Feature unit sampling method and system for surface roughness measurement after laser shock

Abstract

The invention discloses a feature unit sampling method and system for measuring surface roughness after laser shock. The method comprises the following steps: accurately calibrating process parameters of a laser shock process; performing laser shock treatment on the sample based on the process parameters, respectively obtaining surface topography data of different scales of a laser shock treatment area, mapping the surface topography data of different scales to a unified coordinate system, and establishing a correlation model of cross-scale topography; based on the correlation model of the cross-scale morphology, designing a gradient sampling range set, and screening out candidate sampling ranges; roughness parameters of the candidate sampling ranges are obtained, a multiple linear regression model of the roughness parameters and performance indexes is established, and the optimal sampling range is screened out based on the multiple linear regression model; according to the method, the multi-scale features of the optimal sampling range are extracted, the representativeness of the optimal sampling range is verified based on the multi-scale features, the verified optimal sampling range is determined as the minimum feature unit, and the inherent contradiction between precision and efficiency in traditional surface roughness measurement is broken through.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of surface roughness measurement, in particular to a feature unit sampling method and system for surface roughness measurement after laser shock processing. BACKGROUND

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute the prior art.

[0003] Laser shock processing (LSP) is a high-efficiency surface strengthening technology that can form multi-scale composite topography features on the surface of metal materials, including micron-level pits, nanometer-level ripples, and macroscopic lap textures, through inducing high strain rate plastic deformation. Such topography features have a significant impact on the wettability, wear resistance, and fatigue performance of the material, and their accurate characterization is a key link in process optimization and quality control. However, the surface after laser shock presents significant multi-scale spatial heterogeneity: the random distribution of micro-pits coexists with the periodic ripples formed by the macroscopic spot lap, leading to inherent contradictions between precision and efficiency in traditional surface roughness measurement methods.

[0004] In current industrial detection, two methods are commonly used: global measurement and local sampling. Global measurement can completely cover the surface topography, but it requires high-resolution equipment (such as a white light interferometer) to perform point-by-point scanning on a large area, which is time-consuming, energy-consuming, and costly. While local sampling improves efficiency, existing methods often choose sampling areas based on experience, which can lead to statistical bias due to neglecting the spot lap pattern or pit distribution characteristics, and cannot meet the reliability requirements of batch detection. Especially for laser shock, which has strong process-related surface, the blindness of sampling range may introduce ripple interference or miss critical topography units, causing the failure of the correlation between roughness parameters and functional performance. For example, a small sampling range only reflects the local deformation of a single pit, while a large sampling range can lead to data smoothing due to the inclusion of multiple lap cycles, both of which are difficult to accurately characterize the overall characteristics of the surface.

[0005] Existing researches mostly focus on single-scale topography analysis, lack of coupling analysis of multi-scale features of laser shock, and have not formed a scientific method for determining the sampling range. SUMMARY

[0006] To overcome the shortcomings of the prior art, the present application provides a feature unit sampling method and system for surface roughness measurement after laser shock processing, which extracts feature units that can represent statistical rules and have the smallest spatial dimension, realizes efficient and accurate batch detection, and breaks through the "zero-sum game" bottleneck of precision and efficiency in existing technology.

[0007] To achieve the above purpose, one or more embodiments of the present application provide the following technical solutions: In a first aspect, the present application provides a feature unit sampling method for laser shock back surface roughness measurement, comprising: accurately calibrating process parameters of a laser shock process; performing laser shock processing on a sample based on the process parameters, obtaining surface topography data of different scales in the laser shock processing area respectively, mapping the surface topography data of different scales to a unified coordinate system, and establishing a cross-scale topography correlation model; designing a gradient sampling range set based on the cross-scale topography correlation model, and screening a candidate sampling range; obtaining roughness parameters of the candidate sampling range, establishing a multiple linear regression model of the roughness parameters and performance indicators, and screening an optimal sampling range based on the multiple linear regression model; extracting multi-scale features of the optimal sampling range, verifying the representativeness of the optimal sampling range based on the multi-scale features, and determining the optimal sampling range that passes the verification as a minimum feature unit.

[0008] In a further technical solution, a region containing a complete light spot period is selected as a reference observation area, and a device is used to obtain surface topography data at different scales.

[0009] In a further technical solution, a spatial coordinate system registration technology is used to map the macro-waviness data and the micro-roughness data to a unified coordinate system, and a cross-scale topography correlation model is established.

[0010] In a further technical solution, in the area sampling process, for a surface with a significant directional texture, the long side of the sampling range is preferentially aligned with the main texture direction.

[0011] In a further technical solution, a data volatility analysis is used to screen a candidate sampling range with high statistical stability.

[0012] In a further technical solution, the screening rule of the optimal sampling range is to preferentially select a sampling range that maximizes the correlation coefficient between the roughness parameters and the performance indicators.

[0013] In a further technical solution, the feature unit sampling method is converted into an industrial detection specification, and the sampling range size is scaled according to different process parameters.

[0014] In a second aspect, the present application provides a feature unit sampling system for laser shock back surface roughness measurement, comprising: a parameter calibration module configured to accurately calibrate process parameters of a laser shock process; The feature extraction module is configured to: perform laser shock processing on the sample based on the process parameters, respectively acquire surface topography data of different scales of the laser shock processing area, map the surface topography data of different scales to a unified coordinate system, and establish a correlation model of cross-scale topography. The candidate screening module is configured to: design a gradient sampling range set based on the correlation model of cross-scale topography, and screen a candidate sampling range. The optimal screening module is configured to: acquire roughness parameters of the candidate sampling range, establish a multiple linear regression model of the roughness parameters and performance indicators, and screen an optimal sampling range based on the multiple linear regression model. The verification module is configured to: extract multi-scale features of the optimal sampling range, verify the representativeness of the optimal sampling range based on the multi-scale features, and determine an optimal sampling range that passes the verification as a minimum feature unit.

[0015] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the feature unit sampling method for surface roughness measurement after laser shock as described in the first aspect.

[0016] In a fourth aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps in the feature unit sampling method for surface roughness measurement after laser shock as described in the first aspect when executing the program.

[0017] The above one or more technical solutions have the following beneficial effects: The present application breaks through the inherent contradiction between precision and efficiency in traditional surface roughness measurement by establishing a technical path of "global characterization-optimal screening and verification-unit standardization", and provides a theoretical guidance and an industrial adaptation scheme for surface roughness measurement.

[0018] The present application breaks through the inherent contradiction between precision and efficiency in traditional surface roughness measurement by establishing a technical path of "global characterization-optimal screening and verification-unit standardization", and provides a theoretical guidance and an industrial adaptation scheme for surface roughness measurement.

[0019] The present application breaks through the inherent contradiction between precision and efficiency in traditional surface roughness measurement by establishing a technical path of "global characterization-optimal screening and verification-unit standardization", and provides a theoretical guidance and an industrial adaptation scheme for surface roughness measurement.

[0020] Based on the geometric characteristics of the light spot and the overlap rules, the present invention establishes the design criteria for the gradient sampling range, and combines the statistical correlation analysis of performance indicators such as surface wettability and wear resistance to achieve a paradigm breakthrough in the sampling range from empirical selection to scientific verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1 is a flow chart of a characteristic unit sampling method according to an embodiment of the present invention; Figure 2 Schematic diagram of the design of the multi-scale surface topography sampling range according to an embodiment of the present invention; Figure 3 is a comparison chart of different line roughness (Ra) of different sampling lengths in an embodiment of the present invention; Figure 4 is a comparison diagram of different surface roughness (Sa) of different sampling areas in an embodiment of the present invention; Figure 5 These are the contact angle and surface energy test results of the embodiments of the present invention. DETAILED DESCRIPTION

[0023] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0024] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0025] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0026] Example 1 like Figure 1 As shown, this embodiment discloses a characteristic unit sampling method for measuring surface roughness after laser shock, which includes the following steps: S1: Accurately calibrate the process parameters of the laser shock process; In this embodiment, before the surface roughness measurement of laser shock processing is implemented, the process parameters of laser shock processing, i.e. the spot size, need to be systematically calibrated first to ensure the consistency between the actual value and the theoretical set value. Specifically, under the dynamic processing conditions, the non-contact optical measurement equipment (such as high-resolution optical microscope or high-speed camera system) is used to sample the processed surface at multiple points, and the actual diameter of the spot and the uniformity of the energy distribution are analyzed.

[0027] Specifically, a plurality of representative processing points are selected, the optical microscope is used to shoot the spot image at different magnifications, the spot diameter is measured through the image analysis software, and the energy sensor is used to record the energy distribution of the spot. If the deviation of the actual spot diameter from the theoretical value exceeds the preset threshold (such as ±3%), the laser output power or the position of the focusing mirror needs to be adjusted to calibrate the spot size. The verification of the overlap rate (the proportion of the overlapping part of the adjacent spots to the area of a single spot) needs to be based on the actual processing trajectory, and the overlapping proportion between the adjacent spots is calculated through the developed traces on the surface after laser shock. If the deviation of the overlap rate exceeds the allowable range (such as ±3%), the laser scanning path or the pulse trigger timing needs to be adjusted again.

[0028] S2: laser shock processing is performed on the test sample based on the process parameters, surface topography data of different scales in the laser shock processing area are obtained respectively, the surface topography data of different scales are mapped to a unified coordinate system, and a correlation model of cross-scale topography is established; In this embodiment, after the process parameter verification is completed, the test sample is subjected to laser shock processing, and the cross-scale surface topography data of the laser shock processing area needs to be collected. A region containing a complete spot cycle is selected as the reference observation area, and a white light interferometer, a laser confocal microscope or a three-dimensional profilometer is used to obtain surface topography data at different scales, such as the range covered by the center pit and the edge overlap area of the spot, respectively.

[0029] Taking the white light interferometer as an example, the test sample is placed on the measurement platform of the white light interferometer, the appropriate measurement range and resolution are set, and the surface topography data (surface topography image) is collected. The surface topography data includes the depth of the local pit and the micro-roughness distribution.

[0030] The collected surface topography is globally characterized, and the characterization is performed on the largest sampling range as possible. For example, the pit and the boundary are single features, and different sampling ranges must include combinations of different single topography features. During the characterization process, the composite topography features of the spot overlap area need to be focused on, such as the slope structure or the material accumulation area formed by the superposition of adjacent spots, and the geometric characteristics are recorded completely through multi-angle scanning and three-dimensional reconstruction technology. Specifically, the overlap area is taken as the center, and the preset angle sequence (such as 0°, 45°, 90° and 135°) is used to scan the surface topography data of the overlap area, and the three-dimensional reconstruction technology is used to reconstruct the surface topography data of the overlap area. 、 、 ) Tilt the sample and collect local data at each angle; use a registration algorithm to align data at different angles to the same coordinate system, and use a Poisson surface reconstruction algorithm to generate a continuous surface model from the registered data; extract geometric characteristics such as slope inclination and stacking height from the reconstructed model.

[0031] To ensure the comparability of data from different devices, it is necessary to map the macroscopic waviness data and the microscopic roughness data to a unified coordinate system through spatial coordinate system registration technology, and establish a cross-scale morphology correlation model, that is, a surface morphology image in which both the macroscopic and microscopic details of the undulating morphology of the material surface exist. This can not only reflect the changes in waviness at a larger scale, but also show the differences in roughness at a smaller scale, providing a basis for the subsequent sampling range design.

[0032] S3: Based on the cross-scale morphology correlation model, design a gradient sampling range set and screen candidate sampling ranges; In this example, based on the cross-scale topography correlation model established by S2 (i.e., obtaining surface relief data of different scales within the same sampling range), a set of sampling ranges covering different spatial scales was designed. Using the spot diameter as the core unit of reference, the line sampling length was set to a range of 0.5 to 2 times the spot diameter, and the surface sampling area was defined as a rectangular area with a side length of 0.5 to 1 times the spot diameter. Within the topography data, multiple representative locations (randomly selected) were selected along the spot overlap direction (i.e., the path of the laser beam during the laser impact process). Line sampling segments of varying lengths or surface sampling segments of varying areas were set, and roughness parameters (such as Ra values) were recorded for each segment.

[0033] For the sampling range, a gradient sampling range set is designed to cover different spatial scales. For example, the line sampling length can be set to 100 , 200 , 400 The surface sampling area adjusts the aspect ratio according to the overlap direction of the light spot, for example, the length is extended along the laser scanning direction and the width is controlled in the vertical direction to form a 100×100 , 100×200 , 200×200 Equirectangular area.

[0034] Specifically, during surface sampling, for surfaces with significant directional texture (e.g., anisotropy caused by unidirectional scanning), the long side of the sampling area should be aligned with the dominant texture direction to ensure complete coverage of the pit periodic unit. The dominant texture direction is determined in the topographic data, and the sampling area is arranged along this direction, with the roughness parameters of the different areas recorded.

[0035] Each sampling range is measured repeatedly along different directions (e.g., scanning direction, vertical direction, and diagonal direction). Data fluctuation analysis (e.g., calculating the coefficient of variation) is used to select candidate ranges with high statistical stability. For data fluctuation analysis, a linear sampling length is considered representative when the roughness measurement value fluctuates less than 10% in different directions.

[0036] This step determines the sampling range at different scales through the analysis of morphological data and statistical methods, and screens out candidate sampling ranges with high stability.

[0037] S4: obtaining roughness parameters of the candidate sampling range, establishing a multiple linear regression model of the roughness parameters and performance indicators, and screening out the optimal sampling range based on the multiple linear regression model; In this example, a statistical model was used to establish a correlation between surface roughness parameters and material functional properties (such as wettability and wear resistance). Material functional properties can be characterized using performance indices. Regression analysis was performed between the linear roughness Ra and surface roughness Sa values ​​across different sampling ranges and performance indices (such as contact angle and coefficient of friction) to identify key roughness parameters that are sensitive to performance changes.

[0038] Specifically, for each candidate sampling range, the roughness parameters and corresponding performance index data within the range are collected; for each candidate sampling range, multiple linear regression analysis is used to determine the mathematical relationship between the roughness parameters and the performance index, and a multiple linear regression model of the roughness parameters (independent variables) and the performance index (dependent variables) is independently established, which is expressed as:

[0039] in, Indicates performance indicators (such as contact angle, friction coefficient, etc.), Indicates roughness parameters (such as line roughness, surface roughness, etc.), represents the intercept, Represents the regression coefficient (reflecting the influence of roughness parameters on performance indicators), Represents random error.

[0040] Calculate and record the key goodness-of-fit indicators for each multivariate linear regression model, including the coefficient of determination, correlation coefficient, and significance of the regression coefficient. The optimal sampling range screening rule is to prioritize the sampling range that maximizes the correlation coefficient between the roughness parameter and the performance index.

[0041] To ensure that the roughness parameters within the selected sampling range can stably and reliably represent the true surface characteristics, a hypothesis testing method is used to verify statistical consistency and stability. Multiple repeated measurements are performed within the selected candidate sampling range to obtain a set of roughness parameter values ​​within that sampling range. A baseline roughness parameter value representing the global surface characteristics is obtained. The mean of the roughness parameter samples measured within this sampling range is tested for statistically significant differences from the global baseline value. The selection criteria are: the mean of the measured data within the optimal sampling range should not differ significantly from the global baseline value, ensuring the representativeness and reliability of the sampling range.

[0042] For example, if the Ra value of a particular line sampling length shows a significant correlation with contact angle variation (e.g., a correlation coefficient exceeding 0.85), while a larger sampling range shows a decreased correlation due to the introduction of waviness interference, then that line length is the preferred value. At this stage, hypothesis testing methods (such as t-tests or analysis of variance) are required to verify the statistical consistency of the local sampling data with the global benchmark value to ensure the reliability of the screening results. The global benchmark value serves as an overall reference, a benchmark for theoretically correct patterns. When sampling locally, the patterns reflected by the local characteristics must be consistent with the overall benchmark.

[0043] In particular, for surfaces that exhibit direction-dependence—that is, surfaces where the surface roughness data fluctuates inconsistently when sampled from different directions—the sensitivity of each sampling direction must be evaluated separately, with the direction that minimizes the fluctuation in surface roughness data within a sampling range of the same length or area being prioritized as the standardization benchmark. The specific method involves acquiring multiple sets of roughness parameter data along multiple directions within the selected optimal sampling range; performing variance analysis on the data for the same roughness parameter across different directions; calculating the variance of each set of data (in each direction), and selecting the direction with the smallest variance as the optimal sampling direction.

[0044] S5: extracting multi-scale features of the optimal sampling range, verifying the representativeness of the optimal sampling range based on the multi-scale features, and determining the verified optimal sampling range as a minimum feature unit.

[0045] Verify the representativeness of the optimal sampling range and determine the verified optimal sampling range as the minimum characteristic unit. If there are multiple verified optimal sampling ranges, select the sampling range with the smallest area or shortest length as the minimum characteristic unit. Under the requirement and principle that the local reflects the whole, the smallest sampling area or shortest sampling length that accurately corresponds to the overall pattern is the range of the minimum characteristic unit.

[0046] Perform 3D morphology analysis and verification on the selected minimum feature units. Quantify the multi-scale features of the morphology within the feature units, such as the plastic deformation depth of the pit core area and the slope distribution of the overlapping transition area.

[0047] Specifically: (1) Obtain the microscopic geometric features within the smallest feature unit. Use a white light interferometer or laser confocal microscope to perform a three-dimensional topographic scan of the selected optimal sampling range; generate a high-precision three-dimensional surface model through a point cloud reconstruction algorithm (such as ICP registration and Poisson reconstruction); and obtain the surface height matrix and key geometric parameters (pit depth, ripple amplitude, slope distribution, etc.) based on the surface model.

[0048] (2) Quantify the multi-scale features of the morphology within the smallest feature unit. The box counting method is used to calculate the fractal dimension of the feature unit. The height matrix is ​​subjected to fast Fourier transform to extract the dominant frequency and spectral energy distribution. The local features of the feature unit are extracted. The pit core area is identified by morphological segmentation and the average plastic deformation depth is calculated. The slope angle distribution along the gradient direction of the overlap transition area is extracted and the slope standard deviation is calculated.

[0049] (3) Perform a global consistency test on the minimum characteristic unit to determine whether the minimum characteristic unit can represent the entire surface. If the deviation between the fractal dimension, spatial frequency distribution and other indicators of the characteristic unit and the global data is less than a preset threshold (e.g., 5%), it can be determined to be representative.

[0050] In addition, the stability of the characteristic unit needs to be verified through repeated sampling. For example, under the same process conditions of different specimens, the roughness fluctuation within the characteristic unit should be smaller than the fluctuation range of the global data.

[0051] During this phase, special attention must be paid to avoiding misjudgments due to local defects (such as microcracks or impurities). Outlier removal mechanisms or regional smoothing techniques can be used to improve the robustness of the verification results. Specifically, this involves repeatedly measuring characteristic units on multiple specimens, using statistical methods to analyze data volatility, and removing outliers to ensure the stability and representativeness of the characteristic units.

[0052] In this embodiment, the aforementioned characteristic unit sampling method is converted into an executable industrial inspection specification. The sampling range size is scaled proportionally to varying process parameters (e.g., spot size variation, overlap ratio adjustment). Furthermore, clear measurement area positioning rules (e.g., a distance of at least three times the spot diameter from the material edge), direction selection criteria, and data processing procedures are established.

[0053] Specifically, when the spot diameter increases, the line sampling length and the surface sampling area need to be expanded synchronously to maintain complete coverage of the single spot feature; conversely, the sampling range needs to be reduced synchronously.

[0054] For new materials or new process scenarios, the applicability of the feature unit needs to be verified through pre-experiments, and if necessary, additional parameters such as surface energy and hardness are combined to optimize the sampling strategy. Finally, a reusable operation guide is formed, covering equipment selection, parameter setting, data recording and abnormal handling process, to ensure efficient transplantation and large-scale application of the method in industrial field.

[0055] The present application first ensures the controllability of the morphology generation through process parameter calibration, and then uses multi-scale joint representation technology to obtain cross-dimensional morphology feature correlation model; based on the gradient sampling range set designed by the spot geometric features, the optimal sampling scale is screened by using statistical analysis and performance correlation verification; finally, the spatial distribution rule of the feature unit is deconstructed, and a reusable industrial detection specification is established. This method innovatively combines the surface morphology generation rule with statistical process control, realizes multi-level feature coverage from macro-connection cycle to micro-plastic deformation, and improves the detection efficiency by 3-5 times while ensuring the measurement accuracy.

[0056] The following gives a specific implementation example: This example takes aerospace titanium alloy Ti60 and TC11 as the test object, and verifies the scientificity and necessity of LSP surface roughness feature unit sampling method by introducing mechanical shot peening (MSP) process as a control experiment. The following is the specific implementation process: 1. Process parameter calibration and verification The samples are divided into two groups: LSP group: including 2#, 3# (Ti60 matrix) and 5#, 6# (TC11 matrix), laser energy is 50 mJ and 80 mJ respectively, spot diameter (D) is 0.2 mm, overlap rate is 20%, and shot peening medium is ASH230 (diameter 0.23 mm, Almen strength 0.3 mmA).

[0057] MSP control group: 1# (Ti60), 4# (TC11) use the same shot peening medium (ASH230, 0.3 mmA).

[0058] The LSP process parameters are verified by high-resolution optical microscope: the actual spot diameter is 198~202 μm (theoretical value 200 μm), the overlap rate is 19.8%~20.2%, and the shot peening medium ASH230 particle size distribution D90=230±15 μm. The MSP control group (1#, 4#) uses the same shot peening parameters, only changes the processing method to exclude the interference of medium difference. This stage ensures the process repeatability of LSP and MSP samples, and establishes the benchmark for subsequent analysis.

[0059] 2. Multi-scale morphology global representation For example Figure 2As shown in the figure, an area (4×4 mm²) containing 36 complete light spots was selected for LSP samples (2#, 3#, 5#, and 6#), and white light interferometry and laser confocal microscopy were used to obtain the macroscopic waviness and microscopic pit morphology, respectively. The white light interferometry data showed that the overlapping light spots formed periodic ripples (wavelength 210±15 μm), while the surface of MSP samples (1# and 4#) had only random pit distribution ( Figure 2 Through three-dimensional coordinate registration, a cross-scale morphology correlation model was established, clarifying the triple coupling characteristics of the LSP surface: micron-scale pits (depth 12.5-15.8 μm), nanoscale ripples (Ra ≈ 85 nm), and macroscopic overlapping textures.

[0060] 3. Gradient sampling range design Construct a gradient sampling set based on the spot diameter (200 μm) Figure 2 Marking): Line sampling: Area A contains 50 μm, 100 μm, 200 μm, and 500 μm line segments, arranged along the laser scanning direction (X) and the vertical direction (Y); Surface sampling: Areas B (100×100 μm²), C (100×200 μm²), D (200×200 μm²), and E (500×500 μm²) cover different spot periods.

[0061] For MSP specimens (1# and 4#), only 200×200 μm² surface sampling was set as a control to verify the limitations of the traditional method.

[0062] like Figure 3 、 4 As shown, in different ranges such as the above-mentioned sampling length or area, the surface morphology and surface roughness value of each sampling range are tested and determined.

[0063] 4. Statistical Correlation and Range Screening Table 1 Comparison of surface roughness (Sa) and contact angle of LSP and MSP samples with sampling range

[0064] Based on the data in Table 1 and Figure 5 As shown in the figure, the correlation between surface roughness (Sa) and contact angle was analyzed using the Pearson correlation coefficient (r), and the statistical significance of the results was assessed using a significance test (p-value). The Pearson correlation coefficient r ranges from -1 to +1, with absolute values ​​closer to 1 indicating a stronger correlation, and the sign indicating a positive or negative association. A p-value less than 0.05 is considered statistically significant and non-random.

[0065] For laser shock processed (LSP) samples, within a 100×200 μm² sampling range, the correlation coefficient between Sa values ​​(2.7–3.4 μm) and contact angles (78.92°–88.08°) was r=0.82 (p=0.018), indicating a significant positive correlation between roughness and wettability. When the sampling range was expanded to 500×500 μm², the correlation coefficient between Sa values ​​(2.9–3.0 μm) and contact angles dropped to r=0.47 (p=0.26), indicating that the correlation disappeared. This is primarily due to the smoothing of critical topographic gradients in the laser spot overlap region caused by the large sampling range. Compared with MSP samples, the correlation coefficient between the Sa value (1.5–1.6 μm) and the contact angle (74.83°–76.91°) within the traditional 200×200 μm² sampling range is r=0.89 (p=0.006), verifying that this method is suitable for surfaces without periodic features. However, when directly applied to LSP, ignoring the spot period can lead to an Sa value error of up to 38% (for example, 3.4 μm for LSP sample #6 vs. 1.6 μm for MSP).

[0066] If a 100×200 μm² standardized range is used, the detection efficiency will inevitably be improved compared to full-area measurement, and the Sa prediction error will be reduced from 38% to 9.7%, which provides a reliable solution for laser shock surface quality assessment.

[0067] 5. Feature unit deconstruction verification like Figure 4 As shown, 3D topography analysis of the LSP's preferred range (100 × 200 μm²) confirmed that it covers the core area of ​​a single spot and the adjacent overlapping transition zone. This region contains 1.2 pit periods, and the nanoripple spectrum matches the global data with a 92% accuracy. The Sa value fluctuates within ±0.02 μm (±0.05 μm for the entire region). Because the MSP sample lacks periodicity, the pit distribution within the 200 × 200 μm² range is random, confirming that the proposed method is only applicable to LSP multi-scale surfaces.

[0068] 6. Establishment of standardized testing specifications Finally, LSP exclusive testing standards were established: Feature unit: Line length 100 μm (0.5D), surface area 100×200 μm² (0.5D×1D), direction strictly aligned with the scanning trajectory; Positioning rules: ≥600 μm from the edge (3D) to avoid boundary effects; Significance of MSP comparison: Directly applying the 200×200 μm² sampling range of MSP to LSP will result in a 38% error in the Sa value due to ignoring the spot period, proving that the traditional method is not suitable for laser-impacted surfaces.

[0069] This example introduces MSP as a process control to verify that roughness parameters can only be accurately correlated with material functional properties (such as wetting angle) when the characteristic unit strictly matches the spot period of LSP.

[0070] Example 2 This embodiment discloses a feature unit sampling system for measuring surface roughness after laser shock, including: A parameter calibration module, configured to: accurately calibrate process parameters of the laser shock process; a feature extraction module configured to: perform laser shock processing on the sample based on the process parameters, obtain surface morphology data of different scales in the laser shock processing area, map the surface morphology data of different scales to a unified coordinate system, and establish a correlation model of cross-scale morphologies; a candidate screening module configured to: design a gradient sampling range set based on the cross-scale morphology correlation model, and screen out candidate sampling ranges; an optimal screening module configured to: obtain roughness parameters of the candidate sampling range, establish a multiple linear regression model of the roughness parameters and performance indicators, and screen out the optimal sampling range based on the multiple linear regression model; The verification module is configured to: extract multi-scale features of the optimal sampling range, verify the representativeness of the optimal sampling range based on the multi-scale features, and determine the verified optimal sampling range as the minimum feature unit.

[0071] Example 3 The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of embodiment 1 when executing the program.

[0072] Example 4 The purpose of this embodiment is to provide a computer-readable storage medium, a computer-readable storage medium having a computer program stored thereon, which performs the steps of the method of embodiment 1 when executed by a processor.

[0073] The steps involved in the apparatuses of Examples 3 and 4 above correspond to those of Method Example 1. For detailed implementation, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any of the methods of the present invention.

[0074] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0075] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0076] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A characteristic unit sampling method for measuring surface roughness after laser shock, characterized in that: include: Accurately calibrate the process parameters of laser shock process; Performing laser shock processing on the sample based on the process parameters, obtaining surface morphology data of different scales in the laser shock processing area, mapping the surface morphology data of different scales to a unified coordinate system, and establishing a correlation model of cross-scale morphology; Based on the correlation model of the cross-scale morphology, a gradient sampling range set is designed, and candidate sampling ranges are screened; Obtaining roughness parameters of the candidate sampling range, establishing a multiple linear regression model of roughness parameters and performance indicators, and screening out the optimal sampling range based on the multiple linear regression model; Extracting multi-scale features of the optimal sampling range, verifying the representativeness of the optimal sampling range based on the multi-scale features, and determining the verified optimal sampling range as a minimum feature unit.

2. The characteristic unit sampling method for surface roughness measurement after laser shock as claimed in claim 1, characterized in that: An area containing a complete spot period is selected as the reference observation area, and the equipment is used to obtain surface morphology data at different scales.

3. The characteristic unit sampling method for surface roughness measurement after laser shock as claimed in claim 2, characterized in that: The macroscopic waviness data and microscopic roughness data are mapped to a unified coordinate system through spatial coordinate system registration technology to establish a cross-scale morphology correlation model.

4. The characteristic unit sampling method for surface roughness measurement after laser shock as claimed in claim 1, characterized in that: During face sampling, for surfaces with significant directional texture, the long side of the sampling range is preferably aligned with the main texture direction.

5. The characteristic unit sampling method for surface roughness measurement after laser shock as claimed in claim 4, characterized in that: The candidate sampling range with high statistical stability is screened out through data volatility analysis.

6. The characteristic unit sampling method for surface roughness measurement after laser shock as claimed in claim 1, characterized in that: The screening rule for the optimal sampling range is: preferentially selecting the sampling range that maximizes the correlation coefficient between the roughness parameter and the performance index.

7. The characteristic unit sampling method for surface roughness measurement after laser shock as claimed in claim 1, characterized in that: The characteristic unit sampling method is converted into industrial inspection specifications, and the sampling range size is proportionally scaled according to different process parameters.

8. Feature unit sampling system for surface roughness measurement after laser shock, characterized by: include: A parameter calibration module, configured to: accurately calibrate process parameters of the laser shock process; a feature extraction module configured to: perform laser shock processing on the sample based on the process parameters, obtain surface morphology data of different scales in the laser shock processing area, map the surface morphology data of different scales to a unified coordinate system, and establish a correlation model of cross-scale morphologies; a candidate screening module configured to: design a gradient sampling range set based on the cross-scale morphology correlation model, and screen out candidate sampling ranges; an optimal screening module configured to: obtain roughness parameters of the candidate sampling range, establish a multiple linear regression model of the roughness parameters and performance indicators, and screen out the optimal sampling range based on the multiple linear regression model; The verification module is configured to: extract multi-scale features of the optimal sampling range, verify the representativeness of the optimal sampling range based on the multi-scale features, and determine the verified optimal sampling range as a minimum feature unit.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the feature unit sampling method for measuring surface roughness after laser shock are realized as claimed in any one of claims 1 to 7.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the feature unit sampling method for surface roughness measurement after laser shock are implemented as described in any one of claims 1 to 7.

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