Automatic size measurement method and system based on automobile hub

Abstract

The invention relates to the technical field of optical dimension measurement, in particular to an automatic dimension measurement method and system based on an automobile hub, and the method comprises the following steps: controlling an industrial personal computer to set a light source and exposure and record an angle time sequence, collecting images according to angles, extracting and recombining edge points, comparing distribution, eliminating jumping interference, and screening a continuous sequence; and aligning the edge points and extracting boundary lines to generate a hub contour graph set. According to the invention, by constructing an image acquisition time sequence based on angle triggering, accurate correspondence of edge points and angle information is realized, stable association of an edge point sequence in the axial direction is ensured, jump interference is eliminated by means of change detection, continuous edge data is screened, and equal-interval reference positions are set in an image to extract boundary elements. A wire frame diagram set with a clear structure is generated, and a complete contour graph is constructed through superposition of sectional diagrams, so that the precision and consistency of contour reconstruction are improved, the recognition capability of a complex structure is enhanced, and the stability of measured data and the anti-interference performance of image processing are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical size measurement, in particular to a size automatic measurement method and system based on automobile wheel hub. BACKGROUND

[0002] The technical field of optical size measurement involves the non-contact detection and evaluation of the geometric dimensions of objects using optical principles. The core includes precise measurement of linear or curved dimensions such as length, diameter, profile, displacement, and thickness based on methods such as laser triangulation, structured light, interference, and optical imaging. This technology is widely used in industrial manufacturing, quality control, and automated detection, with characteristics of high precision and high efficiency. It often cooperates with image processing and numerical control technology to achieve automated detection and feedback control. Traditional size automatic measurement methods involve capturing images or laser reflection signals of key parts of the wheel hub during production or assembly using industrial cameras and laser ranging sensors. Subsequently, image analysis algorithms or laser profile analysis methods are used to extract profile edges, measure diameters, depths, and thicknesses, and compare them with manually set reference values to identify and determine size deviations. The methods usually use two-dimensional image recognition or single-point laser scanning, which are limited by device placement and workpiece motion trajectories, resulting in measurement precision fluctuations, weak interference resistance, and inability to adapt to complex structures.

[0003] Existing technologies lack precise control of the rotation angle of the wheel hub during image acquisition, and there is no uniform angle reference between image frames, resulting in discontinuous and misaligned edge point distribution, making it impossible to construct a stable image sequence for subsequent analysis. Some areas may produce false edges due to insufficient angle coverage or image interference, which can interfere with size determination accuracy. The edge extraction and image comparison results are sensitive to changes in light and workpiece surface, making it difficult to identify and eliminate jump errors, causing radial size measurement fluctuations, affecting overall detection stability and image restoration completeness, and making it difficult to support detailed analysis and high consistency determination of profile structures. SUMMARY

[0004] To achieve the above purpose, the present application adopts the following technical scheme: a size automatic measurement method based on automobile wheel hub, comprising the following steps: S1: Control the industrial computer to set the light source state and laser exposure period, read the reference trigger signal and equal-angle trigger signal, record the response time of the angle phase, arrange the response time in order of angle, and construct an optical trigger time sequence set; S2: Call the optical trigger time sequence set to control image acquisition, extract edge points in the axial and radial directions of the image frame, classify the edge points by angle phase number, reorganize the edge points of the same angle phase in axial order, and generate an initial sequence set of image edge points; S3: calling the initial image edge point sequence set, comparing the edge point distribution in the radial direction of adjacent angle phases, identifying the jumping interference point column and removing it, and screening the continuous image edge point sequence set; S4: calling the continuous image edge point sequence set, aligning the edge point column based on the angle phase number, setting the axial reference position, extracting the boundary point and drawing the boundary line, and combining to generate the contour cross-section line frame graph set; S5: calling the contour cross-section line frame graph set, extracting the radial distance between the boundary lines, drawing the diameter line and the outer boundary line pattern in the order of angle phase, and combining to generate the automobile hub measurement contour pattern set.

[0005] As a further scheme of the present application, the optical trigger timing set includes a reference trigger signal time, an equal angle trigger signal time, an angle phase number, and a trigger time arrangement sequence. The initial image edge point sequence set includes an axial edge point set, a radial edge point set, a phase number correspondence relationship, and an edge point classification result. The continuous image edge point sequence set includes a radial distribution change point, a jumping interference marker point, and a continuous screening edge point sequence. The contour cross-section line frame graph set includes an equal interval reference point, a boundary line node structure, and an angle phase corresponding line frame graph. The automobile hub measurement contour pattern set includes a diameter distribution line graph, an outer boundary line graph of the contour, and an angle sequence combined pattern structure.

[0006] As a further scheme of the present application, the identifying the jumping interference point column and removing it refers to comparing the edge point distribution in the radial direction of adjacent angle phases, identifying the changed point column, and removing it from the edge point sequence.

[0007] As a further scheme of the present application, the classifying the edge points according to the angle phase number refers to classifying and arranging the extracted edge points according to the corresponding angle phase number.

[0008] As a further scheme of the present application, the specific steps of S1 are: S101: based on the industrial computer control signal, configuring the exposure period parameters of the light source control signal and the laser profile acquisition, calling the exposure duration and light emission period parameters in the laser profiler and light source controller, establishing the time synchronization relationship between light source emission and laser acquisition, and generating an exposure period synchronization configuration group; S102: based on the exposure period synchronization configuration group, collecting the reference trigger signal and the equal angle trigger signal output by the rotating clamp, extracting the time index representing the hub angle, and normalizing and rearranging all the equal angle trigger times to generate a hub equal angle response time sequence; S103: calling the wheel hub isometric angle response time sequence, sequentially sorting the angle positions in the rotation period according to the time index, performing linear fitting processing, obtaining the time mapping points corresponding to the angles, and establishing an optical trigger time sequence set.

[0009] As a further scheme of the present application, the specific steps of S2 are: S201: calling the optical trigger time sequence set, controlling the line laser profiler to collect profile image frames according to the trigger time, synchronously starting image collection for each trigger time point, storing the obtained image frames according to the trigger sequence number, and establishing a profile image frame sequence set; S202: based on the profile image frame sequence set, detecting the gray scale gradient change in the axial direction and the radial direction region in each frame of image, extracting the image coordinate points with an edge strength value greater than a preset edge extraction threshold, performing structure splitting according to the direction where the coordinates are located, and obtaining a direction-separated edge point set; S203: calling the direction-separated edge point set, classifying the edge point data frames according to the angle phase number corresponding to the image, and reorganizing the edge point sequence in each angle phase according to the axial position index, to establish an image edge point initial sequence set.

[0010] As a further scheme of the present application, the specific steps of S3 are: S301: calling the image edge point initial sequence set, performing continuity detection on the image edge point sequence corresponding to all angle phases, constructing a phase pair according to the phase number sequence, calculating the position difference value of the edge point coordinates in the radial direction of adjacent phases, and generating a phase radial displacement sequence; S302: based on the phase radial displacement sequence, comparing the radial displacement amount in the phase pair with a preset radial runout judgment threshold, marking the phase index that exceeds the preset radial runout judgment threshold, and judging the edge point sequence in the corresponding phase as an abnormal point column, logically splitting the abnormal phase and the normal phase, and obtaining a runout interference phase identification set; S303: calling the runout interference phase identification set, performing a rejection operation on the edge point sequence corresponding to the abnormal phase in the image edge point initial sequence set, and re-aggregating the edge point sequence in the remaining phase according to the phase sequence to form a data structure composed of only continuous phase edge points, and screening and establishing a continuous image edge point sequence set.

[0011] As a further scheme of the present application, the specific steps of S4 are: S401: call the continuous image edge point sequence set, compare the coordinate offset of adjacent edge point columns in the axial and radial directions according to the angle phase number, and adjust the position parameter of the edge point column in the axial coordinate system according to the comparison result, so that all edge point sequences are aligned with the reference benchmark in the axial direction, and an aligned edge point sequence set is established; S402: based on the aligned edge point sequence set, set equally spaced reference positions in the axial direction in each edge point column, extract the radial boundary point coordinates on both sides of the reference positions, and perform linear connection operation on adjacent boundary point coordinates to construct boundary line segments under corresponding angles, and obtain a multi-phase boundary line segment set; S403: call the multi-phase boundary line segment set, perform structure combination on the boundary line segment data frames under all angle phases according to the phase number sequence, and perform coordinate unified conversion on the combined boundary line segments to construct a contour cross-section line frame set.

[0012] As a further scheme of the present application, the specific steps of S5 are: S501: call the contour cross-section line frame set, extract the distance values of the coordinate points between the boundary line pairs in the radial direction in each cross-section graph in turn, calculate the coordinate difference values corresponding to each axial position of the upper and lower boundaries, organize all distance data in the axial sequence, and establish a cross-section radial distance sequence set; S502: based on the cross-section radial distance sequence set, set uniformly spaced interpolation reference points in the boundary line frame structure under the corresponding angle phase in the axial direction, construct a diameter line according to the corresponding radial distance value, and superimpose and draw the outer boundary line pattern in each cross-section graph to obtain an angle phase contour pattern sequence; S503: call the angle phase contour pattern sequence, aggregate all pattern structures according to the phase number sequence, and perform coordinate unified conversion and structure reorganization on all angle bitmaps to construct an automobile hub measurement contour pattern set.

[0013] Based on the size automatic measurement system of the automobile hub, comprising: The trigger timing generation module is used to realize S1: controlling the industrial computer to set the light source state and the laser exposure period, reading the reference trigger signal and the equal-angle trigger signal, recording the response time of the angle phase, arranging the response time in the angle sequence, and constructing an optical trigger timing set; The edge point extraction module is used to realize S2: calling the optical trigger timing set to control image acquisition, extracting the edge points in the axial direction and the radial direction in the image frame, classifying the edge points according to the angle phase number, reorganizing the edge points of the same angle phase in the axial sequence, and generating an image edge point initial sequence set; The interference elimination module is used for realizing S3: calling the initial sequence set of the image edge points, comparing the edge point distribution of adjacent angle phases in the radial direction, identifying the jumping interference point column and eliminating, and screening the continuous image edge point sequence set; The contour line construction module is used for realizing S4: calling the continuous image edge point sequence set, aligning the edge point column based on the angle phase number, setting the axial reference position, extracting the boundary point and drawing the boundary line, and combining to generate the contour cross-section line frame graph set; The measurement graph generation module is used for realizing S5: calling the contour cross-section line frame graph set, extracting the radial distance between the boundary lines, drawing the diameter line and the outer boundary line pattern in the order of the angle phase, and combining to generate the automobile hub measurement contour pattern set.

[0014] Compared with the prior art, the advantages and positive effects of the present application are that: In the present application, the image acquisition timing based on angle triggering is constructed, the accurate correspondence between the edge points and the angle information is realized, the stable correlation of the edge point sequence in the axial direction is ensured, the jumping interference is eliminated by means of change detection, the continuous edge data is screened, the boundary elements are extracted by setting the equidistant reference position in the image, the line frame graph set with clear structure is generated, the complete contour pattern is constructed by cross-section graph superposition, the precision and consistency of the contour reconstruction are improved, the recognition ability for complex structures is enhanced, and the stability of the measurement data and the anti-interference performance of the image processing are improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 The step flowchart of the present application is shown in the figure; Figure 2 The S1 refinement diagram of the present application is shown in the figure; Figure 3 The S2 refinement diagram of the present application is shown in the figure; Figure 4 The S3 refinement diagram of the present application is shown in the figure; Figure 5 The S4 refinement diagram of the present application is shown in the figure; Figure 6 The S5 refinement diagram of the present application is shown in the figure; Figure 7 The system module diagram of the present application is shown in the figure. DETAILED DESCRIPTION

[0017] The technical solutions in the present application will be described below with reference to the drawings.

[0018] To make the technical problems, technical solutions and advantages of the present application clearer, specific embodiments will be described in detail below with reference to the drawings.

[0019] Please refer to Figure 1 The embodiment of the present application provides an automatic measurement method based on the size of an automobile hub, comprising the following steps: S1: In the automobile hub detection station, a collection link of an optical size collection task is constructed, an industrial computer controls the light-emitting state of a light source and the exposure period of laser profile collection, during one rotation of the hub, a reference trigger signal and an equal-angle trigger signal output by a rotating clamp are read, the response time corresponding to each angle phase of the hub is recorded, all time points are arranged in order of angle, and an optical trigger time sequence set is constructed; S2: The optical trigger time sequence set is called to control the line laser profile collection to collect profile image frames according to the trigger time, edge points in the axial direction and the radial direction are extracted in each image, the edge points are classified through the angle phase number corresponding to the image, the edge point sequence of the same phase is reorganized according to the axial order, and an initial sequence set of image edge points is generated; S3: The initial sequence set of image edge points is called to detect the continuity of the image edge point sequence corresponding to all angle phases, the distribution of the change position is analyzed by comparing the edge point distribution in the radial direction of adjacent phases, the image point column including the runout interference is identified and removed, and a continuous image edge point sequence set is screened; S4: The continuous image edge point sequence set is called to perform image profile alignment processing based on the angle phase number, equal-interval reference positions are set in the axial direction for each group of edge point columns, boundary points on both sides are extracted at each reference position and a boundary line structure is drawn, the boundary line set generated under all angle phases is combined, and a profile cross-sectional line frame set is constructed; S5: The profile cross-sectional line frame set is called to extract the radial distance information between the boundary lines in each cross-sectional diagram, the diameter line and the profile outer boundary line pattern are drawn in order according to the angle phase sequence, and the pattern structures of all angle positions are combined to construct an automobile hub measurement profile pattern set.

[0020] The optical trigger timing set includes a reference trigger signal time, an equal-angle trigger signal time, an angle phase number, and a trigger time arrangement sequence. The initial sequence set of image edge points includes an axial edge point set, a radial edge point set, a phase number correspondence, and an edge point classification result. The continuous image edge point sequence set includes a radial distribution change point, a beat interference marker point, and a continuity screened edge point sequence. The contour cross-section wireframe set includes an equal-interval reference point, a boundary line node structure, and an angle phase corresponding wireframe. The automobile hub measurement contour graph set includes a diameter distribution line graph, a contour outer boundary line graph, and an angle sequence combined graph structure.

[0021] Please refer to Figure 2 The specific steps of S1 are as follows: S101: Based on the industrial computer control signal, the exposure period of the light source control signal and the laser profile collection is configured with parameters. The exposure duration and the light-emitting period parameters in the laser profiler and the light source controller are called to establish the time synchronization relationship between the light source light-emitting and the laser collection, and an exposure period synchronization configuration group is generated. In the initialization stage of the industrial automation detection process, the industrial computer establishes a bidirectional communication connection with the field programmable logic gate array controller through the gigabit Ethernet interface. The industrial computer sends instructions to the controller to configure the light source control signal and the collection parameters of the laser profiler. This process first defines the stroboscopic mode of the light source, and sets the working mode of the light source controller to the external trigger pulse following mode. The industrial computer calls the pre-stored configuration file to read the exposure duration parameters and the light-emitting period parameters therein. For example, the sensor exposure time of the laser profiler is set to 200 microseconds, and the light-emitting pulse width of the light source controller is set to 250 microseconds, so as to ensure that the light source brightness covers the integration time of the sensor. Subsequently, the time synchronization logic is executed, and the crystal oscillator clock inside the controller is used as the main clock source to send a synchronization reset signal to the laser profiler to clear the internal counters of the two. On this basis, according to the pre-set rotating speed of the rotating clamp, for example, 60 revolutions per minute, the minimum trigger interval required is calculated. If the rotating speed is 60 revolutions per minute, i.e. 1 revolution per second, the corresponding single-circle period is 1000000 microseconds. If the number of single-circle collection points is 1000, the theoretical trigger interval is 1000 microseconds. The calculated 200 microseconds of exposure time, 1000 microseconds of light-emitting period, and 250 microseconds of pulse width are packaged into a group of configuration data packets. The data packets are written into the underlying registers through the communication protocol to complete the generation of the exposure period synchronization configuration group. This process ensures that the light source and the camera can synchronize actions with microsecond-level accuracy every time a physical trigger signal is generated, eliminating the phenomenon of missed shots or uneven brightness caused by communication delay.

[0022] As shown in Table 1, detailed synchronization configuration parameters are recorded, which serve as reference data for subsequent steps.

[0023] Table 1 exposure synchronization configuration parameter table

[0024] Table 1 lists the key parameter setting values for synchronization control in the embodiment. For the above logic, in order to ensure that the light source brightness fully covers the exposure period of the image sensor, the light source opening time needs to be set to an earlier time than the image exposure start time, the exposure pulse width minus the exposure duration plus the synchronization compensation time. Taking the exposure duration 200 microseconds, the light source pulse width 250 microseconds, and the synchronization compensation 5 microseconds as an example, the calculation of the light source opening advance is (250-200)+5 = 55 microseconds, that is, the light source should be turned on 55 microseconds before the exposure starts, to ensure that it continues to emit light during the entire exposure period of the sensor; then the light source pulse width value 250 microseconds is calculated by ratio with the minimum trigger period value 1000 microseconds, and the duty cycle parameter of the light source is 0.25. The benefit of this operation logic is that by accurately calculating the duty cycle and the advance, the trailing problem of the stroboscopic light source in high-speed rotating detection is effectively avoided, while the heat load of the LED light source is reduced, and the equipment life is prolonged.

[0025] S102: Based on the exposure period synchronization configuration group, collect the reference trigger signal and the equal-angle trigger signal output by the rotating clamp, extract the time index representing the hub angle, and normalize and rearrange all equal-angle trigger times to generate a hub equal-angle response time sequence; The motion control module of the rotary clamp is activated, and the pulse signals fed back by the encoder are collected in real time through the high-speed input / output interface. The rotary clamp is equipped with a high-resolution incremental encoder that outputs a reference trigger signal, i.e., a Z-phase pulse, and a number of equal-angle trigger signals, i.e., A-phase pulses, per rotation. An internal high-speed counter records the time of arrival of each pulse in microseconds. When the rising edge of the reference trigger signal is detected, the current timestamp is marked as the zero index, and the timing capture of the subsequent equal-angle trigger signals is started. Assuming that the encoder resolution is 3600 pulses per revolution, 3600 timestamp data will be continuously captured within one rotation period. Extract these time data and store them in a double-precision floating-point array to form the original time sequence. Then, the normalization rearrangement operation is performed to eliminate the nonlinear errors caused by motor speed fluctuations. This process first obtains the total length of the rotation period, i.e., the time difference between two adjacent reference trigger signals, for example, 1000500 microseconds. Then, the relative timestamp of each equal-angle trigger signal is divided by the total length to obtain a normalized time coefficient between 0 and 1. According to the size of these coefficients, all the trigger time points collected are reordered to ensure that the time index is strictly monotonically increasing, thereby generating the equal-angle response time sequence of the hub. This sequence accurately reflects the absolute time corresponding to each small angle position of the hub during rotation, providing a high-precision time domain reference for subsequent image acquisition.

[0026] For the normalization calculation logic, the relative timestamp value of the 500th trigger signal is 138900 microseconds, and the total length value of the current rotation period is 1000000 microseconds. Perform division operation, 138900 divided by 1000000, get the normalized time coefficient 0.1389. Then, multiply this coefficient by 360 degrees to get the corresponding physical angle of 50.004 degrees. The benefit of this operation logic is that it automatically compensates for the slight speed jitter of the motor during single rotation through normalization processing, ensuring the accuracy of angle calculation.

[0027] S103: Call the equal-angle response time sequence of the hub, sequentially sort the angle positions in the rotation period according to the time index, and perform linear fitting processing to obtain the time mapping points corresponding to the angles, and establish the optical trigger time sequence set; The hub's angular response time series in memory is called, which contains the time point data after normalization processing. According to the time index, these time points are mapped into the 0 to 360 degree angle space of the rotation period. Due to the existence of clearance and elastic deformation in mechanical transmission, the directly mapped time points may have local nonlinearity. Therefore, linear fitting processing is performed to optimize this mapping relationship. Select 10 consecutive time points as a sliding window, and use the least squares method to perform regression analysis on the time and angle relationship in the window. The objective function is constructed, that is, the sum of the squares of the distances of each data point to the fitted straight line, and the slope and intercept parameters that minimize the objective function are calculated by derivation. Using the straight line equation obtained by fitting, the theoretical accurate time points corresponding to each angle position are recalculated, which are the angle corresponding time mapping points. These corrected time points are arranged in order to establish the optical trigger timing set. This set defines the exact moment when the laser profiler should collect at each predetermined angle, eliminating the influence of mechanical jitter on the imaging geometric position.

[0028] In the specific example of performing linear fitting, a group of data points are selected, in which the time variables are 10000 microseconds, 20000 microseconds, 30000 microseconds, and the corresponding angle variables are 3.6 degrees, 7.2 degrees, and 10.8 degrees. First, calculate the average value of the time variable 20000 microseconds and the average value of the angle variable 7.2 degrees. Then, calculate the covariance value of the time variable and the angle variable, and the variance value of the time variable. By dividing the covariance by the variance, the slope parameter of the fitted straight line is 0.00036 degrees per microsecond. Then, use the average value and the slope to calculate the intercept parameter, the calculation process is 7.2-0.00036x20000=0. Finally, use the linear model to predict the angle corresponding to the time point 40000 microseconds, the calculation method is 0.00036x40000+0=14.4 degrees. The benefit of this operation logic is that it smooths high-frequency noise by local linearization, making the trigger timing more uniform and reliable.

[0029] Please refer to Figure 3 , the specific steps of S2 are: S201: Call the optical trigger timing set, control the line laser profiler to collect the profile image frame according to the trigger time, start the image acquisition simultaneously for each trigger time point, and store the acquired image frame according to the trigger number, establish the profile image frame sequence set; Precise optical trigger timing set, which contains a series of trigger time stamps accurate to microseconds. The controller sends trigger pulses to the line laser profiler through hardware interrupts according to these time stamps. Whenever the clock matches the trigger time, the laser profiler starts the CMOS sensor for exposure. For example, at the 1st trigger time, the sensor is turned on, and after 200 microseconds of integration, it is turned off. The optical signal is converted to an electrical signal and read out as a frame of digital image. This image captures the cross-sectional shape of the laser line projected on the hub surface. This operation is performed for each trigger time point in turn, for example, 3600 frames of images are collected within one rotation. Each frame of image is assigned a unique trigger sequence number ranging from 1 to 3600, and the image data is stored in the cache area in the form of a two-dimensional array. These image frames are strictly arranged according to the trigger sequence number, and together they form a set of profile image frame sequences. Each frame of image not only contains the height information of the profile, but also implies the reflectivity information at that moment, providing raw materials for subsequent feature extraction.

[0030] To verify the effectiveness of the acquisition logic, the trigger signal and the image transmission state are monitored. Assuming that the current trigger sequence number is 500, the time stamp of the trigger signal is 500500 microseconds, and the time when the image acquisition is completed and transmitted to the memory is 502000 microseconds. The time difference between the two is 1500 microseconds, which is compared with the preset timeout threshold of 2000 microseconds. Since 1500 is less than 2000, it is determined that the frame acquisition is valid. If the difference exceeds the threshold, the frame is marked as a lost frame and an interpolation compensation program is started.

[0031] S202: Based on the set of profile image frame sequences, detect the gray scale gradient change in the axial direction and the radial direction region of each frame of image, extract the image coordinate points with edge strength value greater than the preset edge extraction threshold, and perform structure splitting according to the direction of the coordinates to obtain a set of direction-separated edge points; Each frame of image in the set of profile image frame sequences is processed in parallel. For a single frame of image, set the region of interest to cover the entire effective range of the axial direction and the radial direction. Apply the Sobel operator or Canny operator to perform convolution operation on the image to calculate the gray scale gradient amplitude of each pixel point. Specifically, calculate the gray scale difference in the horizontal direction and the vertical direction respectively, and take the square root of the sum of their squares to obtain the gradient strength. A preset edge extraction threshold is set, for example, a gray scale value of 50. Traverse the entire image and mark the pixel coordinate points with gradient strength greater than or equal to 50 as potential edge points. Then, perform structure splitting according to the geometric position of the coordinates. For example, classify the points with Y coordinate less than half of the image height as outside edges, and the points with Y coordinate greater than half of the image height as inside edges, thereby obtaining a set of direction-separated edge points. This step effectively separates the complex light bar formed by the laser line on the hub surface into independent geometric feature lines.

[0032] As shown in Table 2, the edge extraction parameters of different material surfaces are set to adapt to different reflection characteristics.

[0033] Table 2 edge detection parameter configuration table

[0034] Table 2 provides specific threshold settings for edge detection. In a specific operation, the horizontal gradient value 30 and the vertical gradient value 40 of a certain pixel point are read. The square and square root operation is performed, that is, the square of 30 plus the square of 40 equals 2500, and the square root after the operation is the gradient amplitude 50. The calculation result 50 is compared with the edge extraction threshold 50, and it is determined that the point is an effective edge point, and its coordinates are recorded. The beneficial effect of this operation logic is that the gradient module length can robustly identify the laser center line under weak contrast, overcoming the interference of ambient light.

[0035] S203: Call the direction-separated edge point set, classify and process the edge point data frame according to the angle phase number corresponding to the image, and reorganize the edge point sequence in each angle phase according to the axial position index, to establish an initial sequence set of image edge points; The direction-separated edge point set is called, which contains thousands of discrete coordinate points. In order to construct an ordered 3D model, these points need to be structured and reorganized. First, according to the trigger sequence number of each frame of image, it is mapped back to the corresponding angle phase number, for example, the first frame corresponds to 0.1 degrees, and the second frame corresponds to 0.2 degrees. In each specific angle phase, further arrange the radial edge point data, i.e. the column coordinates, in ascending order according to the axial position index, i.e. the row coordinates of the image. If there are multiple edge points at the same angle and the same axial position, use the median filtering strategy to retain the most reliable point. Through this double indexing mechanism, a three-dimensional array structure is established, with dimensions of angle phase, axial position and radial position, which is the initial sequence set of image edge points. This set converts the chaotic pixel points into regular grid data, laying a topological foundation for subsequent contour analysis.

[0036] In the reorganization logic, the data frame with angle phase number 100 is processed. This frame contains two edge points with coordinates (row 200, column 500) and (row 201, column 502). Check the axial position indexes 200 and 201 to confirm their continuity. If the axial index is missing, for example, only 200 and 202, perform a linear interpolation operation. Read the column coordinates 500 of row 200 and the column coordinates 504 of row 202, calculate the average value 502 of the two, and insert (row 201, column 502) into the sequence. The beneficial effect of this operation logic is that it fills the local data void caused by high surface reflection or occlusion, ensuring the continuity of the contour curve.

[0037] Please refer to Figure 4 The specific steps of S3 are as follows: S301: Call the initial sequence set of image edge points, perform continuity detection on the image edge point sequence corresponding to all angle phases, construct adjacent phase pairs according to the phase number order, and calculate the position difference of the edge point coordinates of adjacent phases in the radial direction to generate a phase radial displacement sequence; The continuity detection is performed on the initial sequence set of image edge points, aiming to identify sudden noise caused by mechanical vibration or electrical interference. According to the order of phase numbers, for example, from the 1st phase to the 3600th phase, adjacent phase pairs are constructed one by one, i.e. the kth phase and the k+1th phase. For each pair of phases, the radial coordinates of the edge points at the same axial position are extracted. The algebraic difference of the two coordinates is calculated to obtain the radial displacement. For example, the radial coordinate of the kth phase at the axial position 100 is 50.5 mm, and the coordinate of the k+1th phase at the same position is 50.6 mm, then the displacement is 0.1 mm. Repeat this calculation for all axial positions to generate a radial displacement sequence corresponding to the phase pair. Finally, the calculation results of all phase pairs are summarized to generate a phase radial displacement sequence covering the entire rotation period. This sequence directly reflects the micro undulations and macro jumps of the hub surface in the circumferential direction.

[0038] In a specific example of displacement calculation, adjacent phase pairs A and B are selected. The radial coordinate value of phase A at a certain axial point is 150.25 mm, and the coordinate value of phase B at the same point is 150.35 mm. Perform subtraction operation to calculate 150.35 minus 150.25, and get displacement 0.10 mm. At the same time, calculate the change rate of this point, assuming that the time interval of the two phases is 1 millisecond, then the change rate is 0.10 mm per millisecond. The benefit of this operation logic is that it sensitively captures the high-frequency sudden signal of the surface through difference operation.

[0039] S302: Based on the phase radial displacement sequence, compare the radial displacement of the phase pair with the preset radial jump judgment threshold, mark the phase index that exceeds the preset radial jump judgment threshold, and judge the edge point sequence in the corresponding phase as an abnormal point column, perform logical splitting of abnormal phases and normal phases, and obtain a set of jump interference phase identifiers; Based on the generated phase radial displacement sequence, screening and marking of abnormal phases is performed. A radial runout determination threshold is preset, which is set according to the tolerance range of hub machining accuracy, for example, 0.5 mm. Each displacement value in the phase radial displacement sequence is traversed, and its absolute value is compared with the threshold. If the absolute value of the displacement of a certain phase pair exceeds 0.5 mm, it is determined that the position has an abnormal violent runout, which may be caused by fixture loosening, hub burr or foreign matter attachment. The phase index is marked as abnormal, and the corresponding whole-column edge point sequence in the phase is determined as an abnormal point column. Subsequently, a logical splitting operation is performed to separate all the phase indexes marked as abnormal and store them in a runout interference phase identification set, while the unmarked phases are retained as normal phases.

[0040] As shown in Table 3, the displacement detection results and determination states of some phase pairs are recorded.

[0041] Table 3 radial runout detection result table

[0042] Table 3 shows the screening process of abnormal points. For the data in the table, the radial displacement value of phase pairs 102 to 103 is 0.65 mm, and the determination threshold is 0.50 mm. A comparison operation is performed to determine that 0.65 is greater than 0.50, and the condition is true. A marking action is triggered to record index 103 into the interference set. At the same time, the proportion of abnormal phases is calculated. If the total number of detected phases is 3600 and the number of abnormal phases is 36, the abnormal rate is 1%. The beneficial effect of this operation logic is that it realizes automatic quality preliminary screening and can quickly locate the process deviation on the production line.

[0043] S303: Call the runout interference phase identification set, perform a rejection operation on the edge point sequence corresponding to the abnormal phase in the initial edge point sequence set of the image, and re-aggregate the edge point sequence in the remaining phase according to the phase order to form a data structure composed of only continuous phase edge points, screen and establish a continuous image edge point sequence set; The runout interference phase identification set is called, and the cleaning operation is performed in the initial edge point sequence set of the image. For each abnormal phase index recorded in the identification set, the corresponding edge point data frame is directly rejected and set to empty or marked as invalid. After the rejection operation is completed, there will be discontinuous gaps in the data sequence. In order to restore the topological continuity of the data, the edge point sequence in the remaining normal phase is re-aggregated. According to the original phase order, the scattered data frames are closely arranged to eliminate the index jumps and form a new data structure composed of only continuous and smooth phase edge points. Finally, a continuous image edge point sequence set is established. This set represents the clean contour data after removing high-frequency noise and outliers, and truly restores the main geometric features of the contour.

[0044] In the example of data re-aggregation, assume that the original sequence contains phases 1, 2, 3, 4, 5, in which phase 3 is marked as abnormal. After the rejection, the sequence becomes 1, 2, 4, 5. Read the data frame of phase 4, and remap its logical index to 3. Read the data frame of phase 5, and remap it to 4. At the same time, calculate the spatial distance between the end of phase 2 and the beginning of the original phase 4. Assume that the end of phase 2 has coordinates (100, 50.0), and the beginning of the original phase 4 has coordinates (102, 50.1). Calculate the Euclidean distance using the Pythagorean theorem, i.e., the square of (102 minus 100) plus the square of (50.1 minus 50.0), and the result is approximately 2.002. If the distance is less than the preset continuity threshold of 5.0, confirm the smoothness of the boundary after re-aggregation. The benefit of this operation logic is that it maximizes the preservation of effective geometric information while removing noise, ensuring the authenticity of model reconstruction.

[0045] Please refer to Figure 5 The specific steps of S4 are as follows: S401: Call the continuous image edge point sequence set, compare the coordinate offset of adjacent edge point columns in the axial and radial directions according to the angle phase number, and adjust the position parameters of the edge point columns in the axial coordinate system according to the comparison result, so that all edge point sequences are aligned in the reference benchmark in the axial direction, and an aligned edge point sequence set is established. Call the continuous image edge point sequence set, and start to perform fine geometric correction. Due to the possible slight axial movement of the rotating clamp, the profiles at different angles cannot be strictly aligned in the axial position. According to the angle phase number, compare the coordinate offset of adjacent edge point columns in the axial and radial directions one by one. Select a reference phase, usually the starting phase, as the reference coordinate system. For each subsequent phase, calculate the axial distance of the feature point (such as the hub-flange inflection point) from the previous phase. According to the comparison result, calculate an axial correction vector, and apply the vector to all coordinate points of the edge point column to adjust its position parameters in the axial coordinate system. This process makes all edge point sequences aligned in the reference benchmark in the axial direction, eliminates the "snake" error caused by mechanical movement, and establishes an aligned edge point sequence set.

[0046] In the alignment calculation, phase K and phase K+1 are selected. The axial coordinate value of the rim inflection point in phase K is identified as 200.5 mm, and the corresponding point in phase K+1 is 200.8 mm. Perform subtraction operation, calculate 200.5 minus 200.8 to get the offset of negative 0.3 mm. Determine that all data points in phase K+1 need to perform forward translation operation, and the translation amount is 0.3 mm. Read the original axial coordinate of a certain point in phase K+1, which is 300.0 mm, and add the translation amount of 0.3 mm to get the corrected coordinate of 300.3 mm. The beneficial effect of this operation logic is that the mechanical precision deficiency of the hardware fixture is compensated by the software algorithm, greatly reducing the dependence on precision machinery.

[0047] S402: Based on the alignment edge point sequence set, set equal interval reference positions in the axial direction in each edge point column, extract the radial boundary point coordinates on both sides of the reference positions, and perform linear connection operation on the adjacent boundary point coordinates to construct the boundary line segment under the corresponding angle, and obtain a multi-phase boundary line segment set; The alignment edge point sequence set is set in each edge point column. For example, define a sampling slice every 0.1 mm along the axial direction. At each reference position, extract the nearest radial boundary point coordinates on both sides of the position. Since the actually collected point cloud may be discrete, the linear interpolation method is used to determine the accurate radial value at the reference position. Then, linear connection operation is performed on the adjacent boundary point coordinates, i.e. connecting adjacent sampling points with a straight line segment to construct a continuous boundary line segment under the corresponding angle. Repeat this step for all angle phases to obtain a multi-phase boundary line segment set. This set converts the discrete point cloud into a series of continuous vector contour lines, which is convenient for subsequent geometric measurement.

[0048] In the example of constructing the boundary line segment, the axial reference position X is set to 150.0 mm. Find the nearest point A (149.8, 60.2) in the data set where X is less than 150.0, and the nearest point B (150.2, 60.4) where X is greater than 150.0. Calculate the axial distance between A and B as 0.4 mm, and the radial distance as 0.2 mm. Calculate the axial offset of reference position 150.0 relative to point A as 0.2 mm. Using the proportional relationship, calculate the radial increment as (0.2 ÷ 0.4) × 0.2 = 0.1 mm. Add the radial coordinate of point A 60.2 to the increment 0.1 to get the interpolated radial coordinate at the reference position, which is 60.3 mm. The beneficial effect of this operation logic is that it realizes sub-pixel level contour reconstruction, so that the measurement result is not limited by the sampling resolution.

[0049] S403: Call the multi-phase boundary line segment set, combine the boundary line segment data frames at all angle phases in the order of phase number, and uniformly convert the combined boundary line segments, to construct a contour cross-section line frame set; Call the multi-phase boundary line segment set, combine the boundary line segment data frames at all angle phases in the order of phase number. These line segments form a cylindrical grid structure in space. In order to unify the measurement standard, uniformly convert the combined boundary line segments, convert the relative sensor coordinates to the hub's own cylindrical coordinate system. Using the calibration matrix, convert the (x, z) sensor coordinates of each point to (r, h, theta) cylindrical coordinates, where r represents the radius, h represents the height, and theta represents the angle. Through this conversion, a contour cross-section line frame set is constructed. This set completely describes the geometric skeleton of the hub in three-dimensional space, providing a fully digital twin model for the final dimensional tolerance evaluation.

[0050] In the specific operation of coordinate conversion, the offset of the sensor coordinate system origin relative to the hub rotation center is set as (dx = 500 mm, dz = 100 mm). For a point with sensor coordinates (x = 20 mm, z = 50 mm), calculate its absolute radius r. Perform addition operation, r is equal to dx plus x, that is, 500 plus 20 equals 520 mm. Calculate the absolute height h, h is equal to dz plus z, that is, 100 plus 50 equals 150 mm. If the current phase corresponds to an angle theta of 90 degrees, the point's coordinates in the cylindrical coordinate system are (520, 150, 90). The benefit of this operation logic is to unify local measurement data to a global reference, allowing geometric features at different positions to be directly compared and evaluated.

[0051] Please refer to Figure 6 , the specific steps of S5 are: S501: Call the contour cross-section line frame set, extract the distance values of the coordinate points between the boundary line pairs in the radial direction in each cross-section graph, calculate the coordinate difference values of the upper and lower boundaries at each axial position, and organize all distance data in the axial sequence to establish a cross-sectional radial distance sequence set; The set of profile section wireframe diagrams is invoked to enter the dimensional analysis stage. In each section diagram, the radial distance values ​​of the coordinate points between boundary line pairs are extracted sequentially. Specifically, for features such as the rim thickness or spoke width of the wheel hub, its upper and lower boundaries are identified. The coordinate difference between the upper and lower boundaries at each axial position is calculated. For example, at axial position h, if the upper boundary radius is r_outer and the lower boundary radius is r_inner, then the radial distance is r_outer minus r_inner. All distance data are organized according to the axial sequence to establish a set of radial distance sequences for the sections. This set of sequences quantifies the solid thickness distribution of various parts of the wheel hub.

[0052] In the distance calculation example, a section at an axial position h = 50 mm is selected. The radial coordinates of the upper boundary point are measured to be 225.50 mm, and the radial coordinates of the lower boundary point are 215.50 mm. A subtraction operation is performed, subtracting 215.50 from 225.50, yielding a radial distance of 10.00 mm. To verify compliance, a standard value of 10.00 mm and a tolerance range of ±0.10 mm are introduced. The absolute value of the difference between the measured value and the standard value is calculated to be 0.00 mm, indicating that the dimension at that location is acceptable. The advantage of this calculation logic is that it enables full-section wall thickness detection, allowing for the discovery of shrinkage cavities or uneven wall thickness defects during the casting process.

[0053] S502: Based on the cross-sectional radial distance sequence set, in the boundary wireframe structure corresponding to the angle phase, interpolation reference points are set at uniform intervals in the axial direction, a diameter line is constructed according to the corresponding radial distance value, and the outer boundary line shape graphic is superimposed and drawn in each cross-sectional view to obtain the angle phase contour graphic sequence; Based on the radial distance sequence set of the cross-section, a visualized contour graphic is further constructed. In the boundary wireframe structure corresponding to the angular phase, interpolation reference points are set at uniform intervals along the axial direction, for example, one point per millimeter. According to the corresponding radial distance value, a diameter line is constructed on the screen; that is, a circle or arc is drawn with the rotation center as the center and half the radial distance as the radius. The outer boundary line shape graphic is overlaid on each cross-sectional view, connecting the data points into a smooth curve. In this way, an angular phase contour graphic sequence is obtained. These graphics not only contain numerical information but also intuitively display the contour features of the wheel hub with geometric shapes, facilitating visual inspection by operators.

[0054] In the logic of graphic construction, the radial distance value of a certain point is processed as 450.0 mm. A division operation is performed, dividing 450.0 by 2, to obtain the radius value of 225.0 mm. Using the drawing engine, a circle with a radius of 225.0 pixels is drawn at the center coordinates (1000, 1000) of the canvas. At the same time, the radius of the adjacent point, 225.1 mm, is read, and a line segment is drawn between the two points. The slope of the line segment is calculated as (225.1 - 225.0) ÷ 1.0 = 0.1. The advantage of this operation logic is that it transforms abstract data into an intuitive engineering drawing view, helping engineers quickly understand deformation trends.

[0055] S503: Call the angle phase contour graphic sequence, aggregate all graphic structures according to the phase number order, and perform coordinate transformation and structural reorganization on all angle bitmaps to construct a set of automotive wheel hub measurement contour graphics; The process involves calling the sequence of angle phase contour graphics and performing the final 3D compositing. Based on the phase numbering order, all graphic structures are aggregated. Two-dimensional cross-sectional graphics are rotated and arranged around the central axis, filling the gaps to form closed curved surfaces. All angle bitmaps undergo coordinate transformation and structural reorganization, and using OpenGL or DirectX rendering technology, a set of automotive wheel hub measurement contour graphics is constructed. This set is a complete high-fidelity 3D model, which users can rotate, scale, and section on the interface to comprehensively examine the wheel hub's machining quality.

[0056] In the final example of data aggregation, the number of vertices in the entire model was counted. Assuming 3600 phases per cycle, 1000 points per phase, and a total of 3.6 million points, the memory usage of each point was calculated. Assuming each point is represented by 3 floating-point numbers (12 bytes), the total data volume is approximately 43.2 megabytes. This 43.2 megabytes of data was loaded into video memory, and lighting was applied to render the model. The model's surface area was calculated by summing the areas of all the tiny triangular facets. Assuming the calculated total surface area is 1.5 square meters, it was compared with the theoretical surface area of ​​1.48 square meters from the CAD design model. The deviation rate was calculated as (1.5 - 1.48) divided by 1.48, approximately 1.35%. The advantage of this calculation logic is that it provides a macro-level quality evaluation indicator, making the consistency assessment of the product more objective and scientific.

[0057] Please see Figure 7 An automated measurement system for automotive wheel hub dimensions includes: The trigger timing generation module is used to implement S1: control the industrial computer to set the light source state and laser exposure cycle, read the reference trigger signal and the equal angle trigger signal, record the response time of the angle phase, arrange the response times in angle order, and construct an optical trigger timing set; The edge point extraction module is configured to implement S2: calling optical trigger timing set control image acquisition, extracting edge points in axial direction and radial direction in the image frame, classifying the edge points according to angle phase numbers, reorganizing the edge points of the same angle phase according to axial sequence, and generating an initial sequence set of image edge points; The interference elimination module is configured to implement S3: calling the initial sequence set of image edge points, comparing edge point distribution in the radial direction of adjacent angle phases, identifying and eliminating a column of jumping interference points, and screening a continuous image edge point sequence set; The contour line construction module is configured to implement S4: calling the continuous image edge point sequence set, aligning the edge point column based on angle phase numbers, setting an axial reference position, extracting boundary points and drawing boundary lines, and combining to generate a contour cross-sectional line frame set; The measurement graph generation module is configured to implement S5: calling the contour cross-sectional line frame set, extracting radial distance between boundary lines, drawing diameter lines and outer boundary line graphics in angle phase sequence, and combining to generate a set of automobile hub measurement contour graphics.

[0058] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the technical solution.

Claims

1. An automated measurement method for the dimensions of automobile wheel hubs, characterized in that, Includes the following steps: S1: Control the industrial computer to set the light source status and laser exposure cycle, read the reference trigger signal and the equal angle trigger signal, record the response time of the angle phase, arrange the response times in angle order, and construct an optical trigger timing set; S2: Call the optical trigger timing set to control image acquisition, extract edge points in the axial and radial directions of the image frame, classify the edge points according to the angle phase number, reorganize the edge points of the same angle phase in axial order, and generate an initial sequence set of image edge points. S3: Call the initial sequence set of image edge points, compare the distribution of edge points in the radial direction of adjacent angular phases, identify and remove jittery interference point sequences, and filter the continuous image edge point sequence set; S4: Call the continuous image edge point sequence set, align the edge point column based on the angle phase number, set the axial reference position, extract the boundary points and draw the boundary lines, and combine them to generate a set of contour section wireframe diagrams; S5: Call the set of contour section wireframe diagrams, extract the radial distance between boundary lines, draw the diameter line and outer boundary line graphics in the order of angle and phase, and combine them to generate a set of automotive wheel hub measurement contour graphics.

2. The automated measurement method for the dimensions of automobile wheel hubs according to claim 1, characterized in that, The optical trigger timing set includes the reference trigger signal time, the equal angle trigger signal time, the angle phase number, and the trigger time arrangement sequence. The image edge point initial sequence set includes the axial edge point set, the radial edge point set, the phase number correspondence, and the edge point classification result. The continuous image edge point sequence set includes radial distribution change points, jump interference marker points, and continuous filtering edge point sequences. The contour cross-section wireframe diagram set includes equally spaced reference points, boundary line node structures, and angle phase corresponding wireframe diagrams. The automobile wheel hub measurement contour graphic set includes diameter distribution line diagrams, contour outer boundary line diagrams, and angle sequence combination graphic structures.

3. The automated measurement method for the dimensions of automobile wheel hubs according to claim 1, characterized in that, The process of identifying and removing interfering points involves comparing the distribution of edge points in the radial direction of adjacent angular phases, identifying changing point sequences, and removing them from the edge point sequence.

4. The automated measurement method for the dimensions of automobile wheel hubs according to claim 1, characterized in that, The classification of edge points by angle and phase number refers to classifying and organizing the extracted edge points according to their corresponding angle and phase numbers.

5. The automated measurement method for the dimensions of automobile wheel hubs according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Based on the industrial control computer control signal, the parameters of the light source control signal and the exposure period of the laser profile acquisition are configured. The exposure duration and emission period parameters in the laser profiler and the light source controller are called to establish the time synchronization relationship between the light source emission and the laser acquisition, and generate the exposure period synchronization configuration group. S102: Based on the exposure cycle synchronization configuration group, collect the reference trigger signal and the constant angle trigger signal output by the rotating fixture, extract the time index representing the hub angle, and normalize and rearrange all constant angle trigger times to generate the hub constant angle response time sequence. S103: Call the wheel hub angle response time series, sort them sequentially according to the angle position within the rotation cycle based on the time index, perform linear fitting processing, obtain the time mapping point corresponding to the angle, and establish an optical triggering time series set.

6. The automated measurement method for the dimensions of automobile wheel hubs according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Call the optical trigger timing set, control the line laser profilometer to acquire contour image frames according to the trigger time, synchronously start image acquisition for each trigger time point, and number and store the acquired image frames according to the trigger sequence number to establish a contour image frame sequence set; S202: Based on the contour image frame sequence set, detect the gray-level gradient changes in the axial and radial regions of each frame image, extract image coordinate points whose edge intensity values ​​are greater than the preset edge extraction threshold, perform structural splitting according to the direction of the coordinates, and obtain a set of direction-separated edge points. S203: Call the direction-separated edge point set, classify the edge point data frames according to the angle phase number corresponding to the image, and reorganize the edge point sequence according to the axial position index within each angle phase to establish an initial sequence set of image edge points.

7. The automated measurement method for the dimensions of automobile wheel hubs according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Call the initial sequence set of image edge points, perform continuity detection on the image edge point sequences corresponding to all angle phases, construct adjacent phase pairs according to the phase number order, and calculate the position difference of the edge point coordinates of adjacent phases in the radial direction to generate a phase radial displacement sequence. S302: Based on the phase radial displacement sequence, compare the radial displacement of the phase pair with the preset radial runout judgment threshold, mark the phase index that exceeds the preset radial runout judgment threshold, and determine the edge point sequence within the corresponding phase as an abnormal point sequence. Perform logical splitting of abnormal phases and normal phases to obtain a runout interference phase identifier set. S303: Call the jitter interference phase identifier set, perform a removal operation on the edge point sequence corresponding to the abnormal phase in the initial sequence set of image edge points, and re-aggregate the edge point sequences in the remaining phase according to the phase order to form a data structure consisting only of continuous phase edge points, and filter and establish a continuous image edge point sequence set.

8. The automated measurement method for the dimensions of automobile wheel hubs according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Call the continuous image edge point sequence set, compare the coordinate offsets of adjacent edge point columns in the axial and radial directions according to the angle phase number, and adjust the position parameters of the edge point columns in the axial coordinate system according to the comparison results, so that all edge point sequences are aligned with the reference reference in the axial direction, and establish an aligned edge point sequence set. S402: Based on the set of aligned edge point sequences, set equally spaced reference positions in the axial direction in each edge point column, extract the coordinates of radial boundary points on both sides of the reference positions, and perform a linear connection operation on the coordinates of adjacent boundary points to construct boundary line segments at the corresponding angles and obtain a set of multi-phase boundary line segments. S403: Call the multi-phase boundary line segment set, perform structural combination of boundary line segment data frames under all angle phases in phase number order, and perform coordinate transformation on the combined boundary line segments to construct a contour section wireframe set.

9. The automated measurement method for the dimensions of automobile wheel hubs according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Call the set of outline cross-section wireframe diagrams, extract the distance values ​​of coordinate points between boundary line pairs in the radial direction in each cross-section diagram, calculate the coordinate difference values ​​of the upper and lower boundaries at each axial position, and organize all distance data according to the axial sequence to establish a cross-section radial distance sequence set; S502: Based on the radial distance sequence set of the cross section, in the boundary wireframe structure corresponding to the angle phase, interpolation reference points are set at uniform intervals in the axial direction, a diameter line is constructed according to the corresponding radial distance value, and the outer boundary line shape graphic is superimposed and drawn in each cross section to obtain the angle phase contour graphic sequence. S503: Call the angle phase contour graphic sequence, aggregate all graphic structures according to the phase number order, and perform coordinate transformation and structural reorganization on all angle bitmaps to construct a set of automotive wheel hub measurement contour graphics.

10. An automated measurement system for the dimensions of automobile wheel hubs, characterized in that, The system is used to implement the automated measurement method for the dimensions of automobile wheel hubs according to any one of claims 1-9, and the system includes: The trigger timing generation module is used to implement S1: control the industrial computer to set the light source state and laser exposure cycle, read the reference trigger signal and the equal angle trigger signal, record the response time of the angle phase, arrange the response times in angle order, and construct an optical trigger timing set; The edge point extraction module is used to implement S2: calling the optical trigger timing set to control image acquisition, extracting edge points in the axial and radial directions of the image frame, classifying edge points by angle phase number, reorganizing edge points of the same angle phase in axial order, and generating an initial sequence set of image edge points; The interference removal module is used to implement S3: call the initial sequence set of image edge points, compare the distribution of edge points in the radial direction of adjacent angular phases, identify and remove jumping interference point sequences, and filter the continuous image edge point sequence set; The contour line construction module is used to implement S4: calling the continuous image edge point sequence set, aligning the edge point column based on the angle phase number, setting the axial reference position, extracting boundary points and drawing boundary lines, and combining them to generate a contour section wireframe set; The measurement diagram generation module is used to implement S5: call the set of contour section wireframe diagrams, extract the radial distance between boundary lines, draw the diameter line and outer boundary line graphics in the order of angle and phase, and combine them to generate a set of automobile wheel hub measurement contour graphics.

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