High-precision Inner Diameter Measurement Method and System for Automotive Nuts

Abstract

This application relates to the technical field of dimensional measurement, and specifically relates to a high-precision inner diameter measurement method and system for automotive nuts. The method includes: obtaining the sectional curves of multiple raceways of the nut on the raceway cross-section; dividing the sectional curves into respective sub-curves; determining the difference between any two sub-curves based on the difference in values of any two sub-curves; obtaining the midpoint of the sectional curve on the horizontal axis, and determining the cutting direction of the nut based on the difference between the degrees of slow change of the sub-curves on both sides of the midpoint; adjusting the formula for calculating the local reachability density of each sub-curve by the LOF anomaly detection algorithm; determining whether the nut meets the standard. If the nut does not meet the standard, no subsequent processing is performed; otherwise, determining the ball radius of the nut; and further determining the inner diameter of the nut. This application aims to select a suitable measurement tool based on the actual situation of the internal thread of the nut to improve the measurement accuracy of the inner diameter of the nut.

Description

Technical Field

[0001] This application relates to the technical field of dimensional measurement, and particularly to a high-precision inner diameter measurement method and system for automotive nuts. Background Art

[0002] With the rapid development of the automotive industry, the precision requirements for automotive parts are constantly increasing. As an important component for connection and fixation, the measurement accuracy of the inner diameter of nuts directly affects the mating quality of bolts and the safety of the overall structure. In recent years, laser displacement sensor measurement technology has been gradually applied to the inner diameter measurement of nuts, but there are still many challenges in practical applications.

[0003] In the high-precision measurement of the inner diameter of automotive nuts, the machining accuracy of multiple internal threads of the nut has a significant impact on the measurement results. Due to the non-uniformity in the machining process of the threads, there are differences in the thread shape, depth, or pitch, which cause the measuring tools (such as steel needles or steel balls) to be unable to accurately contact the thread positions, resulting in unstable contact points during measurement, thus introducing excessive measurement deviations and affecting the reading accuracy of the sensor. Summary of the Invention

[0004] In view of the above, it is necessary to provide a high-precision inner diameter measurement method and system for automotive nuts. Compared with the traditional nut inner diameter measurement method, a suitable measuring tool is selected according to the actual situation of the internal threads of the nut to improve the measurement accuracy of the nut inner diameter:

[0005] In a first aspect, an embodiment of the present application provides a high-precision inner diameter measurement method for automotive nuts, and the method includes the following steps:

[0006] Denote the cross-section perpendicular to the nut raceway as the raceway cross-section, and obtain the cross-section curves of multiple raceways of the nut on the raceway cross-section;

[0007] Segment the cross-section curve based on the trend change situation within the neighborhood range of each point on the cross-section curve to obtain each sub-curve;

[0008] Determine the difference between any two sub-curves based on the difference in values between any two sub-curves;

[0009] Obtain the midpoint of the cross-section curve on the horizontal axis, and determine the cutting direction of the nut based on the difference in the slow change degree of the sub-curves on both sides of the midpoint;

[0010] Based on the difference, obtain the neighborhood sub-curve set of each sub-curve; based on the direction, and in combination with the difference in position between each sub-curve and the other sub-curves, and the neighborhood sub-curve set, adjust the formula for calculating the local reachability density of each sub-curve by the LOF anomaly detection algorithm;

[0011] Obtain the anomaly scores of each sub - curve based on the adjusted LOF anomaly detection algorithm, and determine whether the nut meets the standard based on the anomaly scores. If the nut does not meet the standard, no subsequent processing is performed; otherwise, combine the circle fitting results of each sub - curve and the anomaly scores to determine the ball radius of the nut.

[0012] Based on the ball radius, the outer diameter of the nut, and the distance from the ball placed in the raceway to the outer wall of the nut, determine the inner diameter of the nut.

[0013] In one embodiment, the determination process of each sub - curve is as follows:

[0014] The expression of the segment probability at each point on the cross - section curve is:

[0015] where represents the segment probability at the point (x0, z0) on the cross - section curve; (x0 - ω, x0) and (x0, x0 + ω) respectively represent the neighborhood intervals of ω sampling points forward and backward with the abscissa x0 as the starting point; respectively represent the mean values of the first - order differences of the ordinates within the neighborhood intervals (x0 - ω, x0) and (x0, x0 + ω); respectively represent the degrees of dispersion of the first - order differences of the ordinates within the neighborhood intervals (x0 - ω, x0) and (x0, x0 + ω);

[0016] Take each maximum value point in the distribution probabilities at all points on the cross - section curve as each segmentation point, and divide the cross - section curve into each sub - curve according to each segmentation point.

[0017] In one embodiment, the determination process of the difference is: arrange the ordinates of all points on each sub - curve in order to form a height sequence; the difference is the distance between the height sequences of any two sub - curves.

[0018] In one embodiment, the determination process of the cutting direction of the nut is:

[0019] Calculate the second - order difference sequence of the height sequence of each sub - curve, obtain the absolute values of all data in each second - order difference sequence, and arrange them in ascending order; take the mean value of all the absolute values in the front preset proportion of all the absolute values corresponding to each sub - curve as the roughness of each sub - curve;

[0020] Calculate the average values of the roughness of all sub - curves on the left side and the right side of the mid - point respectively;

[0021] When the average value on the left side of the midpoint is greater than the average value on the right side of the midpoint, the direction in which the nut is cut is from the positive half-axis to the negative half-axis of the horizontal axis; otherwise, the direction in which the nut is cut is from the negative half-axis to the positive half-axis of the horizontal axis.

[0022] In one embodiment, the set of neighborhood sub-curves is a set composed of all sub-curves with a difference from each sub-curve less than the K-nearest distance of each sub-curve, where K is a preset value greater than 0, and the K-nearest distance of each sub-curve is obtained from the difference between each sub-curve and the rest of the sub-curves.

[0023] In one embodiment, the adjusted calculation formula for calculating the local reachability density of each sub-curve by the LOF anomaly detection algorithm is:

[0024] Where, Lrd i ′ represents the corrected local reachability density of the i-th sub-curve; X i , X j represent the i-th and j-th sub-curves respectively; O(X i ) represents the set of neighborhood sub-curves of the i-th sub-curve; reach_dist k (X j , X i ) represents the K-th reachability distance from X j to X i calculated based on the difference; ‖O(X i )‖ represents the number of elements in the set of neighborhood sub-curves O(X i ); exp() represents the exponential function with the natural constant as the base; X i,0 , X j,0 represent the abscissa values of the left endpoints of the i-th and j-th sub-curves respectively; V represents the machining direction parameter, and when the direction in which the nut is cut is from the positive half-axis to the negative half-axis of the horizontal axis, the value of V is -1; otherwise, the value of V is 1.

[0025] In one embodiment, the method for determining whether the nut meets the standard based on the anomaly score is: when the anomaly score of any sub-curve is greater than the preset anomaly threshold, it is determined that the nut does not meet the standard; otherwise, it is determined that the nut meets the standard.

[0026] In one embodiment, the process of determining the ball radius is: respectively fitting a circle to each sub-curve, and the ball radius is negatively correlated with the anomaly score of each sub-curve and positively correlated with the radius of the fitting circle of each sub-curve.

[0027] In one embodiment, the calculation formula of the ball radius is: Among them, R represents the ball radius of the nut; N represents the number of sub-curves; LOF i and LOF j respectively represent the anomaly scores of the i-th and j-th sub-curves; LOF max and LOF min respectively represent the maximum and minimum values among the anomaly scores of all sub-curves; r i represents the radius of the fitting circle of the i-th sub-curve.

[0028] In a second aspect, the embodiments of the present application further provide an inner diameter high-precision measurement system for automotive nuts, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned inner diameter high-precision measurement method for automotive nuts are implemented.

[0029] The present application has at least the following beneficial effects:

[0030] The present application performs segmented processing on the internal laser scanning data of the nut, accurately captures the detailed features of each raceway, which is convenient for subsequent analysis of the differences between different raceways; by calculating the roughness of each raceway based on the slow change of the height of each raceway, and analyzing the cutting direction of the nut during processing according to the distribution of the roughness of all raceways; furthermore, based on the differences between different raceways and the cutting direction of the nut, the LOF anomaly detection algorithm is adjusted. When calculating the anomaly degree of each raceway, the positional relationship between each raceway and the remaining raceways is considered, and higher weights are assigned to the raceways that are closer in position to each raceway and have an earlier processing time, avoiding misidentifying multiple similar abnormal raceways as normal raceways;

[0031] Furthermore, based on the geometric shapes of multiple normal raceways, the most suitable ball radius is calculated by weighting, and then based on the most suitable ball radius, the inner diameter of the nut is obtained, improving the measurement accuracy of the inner diameter of the nut. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1 is a flowchart of the steps of an inner diameter high-precision measurement method for automotive nuts provided by an embodiment of the present application;

[0034] Figure 2Schematic layout diagram of nut and laser displacement sensor;

[0035] Figure 3 Schematic diagram of cross-sectional curve;

[0036] Figure 4 Schematic diagram of dividing the cross-sectional curve into sub-curves;

[0037] Figure 5 Schematic diagram of the acquisition process for the cutting direction of the nut;

[0038] Figure 6 Schematic diagram of the distance from the ball to the outer wall of the nut, the outer diameter of the nut, and the inner diameter of the nut. Detailed implementation manners

[0039] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example", etc. are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "or", "for example" is intended to present relevant concepts in a specific manner.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. It should be understood that unless otherwise specified in this application, " / " means "or".

[0041] In addition, it should be noted that the terms "first" and "second" in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0042] The following specifically describes the specific solutions of the method and system for high-precision measurement of the inner diameter of automotive nuts provided by this application in conjunction with the accompanying drawings.

[0043] Please refer to Figure 1 , which shows the flowchart of the steps of the method for high-precision measurement of the inner diameter of automotive nuts provided by an embodiment of this application. The method includes the following steps:

[0044] Step 1: Denote the cross-section perpendicular to the nut raceway as the raceway cross-section, and obtain the cross-sectional curves of multiple nut raceways on the raceway cross-section.

[0045] Ball nuts are commonly used in the electric or hydraulic power steering systems of automobiles. As transmission components, they can provide smooth steering control and reduce friction. High-precision ball nuts can reduce the clearance and friction between the balls and the threads, thereby extending the service life of the components. Insufficient precision will lead to more friction and wear during the movement process, which will affect the service life of the ball nut and related mechanical components and increase the maintenance cost.

[0046] By connecting the laser displacement sensor to the right-angle prism, the change of the internal thread of the nut is detected. The layout schematic diagram of the nut and the laser displacement sensor is as Figure 2 shown, Figure 2 In the left side of the figure is the schematic diagram of the nut, and the device on the right side is the laser displacement sensor. The arrow direction is the schematic diagram of the light path reflected back to the laser displacement sensor. The light is reflected by the right-angle prism, and then the laser probe can move inside the nut to observe the specific situation of the thread surface.

[0047] The thread track inside the ball nut, that is, the raceway used to accommodate and guide the movement of the balls. Therefore, the cross-section perpendicular to the nut raceway is recorded as the raceway cross-section. The cross-section curves of multiple raceways of the ball nut on the raceway cross-section are obtained by using the laser displacement sensor. The schematic diagram of the cross-section curve is as Figure 3 shown, Figure 3 In the figure, the horizontal axis is the position of the raceway in the axial direction, with the unit of mm, and the vertical axis is the height of the raceway, with the unit of mm.

[0048] In this embodiment, the main parameter data of the laser displacement sensor are as follows: measurement range: between 2 and 1250 mm; measurement frequency: 9400 Hz; laser type: red semiconductor laser 660 nm; power supply and power: 32 VDC, 2 W. As other implementation manners, on the basis of obtaining the cross-section curve, the implementer can also select other laser displacement sensors with different main parameter data from those in this embodiment, and this application does not make special restrictions.

[0049] Step 2: According to the obtained cross-section curve, perform abnormal detection on the raceways inside the nut, and then obtain a more accurate measurement result of the inner diameter of the nut.

[0050] When the balls move in the raceways, they move along the inner wall of the nut. If the precision of the processing equipment is insufficient, or there is vibration or deviation during the processing, it will cause the tool to gradually wear during the processing, and then lead to errors in the cutting depth and cycle of the raceway. In order to avoid the influence of abnormal raceways on the measurement precision of the inner diameter of the nut, therefore, before measuring the inner diameter, it is necessary to analyze different raceways to exclude the influence of abnormal raceways on the inner diameter measurement.

[0051] Step 2.1, based on the trend change within the neighborhood of each point on the cross-sectional curve, the cross-sectional curve is segmented to obtain each sub-curve.

[0052] In order to allow the balls to move in the raceways without affecting each other, there is a smooth area between two adjacent raceways, namely the raceway gap. The spacing of a single raceway usually needs to match the diameter of the ball to ensure that the ball can roll smoothly in the raceway without getting stuck. If the raceway spacing is too small, the ball may not roll smoothly, while if it is too large, the ball may move unstably in the track, affecting the transmission efficiency. Therefore, in order to analyze the abnormal conditions of different raceways, it is necessary to segment the obtained cross-sectional curve and divide a single raceway into a segment.

[0053] Considering that the scanning result at the raceway gap is relatively flat, and the corresponding scanning results at both ends of the raceway vary greatly, that is, the cross-sectional curves corresponding to the raceway gap have similar values ​​on the vertical axis, while the cross-sectional curves corresponding to the two ends of the raceway have large variations on the vertical axis.

[0054] Based on the above analysis, the segmentation probability at each point on the cross-sectional curve is determined based on the trend change within the neighborhood of each point on the cross-sectional curve. The expression is:

[0055] in, represents the segment probability at the point (x0,z0) on the cross-sectional curve; (x0-ω,x0) and (x0,x0+ω) represent the neighborhood intervals of ω sampling points forward and backward from the abscissa x0, respectively; They represent the means of the first-order differences of the ordinates in the neighborhood intervals (x0-ω, x0) and (x0, x0+ω); They respectively represent the degree of discreteness of the first-order difference of the vertical coordinate in the neighborhood intervals (x0-ω,x0) and (x0,x0+ω).

[0056] In this embodiment, the degree of dispersion of the first-order difference is the standard deviation. As other implementation methods, on the basis of being able to measure the degree of uneven distribution of the first-order difference, the implementer may adopt other existing technologies for measurement, such as variance, coefficient of variation, etc., and this application does not impose any special restrictions.

[0057] It should be noted that there is a turning point between the raceway clearances. Compared with other points on the cross-sectional curve, the change trend of the ordinates on the left and right sides of the turning point is more different. Therefore, the probability of segmentation at each point is determined based on the difference in the change trend of the ordinates on the left and right sides of each point on the cross-sectional curve. The larger the value of , the higher the possibility of segmentation at the point (x0,z0).

[0058] Take each maximum point among the distribution probabilities at all points on the cross-sectional curve as each segmentation point, and take the cross-sectional curve between every two adjacent segmentation points as a sub-curve. The schematic diagram of the cross-sectional curve segmented into each sub-curve is as shown in Figure 4 shown in Figure 4 where X i-1 、X i 、X i+1 are the (i - 1)-th segment, the i-th segment, and the (i + 1)-th sub-curve respectively.

[0059] Step 2.2: Based on the difference in values between any two sub-curves, determine the difference between the two sub-curves.

[0060] If there is a tool tip wear during the raceway machining process, uneven burrs will appear inside the raceway at this time, resulting in increased wear between the machined raceway and the ball. After obtaining the sub-curves corresponding to each raceway, the difference between different raceways is reflected through the difference between different sub-curves. The specific process is as follows: Arrange the ordinates of all points on each sub-curve in the order of the points to form a height sequence, and take the distance between the height sequences of any two sub-curves as the difference between the two sub-curves.

[0061] In this embodiment, the distance between height sequences is the DTW (Dynamic Time Warping) distance. As other implementation manners, based on the ability to measure the distance between two height sequences, the implementer can use other existing technologies to obtain the distance between two height sequences, such as Euclidean distance, Manhattan distance, etc., and this application does not make special restrictions.

[0062] It should be noted that: when the difference between any two sub-curves is greater, it indicates that the difference between the raceways corresponding to the two sub-curves is greater.

[0063] Step 2.3: Obtain the midpoint of the cross-sectional curve on the horizontal axis, and based on the difference in the slow change degree between the sub-curves on both sides of the midpoint, determine the cutting direction of the nut.

[0064] When finishing the raceway of the ball nut, a one-time machining and forming method is usually adopted to ensure the geometric accuracy and surface quality of the thread. Therefore, the raceway at the front end of machining usually experiences relatively new tools and stable cutting conditions. As the machining process progresses, the tool will gradually wear, and in the later stage of machining, the thermal deformation or cutting force change generated by the workpiece will affect the final machining accuracy, resulting in a relatively lower quality of the workpiece near the end of machining.

[0065] Since within a single raceway, when the cutting condition is relatively smooth, the change in the raceway height is smooth, that is, the absolute value of the second-order difference at any point is small. However, considering that there is a turning point between the raceway and the raceway clearance, the angle change of the inner wall of the nut at the turning point is large, that is, the absolute value of the second-order difference at the turning point is large. Therefore, calculate the second-order difference sequence of the height sequence of each sub-curve, obtain the absolute values of all the data in each second-order difference sequence, and arrange them in ascending order to form each absolute value sequence; take the mean value of all the absolute values in the front preset proportion in the corresponding absolute value sequence of each sub-curve as the roughness of each sub-curve. The reason for only considering the data in the front preset proportion during the calculation of the roughness of each sub-curve is: on the basis of removing the interference of the second-order difference at the turning point, avoid the influence of the noise points existing during the scanning process on the roughness analysis.

[0066] In this embodiment, the value of the preset proportion is The value of the preset proportion is preset manually, and the implementer can set it by himself / herself. This application does not make special restrictions. If the product of the length of the absolute value sequence and the preset proportion is not a positive number when obtaining the data in the front preset proportion of the absolute value sequence, the rounding method is adopted to obtain an integer.

[0067] Furthermore, according to the distribution of the roughness of the sub-curves, the cutting direction of the nut can be determined. The specific process is as follows: obtain the midpoint of the cross-sectional curve on the horizontal axis, and take the sub-curve where the midpoint is located as the central sub-curve, and calculate the average values of the roughness of all the sub-curves on the left and right sides of the central sub-curve respectively. When the average value on the left side of the central sub-curve is greater than the average value on the right side of the central sub-curve, the cutting direction of the nut is from the positive half-axis of the horizontal axis to the negative half-axis, otherwise, the cutting direction of the nut is from the negative half-axis of the horizontal axis to the positive half-axis. The schematic diagram of the acquisition process of the cutting direction of the nut is as Figure 5 shown.

[0068] Step 2.4, based on the difference, obtain the set of neighborhood sub-curves of each sub-curve; based on the direction, and combined with the difference in position between each sub-curve and the rest of the sub-curves, as well as the set of neighborhood sub-curves, adjust the formula for calculating the local reachability density of each sub-curve by the LOF outlier detection algorithm.

[0069] In order to reduce the influence of abnormal raceways on the final inner diameter of the nut, it is considered to calculate the degree of abnormality of each raceway through LOF anomaly detection. However, traditional anomaly detection algorithms only detect based on the similarity relationship between multiple samples. When the tool wears severely, it may cause multiple similar abnormal raceways to be misidentified as normal. Therefore, in order to simultaneously detect multiple raceway abnormalities caused by tool tip wear during machining and a single raceway abnormality caused by the movement of the tool tip, it is considered to introduce the cutting direction of the nut when calculating the anomaly score of each sample using the traditional LOF anomaly detection algorithm. The specific process is as follows:

[0070] The original calculation formula for the local reachability density of each sub-curve is:

[0071] Among them, Lrd i represents the local reachability density of the i-th sub-curve; X i , X j respectively represent the i-th and j-th sub-curves; O(X i ) represents the set of neighborhood sub-curves of the i-th sub-curve; reach_dist k (X j , X i ) represents the k-th reachability distance from X j to X i calculated based on the said difference; ‖O(X i )‖ represents the number of elements in the set of neighborhood sub-curves O(X i ). Among them, the set of neighborhood sub-curves of the i-th sub-curve is: the set composed of all sub-curves whose difference from the i-th sub-curve is less than the k-nearest distance of the i-th sub-curve; arrange the differences between the i-th sub-curve and the other sub-curves in ascending order, and the difference at the k-th position is the k-nearest distance of the i-th sub-curve. When calculating the k-th reachability distance from X j to X i , only need to replace the distance between points in the reachability distance calculation formula with the difference between sub-curves. The specific reachability distance calculation formula is well-known technology and will not be elaborated in this application.

[0072] In this embodiment, the value of k is 10, and the value of k is preset manually. The implementer can set it by himself, and this application does not make special restrictions.

[0073] Furthermore, considering the machining direction of the ball nut, the adjusted calculation formula for the local reachability density of each sub-curve is:

[0074] Among them, Lrd i ′Denote the corrected local reachability density of the i-th sub-curve; X i , X j respectively denote the i-th and j-th sub-curves; O(X i ) denotes the set of neighborhood sub-curves of the i-th sub-curve; reach_dist k (X j , X i ) denotes the k-th reachability distance from X j to X i calculated based on the said difference; ‖O(X i )‖ denotes the number of elements in the set of neighborhood sub-curves O(X i ); exp() represents the exponential function with the natural constant as the base, aiming to map to a positive number; X i,0 , X j,0 respectively denote the abscissa values of the left endpoints of the i-th and j-th sub-curves; V represents the machining direction parameter. When the direction in which the nut is cut is from the positive half-axis to the negative half-axis of the horizontal axis, the value of V is -1; otherwise, the value of V is 1.

[0075] It should be noted that: taking as the weight, when two sub-curves are closer in position, and the machining time of the raceway corresponding to X j is earlier than the machining time of the raceway corresponding to X i , then has a larger value, that is, for the raceway at the front end of machining, its reference is stronger than that of the subsequent raceways.

[0076] Step 2.5, obtain the anomaly scores of each sub-curve based on the adjusted LOF anomaly detection algorithm, and determine whether the nut meets the standard based on the anomaly scores. If the nut does not meet the standard, no subsequent processing is performed; otherwise, combine the circle fitting results of each sub-curve with the anomaly scores to determine the ball radius of the nut.

[0077] Furthermore, based on the improved local reachability density, use the LOF anomaly detection algorithm to obtain the anomaly scores of each sub-curve. When the anomaly score of any sub-curve is greater than the preset anomaly threshold, it is determined that the nut does not meet the standard, and no subsequent nut inner diameter measurement processing is performed; otherwise, it is determined that the nut meets the standard, and the nut is subjected to inner diameter measurement processing. Among them, the specific process of the LOF anomaly detection algorithm is a well-known technology, and this application will not elaborate.

[0078] In this embodiment, the value of the preset anomaly threshold is 1.1. The value of the preset anomaly threshold is preset manually, and the implementer can set it by himself / herself. This application does not make special restrictions.

[0079] Further, combining the circle fitting results of each sub-curve with the abnormal scores, the ball radius of the nut is determined, and the expression is:

[0080] wherein, R represents the ball radius of the nut; N represents the number of sub-curves; LOF i and LOF j respectively represent the abnormal scores of the i-th and j-th sub-curves; LOF max and LOF min respectively represent the maximum and minimum values among the abnormal scores of all sub-curves; r i represents the radius of the fitting circle of the i-th sub-curve.

[0081] In this embodiment, the least squares method is used to perform circle fitting on each sub-curve to obtain the fitting circle of each sub-curve.

[0082] It should be noted that: taking as the weight, the smaller the abnormal degree of the sub-curve, the greater the weight is given, and the greater the abnormal degree of the sub-curve, the smaller the weight is given, and the sum of the weights of all sub-curves is 1.

[0083] Step 2.6, based on the ball radius and the outer diameter of the nut, and the distance between the ball placed in the raceway and the outer wall of the nut, determine the inner diameter of the nut.

[0084] According to the weighted average result of the fitting radii of each raceway, select the ball with the best radius and place it in the raceway with the smallest abnormal score. After the ball is stable in the nut, measure the distance from the ball placed in the raceway to the outer wall of the nut and the outer diameter of the nut. Then, based on the ball radius and the outer diameter of the nut, and the distance from the ball placed in the raceway to the outer wall of the nut, determine the inner diameter of the nut, and the expression is:

[0085] D3 = D1 - 2×D2 + 2×R; wherein, D3 represents the inner diameter of the nut; D1 represents the outer diameter of the nut; D2 represents the distance from the ball placed in the raceway to the outer wall of the nut; R represents the ball radius of the nut.

[0086] The schematic diagram of the distance from the ball to the outer wall of the nut, the outer diameter of the nut, and the inner diameter of the nut is as Figure 6 shown, Figure 6 where G represents the ball and wall represents the outer wall of the nut.

[0087] Based on the same inventive concept as the above method, the embodiment of the present application also provides an inner diameter high-precision measurement system for automotive nuts, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for the inner diameter high-precision measurement method for automotive nuts.

[0088] In summary, the present application performs segmented processing on the internal laser scanning data of the nut, accurately captures the detailed features of each raceway, facilitating subsequent analysis of the differences between different raceways; calculates the roughness of each raceway based on the slow change of the height of each raceway, and analyzes the cutting direction when the nut is machined according to the distribution of the roughness of all raceways; furthermore, based on the differences between different raceways and the cutting direction of the nut, adjusts the LOF anomaly detection algorithm. When calculating the anomaly degree of each raceway, consider the positional relationship between each raceway and the remaining raceways, and assign a higher weight to the raceway that is closer in position to each raceway and has an earlier machining time, so as to avoid misidentifying multiple similar abnormal raceways as normal raceways;

[0089] Furthermore, based on the geometric shapes of multiple normal raceways, weighted calculation of the most suitable ball radius is performed, and then based on the most suitable ball radius, the inner diameter of the nut is obtained, improving the measurement accuracy of the inner diameter of the nut.

[0090] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0091] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the basic features of the present application. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-limiting.

Claims

1. A high-precision measurement method for the inner diameter of automobile nuts, characterized in that: The method comprises the following steps: Record the section perpendicular to the nut raceway as the raceway section, and obtain the section curves of multiple raceways of the nut on the raceway section; Based on the trend change within the neighborhood of each point on the cross-sectional curve, the cross-sectional curve is segmented to obtain each sub-curve; Based on the difference in values ​​of any two segments of sub-curves, determining the difference between the any two segments of sub-curves; Obtaining the midpoint of the cross-sectional curve on the horizontal axis, and determining the direction in which the nut is cut based on the difference between the slowness of changes of the sub-curves on both sides of the midpoint; Based on the difference, a neighborhood sub-curve set of each sub-curve is obtained; based on the direction, and in combination with the difference in position between each sub-curve and the other sub-curves, and the neighborhood sub-curve set, a formula for calculating the local reachable density of each sub-curve by the LOF anomaly detection algorithm is adjusted; Based on the adjusted LOF anomaly detection algorithm, an anomaly score of each sub-curve is obtained, and whether the nut meets the standard is judged based on the anomaly score. If the nut does not meet the standard, no subsequent processing is performed; otherwise, the ball radius of the nut is determined by combining the circle fitting results of each sub-curve with the anomaly score; Determine the inner diameter of the nut based on the ball radius and the outer diameter of the nut, and the distance from the ball placed in the raceway to the outer wall of the nut; The calculation formula after adjusting the formula for calculating the local reachable density of each sub-curve by the LOF anomaly detection algorithm is: ;in, represents the modified local reachability density of the i-th subcurve; , Respectively represent the i-th and j-th sub-curves; represents the neighborhood subcurve set of the i-th subcurve; Indicates the value calculated based on the difference arrive The Kth reachable distance of Represents a set of neighborhood subcurves The number of elements in; exp() represents an exponential function with a natural constant as the base; , Respectively represent the horizontal coordinate values ​​of the left endpoints of the i-th and j-th sub-curves; Represents the machining direction parameter. When the nut is cut in the direction where the positive half axis of the horizontal axis points to the negative half axis, the value of V is -1; otherwise, the value of V is 1.

2. The high-precision inner diameter measurement method for automobile nuts according to claim 1, characterized in that: The determination process of each sub-curve is as follows: The expression of the segment probability at each point on the cross-sectional curve is: ;in, Indicates a point on the cross-section curve The segment probability at ; Respectively, the horizontal axis is the neighborhood interval of ω sampling points forward and backward from the starting point; Respectively represent the neighborhood interval the mean of the first differences of the inner ordinates; Respectively represent the neighborhood interval The degree of dispersion of the first-order difference of the inner ordinate; The maximum points in the distribution probability at all points on the cross-sectional curve are used as segmentation points, and the cross-sectional curve is divided into sub-curves according to the segmentation points.

3. The high-precision inner diameter measurement method for automobile nuts according to claim 1, characterized in that: The process of determining the difference is: arranging the ordinates of all points on each sub-curve in order of the points to form a height sequence; the difference is the distance of the height sequence between any two sub-curves.

4. The high-precision inner diameter measurement method for automobile nuts according to claim 3, characterized in that: The process of determining the direction in which the nut is cut is as follows: Calculate the second-order difference sequence of the height sequence of each sub-curve, obtain the absolute values ​​of all data in each second-order difference sequence, and arrange them in order from small to large; take the average of all the absolute values ​​corresponding to each sub-curve that are in a preset ratio as the roughness of each sub-curve; Calculate the average roughness of all sub-curves on the left side of the midpoint and on the right side of the midpoint respectively; When the average value on the left side of the midpoint is greater than the average value on the right side of the midpoint, the direction in which the nut is cut is the direction from the positive semi-axis of the horizontal axis to the negative semi-axis; otherwise, the direction in which the nut is cut is the direction from the negative semi-axis of the horizontal axis to the positive semi-axis.

5. The high-precision inner diameter measurement method for automobile nuts according to claim 1, characterized in that: The neighborhood subcurve set is a set consisting of all subcurves whose difference with each subcurve is less than the K neighborhood distance of each subcurve, wherein K is a preset value greater than 0, and the K neighborhood distance of each subcurve is obtained by the difference between each subcurve and the other subcurves.

6. The high-precision inner diameter measurement method for automobile nuts according to claim 1, characterized in that: The method for judging whether the nut meets the standard based on the abnormal score is: when the abnormal score of any sub-curve is greater than a preset abnormal threshold, the nut is judged to be unqualified; otherwise, the nut is judged to be qualified.

7. The high-precision inner diameter measurement method for automobile nuts according to claim 1, characterized in that: The process of determining the ball radius is: fitting a circle to each sub-curve respectively, and the ball radius is negatively correlated with the abnormality score of each sub-curve and positively correlated with the radius of the fitting circle of each sub-curve.

8. The high-precision inner diameter measurement method for automobile nuts according to claim 1, characterized in that: The calculation formula of the ball radius is: ; Where R represents the ball radius of the nut; N represents the number of sub-curves; They represent the abnormal scores of the i-th and j-th sub-curves respectively; Respectively represent the maximum and minimum values ​​of the anomaly scores of all sub-curves; Represents the radius of the fitting circle of the i-th sub-curve.

9. A high-precision inner diameter measurement system for automobile nuts, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the high-precision inner diameter measurement method applied to automobile nuts as described in any one of claims 1 to 8 are implemented.

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