, and identify all abnormal peak signals in the two spectra; based on the abnormal peak signals in the second spatial spectrum and the second time spectrum, identify noise peaks caused by external vibration interference; extract the frequency of the noise peaks to obtain the second abnormal peak frequency set; S504. If the first abnormal peak frequency set and the second abnormal peak frequency set have the same value, then they are considered to overlap on the frequency axis.

5. The method for precise measurement of surface morphology to reduce vibration and noise interference according to claim 4, characterized in that, Step S6 specifically includes: taking the union of the first abnormal peak frequency set and the second abnormal peak frequency set to obtain the vibration frequency set of the spatial spectrum.

6. The method for precise measurement of surface morphology with reduced vibration and noise interference according to claim 5, characterized in that, Step S7 specifically includes: S701. Create a copy of the first spatial spectrum, and define it as a copy of the first spatial spectrum; S702. Extract all peak frequencies from the vibration frequency set and obtain the frequency replacement range based on the preset replacement radius; S703. Calculate the frequency index range corresponding to the frequency replacement range; S704. Extract the amplitude data corresponding to the frequency index range in the spatial spectrum of the second surface topography measurement data, and replace it with the same index range of the first spatial spectrum copy to obtain the copy spatial spectrum; S705. Perform an inverse Fourier transform in the spatial domain on the replica spatial spectrum to obtain the third surface topography measurement data.

7. The method for precise measurement of surface morphology with reduced vibration and noise interference according to claim 6, characterized in that, Step S703 specifically includes: S7031. The frequency replacement range is determined as follows: ;where: f t The frequency r represents the t-th peak frequency in the set of vibration frequencies; r is the preset replacement radius. S7032. Determine the starting frequency f of the spectrum. start Termination frequency f end ; S7033. Calculate the frequency index range using the following formula. : ； ； Where: index start This is the lower limit of the frequency index range; index end Δf represents the upper limit of the frequency index range; Δf represents the spectral resolution. This is the floor operator.

8. The method for precise measurement of surface morphology with reduced vibration and noise interference according to claim 6, characterized in that, In step S704, during the replacement process, linear interpolation is used to smooth the amplitude at the boundary of the frequency index range.

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