Brake spring piece flatness detection system and method

Abstract

The invention discloses a brake spring piece flatness detection system and method, and relates to the technical field of automobile part processing. The brake spring piece flatness detection system comprises a data acquisition module, a data analysis module, a data screening module, a dynamic monitoring module and a comprehensive judgment module, wherein the data acquisition module is used for acquiring static detection data of N groups of spring pieces randomly extracted from the same batch; the static detection data comprises top view shooting picture data and side view shooting picture data of N groups of spring pieces, which are acquired at equal intervals by utilizing an optical detection instrument, and size data of the spring pieces, which are acquired by utilizing a laser radar, and the technical key points are as follows: through analysis and processing of the static detection data and the dynamic detection data, the overall quality of the spring pieces is shown; related workers can conveniently and quickly know whether the spring pieces meet the requirements of automobile braking or not, high-quality products can be quickly screened out from numerous spring pieces, use is convenient, secondary detection is not needed in the follow-up process, and good application prospects are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of automobile parts processing, and in particular to a brake spring sheet flatness detection system and method. Background Art

[0002] Braking, also known as braking, refers to the action of stopping or reducing the speed of a running locomotive, vehicle, other means of transportation or machinery. The general principle of braking is to fix a wheel or disc on the high-speed shaft of the machine, install a brake shoe, belt or disc corresponding to it on the machine base, and generate a braking torque under the action of external force. The brake device is a mechanical brake device that can slow down the speed of the vehicle, also known as a reducer and brake.

[0003] Brakes are composed of many structures, among which the more common structures are brake pads and spring leaves. Currently, there are many inspections and studies on brake pads, but there are relatively few inspection technologies for brake spring leaves, which still rely on manual inspection.

[0004] The existing application publication number is CN217898601U, and the utility model patent named return spring sheet, brake and automobile records that the return spring sheet includes a connecting part, a buckle structure, a fixing structure and an elastic structure. The buckle structure is connected to one end of the connecting part, and is used to clamp and fix the return spring sheet to the brake pad. The fixing structure is provided at the other end of the connecting part, and is used to fix the return spring sheet on the caliper bracket. One end of the elastic structure is connected to the fixing structure, and the other end is connected to the buckle structure through the connecting part to pull the buckle structure to reset, so that the brake pad has a tendency to move away from the brake disc. The technical solution of the utility model can reduce the drag torque of the brake, reduce the fuel consumption (power consumption) of the automobile, and extend the service life of the brake pad.

[0005] The existing application publication number is CN216309407U, and the utility model patent named a spring sheet detection device records that it includes a frame, a positioning plate, a rotating plate for driving the spring sheet to move, a feeding mechanism for feeding the spring sheet, and a detection mechanism for detecting the spring sheet. The rotating plate is provided with a receiving notch for accommodating the spring sheet, and the positioning plate is provided with a first detection sensor at the feeding mechanism. The detection mechanism includes a pressing component for pressing the spring sheet in the receiving notch and deforming the spring sheet and a second detection sensor for detecting the condition of the spring sheet after deformation. The beneficial effect of the utility model is that the spring sheet is driven to move by the rotating plate and detected by the detection mechanism, so that the deformation of the spring sheet can be quickly detected, automatic detection can be realized, and product production and processing are convenient.

[0006] However, according to the above content combined with the prior art, the central scheme of the patent recorded in the above patent is to measure whether the spring sheet can reach the set deformation amount when in use, and whether it can return to its original state after deformation. The above detection method is the most basic detection of the spring sheet, which is to judge whether the spring sheet can meet the most basic working requirements. However, its detection is relatively simple, and the detection of the spring sheet is not comprehensive. The detection accuracy is relatively low. It can only reflect that the spring sheet is used in the scene, but cannot reflect the specific situation of the spring sheet in the process, and cannot analyze the risks of long-term application. Therefore, the detected spring sheet needs to be re-inspected later, which is more troublesome.

[0007] Therefore, the above scheme has great defects in actual use and does not meet people's use requirements. For this reason, we have developed a brake spring sheet flatness detection system and method. Summary of the invention

[0008] 1. Technical issues to be solved

[0009] In view of the deficiencies in the prior art, the present invention provides a brake spring sheet flatness detection system and method, which obtain static detection data and dynamic detection data of the spring sheet, and can display the overall quality of the spring sheet through further analysis and processing of the static detection data and the dynamic detection data, so as to facilitate relevant staff to quickly understand whether the spring sheet can meet the requirements of automobile braking, and quickly screen out high-quality spring sheets from a large number of spring sheets. The system and method are convenient to use, and no secondary detection is required subsequently. The system has good use effect and has good application prospects.

[0010] (II) Technical solution

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0012] A brake spring sheet flatness detection system, the brake spring sheet flatness detection system comprises a data acquisition module, a data analysis module, a data screening module, a dynamic monitoring module and a comprehensive evaluation module:

[0013] The data acquisition module is used to acquire static detection data of N groups of spring sheets randomly selected from the same batch, and the static detection data includes overhead view picture data and side view picture data of the N groups of spring sheets equidistantly collected by optical detection instruments and size data of the spring sheets collected by laser radar;

[0014] The data analysis module is used to pre-process the collected static test data, calculate the defect degree, flatness and product consistency of the spring sheet under static conditions, and calculate the standard degree of the spring sheet using the defect degree, flatness and product consistency;

[0015] The data screening module is used to compare the calculated spring sheet standard degree with the preset spring sheet standard interval to determine whether the spring sheet standard degree is within the spring sheet standard interval. If the spring sheet standard degree is within the spring sheet standard interval, the spring sheet is determined to be statically qualified. If the spring sheet standard degree is outside the spring sheet standard interval, the spring sheet is marked and determined to be unqualified.

[0016] The dynamic detection module is used to obtain dynamic detection data of the spring piece whose determination result is statically qualified, and calculate the dynamic flatness based on the dynamic detection data, and calculate the change degree of the spring piece based on the dynamic flatness and the flatness of the spring piece under static conditions;

[0017] The comprehensive evaluation module is used to obtain the degree of change of the spring sheet, and compare the degree of change with the preset degree of change interval to determine whether the spring sheet is qualified, and calculate the average degree of change of qualified spring sheets in the entire batch, and compare the qualified rate and average degree of change with the preset qualified interval and degree of change interval, and execute the corresponding adjustment strategy according to the comparison results.

[0018] Furthermore, the collected static detection data is preprocessed by preprocessing the top view image data and the side view image data of the N groups of spring sheets. The preprocessing steps are as follows:

[0019] Segment the collected images and remove the background of the images;

[0020] Perform filtering and denoising on the image with the background removed;

[0021] The edge detection algorithm is used to calculate and threshold segment the graphics after filtering and denoising.

[0022] Furthermore, the formula for filtering and denoising the image with the background removed is as follows:

[0023]

[0024] In the formula, g(s, t) is the original image, f(x, y) is the image after filtering and denoising, S xy represents a filter window with a center point at (x, y and a size of m×n.

[0025] Furthermore, the formula for calculating the defect degree is as follows:

[0026]

[0027] In the formula, Def is the calculated defect degree, k is the number of defects, s i is the area of ​​the defect at the i-th position, A is a constant coefficient, A>1, is the average gray value of the pixels in the defect area at the i-th location, hb is the standard gray value, q is the number of pixels in the image corresponding to the defect at the i-th location, hd j is the pixel value of the jth pixel in the i-th defect;

[0028] The calculation formula for flatness is as follows:

[0029]

[0030] Where Flatj is the flatness of the spring leaf, S zj is the total forward projection area of ​​the spring sheet, S z is the total forward projection area of ​​the standard spring leaf, S cj is the total lateral projection area of ​​the spring leaf, S f is the total lateral projection area of ​​the standard spring leaf, S fj S is the top projection area of ​​the spring sheet, f It is the top projection area of ​​the standard spring leaf.

[0031] Furthermore, the calculation method of product consistency is as follows:

[0032] Input the size data of the spring piece collected by the laser radar into the three-dimensional model to construct the three-dimensional model of the spring piece;

[0033] Compare the three-dimensional model of the spring sheet with the three-dimensional model of the standard spring sheet, and mark the deviation position and the corresponding deviation volume;

[0034] Calculate product consistency based on deviation position and corresponding deviation volume;

[0035]

[0036] Where Con is the product consistency, w is the number of deviation positions, V i is the volume of the ith deviation position, lx i is the type of the i-th deviation position, zyx i is the importance data of the i-th deviation position;

[0037] The formula for calculating the standard of the spring sheet using defect degree, flatness and product consistency is as follows:

[0038] STD=Def×Dxs+Flatj×Fxs+Con×Cxs

[0039] In the formula, STD is the standard degree of the spring sheet, Dxs is the weight coefficient corresponding to the defect degree, Fxs is the weight coefficient corresponding to the flatness of the spring sheet, and Cxs is the weight coefficient corresponding to the product consistency.

[0040] Furthermore, the dynamic detection data of the spring sheet is the top view picture data and the side view picture data of the static qualified spring sheet of group P under different stress states when in use. The formula for calculating the dynamic flatness based on the dynamic detection data is as follows:

[0041]

[0042] In the formula, Flatd is the dynamic flatness, R is the number of detections, S ci is the total lateral projection area of ​​the spring sheet during the i-th dynamic test;

[0043] The formula for calculating the change of the spring leaf based on the dynamic flatness and the static flatness of the spring leaf is as follows:

[0044] Var=log d (Flatj×α+Flatd+1)

[0045] Where Var is the degree of change of the spring leaf, α and d are constant coefficients, α>d>1.

[0046] Furthermore, the preset change range is

[0047] If the calculated The brake spring sheet is qualified;

[0048] If the calculated The brake spring sheet is unqualified.

[0049] Furthermore, the formula for calculating the average variation of qualified spring sheets in the entire batch is as follows:

[0050]

[0051] In the formula, is the average variation of qualified spring sheets, u is the number of qualified spring sheets, u≤P≤N, Var i is the variation data of the i-th group of spring sheets.

[0052] Furthermore, the qualified interval is [85%, 100%], and the variation interval is

[0053] When executing the corresponding adjustment strategy based on the comparison results:

[0054] If the pass rate Check production equipment and adjust production process;

[0055] If the qualified rate is within the qualified interval, but the average variation is outside the variation interval, calibrate the production equipment and adjust the production process locally;

[0056] If the pass rate is within the pass range and the average variation is within the variation range, no adjustment is required.

[0057] Furthermore, a method for detecting the flatness of a brake spring sheet comprises the following steps:

[0058] Obtain static inspection data of N groups of spring leaves randomly selected from the same batch, the static inspection data including overhead view image data and side view image data of the N groups of spring leaves collected at equal intervals using an optical inspection instrument, and size data of the spring leaves collected using a laser radar;

[0059] Preprocess the collected static test data to calculate the defect, flatness and product consistency of the spring sheet under static conditions, and use the defect, flatness and product consistency to calculate the standard of the spring sheet;

[0060] Compare the calculated standard degree of the spring piece with the preset standard interval of the spring piece to determine whether the standard degree of the spring piece is within the standard interval of the spring piece. If the standard degree of the spring piece is within the standard interval of the spring piece, the spring piece is determined to be statically qualified. If the standard degree of the spring piece is outside the standard interval of the spring piece, the spring piece is marked and determined to be unqualified.

[0061] Acquire dynamic detection data of the spring piece with a static qualified result, calculate dynamic flatness based on the dynamic detection data, and calculate the degree of change of the spring piece based on the dynamic flatness and the flatness of the spring piece under static conditions;

[0062] Obtain the degree of change of the spring piece, and compare the degree of change with the preset degree of change interval, determine whether the spring piece is qualified, and calculate the average degree of change of qualified spring pieces in the entire batch, and compare the qualified rate and the average degree of change with the preset qualified interval and degree of change interval. If the qualified rate is outside the qualified interval, verify the production equipment and adjust the production process. If the qualified rate is within the qualified interval, but the average degree of change is outside the degree of change interval, verify the production equipment and partially adjust the production process.

[0063] If the pass rate is within the pass range and the average variation is within the variation range, no adjustment is required.

[0064] (III) Beneficial effects

[0065] The present invention provides a brake spring sheet flatness detection system and method, which have the following beneficial effects:

[0066] 1. The present invention provides a brake spring sheet flatness detection system and method, which obtains static detection data and dynamic detection data of the spring sheet. Through further analysis and processing of the static detection data and the dynamic detection data, the overall quality of the spring sheet can be displayed, which can facilitate relevant staff to quickly understand whether the spring sheet can meet the requirements of automobile braking, and quickly screen out high-quality spring sheets from a large number of spring sheets. The system is relatively convenient to use, does not require subsequent secondary detection, has good use effect, and has good application prospects.

[0067] 2. The present invention provides a brake spring sheet flatness detection system and method, which collects static detection data of the spring sheet and performs comprehensive analysis and processing on the static detection data of the spring sheet, and can intuitively show whether there are defects on the outer surface of the spring sheet, whether the outer surface of the spring sheet is abnormally deformed, and whether the size of the processed spring sheet is standard. It can realize comprehensive detection of the outside of the spring sheet, complete detection at one time, and have good overall use effect. Moreover, the process processes the same data, which greatly reduces the data processing volume. Therefore, the detection speed is relatively fast and has good application prospects.

[0068] 3. The present invention provides a brake spring sheet flatness detection system and method, which obtains static detection data and dynamic detection data of the spring sheet, and conducts comprehensive analysis on the static detection data and the dynamic detection data, and calculates the degree of change of the spring sheet, so as to facilitate relevant staff to intuitively understand the deformation risk of the spring sheet during use, so as to directly judge the performance of the spring sheet, and analyze the production capacity of the entire batch in combination with the qualified rate and the degree of change, so as to help relevant managers to adjust strategies in time, and better manage the production of the spring sheet, with good use effect and good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flowchart of a brake spring sheet flatness detection system of the present invention;

[0070] Figure 2 This is a state diagram of static detection of a brake spring sheet flatness detection system of the present invention.

[0071] In the picture: 1. 3D scanning laser radar; 2. Industrial camera; 3. Fixture; 4. Inspection table. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] Research reasons

[0074] The existing technical center solution is to measure whether the spring sheet can reach the set deformation amount during use and whether it can return to its original state after deformation. It mainly moves the spring sheet to detect whether the elastic deformation capacity of the spring sheet can meet the requirements of use.

[0075] However, the above-mentioned detection method is the most basic detection of the spring sheet, in order to determine whether the spring sheet can meet the most basic working requirements, but its detection is relatively simple, and the detection of the spring sheet is not comprehensive. For example, during the detection process, the spring sheet undergoes partial twisting and is no longer tightly fitted to the product. It can also meet the requirements and can be reset after use. However, after being used for a period of time, it is easy for the spring sheet to be severely deformed and damaged or the spring sheet to separate from the product due to uneven force on the spring sheet, which can easily cause an accident.

[0076] Therefore, the detection accuracy of the above scheme is relatively low. It can only reflect the use of the spring sheet in this scenario, but cannot reflect the specific situation of the spring sheet in the process, and cannot analyze the risks of long-term application. Therefore, the detected spring sheet needs to be re-inspected later, and needs to go through multiple detection steps, multiple data collection and processing, and multiple screening and processing. The whole process is relatively complicated and troublesome.

[0077] Design ideas

[0078] Most existing detection systems directly move the spring sheet to determine whether the spring sheet can meet the basic deformation test required for brake resetting, but do not consider the deformation of the spring sheet during use, and do not consider whether the spring sheet can be used for a long time.

[0079] Therefore, during the research and development process, the initial idea was to achieve a more comprehensive detection of the spring leaf. Therefore, dynamic detection and static detection were added to detect the deformation of the spring leaf during use, and to judge whether the spring leaf can be used for a long time based on the deformation. This method greatly improves the accuracy of spring leaf detection and can reflect the quality of the spring leaf to a certain extent.

[0080] However, in actual use, it was found that there were various problems in the processing of spring sheets, which were not only the elastic capacity problems described above. For example, there were also problems such as incorrect processing dimensions, defects on the surface of the processed spring sheets, and certain deformations of the spring sheets themselves that did not meet people's usage requirements.

[0081] Therefore, in the middle of the research and development, we changed our thinking and added a basic inspection plan for the spring leaves, comprehensively analyzed the appearance and size of the spring leaves, and further processed the spring leaves.

[0082] However, in actual use, it was found that the above method can achieve an overall analysis and inspection of the appearance of the spring sheet, but the amount of data that needs to be processed during use is relatively large, and each test is relatively independent, which is equivalent to continuous single testing, and the data cannot be borrowed and integrated from each other.

[0083] Therefore, in the later stage of research and development, we fully combined the previous plans and developed a brake spring sheet flatness detection system and method, which can be detected at one time, and the amount of data processed during the detection process is relatively small. Therefore, the detection speed is relatively fast and the detection accuracy is also relatively high. The specific plan is as follows.

[0084] Study plan

[0085] like Figure 1 As shown, a brake spring sheet flatness detection system includes a data acquisition module, a data analysis module, a data screening module, a dynamic monitoring module and a comprehensive evaluation module:

[0086] Among them, the data acquisition module is the hardware part, and the data analysis module, data screening module, dynamic monitoring module and comprehensive evaluation module are the software part.

[0087] The model of the industrial camera is MER-503-20GM-P, and an aperture is applied to the outside of the industrial camera for auxiliary lighting and imaging. The laser radar uses a three-dimensional scanning laser radar, and the model of the laser radar is HD TLS360.

[0088] The hardware of the brake spring flatness detection system is as follows: Figure 2 As shown, it is composed of a detection platform 4, an industrial camera 2 and a three-dimensional scanning laser radar 1. A fixture 3 for limiting the position of a spring sheet is installed on the detection platform 4. When in use, the spring sheet is set on the fixture 3, and the industrial cameras 2 in different directions and the three-dimensional scanning laser radar 1 in different positions collect data of the spring sheet.

[0089] When performing dynamic detection of data, the spring sheet is set on an existing spring sheet elastic force testing device, and the deformation data is collected by an industrial camera.

[0090] Data collection

[0091] To know the processing quality of spring sheets, we must understand the basic properties of spring sheets after processing. To understand the basic properties of spring sheets, we must understand the parameters of spring sheets. Therefore, the basis for analyzing and judging spring sheets is to understand the parameters of spring sheets. Further analysis of the parameters of spring sheets can determine the quality of spring sheets.

[0092] The process of acquiring spring leaf data depends on the data acquisition module.

[0093] The data acquisition module is used to acquire static detection data of N groups of spring sheets randomly selected from the same batch, and the static detection data includes overhead view picture data and side view picture data of the N groups of spring sheets equidistantly collected by optical detection instruments and size data of the spring sheets collected by laser radar;

[0094] When in use, the spring sheets produced are usually sampled according to the processing batch. The results of the sampling can represent the processing quality of the entire batch of spring sheets. Therefore, when obtaining the spring sheet data, static inspection data of N groups of spring sheets are collected by random sampling, and the processing quality of the spring sheets is analyzed through the static inspection data of N groups of spring sheets.

[0095] Data processing

[0096] After the processing data of the spring sheet is collected, in order to facilitate the processing of the processing data, the data of the spring sheet must be analyzed and the required data must be extracted from it. The process of extracting data is based on the data analysis module.

[0097] The data analysis module is used to pre-process the collected static test data, calculate the defect degree, flatness and product consistency of the spring sheet under static conditions, and calculate the standard degree of the spring sheet using the defect degree, flatness and product consistency;

[0098] The collected static detection data is preprocessed by preprocessing the top view image data and the side view image data of N groups of spring sheets. The preprocessing steps are as follows:

[0099] Segment the collected images and remove the background of the images;

[0100] This solution adopts the method of fixed-point fixed-distance measurement, and the detection is carried out on the workbench. Therefore, this step is relatively convenient. The collected data is directly cropped, and then the background of the picture can be removed based on the threshold segmentation method. (The table top of the workbench adopts a pattern with a large color difference from the spring sheet. Usually, a pure color pattern with a grayscale difference greater than 50 is selected, such as red. Therefore, the background can be removed at one time based on the threshold segmentation method)

[0101] Due to interference from the environment, there will inevitably be a certain amount of noise in the collected images. Unless they are located in an independent and interference-free interval, denoising is required. For digital image signals, most of the noise is extreme values. The existence of extreme values ​​will make the image excessive or have dark spots in some parts, which will reduce the image quality and affect the subsequent analysis. Therefore, the collected images need to be denoised.

[0102] Perform filtering and denoising on the image with the background removed;

[0103] The formula for filtering and denoising the image with the background removed is as follows:

[0104]

[0105] In the formula, g(s, t) is the original image, f(x, y) is the image after filtering and denoising, S xy represents a filter window with a center point at (x, y and a size of m×n.

[0106] The smaller the m×n, the higher the accuracy. Generally, it is selected between 3×3 and 20×20. After filtering, the overall image is clearer.

[0107] When the image is clear, in order to more clearly identify the location and area of ​​the defect, it is necessary to extract the drawing of the defect location separately, and this process is also based on image processing.

[0108] The edge detection algorithm is used to calculate and threshold segment the graphics after filtering and denoising.

[0109] The edge detection algorithm uses the Sobel edge detection algorithm to calculate the grayscale gradient direction of the image after mean filtering. The Sobel operator uses two (3×3) matrices to perform convolution operations on the original image to obtain estimated values ​​of grayscale differences in two directions.

[0110] After the grayscale gradient direction is calculated, the grayscale gradient image is segmented by threshold.

[0111] Threshold segmentation is the simplest and most commonly used image processing method, especially when the image target and background occupy different grayscale ranges. This method can not only greatly compress the amount of data, but also greatly simplify the analysis and processing steps. Therefore, in many cases, threshold segmentation is a necessary image preprocessing process before image analysis and feature extraction.

[0112] For example, the grayscale value of a defect is 60, while the grayscale value of a normal defect is 30. There is a large difference. The threshold can be set to 40 to achieve image segmentation. The above image segmentation process is a commonly used image segmentation technology in the prior art. The present invention is a further application of this technology and does not improve the process. Therefore, there is no need to further refine and supplement the metal in this aspect.

[0113] After image processing, the location and number of defects are extracted, so that the degree of defect can be directly calculated.

[0114] The formula for calculating defectivity is as follows:

[0115]

[0116] In the formula, Def is the calculated defect degree, k is the number of defects, s i is the area of ​​the defect at the i-th position, A is a constant coefficient, A>1, is the average gray value of the pixels in the defect area at the i-th location, hb is the standard gray value, q is the number of pixels in the image corresponding to the defect at the i-th location, hd j is the pixel value of the jth pixel in the i-th defect;

[0117] The greater the difference between the average gray value and the standard gray value, the more serious the defect at that location.

[0118] For example, for slight depression and severe depression, under the same light exposure conditions, the deeper the depression, the less light is reflected in the depression area, resulting in a lower grayscale value at that position, which leads to a larger difference ratio and therefore a larger calculated defect degree.

[0119] By the same token, when the defect area is larger, it means that the defect is more serious, so the calculated defect degree is larger. Therefore, using the above formula, the larger the calculated defect degree is, the more serious the defect of the spring sheet is.

[0120] In general, k is usually less than 3.

[0121] The calculation formula for flatness is as follows:

[0122]

[0123] Where Flatj is the flatness of the spring leaf, S zj is the total forward projection area of ​​the spring sheet, S z is the total forward projection area of ​​the standard spring leaf, S cj is the total lateral projection area of ​​the spring leaf, S f is the total lateral projection area of ​​the standard spring leaf, S fj S is the top projection area of ​​the spring sheet, f It is the top projection area of ​​the standard spring leaf.

[0124] The lateral projection can be directly collected by a lateral industrial camera after removing the background, and then the corresponding pixel points can be obtained by computer calculation.

[0125] This method can determine whether the spring sheet is uniform. When there is a local protrusion, the area of ​​the lateral projection will inevitably increase. Therefore, the flatness will inevitably change. The greater the flatness, the lower the quality of the corresponding spring sheet.

[0126] The calculation method of product consistency is as follows:

[0127] Input the size data of the spring piece collected by the laser radar into the three-dimensional model to construct the three-dimensional model of the spring piece;

[0128] Compare the three-dimensional model of the spring sheet with the three-dimensional model of the standard spring sheet, and mark the deviation position and the corresponding deviation volume;

[0129] Calculate product consistency based on deviation position and corresponding deviation volume;

[0130]

[0131] Where Con is the product consistency, w is the number of deviation positions, V i is the volume of the ith deviation position, lx i is the type of the i-th deviation position, zyx i is the importance data of the i-th deviation position;

[0132] There are two types of deviation positions, one is redundant and the other is missing, and there are corresponding coefficients.

[0133] The weight coefficient is determined by the coefficient of variation method, which is a method of weighting each indicator according to the degree of variation between the current value of each evaluation indicator and the target value. If the numerical difference of an indicator is large, it can clearly distinguish the evaluated objects, indicating that the indicator has rich discrimination information, and thus the indicator should be given a larger weight. On the contrary, if the numerical difference of each evaluated object on a certain indicator is small, then the ability of this indicator to distinguish the evaluation objects is weak, and thus the indicator should be given a smaller weight. This method directly uses the information contained in each indicator and obtains the weight of the indicator through calculation, so it is objective.

[0134] Different processing positions have different importances. For example, the contact position with the brake pad and other positions on the spring sheet are more likely to cause an accident due to deviations in the contact position with the brake pad. Therefore, the corresponding coefficients are also different.

[0135] During operation, the possibility of accidents caused by defects at locations with large elastic deformation and locations with small elastic deformation is different, so they also need to be analyzed and handled separately.

[0136] The formula for calculating the standard of the spring sheet using defect degree, flatness and product consistency is as follows:

[0137] STD=Def×Dxs+Flatj×Fxs+Con×Cxs

[0138] In the formula, STD is the standard degree of the spring sheet, Dxs is the weight coefficient corresponding to the defect degree, Fxs is the weight coefficient corresponding to the flatness of the spring sheet, and Cxs is the weight coefficient corresponding to the product consistency.

[0139] The evaluation of spring sheets individually will be interfered by the testing instruments, and the individual testing and analysis is not comprehensive enough. Therefore, it is necessary to analyze and process all the data centrally to obtain the required data to fully judge the quality of the spring sheets, that is, the standard degree of the spring sheets. The standard degree of the spring sheets can clearly reflect the quality of the spring sheets, making it convenient for relevant technical personnel to understand the quality of the processed spring sheets.

[0140] Product consistency mainly reflects the processing quality of the spring leaf through volume. Through model comparison, defects such as deformation of the processed products can also be reflected more clearly.

[0141] Product Filter

[0142] When data that can reflect the processing quality of the spring sheet is obtained, the data can be compared with the corresponding qualified standards to determine whether the processed spring sheet is qualified. In this way, it is possible to intuitively check whether the processed spring sheet is obviously unqualified. This process is based on the data screening module.

[0143] The data screening module is used to compare the calculated spring sheet standard degree with the preset spring sheet standard interval to determine whether the spring sheet standard degree is within the spring sheet standard interval. If the spring sheet standard degree is within the spring sheet standard interval, the spring sheet is determined to be statically qualified. If the spring sheet standard degree is outside the spring sheet standard interval, the spring sheet is marked and determined to be unqualified.

[0144] For example, the STDs of 10 groups of spring sheets are calculated to be 16.3, 11.9, 12.8, 17.6, 13.7, 9.4, 7.8, 16.3, 6.7 and 16.8 respectively, and the standard interval of the spring sheets is [0, 15], then the unqualified products are the first, fourth, eighth and tenth ones.

[0145] The present invention provides a brake spring sheet flatness detection system, which collects static detection data of the spring sheet and performs comprehensive analysis and processing on the static detection data of the spring sheet, and can intuitively show whether there are defects on the outer surface of the spring sheet, whether the outer surface of the spring sheet is abnormally deformed, and whether the size of the processed spring sheet is standard, and can achieve comprehensive detection of the outside of the spring sheet, complete detection at one time, and good overall use effect, and the process processes the same data, greatly reducing the data processing volume, therefore, the detection speed is relatively fast, and has good application prospects.

[0146] Dynamic Detection

[0147] After screening out spring sheets without obvious defects, it is necessary to further perform dynamic testing on the screened spring sheets. In this way, the detection amount of spring sheets can be greatly reduced, thereby shortening the detection time of spring sheets and improving the detection efficiency of spring sheets. Further dynamic testing of the screened spring sheets is based on the dynamic monitoring module.

[0148] The dynamic detection module is used to obtain dynamic detection data of the spring piece whose determination result is statically qualified, and calculate the dynamic flatness based on the dynamic detection data, and calculate the change degree of the spring piece based on the dynamic flatness and the flatness of the spring piece under static conditions;

[0149] The dynamic detection data of the spring sheet is the top view and side view picture data of the static qualified spring sheet of group P under different stress states when in use (that is, the deformation process is divided into multiple stages, and each stage is tested once). The formula for calculating the dynamic flatness based on the dynamic detection data is as follows:

[0150]

[0151] In the formula, Flatd is the dynamic flatness, R is the number of detections, S ci is the total lateral projection area of ​​the spring sheet during the i-th dynamic test;

[0152] Dynamic flatness is obtained based on a static scene.

[0153] In this case, the deformation is more obvious when observed from the side, so the dynamic flatness can be clearly calculated using the image data taken from the side.

[0154] The overhead photo data is archived for review when a new plan is formulated later, which facilitates subsequent adjustments to understand the defects of the spring sheet, so as to better adjust the production plan and processing parameters.

[0155] The formula for calculating the change of the spring leaf based on the dynamic flatness and the static flatness of the spring leaf is as follows:

[0156] Var=log d (Flatj×α+Flatd+1)

[0157] Where Var is the degree of change of the spring leaf, α and d are constant coefficients, α>d>1.

[0158] The degree of change in the spring leaf can reflect the quality of a single spring leaf when in use.

[0159] The present invention provides a brake spring sheet flatness detection system, which obtains static detection data and dynamic detection data of the spring sheet, and comprehensively analyzes the static detection data and the dynamic detection data to calculate the degree of change of the spring sheet, so as to facilitate relevant staff to intuitively understand the deformation risk of the spring sheet when in use, so as to directly judge the performance of the spring sheet, and analyzes the production capacity of the entire batch in combination with the qualified rate and the degree of change, so as to help relevant managers to adjust strategies in time, so as to better manage the production of the spring sheet, have good use effect, and have good application prospects.

[0160] By comprehensively evaluating the spring piece, the change degree of the spring piece is obtained. Through the change degree of the spring piece, it can be clearly understood whether the spring piece can be used in the same environment for a long time.

[0161] The greater the degree of variation of the spring leaf, the greater the difference between the spring leaf and the standard spring leaf, and the greater the risk when using it.

[0162] The comprehensive evaluation module is used to obtain the degree of change of the spring sheet, and compare the degree of change with the preset degree of change interval to determine whether the spring sheet is qualified, and calculate the average degree of change of qualified spring sheets in the entire batch, and compare the qualified rate and average degree of change with the preset qualified interval and degree of change interval, and execute the corresponding adjustment strategy according to the comparison results.

[0163] The preset change range is

[0164] If the calculated The brake spring sheet is qualified;

[0165] If the calculated The brake spring sheet is unqualified.

[0166] This step further evaluates the spring sheet to determine whether the spring sheet is qualified. After two evaluations of the spring sheet, it is determined that the qualified spring sheet can meet the needs of automobile braking and has a good overall use effect.

[0167] The formula for calculating the average variation of qualified spring sheets in the entire batch is as follows:

[0168]

[0169] In the formula, is the average variation of qualified spring sheets, u is the number of qualified spring sheets, u≤P≤N, Var i is the variation data of the i-th group of spring sheets.

[0170] The average variation can reflect the processing quality of the batch of qualified spring sheets.

[0171] The qualified interval is [85%, 100%], and the variation interval is

[0172] When executing the corresponding adjustment strategy based on the comparison results:

[0173] If the pass rate Verify production equipment and adjust production processes. In this case, both equipment and processes have large errors;

[0174] If the qualified rate is within the qualified range, but the average variation is outside the variation range, the production equipment should be verified and the production process should be partially adjusted. This situation usually means that the product has relatively mature processes and processing equipment, but the equipment needs to be corrected and the process needs to be fine-tuned to further improve the qualified rate.

[0175] If the qualified rate is within the qualified range and the average variation is within the variation range, no adjustment is required. In this case, the process is mature and the yield rate is relatively high. There is no need to calibrate the equipment and process, and the use effect is good.

[0176] The present invention provides a brake spring sheet flatness detection system, which obtains static detection data and dynamic detection data of the spring sheet, and can display the overall quality of the spring sheet through further analysis and processing of the static detection data and the dynamic detection data, so as to facilitate relevant staff to quickly understand whether the spring sheet can meet the requirements of automobile braking, and quickly screen out high-quality spring sheets from a large number of spring sheets. The system is convenient to use, does not need to be subjected to subsequent secondary detection, has good use effect, and has good application prospects.

[0177] A method for detecting the flatness of a brake spring sheet comprises the following steps:

[0178] Obtain static inspection data of N groups of spring leaves randomly selected from the same batch, the static inspection data including overhead view image data and side view image data of the N groups of spring leaves collected at equal intervals using an optical inspection instrument, and size data of the spring leaves collected using a laser radar;

[0179] Preprocess the collected static test data to calculate the defect, flatness and product consistency of the spring sheet under static conditions, and use the defect, flatness and product consistency to calculate the standard of the spring sheet;

[0180] Compare the calculated standard degree of the spring piece with the preset standard interval of the spring piece to determine whether the standard degree of the spring piece is within the standard interval of the spring piece. If the standard degree of the spring piece is within the standard interval of the spring piece, the spring piece is determined to be statically qualified. If the standard degree of the spring piece is outside the standard interval of the spring piece, the spring piece is marked and determined to be unqualified.

[0181] Acquire dynamic detection data of the spring piece with a static qualified result, calculate dynamic flatness based on the dynamic detection data, and calculate the degree of change of the spring piece based on the dynamic flatness and the flatness of the spring piece under static conditions;

[0182] Obtain the degree of change of the spring piece, and compare the degree of change with the preset degree of change interval, determine whether the spring piece is qualified, and calculate the average degree of change of qualified spring pieces in the entire batch, and compare the qualified rate and the average degree of change with the preset qualified interval and degree of change interval. If the qualified rate is outside the qualified interval, verify the production equipment and adjust the production process. If the qualified rate is within the qualified interval, but the average degree of change is outside the degree of change interval, verify the production equipment and partially adjust the production process.

[0183] If the pass rate is within the pass range and the average variation is within the variation range, no adjustment is required.

[0184] The above formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0185] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0186] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A brake spring sheet flatness detection system, which is used for the detection of automobile brake spring sheets, characterized in that: The brake spring sheet flatness detection system includes: Data acquisition module: used to obtain static detection data of N groups of spring sheets randomly selected from the same batch, the static detection data including overhead view picture data and side view picture data of N groups of spring sheets collected at equal distances by optical detection instruments and size data of spring sheets collected by laser radar; Data analysis module: used to pre-process the collected static test data, calculate the defect degree, flatness and product consistency of the spring sheet under static conditions, and calculate the standard degree of the spring sheet using the defect degree, flatness and product consistency; Data screening module: used to compare the calculated spring sheet standard degree with the preset spring sheet standard interval to determine whether the spring sheet standard degree is within the spring sheet standard interval. If the spring sheet standard degree is within the spring sheet standard interval, the spring sheet is determined to be statically qualified. If the spring sheet standard degree is outside the spring sheet standard interval, the spring sheet is marked and determined to be unqualified. Dynamic detection module: used to obtain dynamic detection data of the spring piece whose determination result is static qualified, and calculate the dynamic flatness based on the dynamic detection data, and calculate the change degree of the spring piece based on the dynamic flatness and the flatness of the spring piece under static condition; Comprehensive evaluation module: used to obtain the degree of change of the spring sheet, and compare the degree of change with the preset degree of change interval to determine whether the spring sheet is qualified, and calculate the average degree of change of qualified spring sheets in the entire batch, and compare the qualified rate and average degree of change with the preset qualified interval and degree of change interval, and execute the corresponding adjustment strategy according to the comparison results.

2. A brake spring sheet flatness detection system according to claim 1, characterized in that: The collected static detection data is preprocessed by preprocessing the top view image data and the side view image data of N groups of spring sheets. The preprocessing steps are as follows: Segment the collected images and remove the background of the images; Perform filtering and denoising on the image with the background removed; The edge detection algorithm is used to calculate and threshold segment the graphics after filtering and denoising.

3. A brake spring sheet flatness detection system according to claim 2, characterized in that: The formula for filtering and denoising the image with the background removed is as follows: In the formula, g(s, t) is the original image, f(x, y) is the image after filtering and denoising, S xy represents a filter window with a center point at (x, y and a size of m×n.

4. A brake spring sheet flatness detection system according to claim 3, characterized in that: The formula for calculating defectivity is as follows: In the formula, Def is the calculated defect degree, k is the number of defects, s i is the area of ​​the defect at the i-th position, A is a constant coefficient, A>1, is the average gray value of the pixels in the defect area at the i-th location, hb is the standard gray value, q is the number of pixels in the image corresponding to the defect at the i-th location, hd j is the pixel value of the jth pixel in the i-th defect; The calculation formula for flatness is as follows: Where Flatj is the flatness of the spring leaf, S zj is the total forward projection area of ​​the spring sheet, S z is the total forward projection area of ​​the standard spring leaf, S cj is the total lateral projection area of ​​the spring leaf, S f is the total lateral projection area of ​​the standard spring leaf, S fj S is the top projection area of ​​the spring sheet, f It is the top projection area of ​​the standard spring leaf.

5. A brake spring sheet flatness detection system according to claim 4, characterized in that: The calculation method of product consistency is as follows: Input the size data of the spring piece collected by the laser radar into the three-dimensional model to construct the three-dimensional model of the spring piece; Compare the three-dimensional model of the spring sheet with the three-dimensional model of the standard spring sheet, and mark the deviation position and the corresponding deviation volume; Calculate product consistency based on deviation position and corresponding deviation volume; Where Con is the product consistency, w is the number of deviation positions, V i is the volume of the ith deviation position, lx i is the type of the i-th deviation position, zyx i is the importance data of the i-th deviation position; The formula for calculating the standard of the spring sheet using defect degree, flatness and product consistency is as follows: STD=Def×Dxs+Flatj×Fxs+Con×Cxs In the formula, STD is the standard degree of the spring sheet, Dxs is the weight coefficient corresponding to the defect degree, Fxs is the weight coefficient corresponding to the flatness of the spring sheet, and Cxs is the weight coefficient corresponding to the product consistency.

6. A brake spring sheet flatness detection system according to claim 5, characterized in that: The dynamic detection data of the spring sheet is the top view and side view picture data of the static qualified spring sheet of group P under different stress states when in use. The formula for calculating the dynamic flatness based on the dynamic detection data is as follows: In the formula, Flatd is the dynamic flatness, R is the number of detections, S ci is the total lateral projection area of ​​the spring sheet during the i-th dynamic test; The formula for calculating the change of the spring leaf based on the dynamic flatness and the static flatness of the spring leaf is as follows: Was=log d (Flatj×α+Flatd+1) Where Var is the degree of change of the spring leaf, α and d are constant coefficients, α>d>1.

7. A brake spring sheet flatness detection system according to claim 6, characterized in that: The preset change range is If the calculated The brake spring sheet is qualified; If the calculated The brake spring sheet is unqualified.

8. A brake spring sheet flatness detection system according to claim 7, characterized in that: The formula for calculating the average variation of qualified spring sheets in the entire batch is as follows: In the formula, is the average variation of qualified spring sheets, u is the number of qualified spring sheets, u≤P≤N, Var i is the variation data of the i-th group of grid spring sheets.

9. A brake spring sheet flatness detection system according to claim 8, characterized in that: The qualified interval is 85%, 100%], and the variation interval is When executing the corresponding adjustment strategy based on the comparison results: If the pass rate Check production equipment and adjust production process; If the qualified rate is within the qualified interval, but the average variation is outside the variation interval, calibrate the production equipment and adjust the production process locally; If the pass rate is within the pass range and the average variation is within the variation range, no adjustment is required.

10. A method for detecting the flatness of a brake spring sheet, using the system according to any one of claims 1 to 9, characterized in that: The steps include: Obtain static inspection data of N groups of spring leaves randomly selected from the same batch, the static inspection data including overhead view image data and side view image data of the N groups of spring leaves collected at equal intervals using an optical inspection instrument, and size data of the spring leaves collected using a laser radar; Preprocess the collected static test data to calculate the defect, flatness and product consistency of the spring sheet under static conditions, and use the defect, flatness and product consistency to calculate the standard of the spring sheet; Compare the calculated standard degree of the spring piece with the preset standard interval of the spring piece to determine whether the standard degree of the spring piece is within the standard interval of the spring piece. If the standard degree of the spring piece is within the standard interval of the spring piece, the spring piece is determined to be statically qualified. If the standard degree of the spring piece is outside the standard interval of the spring piece, the spring piece is marked and determined to be unqualified. Acquire dynamic detection data of the spring piece with a static qualified result, calculate dynamic flatness based on the dynamic detection data, and calculate the degree of change of the spring piece based on the dynamic flatness and the flatness of the spring piece under static conditions; Obtain the degree of change of the spring piece, and compare the degree of change with the preset degree of change interval, determine whether the spring piece is qualified, and calculate the average degree of change of qualified spring pieces in the entire batch, and compare the qualified rate and the average degree of change with the preset qualified interval and degree of change interval. If the qualified rate is outside the qualified interval, verify the production equipment and adjust the production process. If the qualified rate is within the qualified interval, but the average degree of change is outside the degree of change interval, verify the production equipment and partially adjust the production process. If the pass rate is within the pass range and the average variation is within the variation range, no adjustment is required.

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