Laser Measurement Method and System for Surface Roughness of Automotive Fasteners

Abstract

This invention relates to the field of optical surface roughness measurement technology, specifically to a laser measurement method and system for the surface roughness of automotive fasteners. The method includes the following steps: acquiring a set of spatial configuration parameters for the thread, generating a laser focusing adjustment path, correcting the reflection angle curve, constructing a roughness unit height map, extracting the actual microstructure region, and generating roughness feature structure image data. In this invention, by combining triaxial contour scanning to extract spatial geometric features with real-time encoder positioning, a set of spatial configuration parameters reflecting changes in the thread microstructure is established. The laser focusing path is precisely controlled based on the relationship between the curvature and normal of the incident point, achieving synchronous matching between the focusing position and the thread morphology. Error elimination and interpolation correction are performed by combining the intensity and angle changes of the reflected signal, improving the authenticity of the reflection data. Only microstructure change segments with characterizing value are retained, thus improving the specificity of the measurement data, the accuracy of surface structure identification, and the stability of roughness feature extraction.

Description

Technical Field

[0001] This invention relates to the field of optical surface roughness measurement technology, and in particular to a laser measurement method and system for the surface roughness of automotive fasteners. Background Technology

[0002] The field of optical surface roughness measurement technology involves utilizing optical principles to measure and evaluate the microscopic morphological features of object surfaces. Core aspects include surface profile acquisition, roughness parameter calculation, and the construction of non-contact measurement methods. Commonly used optical measurement methods in this field include interferometry, confocal microscopy, laser scattering, and laser triangulation, enabling high-precision detection of material surface profiles, textures, and microstructures. Optical measurement technology features non-contact operation, high speed, and high resolution, and is widely used in various industries such as semiconductor manufacturing, precision machinery, metal processing, and automotive parts inspection, especially suitable for surface inspection tasks where contact is not advisable or high measurement accuracy is required. In this field, lasers, as a common light source, are widely used in various roughness measurement devices due to their stable wavelength, strong directionality, and good focusing performance. Specifically, the traditional laser measurement method for automotive fastener surface roughness refers to the detection of the microscopic morphological features of automotive fastener surfaces. It uses a laser as the measurement light source, and determines the surface roughness parameters of the fastener by acquiring and analyzing the reflected signal after laser beam irradiation. These methods utilize laser triangulation, projecting a laser beam onto the fastener surface and receiving changes in the reflected light's position using a position-sensitive detector. Based on geometric relationships, they calculate surface height variations and further derive roughness parameters such as Ra and Rz. Some methods also incorporate laser interferometers to analyze interference fringes on minute surface displacements, obtaining height variation data with nanometer-level precision. Scanning laser confocal techniques are also employed, reconstructing the surface's three-dimensional morphology by scanning the surface point-by-point and recording changes in focal point reflection intensity. Traditional measurement procedures generally include: fastener fixing, laser irradiation, reflected signal acquisition, signal conversion, surface contour reconstruction, and roughness parameter extraction, all relying on high-precision photoelectric conversion and signal processing equipment to complete the measurement task.

[0003] Existing technologies employ optical measurement methods based on laser triangulation, interferometry, or confocal methods. However, when dealing with fasteners with complex threaded structures, these methods struggle to accurately capture microscopic curvature changes and spatial geometric features. This is especially true in areas deep within the threads or regions of rapid change, where limited laser focusing and significant interference from reflected signals lead to localized distortion or analytical ambiguity in the acquired contour data. Traditional methods rely on pre-defined models for analyzing reflected light intensity and angle changes, lacking precise mechanisms to eliminate abrupt curvature changes and surface texture interference, which can easily result in misjudgments or information omissions. Furthermore, the accuracy is limited when reconstructing surface morphology in complex structural regions, and the extraction of surface roughness parameters suffers from interference redundancy, making it difficult to accurately reflect the true microscopic morphological features. Overall, these limitations restrict the application capability and data reliability of this technology in refined measurement scenarios. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a laser measurement method for the surface roughness of automotive fasteners, comprising the following steps:

[0005] S1: Obtain standard samples of automotive threaded fasteners, extract the thread valley apex, helical trajectory, local arc curvature and change direction within each pitch using a three-axis profile scanner, calculate the spatial angle difference between adjacent curvature points in the same circle and the trend value of the guiding direction, and generate a set of spatial configuration parameters for the thread segment.

[0006] S2: Call the set of spatial configuration parameters of the thread segment, combine the axial displacement encoder and the rotary encoder to obtain the spatial coordinates of the laser incident point on the thread surface, compare the curvature value corresponding to the laser incident point with the real-time laser focal plane distance, and generate a real-time focusing adjustment path for the laser incident point.

[0007] S3: Based on the laser incident point, the laser emission is performed by real-time focusing and adjusting the path. The light intensity value and reflection angle change curve of the reflection point are collected and returned. The offset segment on the reflection curve caused by curvature change and surface texture is identified, and a threaded surface reflection angle correction sequence is generated.

[0008] S4: Call the threaded surface reflection angle correction sequence, perform vector operation on the correction angle of the laser reflection point and the laser incident direction, solve the normal direction component group, and combine the angle between the normal vectors between the front and rear points and the distance gradient to generate the height distribution map of the threaded surface roughness unit.

[0009] As a further embodiment of the present invention, the set of spatial configuration parameters of the thread segment includes the distribution characteristics of thread valleys, the spatial morphology of the helical trajectory, the local curvature change pattern, the difference in the included angle between adjacent curvature points, the trend information of the guiding direction, and the three-dimensional edge mapping structure. The real-time focusing adjustment path of the laser incident point includes the spatial coordinate response trajectory, the curvature and focal plane distance correlation model, the incident angle and normal relationship parameters, and the servo drive adjustment data. The thread surface reflection angle correction sequence includes reflection angle change data, distortion segment rejection identifier, interpolation correction parameter set, and angle stability mapping. The thread surface roughness unit height distribution map includes normal height distribution information, abrupt change point interpolation data, surface structure hierarchy characteristics, and unit structure density trend.

[0010] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0011] S101: Obtain standard samples of automotive threaded fasteners, use a three-axis profile scanner to scan the thread surface at equal intervals within a single pitch range, extract the three-dimensional spatial coordinate information of the thread valley bottom and thread peak top within each pitch cycle, and combine the positional order of the coordinate points in the scanning trajectory to generate a set of thread feature spatial points.

[0012] S102: Based on the set of spatial points of the thread feature, combined with the spatial coordinate sequence of the thread valley bottom and the thread peak top, obtain the tangent vector formed by local adjacent coordinate points, and take the angle region formed by three consecutive points as the reference to calculate the arc curvature value and the corresponding tangential change direction on each segment of the spiral path, call the rotation trend information of the tangential change direction to perform difference normalization processing, and obtain the local curvature guidance change sequence.

[0013] S103: Call the adjacent curvature points in the local curvature guidance change sequence, calculate the angle difference between the tangent vectors in three-dimensional space, and combine the direction guidance trend to construct a vector group including spatial position, curvature direction and angle difference, and classify and aggregate the vector group to generate a set of thread segment spatial configuration parameters.

[0014] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0015] S201: Call the coordinate set of the spatial position points of the thread surface contained in the thread segment spatial configuration parameter set, and combine the real-time displacement increment provided by the axial displacement encoder and the rotation angle change data fed back by the rotary encoder to perform positioning calculation on the laser scanning path. Obtain the real-time three-dimensional coordinates of the laser incident point on the thread surface through the position and angle mapping relationship, and generate a laser incident point spatial positioning dataset.

[0016] S202: Based on the laser incident point spatial positioning dataset, extract the local curvature value matched by the corresponding point in the thread segment spatial configuration parameter set, and combine it with the real-time focal plane distance value output by laser ranging. By comparing the spatial distance deviation between the location of the curvature point and the real-time focal length, obtain the real-time focusing deviation degree, and call the deviation value and the surface normal angle information of the real-time incident point to generate a laser focal length deviation correction factor group.

[0017] S203: Call the focal length deviation value and normal angle value of each incident point in the laser focal length deviation correction factor group, and input the correction factor into the drive adjustment amount of the laser head to the displacement control of the focusing servo unit to drive the laser head to adjust the displacement in the vertical direction in real time, thereby generating a real-time focusing adjustment path for the laser incident point.

[0018] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0019] S301: Adjust the focusing path in real time according to the laser incident point, control the laser to continuously emit according to the laser head displacement path, receive the reflected signal returned from each incident point, collect the corresponding returned light intensity value and reflection angle change, record the spatial correspondence between laser incident and reflection at the incident point, and generate a set of laser reflection data frames for the threaded surface.

[0020] S302: Call the angle change sequence of each reflection curve in the laser reflection data frame set of the threaded surface, identify the curvature change position and the abnormal offset segment caused by the texture structure in the local light intensity signal, and based on the preset scattering angle threshold, the real-time normal angle change trend and the light intensity amplitude difference between adjacent points, exclude the identified abnormal reflection segments and generate an abnormal reflection segment removal index set.

[0021] S303: Call the abnormal reflection segment removal index set, continuously reconstruct the angle data of the missing interval through linear interpolation, and archive and sort the interpolated reflection angles according to the incident point order to identify the complete and continuous angle change structure within the laser scanning range and generate the threaded surface reflection angle correction sequence.

[0022] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0023] S401: Call the threaded surface reflection angle correction sequence, extract the spatial vector of the laser incident direction, perform vector difference operation according to the angle relationship between the three-dimensional vectors, solve the spatial component of the normal direction corresponding to the reflection point, and perform aggregation processing according to the incident point sequence to generate the threaded surface normal direction component group.

[0024] S402: Call the normal vectors of two consecutive reflection points in the normal direction component group of the threaded surface, calculate the included angle value, and combine the Euclidean distance difference between the three-dimensional coordinates of the reflection points to construct a joint discrimination relationship between the rate of change of the included angle and the spatial distance gradient. Based on the joint discrimination relationship, back-calculate the normal height value of the unit micro-surface segment corresponding to the real-time laser incident point, and generate a micro-surface normal height value sequence.

[0025] S403: Call the data of adjacent points in the micro-surface normal height value sequence where there is a sudden change in normal, sample height value pairs before and after the height change interval, perform linear interpolation to fill in the discontinuous values ​​in the segment, and bind the spatial position coordinates of each point in the whole sequence with the normal height value to generate a height distribution map of the rough element of the thread surface.

[0026] As a further aspect of the present invention, the calculation of the included angle value is based on the vector included angle calculation method determined in the cosine theorem, and the included angle value is set to a small variation range when it is less than 20 degrees.

[0027] The Euclidean distance difference is the straight-line distance between the three-dimensional coordinates of adjacent reflection points. When the straight-line distance difference is less than the set threshold of 0.2 mm, the data is marked as a dense sampling segment.

[0028] The rate of change of the included angle in the joint discrimination relationship is quantified by the ratio of the difference between adjacent included angle values ​​to the corresponding Euclidean distance difference. When the ratio is greater than 1, it is marked as a region with a high abrupt change trend.

[0029] The normal height value of the unit micro-surface segment is determined by the projection value of the normal direction component generated by the interpolation point in the region indicated by the above-mentioned angle change rate and spatial distance gradient on the laser incident direction.

[0030] As a further aspect of the present invention, the method further includes step S5:

[0031] S5: Using the height distribution map of the roughness unit of the thread surface, the curvature change rate and inter-peak spacing of the continuous region are identified. Frequency statistics are performed by sliding the window. The periodic repetitive disturbance area is marked and isolated from the measurement profile, and the real microstructure change area is preserved. The thread roughness feature structure image data is obtained by calculating the inter-peak spacing and the surface structure density distribution trend.

[0032] The image data of the rough thread feature structure includes periodic disturbance zone marking information, actual microstructure distribution map, peak spacing statistics, and structure density trend map.

[0033] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0034] S501: Call the normal height value sequence of the continuous region in the height distribution map of the thread surface roughness unit, construct the tangent vector through adjacent height points and calculate the unit curvature change rate, and extract the spatial spacing between local height extreme points as the inter-peak spacing data. Classify and organize the parameters in each continuous region to generate the curvature change rate and inter-peak spacing identification matrix.

[0035] S502: Based on the peak spacing data of continuous sub-regions in the curvature change rate and peak spacing identification matrix, a fixed window length sliding method is used to count the frequency of peak occurrence in a unit area, identify sub-region sequences with periodic repetitive perturbation characteristics, mark the perturbation area in the original distribution map and set the shielding area, and generate a perturbation area shielding index set.

[0036] S503: Call the disturbance region shielding index set, filter the original thread surface roughness unit height distribution map, remove the marked periodic disturbance regions, retain only the unshielded real microstructure change regions, and perform statistical normalization processing on the peak spacing sequence and the corresponding region normal height density distribution trend in the remaining regions to obtain thread roughness feature structure image data.

[0037] A laser measurement system for the surface roughness of automotive fasteners includes:

[0038] The spatial configuration acquisition module acquires standard samples of automotive threaded fasteners, calls a three-axis profile scanner to detect the pitch area within the same turn, extracts the spatial coordinate data of the thread valley apex, the set of helical trajectory points, and the local arc curvature value and curvature change direction at each point, performs the corresponding calculation of the included angle difference and the guiding direction, and generates a set of spatial configuration parameters for the thread segment.

[0039] The laser focusing path module uses the thread segment spatial configuration parameter set to obtain the real-time axial coordinate value of the axial displacement encoder and the angular coordinate value of the rotary encoder, calculates the real-time laser incident point in the space of the thread surface, judges the difference between the curvature value and the focal plane distance from the laser head to the laser incident point, adjusts the laser head displacement, and generates a real-time focusing adjustment path for the laser incident point.

[0040] The reflection angle correction module calls the laser incident point to focus and adjust the path in real time, performs laser emission on the specified incident point, collects the reflected light intensity value and the corresponding reflection angle change sequence of the laser reflection point within the measurement period, identifies the reflection offset segment based on the reflection segment corresponding to the curvature abrupt value in the reflected light intensity value change curve, and generates the threaded surface reflection angle correction sequence.

[0041] The microstructure extraction module uses the threaded surface reflection angle correction sequence to calculate the vector difference between the correction angle corresponding to each point and the laser incident direction to obtain the normal component value of the incident point. It also calculates the angle and gradient between the normal component vectors of adjacent reflection points and the spatial coordinate difference to generate a threaded surface roughness element height distribution map.

[0042] The feature recognition module uses the height distribution map of the roughness unit on the threaded surface to perform frequency statistics by sliding the window, marks and isolates the periodic repetitive disturbance area from the measurement profile, retains the real microstructure change area, and generates thread roughness feature structure image data by calculating the inter-peak spacing statistics and the surface structure density distribution trend.

[0043] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0044] In this invention, by combining triaxial contour scanning to extract spatial geometric features with real-time encoder positioning, a set of spatial configuration parameters reflecting the changes in the thread microstructure is established. The laser focusing path is precisely controlled based on the relationship between the curvature and normal of the incident point, achieving synchronous matching between the focusing position and the thread morphology. Furthermore, error elimination and interpolation correction are performed by combining the intensity and angle changes of the reflected signal, improving the authenticity of the reflected data. The normal height distribution of the micro-unit structure is obtained through vector back-calculation, and the real texture is reconstructed by combining local structural trends. Periodic disturbance areas are isolated, and only micro-structural change segments with characterization value are retained, improving the relevance of the measurement data, the accuracy of surface structure identification, and the stability of roughness feature extraction. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of the steps of the present invention;

[0047] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0048] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0049] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0050] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0051] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0052] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0053] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0054] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0055] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0056] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0057] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0058] Please see Figure 1 This invention provides a laser method for measuring the surface roughness of automotive fasteners, comprising the following steps:

[0059] S1: Obtain standard samples of automotive threaded fasteners, extract the thread valley apex, helical trajectory, local arc curvature and change direction within each pitch using a three-axis profile scanner, calculate the spatial angle difference and guide direction trend value of adjacent curvature points in the same circle, establish the edge distribution mapping under spatial coordinates, and generate a set of thread segment spatial configuration parameters.

[0060] S2: Call the thread segment spatial configuration parameter set, combine the axial displacement encoder and rotary encoder to obtain the spatial coordinates of the laser incident point on the thread surface, compare the curvature value corresponding to the laser incident point with the real-time laser focal plane distance, combine the real-time incident point with the normal angle, control the focusing servo unit to drive the laser head displacement, and generate the real-time focusing adjustment path of the laser incident point.

[0061] S3: Based on the real-time focusing and path adjustment of the laser incident point, laser emission is performed. The light intensity value and reflection angle change curve of the reflection point are collected and returned. The offset segment on the reflection curve caused by curvature change and surface texture is identified. Based on the scattering angle threshold, the trend of normal angle change and the local light intensity amplitude difference, the distortion segment is eliminated and interpolated for correction, and a threaded surface reflection angle correction sequence is generated.

[0062] S4: Call the thread surface reflection angle correction sequence, perform vector operation on the correction angle of the laser reflection point and the laser incident direction, solve the normal direction component group, combine the angle between the normal vectors between the front and back points and the distance gradient, deduce the normal height value of the micro-surface unit structure, perform linear interpolation at the structural abrupt point, and generate the thread surface roughness element height distribution map.

[0063] S5: Using the height distribution map of the roughness unit of the thread surface, the curvature change rate and inter-peak spacing of the continuous region are identified. Frequency statistics are performed by sliding the window. The periodic repetitive disturbance area is marked and isolated from the measurement profile, and the real microstructure change area is preserved. The image data of the thread roughness feature structure is obtained by calculating the inter-peak spacing statistics and the surface structure density distribution trend.

[0064] The set of spatial configuration parameters for the thread segment includes the distribution characteristics of thread valleys, the spatial morphology of the helical trajectory, the local curvature change pattern, the difference in the included angle between adjacent curvature points, the trend information of the guiding direction, and the three-dimensional edge mapping structure. The real-time focusing adjustment path of the laser incident point includes the spatial coordinate response trajectory, the correlation model between curvature and focal plane distance, the parameters of the relationship between incident angle and normal, and the servo drive adjustment data. The thread surface reflection angle correction sequence includes reflection angle change data, distortion segment rejection markers, interpolation correction parameter sets, and angle stability mapping. The thread surface roughness unit height distribution map includes normal height distribution information, abrupt change point interpolation data, surface structure hierarchy characteristics, and unit structure density trend. The thread roughness feature structure image data includes periodic disturbance zone marking information, real microstructure distribution map, peak spacing statistics, and structure density trend map.

[0065] Please see Figure 2 The specific steps of S1 are as follows:

[0066] S101: Obtain standard samples of automotive threaded fasteners, use a three-axis profile scanner to scan the thread surface at equal intervals within a single pitch range, extract the three-dimensional spatial coordinate information of the thread valley bottom and thread peak top within each pitch cycle, and combine the positional order of the coordinate points in the scanning trajectory to generate a set of thread feature spatial points.

[0067] Based on the specific requirements of different vehicles for fastening performance, the selection of fasteners should be categorized. For example, for high-strength fine-pitch threaded parts commonly used in engine blocks, suspensions, or wheel hubs, M12×1.25 specification products with high fatigue resistance should be given priority. After determining the sample specifications, the fasteners should be placed on a scanning platform and initialized and calibrated using a contour scanner with three-axis linkage function. When setting the scanning path, segmented scanning operations should be performed according to the length of a complete pitch to ensure the integrity and continuity of the contour data. When setting scanning points, equally spaced point distribution should be selected. For example, one pitch can be divided into 100... To ensure uniform scanning intervals and continuous scanning points, a high-precision focus tracking algorithm should be set in the scanning process to dynamically adjust the Z-axis scanning focal length, preventing coordinate errors caused by surface tilt or machining errors. After fixing the threaded part to be tested with a stable fixture, the scanning program is started. The scanning will automatically acquire the three-dimensional coordinate information of each scanning point along the set trajectory. After the data acquisition is completed, the extreme points of the coordinate curve in each cycle are extracted through the built-in analysis program to identify the coordinate positions corresponding to each valley and each peak. The time series sequence of the scanning points is combined to number and record them to generate a set of thread feature spatial points.

[0068] S102: Based on the set of spatial points of the thread feature, combined with the spatial coordinate sequence of the thread valley and the thread peak, obtain the tangent vector formed by the local adjacent coordinate points, and take the angle region formed by the three consecutive points as the reference to calculate the arc curvature value and the corresponding tangential change direction on each segment of the spiral path. Call the rotation trend information of the tangential change direction to perform difference normalization processing to obtain the local curvature guidance change sequence.

[0069] The three-dimensional spatial coordinate sequence of valley bottom and peak top is extracted to construct the feature path of the thread surface. After sorting the point set, the connecting line formed by each pair of adjacent points in space is calculated, and the direction of the connecting line is determined as the local tangent direction. Based on the tangent trend formed between multiple continuous points, the continuity of each directional segment is divided. The turning change of the path in three-dimensional space is identified by the angular relationship formed between every three continuous points. The turning information is converted into a data structure of curvature change, which can characterize the degree of local deformation of the thread surface contour. In each scanning segment, the starting point, ending point and directional change trend are recorded and combined to form the rotation change trend of the path segment. In order to avoid interference from surface noise or scanning error, segment-by-segment comparison and smooth interpolation can be used. For example, the segment with continuous directional change trend is weighted by trend to make the rotation direction of the overall path continuous and stable. The change sequence of each segment is normalized by combining the change of adjacent directions. In the normalization process, the rotation direction change trend of different segments is classified and identified by trend consistency. At the same time, continuous and consistent change direction segments are grouped together to obtain the local curvature-guided change sequence.

[0070] S103: Call the adjacent curvature points in the local curvature guidance change sequence, calculate the angle difference between the tangent vectors in three-dimensional space, and combine the direction guidance trend to construct a vector group including spatial position, curvature direction and angle difference, and classify and aggregate the vector group to generate a set of thread segment spatial configuration parameters;

[0071] Each pair of adjacent change points is processed item by item. The starting point and ending point of each segment are selected, and their spatial positional relationship is analyzed. The tangent direction is used to determine whether the change trend is continuous or deflected. The characteristics of the path change area represented by different points are determined by the positional relationship. For example, if two segments are continuous in direction and have similar change angles, the pair of points is marked as a continuous trend segment. If the change direction is significantly different, it is marked as a turning node. For continuous change segments, a comprehensive vector group containing path direction, curvature change, and spatial position is constructed by analyzing spatial position information. Each vector group records the position center, change amplitude, and direction mark of the path segment. After all vector groups are formed, they are classified and aggregated. Based on the spatial proximity and directional trend consistency between vector groups, the same type of vectors are divided into the same category group by iterative aggregation. During the aggregation process, the classification is adjusted according to information such as the change amplitude of the center position and the proportion of directional consistency, forming several spatially stable configuration segments. Each configuration segment records the spatial center position and change characteristics of the representative path, completing the spatial configuration feature division of the overall thread structure contour and generating a set of spatial configuration parameters for the thread segment.

[0072] Please see Figure 3 The specific steps of S2 are as follows:

[0073] S201: Call the coordinate set of the spatial position points of the thread surface contained in the thread segment spatial configuration parameter set, and combine it with the real-time displacement increment provided by the axial displacement encoder and the rotation angle change data fed back by the rotary encoder to perform positioning calculation on the laser scanning path. Obtain the real-time three-dimensional coordinates of the laser incident point on the thread surface through the position and angle mapping relationship, and generate the laser incident point spatial positioning dataset.

[0074] Each spatial location point is numbered and associated with the path to ensure accurate matching order during positioning. Simultaneously, real-time displacement increments in the Z-axis direction of laser processing are obtained from the axial displacement encoder, and rotation angle changes of the worktable or sample around the spindle are obtained from the rotary encoder. Combining these two sets of data forms the relative displacement trajectory of the current laser scanning head in space. During processing, by establishing a mapping model between axial and angular changes and the sample's thread structure, the relative position coordinates of the laser beam's incident point on the thread surface are calculated in real time. For example, when the sample rotates along the thread axis at a certain speed while the laser head feeds synchronously, each feed step corresponds to a specific spatial point. By combining the rotation angle and axial step distance, the specific three-dimensional coordinate position of the laser beam illuminating the sample's thread surface can be determined. The coordinate position is corrected by calling a point set from the thread segment configuration parameter set, forming a real-time spatial positioning record of the laser incident point on the sample surface. This positioning data is recorded in chronological order, containing X, Y, and Z three-dimensional coordinates and corresponding timestamps or coded sequences, generating a laser incident point spatial positioning dataset.

[0075] S202: Based on the spatial positioning dataset of the laser incident point, extract the local curvature value matched by the corresponding point in the spatial configuration parameter set of the thread segment, and combine it with the real-time focal plane distance value output by laser ranging. By comparing the spatial distance deviation between the location of the curvature point and the real-time focal length, obtain the real-time focusing deviation degree, and call the information of the angle between the deviation value and the surface normal of the real-time incident point to generate a laser focal length deviation correction factor group.

[0076] For each real-time incident point, data matching is performed. The curvature information recorded in the spatial configuration parameter set of the thread segment is called and compared with the spatial position of the incident point coordinates. The local curvature value corresponding to the position is extracted. The focal plane distance value corresponding to each incident point is obtained in real time through laser ranging. This distance value reflects the spatial interval between the actual emission focus of the laser head and the sample surface. If there is a deviation between the distance at the actual incident point and the ideal focusing distance, it means that the current laser has not been accurately focused on the curved target. Therefore, it is necessary to compare the vertical distance difference between the theoretical surface position of the current curvature point and the ranging feedback value. The degree of focal length deviation is determined by the difference. By calculating the angle between the normal direction of the curvature surface where the current incident point is located and the laser beam direction, the degree of influence of the incident angle on the focusing deviation is obtained. A focal length correction factor is generated by combining the above two parameters to characterize the degree of displacement correction required for the current point. The correction factor record includes a data structure consisting of the focusing deviation value and the angle value. A laser focal length deviation correction factor group is generated according to the processing sampling cycle.

[0077] S203: Call the focal length deviation value and normal angle value of each incident point in the laser focal length deviation correction factor group. By mapping the correction factor to the driving adjustment amount of the laser head, input it to the displacement control of the focusing servo unit, drive the laser head to adjust the displacement in the vertical direction in real time, and generate the real-time focusing adjustment path of the laser incident point.

[0078] For each factor, there is a specific laser incident point, which includes the focal length deviation and the angle between the incident direction and the surface normal. In the focusing control logic, each correction factor is deconstructed into a driving adjustment amount for the laser head position. Specifically, it is calculated by analyzing the relationship between the magnitude of the deviation and the slope of the angle. For example, if the focal length deviation at the incident point is large but the angle is small, a large vertical fine adjustment is required. Conversely, if the angle is large, focusing correction can be achieved through small displacement. In this way, each set of correction factors is mapped to specific adjustment data in the vertical displacement direction of the laser head. The displacement data is input as a driving command to the laser focusing servo unit, which controls the rapid response adjustment in the vertical direction. After obtaining the adjustment command, the displacement execution unit in the Z-axis direction is controlled in real time, so that the laser head can dynamically adjust the focal position in a high-frequency micro-displacement manner, so that each laser incident point can obtain a matching focusing state. Recording the displacement adjustment amount, time series, and target incident position of the laser head at each moment during the actual processing is an important control basis for dynamically controlling the stability of the laser focusing state, and generating a real-time focusing adjustment path for the laser incident point.

[0079] Please see Figure 4 The specific steps of S3 are as follows:

[0080] S301: Adjust the path of the laser in real time according to the laser incident point, control the laser to continuously emit according to the laser head displacement path, receive the reflected signal returned from each incident point, collect the corresponding returned light intensity value and reflection angle change, record the spatial correspondence between laser incident and reflection at the incident point, and generate a set of laser reflection data frames for the threaded surface.

[0081] The control of the laser must be completely synchronized with the displacement path of the laser head. The incident position at each moment is used as the trigger point for laser emission. At the same time, the incident angle and time point of each laser emission are recorded. When the laser beam irradiates the thread surface, the reflected signal is captured by a high-sensitivity receiving device configured around the laser head. The intensity value of the reflected light and the change of the reflection angle relative to the incident angle are collected synchronously. This process needs to continuously cover the incident points involved in each real-time focusing adjustment path. To achieve stable signal acquisition, a microsecond-level sampling period can be set at each point to perform high-frequency detection of the reflected light. The effective signal value is extracted using a multiple averaging strategy. By combining the laser incident angle with the corresponding reflection angle and light intensity data into a three-dimensional correspondence, a reflection data entry for each incident point is established. At the same time, the mapping structure between the spatial arrangement of the incident points and the laser return data is recorded according to the scanning trajectory, generating a set of laser reflection data frames for the thread surface.

[0082] S302: Call the angle change sequence of each reflection curve in the laser reflection data frame set of the threaded surface, identify the curvature change position and the abnormal offset segment caused by the texture structure in the local light intensity signal, and based on the preset scattering angle threshold, the real-time normal angle change trend and the light intensity amplitude difference between adjacent points, exclude the identified abnormal reflection segments and generate an abnormal reflection segment removal index set.

[0083] In the scanning data processing, the sequence of reflection angle changes with the path is extracted sequentially. By analyzing the continuity between the reflection angle and the incident point position, it is detected whether there is a sudden change in the reflection characteristics between the curvature change points. For example, if the reflection angle changes abruptly beyond the preset scattering angle threshold or the light intensity signal drops instantaneously within a certain segment, it is marked as an abnormal area. For this purpose, it is necessary to combine the real-time normal angle change trend to observe whether there is a violent fluctuation in the surface normal. If the angle change between the incident points exceeds the continuous trend threshold, it is also one of the abnormal indicators. At the same time, the light intensity amplitude difference between adjacent points is calculated. If it is greater than the set minimum difference threshold, it is also marked as an abnormal reflection segment. The above three conditions are used as identification criteria to remove abnormal data. In the data processing, the above features are compared point by point on the reflection curve. Once any abnormal judgment condition is met, it is marked and recorded as a removal index item. Each index item contains the number of the start point and end point of the abnormal segment and the abnormal type label, generating an abnormal reflection segment removal index set.

[0084] S303: Call the abnormal reflection segment removal index set, continuously reconstruct the angle data of the missing interval through linear interpolation, and archive and sort the interpolated reflection angles according to the incident point order to identify the complete and continuous angle change structure within the laser scanning range and generate the threaded surface reflection angle correction sequence.

[0085] For each set of reflection data regions identified as abnormal, the missing or invalid angle data is repaired and reconstructed using linear interpolation. During the interpolation process, two normal data points before and after the abnormal segment are selected as the interpolation start and end points. The angle change value of the intermediate point is calculated according to the order relationship between the points, and angle filling data corresponding to time or space points is generated step by step. At the same time, the angle values ​​that are repaired by interpolation are archived and sorted according to the order of the incident points to ensure that the interpolation results can be correctly embedded into the original angle change sequence, forming a set of continuous angle change data structure without interference from abnormal segments. The structure completely covers the incident points under the entire laser scanning path, and the reflection angle corresponds one-to-one with the position point. This ensures that surface modeling and structural analysis can be based on continuous and smooth angle curves, realizing a complete and accurate expression of the reflection structure of the thread surface and generating a thread surface reflection angle correction sequence.

[0086] Please see Figure 5 The specific steps of S4 are as follows:

[0087] S401: Call the threaded surface reflection angle correction sequence, extract the spatial vector of the laser incident direction, perform vector difference operation according to the angle relationship between the three-dimensional vectors, solve the spatial component of the normal direction corresponding to the reflection point, and perform aggregation processing according to the incident point sequence to generate the threaded surface normal direction component group.

[0088] This application employs the classic micro-facet theory in optical measurement. Although macroscopically it is diffuse reflection, at the microscale of laser focusing, the surface is considered to be composed of countless tiny "mirrors." S301 collects the intensity and angle of reflected light, while S302 and S303 explicitly perform "abnormal reflection segment removal" and "interpolation correction." This means that the system has extracted the "effective reflection component" (i.e., the component with the strongest energy or conforming to geometric optics) representing the main orientation of the micro-surface through signal processing, eliminated stray light, and extracted the spatial direction vector of the laser beam at each incident point according to the incident direction parameters recorded during laser emission. The vector is composed of the X, Y, and Z components of the incident direction in the three-dimensional coordinate system. For each incident point, the reflection direction vector is constructed based on the angular relationship between the corrected reflection angle and the incident direction. The normal direction vector is derived by calculating the spatial angle between the incident vector and the reflection vector. After the angle is formed by the two incident vectors, the corresponding normal direction is the direction of the bisector of the angle between the two vectors. The spatial component of this direction is calculated by vector interpolation, forming a group of normal direction vectors for each point. This operation is performed sequentially for each incident point, forming a set of record entries containing the point number, spatial position, and normal vector components. At the same time, the normal vectors are aggregated according to the order of the incident points to ensure that the position corresponding to each vector is consistent with the reflection angle. The component group corresponds one-to-one with the incident trajectory in structure, which can provide accurate directional basis for microstructure changes and surface analysis, generating the normal direction component group of the threaded surface.

[0089] S402: Call the normal vectors of two consecutive reflection points in the normal direction component group of the thread surface, calculate the included angle value, and combine it with the Euclidean distance difference between the three-dimensional coordinates of the reflection points to construct a joint discrimination relationship between the rate of change of the included angle and the spatial distance gradient. Based on the joint discrimination relationship, back-calculate the normal height value of the unit micro-surface segment corresponding to the real-time laser incident point, and generate a sequence of micro-surface normal height values.

[0090] By extracting the normal vector of each pair of adjacent reflection points and analyzing the angle between the two vectors, the directional change between adjacent micro-surface units can be identified. At the same time, combined with the three-dimensional coordinates of the two reflection points in space, the difference in Euclidean distance is calculated, which is the rate of normal change within a unit spatial length. The angle change and spatial distance are combined into a joint discrimination relationship. In the specific execution process, for point pairs with significant normal changes but small spatial distances, they are identified as surface abrupt change areas or local curvature anomalies. After establishing the joint discrimination relationship, the normal height value of the unit micro-surface segment corresponding to each incident point can be deduced based on the model. The height value describes the degree of undulation of the threaded surface in the unit normal direction at that point. Combined with the trajectory path sequence, the normal height change amplitude is calculated one by one to generate a sequence of micro-surface normal height values.

[0091] S403: Call the data of adjacent points in the micro-surface normal height value sequence where there is an abrupt change in normal, sample height value pairs before and after the height change interval, perform linear interpolation to fill in the discontinuous values ​​in the segment, and bind the spatial position coordinates of each point in the whole sequence with the normal height value to generate the height distribution map of the rough element of the thread surface.

[0092] Analyzing adjacent data points in the height sequence where there are abrupt changes in the normal direction, the location of the abrupt change is identified. For example, if a large jump in height value occurs in a continuous data segment and the height difference between adjacent points exceeds a set threshold, it is identified as an abrupt change region. Height values ​​are extracted before and after the abrupt change region as the start and end points of interpolation, respectively. Linear interpolation is used to fill in the missing values ​​point by point to ensure the continuity of height values ​​within the segment. The filled height data is then fused with the original sequence to ensure that the entire sequence data is complete and uninterrupted. After the interpolation and repair are completed, the spatial three-dimensional coordinates of each incident point in the entire sequence are bound to the corresponding corrected normal height value and organized into a two-dimensional table or image structure dataset. A visual height map is drawn according to the spatial arrangement order. The map shows the distribution characteristics of roughness unit undulations on the thread surface in the normal direction, which is convenient for extracting local features such as texture anomalies and processing defects through image analysis. This enables the overall presentation and digital expression of the microstructure of the thread surface, generating a height distribution map of roughness unit on the thread surface.

[0093] Please see Figure 6 The specific steps of S5 are as follows:

[0094] S501: Call the normal height value sequence of continuous regions in the height distribution map of the thread surface roughness element, construct the tangent vector through adjacent height points and calculate the unit curvature change rate, and extract the spatial spacing between local height extreme points as the inter-peak spacing data. Classify and organize the parameters in each continuous region to generate the curvature change rate and inter-peak spacing identification matrix.

[0095] The sequence of height points in each continuous segment is extracted by sorting spatial coordinates. Tangent vectors are constructed by the spatial positions of two adjacent height points to form a directional representation of the local micro-contour. Based on adjacent point pairs, a sequence of tangent vectors covering the entire region is constructed sequentially. Then, combined with the angular change trend between continuous vectors, the rate of curvature change per unit length is calculated. The rate of curvature change represents the density of change in the degree of local surface undulation. At the same time, local maxima and minima appearing in each segment are identified, and the horizontal spatial distance between them is extracted and recorded as inter-peak spacing data. For example, if three consecutive extreme points appear in a certain region, the distances are recorded as d1 and d2, respectively, and archived in the order of appearance. The extracted data is classified and organized according to the source region to generate a curvature change rate and inter-peak spacing identification matrix.

[0096] S502: Based on the curvature change rate and the peak spacing identification matrix, the peak spacing data of continuous sub-regions are identified. A fixed window length sliding method is used to count the frequency of peak occurrence in a unit area, identify the sub-region sequence with periodic repetitive perturbation characteristics, mark the perturbation area in the original distribution map and set the shielding area, and generate a perturbation area shielding index set.

[0097] Continuous sub-regions containing periodic perturbation features are identified. The inter-peak spacing data is selected as the main reference object, and a fixed window length is set for sliding scanning. The window length can be selected according to the thread specification, for example, a thread pitch length of 1.25mm. The number of extreme points appearing in each window is counted. If the peak frequency distribution is found to be periodically repeated during multiple window sliding processes, for example, if an approximately the same number of extreme points or similar spacing sequence changes appear in each of the 5 windows, then the region is considered to have a repetitive perturbation phenomenon. Regions exhibiting regular and periodic height fluctuations are marked, and the start and end positions are located in the original height distribution map and set as shielded regions to avoid interference with the judgment of real microstructure changes. The identified perturbation sub-regions are uniformly encoded to generate a perturbation region shielded index set.

[0098] S503: Call the disturbance region shielding index set, filter the original thread surface roughness element height distribution map, remove the marked periodic disturbance regions, retain only the unshielded real microstructure change regions, and perform statistical normalization on the peak spacing sequence and the corresponding region normal height density distribution trend in the remaining regions to obtain thread roughness feature structure image data.

[0099] Based on the starting position marked in the index set, regions identified as periodic perturbation features are removed from the height distribution map, retaining only the data of unmarked microstructure regions. The interpeak spacing sequence of the retained continuous regions is reorganized, and the spatial distance between adjacent extreme values ​​in each region is extracted. At the same time, the distribution trend of normal height values ​​in a unit region is analyzed, the density concentration and distribution uniformity are calculated, and the height density data of different regions are normalized. The original values ​​are proportionally converted to a standardized interval to improve the comparability of data between different samples. The processed interpeak spacing sequence and the normal height density information of the corresponding regions are organized into an image data format, maintaining the spatial arrangement logic in the original map. Each pixel in the image represents the microscopic surface feature of a specific incident point. Combining spatial location and statistical characteristics, it can be used for data support in practical application scenarios such as defect identification, texture classification, and production monitoring to obtain image data of thread roughness feature structure.

[0100] Please see Figure 7 A laser measurement system for the surface roughness of automotive fasteners, including:

[0101] The spatial configuration acquisition module acquires standard samples of automotive threaded fasteners, calls a three-axis profile scanner to detect the pitch area within the same turn, extracts the spatial coordinate data of the thread valley apex, the set of helical trajectory points, and the local arc curvature value and curvature change direction at each point, performs the corresponding calculation of the included angle difference and the guiding direction, and generates a set of spatial configuration parameters for the thread segment.

[0102] The laser focusing path module uses the thread segment spatial configuration parameter set to obtain the real-time axial coordinate values ​​of the axial displacement encoder and the angular coordinate values ​​of the rotary encoder, calculates the real-time laser incident point in the space of the thread surface, judges the difference between the curvature value and the focal plane distance from the laser head to the laser incident point, adjusts the laser head displacement, and generates a real-time focusing adjustment path for the laser incident point.

[0103] The reflection angle correction module calls the laser incident point to focus and adjust the path in real time, performs laser emission at the specified incident point, collects the reflected light intensity value and the corresponding reflection angle change sequence of the laser reflection point within the measurement period, identifies the reflection offset segment based on the reflection segment corresponding to the curvature abrupt value in the reflected light intensity value change curve, and generates the threaded surface reflection angle correction sequence.

[0104] The microstructure extraction module uses the threaded surface reflection angle correction sequence to calculate the vector difference between the correction angle corresponding to each point and the laser incident direction to obtain the normal component value of the incident point. It also calculates the angle and gradient between the normal component vectors of adjacent reflection points and the spatial coordinate difference to generate a threaded surface roughness element height distribution map.

[0105] The feature recognition module uses the height distribution map of the rough unit on the thread surface and performs frequency statistics by sliding the window. It marks and isolates the periodic repetitive disturbance area from the measurement profile, retains the real microstructure change area, and generates thread rough feature structure image data by calculating the inter-peak spacing statistics and the surface structure density distribution trend.

[0106] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A laser method for measuring the surface roughness of automotive fasteners, characterized in that, Includes the following steps: S1: Obtain standard samples of automotive threaded fasteners, extract the thread valley apex, helical trajectory, local arc curvature and change direction within each pitch using a three-axis profile scanner, calculate the spatial angle difference between adjacent curvature points in the same circle and the trend value of the guiding direction, and generate a set of spatial configuration parameters for the thread segment. S2: Call the set of spatial configuration parameters of the thread segment, combine the axial displacement encoder and the rotary encoder to obtain the spatial coordinates of the laser incident point on the thread surface, compare the curvature value corresponding to the laser incident point with the real-time laser focal plane distance, and generate a real-time focusing adjustment path for the laser incident point. S3: Based on the laser incident point, the laser emission is performed by real-time focusing and adjusting the path. The light intensity value and reflection angle change curve of the reflection point are collected and returned. The offset segment on the reflection curve caused by curvature change and surface texture is identified, and a threaded surface reflection angle correction sequence is generated. S4: Call the threaded surface reflection angle correction sequence, perform vector operation on the correction angle of the laser reflection point and the laser incident direction, solve the normal direction component group, and combine the angle between the normal vectors between the front and rear points and the distance gradient to generate the height distribution map of the threaded surface roughness unit.

2. The laser measurement method for surface roughness of automotive fasteners according to claim 1, characterized in that, The set of spatial configuration parameters for the threaded segment includes the distribution characteristics of threaded valleys, the spatial morphology of the helical trajectory, the local curvature change pattern, the difference in the included angle between adjacent curvature points, the trend information of the guiding direction, and the three-dimensional edge mapping structure. The real-time focusing adjustment path of the laser incident point includes the spatial coordinate response trajectory, the correlation model between curvature and focal plane distance, the relationship parameters between incident angle and normal, and servo drive adjustment data. The threaded surface reflection angle correction sequence includes reflection angle change data, distortion segment rejection identifiers, interpolation correction parameter sets, and angle stability mapping. The threaded surface roughness unit height distribution map includes normal height distribution information, abrupt change point interpolation data, surface structure hierarchy characteristics, and unit structure density trend.

3. The laser measurement method for surface roughness of automotive fasteners according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain standard samples of automotive threaded fasteners, use a three-axis profile scanner to scan the thread surface at equal intervals within a single pitch range, extract the three-dimensional spatial coordinate information of the thread valley bottom and thread peak top within each pitch cycle, and combine the positional order of the coordinate points in the scanning trajectory to generate a set of thread feature spatial points. S102: Based on the set of spatial points of the thread feature, combined with the spatial coordinate sequence of the thread valley bottom and the thread peak top, obtain the tangent vector formed by local adjacent coordinate points, and take the angle region formed by three consecutive points as the reference to calculate the arc curvature value and the corresponding tangential change direction on each segment of the spiral path, call the rotation trend information of the tangential change direction to perform difference normalization processing, and obtain the local curvature guidance change sequence. S103: Call the adjacent curvature points in the local curvature guidance change sequence, calculate the angle difference between the tangent vectors in three-dimensional space, and combine the direction guidance trend to construct a vector group including spatial position, curvature direction and angle difference, and classify and aggregate the vector group to generate a set of thread segment spatial configuration parameters.

4. The laser measurement method for surface roughness of automotive fasteners according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Call the coordinate set of the spatial position points of the thread surface contained in the thread segment spatial configuration parameter set, and combine the real-time displacement increment provided by the axial displacement encoder and the rotation angle change data fed back by the rotary encoder to perform positioning calculation on the laser scanning path. Obtain the real-time three-dimensional coordinates of the laser incident point on the thread surface through the position and angle mapping relationship, and generate a laser incident point spatial positioning dataset. S202: Based on the laser incident point spatial positioning dataset, extract the local curvature value matched by the corresponding point in the thread segment spatial configuration parameter set, and combine it with the real-time focal plane distance value output by laser ranging. By comparing the spatial distance deviation between the location of the curvature point and the real-time focal length, obtain the real-time focusing deviation degree, and call the deviation value and the surface normal angle information of the real-time incident point to generate a laser focal length deviation correction factor group. S203: Call the focal length deviation value and normal angle value of each incident point in the laser focal length deviation correction factor group, and input the correction factor into the drive adjustment amount of the laser head to the displacement control of the focusing servo unit to drive the laser head to adjust the displacement in the vertical direction in real time, thereby generating a real-time focusing adjustment path for the laser incident point.

5. The laser measurement method for surface roughness of automotive fasteners according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Adjust the focusing path in real time according to the laser incident point, control the laser to continuously emit according to the laser head displacement path, receive the reflected signal returned from each incident point, collect the corresponding returned light intensity value and reflection angle change, record the spatial correspondence between laser incident and reflection at the incident point, and generate a set of laser reflection data frames for the threaded surface. S302: Call the angle change sequence of each reflection curve in the laser reflection data frame set of the threaded surface, identify the curvature change position and the abnormal offset segment caused by the texture structure in the local light intensity signal, and based on the preset scattering angle threshold, the real-time normal angle change trend and the light intensity amplitude difference between adjacent points, exclude the identified abnormal reflection segments and generate an abnormal reflection segment removal index set. S303: Call the abnormal reflection segment removal index set, continuously reconstruct the angle data of the missing interval through linear interpolation, and archive and sort the interpolated reflection angles according to the incident point order to identify the complete and continuous angle change structure within the laser scanning range and generate the threaded surface reflection angle correction sequence.

6. The laser measurement method for surface roughness of automotive fasteners according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Call the threaded surface reflection angle correction sequence, extract the spatial vector of the laser incident direction, perform vector difference operation according to the angle relationship between the three-dimensional vectors, solve the spatial component of the normal direction corresponding to the reflection point, and perform aggregation processing according to the incident point sequence to generate the threaded surface normal direction component group. S402: Call the normal vectors of two consecutive reflection points in the normal direction component group of the threaded surface, calculate the included angle value, and combine the Euclidean distance difference between the three-dimensional coordinates of the reflection points to construct a joint discrimination relationship between the rate of change of the included angle and the spatial distance gradient. Based on the joint discrimination relationship, back-calculate the normal height value of the unit micro-surface segment corresponding to the real-time laser incident point, and generate a micro-surface normal height value sequence. S403: Call the data of adjacent points in the micro-surface normal height value sequence where there is a sudden change in normal, sample height value pairs before and after the height change interval, perform linear interpolation to fill in the discontinuous values ​​in the segment, and bind the spatial position coordinates of each point in the whole sequence with the normal height value to generate a height distribution map of the rough element of the thread surface.

7. The laser measurement method for surface roughness of automotive fasteners according to claim 6, characterized in that, The angle value is calculated based on the vector angle calculation method determined in the cosine theorem, and a small variation range is set when the angle value is less than 20 degrees. The Euclidean distance difference is the straight-line distance between the three-dimensional coordinates of adjacent reflection points. When the straight-line distance difference is less than the set threshold of 0.2 mm, the data is marked as a dense sampling segment. The rate of change of the included angle in the joint discrimination relationship is quantified by the ratio of the difference between adjacent included angle values ​​to the corresponding Euclidean distance difference. When the ratio is greater than 1, it is marked as a region with a high abrupt change trend. The normal height value of the unit micro-surface segment is determined by the projection value of the normal direction component generated by the interpolation point in the region indicated by the above-mentioned angle change rate and spatial distance gradient on the laser incident direction.

8. The laser measurement method for surface roughness of automotive fasteners according to claim 1, characterized in that, The method further includes step S5: S5: Using the height distribution map of the roughness unit of the thread surface, the curvature change rate and inter-peak spacing of the continuous region are identified. Frequency statistics are performed by sliding the window. The periodic repetitive disturbance area is marked and isolated from the measurement profile, and the real microstructure change area is preserved. The thread roughness feature structure image data is obtained by calculating the inter-peak spacing and the surface structure density distribution trend. The image data of the rough thread feature structure includes periodic disturbance zone marking information, actual microstructure distribution map, peak spacing statistics, and structure density trend map.

9. The laser measurement method for the surface roughness of automotive fasteners according to claim 8, characterized in that, The specific steps of S5 are as follows: S501: Call the normal height value sequence of the continuous region in the height distribution map of the thread surface roughness unit, construct the tangent vector through adjacent height points and calculate the unit curvature change rate, and extract the spatial spacing between local height extreme points as the inter-peak spacing data. Classify and organize the parameters in each continuous region to generate the curvature change rate and inter-peak spacing identification matrix. S502: Based on the peak spacing data of continuous sub-regions in the curvature change rate and peak spacing identification matrix, a fixed window length sliding method is used to count the frequency of peak occurrence in a unit area, identify sub-region sequences with periodic repetitive perturbation characteristics, mark the perturbation area in the original distribution map and set the shielding area, and generate a perturbation area shielding index set. S503: Call the disturbance region shielding index set, filter the original thread surface roughness unit height distribution map, remove the marked periodic disturbance regions, retain only the unshielded real microstructure change regions, and perform statistical normalization processing on the peak spacing sequence and the corresponding region normal height density distribution trend in the remaining regions to obtain thread roughness feature structure image data.

10. A laser measurement system for the surface roughness of automotive fasteners, characterized in that, The system is used to implement the laser measurement method for the surface roughness of automotive fasteners according to any one of claims 1-9, the system comprising: The spatial configuration acquisition module acquires standard samples of automotive threaded fasteners, calls a three-axis profile scanner to detect the pitch area within the same turn, extracts the spatial coordinate data of the thread valley apex, the set of helical trajectory points, and the local arc curvature value and curvature change direction at each point, performs the corresponding calculation of the included angle difference and the guiding direction, and generates a set of spatial configuration parameters for the thread segment. The laser focusing path module uses the aforementioned thread segment spatial configuration parameter set to obtain the real-time axial coordinate values ​​of the axial displacement encoder and the angular coordinate values ​​of the rotary encoder, calculates the real-time laser incident point in the spatial position of the thread surface, judges the difference between the curvature value and the focal plane distance from the laser head to the laser incident point, adjusts the laser head displacement, and generates a real-time focusing adjustment path for the laser incident point. The reflection angle correction module calls the laser incident point to focus and adjust the path in real time, performs laser emission on the specified incident point, collects the reflected light intensity value and the corresponding reflection angle change sequence of the laser reflection point within the measurement period, identifies the reflection offset segment based on the reflection segment corresponding to the curvature abrupt value in the reflected light intensity value change curve, and generates the threaded surface reflection angle correction sequence. The microstructure extraction module uses the threaded surface reflection angle correction sequence to calculate the vector difference between the correction angle corresponding to each point and the laser incident direction to obtain the normal component value of the incident point. It also calculates the angle and gradient between the normal component vectors of adjacent reflection points and the spatial coordinate difference to generate a threaded surface roughness element height distribution map. The feature recognition module uses the height distribution map of the roughness unit on the threaded surface to perform frequency statistics by sliding the window, marks and isolates the periodic repetitive disturbance area from the measurement profile, retains the real microstructure change area, and generates thread roughness feature structure image data by calculating the inter-peak spacing statistics and the surface structure density distribution trend.

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