High-precision inner diameter measuring method and system applied to automobile nut

Abstract

The invention relates to the technical field of size measurement, in particular to an inner diameter high-precision measurement method and system applied to an automobile nut, and the method comprises the steps: obtaining the section curves of a plurality of raceways of the nut on the raceway section; dividing the section curve into sub-curves; determining the difference between any two sections of sub-curves based on the value difference of any two sections of sub-curves; acquiring a midpoint of the section curve on a transverse axis, and determining the cut direction of the nut based on the difference between the change slow degrees of the sub-curves on the two sides of the midpoint; adjusting a formula for calculating the local reachable density of each sub-curve by the LOF anomaly detection algorithm; whether the nut reaches the standard or not is judged, and if the nut does not reach the standard, follow-up processing is not carried out; otherwise, determining the ball radius of the nut; and further determining the inner diameter of the nut. The invention aims to select a proper measuring tool according to the actual condition of the internal thread of the nut so as to improve the measuring precision of the internal diameter of the nut.

Description

Technical Field

[0001] The present application relates to the field of dimension measurement technology, and in particular to a high-precision measurement method and system for the inner diameter of automobile nuts. Background Art

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

[0003] In the high-precision measurement of the inner diameter of automotive nuts, the machining accuracy of multiple threads inside the nut has a significant impact on the measurement results. Due to the unevenness of the thread machining process, the thread shape, depth or spacing are different. These factors make it impossible for the measuring tool (such as a steel needle or a steel ball) to accurately contact the thread position, resulting in an unstable contact point during measurement, thereby introducing excessive measurement deviation 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, the measurement accuracy of the nut inner diameter can be improved by selecting a suitable measuring tool according to the actual situation of the internal thread of the nut:

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

[0006] 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;

[0007] 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;

[0008] Based on the difference in values ​​of any two segments of sub-curves, determining the difference between the any two segments of sub-curves;

[0009] 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;

[0010] 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;

[0011] 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;

[0012] The inner diameter of the nut is determined based on the ball radius and the outer diameter of the nut, as well as the distance from the ball placed in the raceway to the outer wall of the nut.

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

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

[0015] 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) respectively; They represent the degree of discreteness of the first-order difference of the ordinate in the neighborhood intervals (x0-ω, x0) and (x0, x0+ω);

[0016] 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.

[0017] In one embodiment, 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; and the difference is the distance of the height sequence between any two sub-curves.

[0018] In one embodiment, the process of determining the direction in which the nut is cut is as follows:

[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 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;

[0020] Calculate the average roughness of all sub-curves on the left side of the midpoint and on the right side of the midpoint 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 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.

[0022] In one embodiment, 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.

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

[0024] Among them, Lrd i ′ represents the modified 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 neighborhood subcurve set of the i-th subcurve; reach_dist k (X j ,X i ) represents X calculated based on the difference j To X i The Kth reachable distance; ‖O(X i )‖ represents the neighborhood subcurve set O(X i ) The number of elements in ); exp() represents an exponential function with a natural constant as the base; X i,0 , X j,0 They represent the horizontal coordinate values ​​of the left endpoints of the i-th and j-th sub-curves respectively; V represents the machining direction parameter. When the nut is cut in the direction where the positive semi-axis of the horizontal axis points to the negative semi-axis, the value of V is -1; otherwise, the value of V is 1.

[0025] In one embodiment, 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 not up to standard; otherwise, the nut is judged to be up to standard.

[0026] In one embodiment, the process of determining the ball radius is: fitting a circle for 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.

[0027] In one embodiment, the calculation formula of the ball radius is: Where R is the ball radius of the nut; N is the number of sub-curves; LOF i ,LOF j Respectively represent the abnormal scores of the i-th and j-th sub-curves; LOF max ,LOF min Respectively represent the maximum and minimum values ​​of the abnormal 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, an embodiment of the present application also provides 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, wherein when the processor executes the computer program, the system implements any of the steps of the high-precision inner diameter measurement method for automobile nuts described above.

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

[0030] This application processes the laser scanning data inside the nut in segments, accurately captures the detailed features of each raceway, and facilitates the subsequent analysis of the differences between different raceways; calculates the roughness of each raceway through the slow change of the height of each raceway, and analyzes the cutting direction of the nut when it is processed according to the distribution of the roughness of all raceways; and then adjusts the LOF anomaly detection algorithm based on the differences between different raceways and the direction in which the nut is cut. When calculating the degree of abnormality of each raceway, the positional relationship between each raceway and the remaining raceways is considered, and a higher weight is given to the raceway that is closer in position to each raceway and has an earlier processing time, so as to avoid mistakenly identifying 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 weight, and then based on the most suitable ball radius, the inner diameter of the nut is obtained, thereby 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 drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A flowchart of a method for high-precision measurement of the inner diameter of automobile nuts provided in one embodiment of the present application;

[0034] Figure 2It is a schematic diagram of the layout of the nut and the laser displacement sensor;

[0035] Figure 3 It is a schematic diagram of the cross-sectional curve;

[0036] Figure 4 It is a schematic diagram of dividing the cross-section curve into sub-curves;

[0037] Figure 5 A schematic diagram of a process for obtaining a direction in which a nut is cut;

[0038] Figure 6 This is a schematic diagram of the distance from the ball to the outer wall of the nut and the outer diameter and inner diameter of the nut. DETAILED DESCRIPTION

[0039] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

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

[0041] It should also be noted that the terms "first" and "second" in the present application are used to distinguish similar objects rather than to describe a specific order or sequence.

[0042] The specific scheme of the high-precision inner diameter measurement method and system for automobile nuts provided by the present application is described in detail below with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a flowchart of a method for high-precision measurement of the inner diameter of automobile nuts provided by an embodiment of the present application, the method comprising the following steps:

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

[0045] Ball nuts are commonly used in electric or hydraulic power steering systems in cars. As transmission elements, they can provide smooth steering control and reduce friction. High-precision ball nuts can reduce the gap and friction between the ball and the thread, thereby extending the service life of parts. Insufficient precision will cause more friction and wear during movement, which will affect the service life of ball nuts and related mechanical parts and increase maintenance costs.

[0046] The laser displacement sensor is connected to a right-angle prism to detect the changes in the internal threads of the nut. The layout diagram of the nut and the laser displacement sensor is shown in the figure. Figure 2 As shown, Figure 2 The left side is a schematic diagram of the nut, the right side device is a laser displacement sensor, and the direction of the arrow is a schematic diagram of the light path reflected back to the laser displacement sensor. The light is reflected by a right-angle prism, and then the laser probe can be moved inside the nut to observe the specific conditions of the thread surface.

[0047] The threaded track inside the ball nut is the raceway used to accommodate and guide the movement of the ball. Therefore, the section perpendicular to the nut raceway is recorded as the raceway section. The cross-sectional curves of multiple raceways of the ball nut on the raceway section are obtained using a laser displacement sensor. The schematic diagram of the cross-sectional curve is shown in Figure 3 As shown, Figure 3 The horizontal axis is the axial position of the raceway, in mm, and the vertical axis is the height of the raceway, in 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, power: 32 VDC, 2 W. As other implementation methods, based on the obtainable cross-sectional curve, the implementer may also select other laser displacement sensors with different main parameter data from those in this embodiment, and this application does not impose any special restrictions.

[0049] Step 2: Based on the obtained cross-sectional curve, anomaly detection is performed on the raceway inside the nut to obtain a more accurate measurement result of the inner diameter of the nut.

[0050] When the ball moves in the raceway, it moves along the inner wall of the nut. If the processing equipment is not accurate enough, or if vibration or deviation occurs during the processing, the tool will gradually wear out during the processing, which will cause errors in the cutting depth and cycle of the raceway. In order to avoid the abnormal raceway affecting the measurement accuracy of the inner diameter of the nut, it is necessary to analyze different raceways before measuring the inner diameter to eliminate 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) respectively; 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 probability of segmentation at the point (x0,z0).

[0058] The maximum points in the distribution probability at all points on the cross-sectional curve are taken as segmentation points, and the cross-sectional curve between every two adjacent segmentation points is taken as a sub-curve. The schematic diagram of the cross-sectional curve being divided into sub-curves is shown in Figure 4 As shown, Figure 4 Medium X i-1 , X i , X i+1 They are the i-1th, ith, and i+1th sub-curves respectively.

[0059] Step 2.2: Determine the difference between any two sub-curves based on the difference in values ​​between the two sub-curves.

[0060] If the cutter head is worn during the raceway machining process, uneven burrs will appear inside the raceway, resulting in increased wear between the machined raceway and the ball. After obtaining the sub-curves corresponding to each raceway, the differences between different sub-curves are used to reflect the differences between different raceways. The specific process is: the ordinates of all points on each sub-curve are arranged in order to form a height sequence, and the distance of the height sequence between any two sub-curves is used as the difference between the two sub-curves.

[0061] In this embodiment, the distance between the height sequences is the DTW (Dynamic Time Warping) distance. As other implementation methods, on the basis of the measurable distance between the two height sequences, the implementer may adopt other existing technologies to obtain the distance between the two height sequences, such as Euclidean distance, Manhattan distance, etc., and this application does not impose any special restrictions.

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

[0063] Step 2.3, 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 change of the sub-curves on both sides of the midpoint.

[0064] When finishing the raceway of a ball nut, a one-time processing 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 the processing usually experiences relatively new tools and stable cutting conditions. As the processing progresses, the tool will gradually wear out, and in the later stage of processing, the thermal deformation or cutting force changes generated by the workpiece will affect the final processing accuracy, resulting in a relative decrease in the quality of the workpiece near the rear end of the processing.

[0065] Since inside a single raceway, when the cutting condition is relatively smooth, the change of the raceway height is smooth, that is, the absolute value of the second-order difference at any point is small, but considering that there is a turning point between the raceway and the raceway gap, the angle of the inner wall of the nut at the turning point changes greatly, that is, the absolute value of the second-order difference at the turning point is large, so the second-order difference sequence of the height sequence of each sub-curve is calculated, the absolute value of all data in each second-order difference sequence is obtained, and arranged in order from small to large to form each absolute value sequence; the average of all the absolute values ​​in the absolute value sequence corresponding to each sub-curve that is in the pre-set ratio is used as the roughness of each sub-curve. In the process of calculating the roughness of each sub-curve, only the data of the pre-set ratio is considered because: on the basis of removing the second-order difference interference at the turning point, the influence of noise points existing in the scanning process on the roughness analysis is avoided.

[0066] In this embodiment, the preset ratio is The value of the preset ratio is preset by humans and can be set by the implementer. This application does not impose any special restrictions. If the product of the length of the absolute value sequence and the preset ratio is not a positive number when obtaining the data of the preset ratio in the absolute value sequence, the integer is rounded off.

[0067] Furthermore, according to the distribution of the roughness of the sub-curves, the direction in which the nut is cut can be determined. The specific process is: obtain the midpoint of the cross-sectional curve on the horizontal axis, take the sub-curve where the midpoint is located as the central sub-curve, and calculate the average roughness of all sub-curves on the left and right sides of the central sub-curve. 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 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. The schematic diagram of the process of obtaining the direction in which the nut is cut is shown in FIG. Figure 5 shown.

[0068] Step 2.4, based on the difference, obtain a set of neighborhood subcurves of each subcurve; based on the direction, and in combination with the difference in position between each subcurve and the remaining subcurves, and the set of neighborhood subcurves, adjust the formula for calculating the local reachable density of each subcurve by the LOF anomaly detection algorithm.

[0069] In order to reduce the impact of abnormal raceways on the final nut inner diameter, the LOF abnormality detection method is considered to calculate the abnormality degree of each raceway. However, the traditional abnormality detection algorithm only detects based on the similarity relationship between multiple samples. When the tool is severely worn, multiple similar abnormal raceways may be mistakenly identified as normal. Therefore, in order to simultaneously detect multiple raceway abnormalities caused by tool head wear during processing and single raceway abnormalities caused by tool head movement, it is considered to introduce the direction in which the nut is cut when calculating the abnormality score of each sample using the traditional LOF abnormality 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 subcurve; X i , X j Respectively represent the i-th and j-th sub-curves; O(X i ) represents the neighborhood subcurve set of the i-th subcurve; reach_dist k (X j ,X i ) represents X calculated based on the difference j To X i The k-th reachable distance; ‖O(X i )‖ represents the neighborhood subcurve set O(X i ). The neighborhood subcurve set of the i-th subcurve is: the set of all subcurves whose difference with the i-th subcurve is less than the k-neighboring distance of the i-th subcurve; the differences between the i-th subcurve and the remaining subcurves are arranged in ascending order, and the difference at the kth position is the k-neighboring distance of the i-th subcurve. j To X i When calculating the kth reachable distance, it is only necessary to replace the distance between points in the reachable distance calculation formula with the difference between sub-curves. The specific reachable distance calculation formula is a well-known technology and will not be described in detail in this application.

[0072] In this embodiment, the value of k is 10. The value of k is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0073] Furthermore, considering the processing direction of the ball nut, the calculation formula of the local reachable density of each sub-curve is adjusted to:

[0074] Among them, Lrd i ′represents the modified 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 neighborhood subcurve set of the i-th subcurve; reach_dist k (X j ,X i ) represents X calculated based on the difference j To X i The k-th reachable distance; ‖O(X i )‖ represents the neighborhood subcurve set O(X i ) The number of elements in ; exp() represents an exponential function with a natural constant as the base, and its purpose is to convert Mapped to positive numbers; X i,0 , X j,0 They represent the horizontal coordinate values ​​of the left endpoints of the i-th and j-th sub-curves respectively; V represents the machining direction parameter. When the nut is cut in the direction where the positive semi-axis of the horizontal axis points to the negative semi-axis, the value of V is -1; otherwise, the value of V is 1.

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

[0076] Step 2.5, based on the adjusted LOF anomaly detection algorithm, obtain the anomaly score of each sub-curve, and judge whether the nut meets the standard 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.

[0077] Furthermore, based on the improved local reachable density, the LOF anomaly detection algorithm is used to obtain the anomaly score of each sub-curve. When the anomaly score of any sub-curve is greater than the preset anomaly threshold, the nut is judged to be substandard and no subsequent nut inner diameter measurement is performed; otherwise, the nut is judged to be up to standard and the inner diameter of the nut is measured. The specific process of the LOF anomaly detection algorithm is a well-known technology and will not be described in detail in this application.

[0078] In this embodiment, the value of the preset abnormal threshold is 1.1. The value of the preset abnormal threshold is preset manually and can be set by the implementer. This application does not impose any special restrictions.

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

[0080] Where R is the ball radius of the nut; N is the number of sub-curves; LOF i ,LOF j Respectively represent the abnormal scores of the i-th and j-th sub-curves; LOF max ,LOF min Respectively represent the maximum and minimum values ​​of 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 square method is used to perform circle fitting on each sub-curve to obtain a fitting circle of each sub-curve.

[0082] It should be noted that: As the weight, a larger weight is assigned to a sub-curve with a smaller abnormality, and a smaller weight is assigned to a sub-curve with a larger abnormality, and the sum of the weights of all sub-curves is made to be 1.

[0083] Step 2.6, determining the inner diameter of the nut 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.

[0084] According to the weighted average result of each raceway fitting radius, the ball with the best radius is selected and placed in the raceway with the smallest abnormal score. After the ball is stable in the nut, the distance from the ball placed in the raceway to the outer wall of the nut and the outer diameter of the nut are measured. 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, the inner diameter of the nut is determined. 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; and R represents the ball radius of the nut.

[0086] The distance from the ball to the outer wall of the nut and the schematic diagram of the nut outer diameter and nut inner diameter are shown in Figure 6 As shown, Figure 6 In the text, G stands for ball, and wall stands for the outer wall of the nut.

[0087] Based on the same inventive concept as the above method, an embodiment of the present application also provides a high-precision inner diameter measurement system for automobile 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 any one of the above-mentioned high-precision inner diameter measurement methods for automobile nuts are implemented.

[0088] In summary, the present application processes the laser scanning data inside the nut in segments, accurately captures the detailed features of each raceway, and facilitates the subsequent analysis of the differences between different raceways; the roughness of each raceway is calculated by the slow change of the height of each raceway, and the cutting direction of the nut when it is processed is analyzed according to the distribution of the roughness of all raceways; and then based on the differences between different raceways and the direction in which the nut is cut, the LOF anomaly detection algorithm is adjusted. When calculating the degree of abnormality of each raceway, the positional relationship between each raceway and the remaining raceways is considered, and a higher weight is given to the raceway that is closer in position to each raceway and has an earlier processing time, so as to avoid mistakenly identifying multiple similar abnormal raceways as normal raceways;

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

[0090] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0091] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

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; The inner diameter of the nut is determined based on the ball radius and the outer diameter of the nut, as well as the distance from the ball placed in the raceway to the outer wall of the nut.

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, 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) respectively; They represent the degree of discreteness of the first-order difference of the ordinate in the neighborhood intervals (x0-ω, x0) and (x0, x0+ω); 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 4, characterized in that: The calculation formula after adjusting the formula for calculating the local reachable density of each sub-curve by the LOF anomaly detection algorithm is: Among them, Lrd i ′ represents the modified 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 neighborhood subcurve set of the i-th subcurve; reach_dist k (X j ,X i ) represents X calculated based on the difference j To X i The Kth reachable distance; ‖O(X i )‖ represents the neighborhood subcurve set O(X i ) The number of elements in ); exp() represents an exponential function with a natural constant as the base; X i,0 , X j,0 They represent the horizontal coordinate values ​​of the left endpoints of the i-th and j-th sub-curves respectively; V represents the machining direction parameter. When the nut is cut in the direction where the positive semi-axis of the horizontal axis points to the negative semi-axis, the value of V is -1; otherwise, the value of V is 1.

7. 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.

8. 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.

9. 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 is the ball radius of the nut; N is the number of sub-curves; LOF i ,LOF j Respectively represent the abnormal scores of the i-th and j-th sub-curves; LOF max LOF min Respectively represent the maximum and minimum values ​​of the abnormal scores of all sub-curves; r i Represents the radius of the fitting circle of the i-th sub-curve.

10. 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 9 are implemented.

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