Grinding machine flatness-based detection system

Abstract

The invention discloses a detection system based on the flatness of a grinding machine, relates to the technical field of flatness measurement, and solves the technical problem that an existing grinding disc flatness detection system lacks pertinence to detection of different grinding discs, so that the accuracy of a grinding disc flatness detection result is not enough. The data acquisition module is used for acquiring scanning data and the working temperature of the line laser; the detection signal generation module is used for acquiring a plurality of historical grinding data and generating a grinding disc detection signal according to the historical grinding data; the detection scheme generation module is used for acquiring grinding disc feature data of the grinding disc through the database; generating a detection scheme according to the grinding disc feature data; the line laser detection module is used for acquiring the detection scheme and detecting the grinding disc according to the detection scheme; the working temperature is obtained in real time, a calibration scheme is generated according to the working temperature, and the line laser is adjusted according to the calibration scheme; generating a detection result corresponding to the grinding disc according to the scanning data; and the accuracy of flatness detection of the grinding disc is ensured.

Description

Technical Field

[0001] The present application belongs to the field of flatness measurement technology, and relates to flatness measurement technology, and specifically is a detection system based on the flatness of a grinding machine. Background Art

[0002] During the use of the grinding machine, as the cumulative amount of material removed increases, the grinding disc will wear out, and due to the different trajectories at different positions on the grinding disc surface, the grinding disc surface wears unevenly. When the surface shape of the grinding disc deteriorates, the processing stability will be reduced and the surface quality of the machined workpiece will deteriorate. Therefore, when the surface shape of the grinding disc reaches a certain error, it must be trimmed.

[0003] The prior art (the invention patent with announcement number CN114659474B) discloses a method, device, equipment and storage medium for detecting the flatness of a grinding disc, and relates to the field of flatness measurement technology. Among them, this detection method comprises steps S1 to S5. S1 obtains N radial measurement data of the grinding disc. S2 eliminates the mechanical errors of the N radial measurement data respectively to obtain N initial data. S3 averages the height information of each grinding block according to the N initial data, and deletes the height information of non-grinding blocks to obtain N grinding block surface height data. S4 scales the N grinding block surface height data to the same data length respectively, and then calculates the average value to obtain the radial height data of the grinding disc. S5 performs three-dimensional reconstruction based on the radial height data of the grinding disc to obtain the surface model of the grinding disc.

[0004] The above-mentioned grinding wheel flatness detection method analyzes the flatness of the grinding wheel by detecting N measurement data and then removing the corresponding mechanical errors. However, the degree of wear of different areas of the grinding wheel during use is different. The flatness is analyzed by analyzing the radial data of a set number of strips, which makes the analysis result inaccurate. Due to the different materials of the grinding wheel, the degree of wear will also be different. Using the same scanning parameters to scan different grinding wheels will also reduce the accuracy of the scanning results. Therefore, a detection system based on the flatness of the grinder is needed. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a detection system based on the flatness of a grinding machine, which is used to solve the technical problem that the existing grinding disc flatness detection system lacks the specificity to detect different grinding discs, resulting in insufficient accuracy of the grinding disc flatness detection results.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a detection system based on the flatness of a grinding machine, comprising: a data acquisition module, a detection signal generation module, a detection scheme generation module, a line laser detection module and a database; The data acquisition module is used to acquire scanning data and the working temperature of the line laser through a data acquisition device connected thereto; The detection signal generating module is configured to obtain a number of historical grinding data through a database connected thereto, and to generate a grinding disc detection signal according to the historical grinding data; The detection scheme generating module is used to obtain the grinding disc characteristic data of the grinding disc through the database; and to generate a detection scheme according to the grinding disc characteristic data; The line laser detection module: obtains a detection plan, and performs grinding disc detection according to the detection plan; obtains the working temperature in real time, generates a calibration plan according to the working temperature, and adjusts the line laser according to the calibration plan; and obtains a number of scanning data, and generates a corresponding grinding disc detection result according to the number of scanning data.

[0007] The present application obtains the grinding disc characteristic data of the grinding disc and generates a detection scheme according to the grinding disc characteristic data; obtains a number of historical grinding data and generates a grinding disc detection signal according to the historical grinding data; if a grinding disc detection signal is generated, then obtains the detection scheme and detects the grinding disc according to the detection scheme. At the same time, during the detection process, the working temperature of the line laser is obtained in real time, and a calibration scheme is generated in a targeted manner according to the working temperature. Finally, a corresponding detection result is generated according to the scanning data obtained by the detection. Through the above-mentioned operation method, the grinding disc is detected in a targeted manner to ensure the accuracy of the grinding disc flatness detection.

[0008] Preferably, generating a grinding disc detection signal according to historical grinding data comprises: Extracting grinding time and grinding object characteristic data from a number of historical grinding data; inputting the grinding time and grinding object characteristic data into an influence coefficient score generation model to obtain a corresponding influence coefficient score; the influence coefficient score generation model is obtained by training an artificial intelligence model; Obtain the influence coefficient score corresponding to the grinding object feature in each historical grinding data, add up the influence coefficient scores to get the total influence coefficient score, and judge whether the total influence coefficient score is greater than the set influence coefficient score threshold. If so, generate a grinding disc detection signal; if not, update the total influence coefficient score when the next grinding work is completed, and judge again whether the total influence coefficient score is greater than the set influence coefficient score threshold.

[0009] Preferably, the impact coefficient scoring generation model is obtained by training an artificial intelligence model, including: Obtain a number of historical grinding data and their corresponding impact coefficient scores through a database; integrate the grinding time and grinding object feature data in the historical grinding data, as well as the impact coefficient scores, into a number of training data and test data; The training data is used to train the artificial intelligence model, and the test data is used to test the trained artificial intelligence model, and finally an influence coefficient score generation model is obtained, whose input is grinding time and grinding object characteristic data, and output is influence coefficient score, wherein the artificial intelligence model includes BP neural network model and RBF neural network model.

[0010] Preferably, generating a detection scheme according to the grinding disc characteristic data includes: The grinding disc is divided into several detection areas, and the regional material feature data and regional structure feature data in the grinding disc feature data corresponding to each detection area are extracted. Extracting material hardness and material heat resistance scores from regional material features, as well as bump quantity, bump size, and bump height from regional structural feature data; Generate a preset scanning speed for the corresponding inspection area based on material hardness, number of bumps, bump size, and bump roundness; Generate a preset scanning intensity for the corresponding inspection area based on the material heat resistance score, bump quantity, bump size, and bump roundness; Generate a scanning area ranking table according to the preset scanning intensity of each detection area; The detection plan is integrated according to the scanning area ranking table and the preset scanning speed corresponding to each detection area.

[0011] Preferably, the preset scanning speed of the corresponding detection area is generated according to the material hardness, the number of bumps, the size of the bumps and the roundness of the bumps, including: The material hardness is marked as CY, the number of bumps is marked as TS, the bump size is marked as TC, and the bump roundness is marked as TR; By formula The scanning accuracy requirement score SJP is calculated; where α1, β1 and β2 are all proportional coefficients; ε1 is the adjustment coefficient; DTC is the set unit bump size; TRY is the set roundness threshold; By formula The preset scanning speed YMS is calculated, where α2 is the proportional coefficient, 0<α2<1; ZJP is the maximum accuracy requirement score; and XMS is the scanning speed limit.

[0012] Preferably, the method of generating a preset scanning intensity of a corresponding detection area according to the material heat resistance score, the number of bumps, the size of the bumps and the roundness of the bumps includes: The material heat resistance score is marked as NP; By formula The demand intensity impact score XQP is calculated; among them, α3, β3 and β4 are proportional coefficients; ε2 is the adjustment coefficient; MNP is the maximum material heat resistance score; XMQ is the scanning intensity limit.

[0013] Preferably, generating a scanning area ranking table according to the preset scanning intensity of each detection area includes: The detection areas with preset scanning intensity greater than the low-intensity threshold are marked as high-intensity scanning areas, and the remaining detection areas are marked as low-intensity scanning areas; the high-intensity scanning areas are arranged in order from large to small according to the preset scanning intensity to obtain a high-intensity scanning area sorting table, and the low-intensity scanning areas are arranged in order from small to large according to the preset scanning intensity to obtain a low-intensity scanning sequence table, and a new scanning area sorting table is cross-arranged in order from high to low of the high-intensity scanning area sorting table. To ensure that each high-intensity scanning area and low-intensity scanning area can appear alternately, if the next scanning area after the high-intensity scanning area appears is still a high-intensity scanning area, then it is necessary to set a waiting time when the previous high-intensity scanning area scans, and scan the next high-intensity scanning area when the waiting time is reached, to ensure that there will not be two high-intensity scanning areas scanning continuously, so that the line laser can work intermittently at high intensity during operation.

[0014] Preferably, generating a calibration scheme according to the operating temperature includes: Get the working temperature and determine whether the working temperature is greater than the set safety temperature threshold; if yes, get the current scanning intensity, reduce the scanning intensity according to the set step size, mark the reduced scanning intensity as the calibration scanning intensity, and generate a calibration scheme based on the calibration scanning intensity; if no, continue to get the working temperature at the next moment, and determine whether the corresponding working temperature is greater than the set safety temperature threshold.

[0015] Due to the long-term, high-intensity operation of the laser, the temperature of some parts of the laser may rise, that is, the operating temperature increases; due to the higher operating temperature, the parameters of some electronic components of the line laser may drift, resulting in distortion of the scanning signal, thereby reducing the detection effect. Therefore, by reducing the scanning intensity, that is, reducing the laser emission power of the line laser, the temperature can be reduced to enhance the detection effect.

[0016] Preferably, the generating of the detection result of the corresponding grinding disc according to the plurality of scanning data comprises: Obtain scanning data of each detection area, extract three-dimensional point cloud data from the scanning data, and obtain the distance between each adjacent scanning point in the three-dimensional point cloud data; determine whether there is a distance exceeding a distance threshold set for the corresponding detection area; If yes, the ratio of the number of distances exceeding the distance threshold set for the corresponding sector to the number of distances between all adjacent scanning points is obtained and recorded as the sparse ratio. It is determined whether the sparse ratio is greater than the set re-inspection threshold, the detection area is set as the re-inspection area, and the re-inspection area is re-inspected according to the sparse ratio; if no, the detection area is set as the scanned area; If no, the detection area is set as the scan completion area; Acquire the three-dimensional point cloud data of each scanned area, acquire the standard grinding disc scanning surface, map each scanning point in the three-dimensional point cloud data to the three-dimensional space of the standard grinding disc scanning surface to form a detection surface; acquire the closest distance between each scanning point in the detection surface and the standard grinding disc scanning surface and mark it as the error distance, and judge whether the error distance is less than the set error distance standard; if yes, mark the scanning point as a normal scanning point; if no, mark the scanning point as an abnormal scanning point; The ratio of abnormal scanning points to normal scanning points is set as the detection score; the abnormal scanning points and their corresponding error distances are marked on the abnormal scanning points corresponding to the detection surface to generate a detection result map; and the detection score and the detection result map are integrated into the detection result.

[0017] The present application determines whether the particle density of each area meets the particle density standard of the corresponding area, that is, whether the distance between each scanning point is greater than the set distance threshold, and whether the sparse ratio is greater than the corresponding re-inspection threshold, to determine whether the scanning result of the area is compliant. If it is compliant, subsequent operations are performed; if it is not compliant, the area is rescanned to increase the accuracy of the final detection result.

[0018] Preferably, re-inspecting the re-inspected area according to the sparse ratio includes: Obtaining a sparse ratio XS and a preset scanning speed YMS corresponding to the re-inspection area; By formula The re-inspection scanning speed CMS is calculated; wherein α4 is a proportionality coefficient, and a specific value is set based on experience; the re-inspection area is re-inspected based on the re-inspection scanning speed and the predicted scanning intensity of the re-inspection area.

[0019] Compared with the prior art, the beneficial effects of this application are: 1. The present application obtains the grinding disc characteristic data of the grinding disc and generates a detection scheme according to the grinding disc characteristic data; obtains a number of historical grinding data and generates a grinding disc detection signal according to the historical grinding data; if a grinding disc detection signal is generated, then obtains the detection scheme and detects the grinding disc according to the detection scheme. At the same time, during the detection process, the working temperature of the line laser is obtained in real time, and a calibration scheme is generated in a targeted manner according to the working temperature. Finally, a corresponding detection result is generated according to the scanning data obtained by the detection. Through the above-mentioned operation method, the grinding disc is detected in a targeted manner to ensure the accuracy of the grinding disc flatness detection.

[0020] 2. This application determines whether the particle density of each area meets the particle density standard of the corresponding area, that is, whether the distance between each scanning point is greater than the set distance threshold, and whether the sparse ratio is greater than the corresponding re-inspection threshold, to determine whether the scanning result of the area is compliant. If it is compliant, subsequent operations are performed; if it is not compliant, the area is rescanned to increase the accuracy of the final detection result. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be 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 paying creative work.

[0022] Figure 1 This is a schematic diagram of the principle of the grinding machine flatness detection system in this application; Figure 2 This is a schematic diagram of the execution steps of the grinding machine flatness detection system in this application; Figure 3 Generate a flow chart for the detection scheme in this application. DETAILED DESCRIPTION

[0023] The technical solution of the present application will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0024] See also Figure 1-Figure 2 , the first aspect of the present application provides a detection system based on the flatness of a grinding machine, including: a data acquisition module, a detection signal generation module, a detection scheme generation module, a line laser detection module and a database; Data acquisition module: acquires scanning data and the working temperature of the line laser through the data acquisition device connected thereto; the scanning data is the scanning data of the line laser corresponding to each scanning area during the scanning process; the working temperature is the temperature of the line laser device when the line laser is working; the data acquisition device includes a line laser scanning device and a temperature detection device, etc.; Detection signal generation module: obtains some historical grinding data through the database connected to it. The historical grinding data is the grinding data recorded during each grinding work between the last flatness detection and the current flatness detection, and generates a grinding disc detection signal according to the historical grinding data; the grinding disc detection signal is a detection signal generated according to the use of the grinding disc. When the use of the grinding disc reaches the set standard, the grinding disc needs to be detected, and the grinding disc needs to be detected in time to ensure the yield rate of the grinding disc; Detection scheme generation module: obtains the grinding disc feature data of the grinding disc through the database; the grinding disc feature data is the feature of the grinding disc, including the regional material feature data and regional structural feature data of the grinding disc; the regional material feature data is the relevant characteristic data of the material used for the grinding disc, and the regional structural feature data is the structural relevant feature data of the surface of the grinding disc; generates a detection scheme according to the grinding disc feature data; the detection scheme is a scanning scheme for scanning the grinding disc using a line laser device and performing flatness detection; Line laser detection module: obtain the detection plan, and perform grinding disc detection according to the detection plan; obtain the working temperature in real time, and generate a calibration plan according to the working temperature; it can be understood that the data acquisition module starts to obtain scanning data only after the line laser detection module starts scanning; the calibration plan is a plan for adjusting the detection plan currently executed by the line laser equipment based on the real-time working temperature of the grinding disc, and the adjustment items include the line laser intensity during scanning; adjust the line laser according to the calibration plan; and obtain a number of scanning data, and generate the detection result of the corresponding grinding disc according to the number of scanning data; the detection result is the scanning result of this grinding disc.

[0025] This embodiment obtains grinding disc characteristic data of the grinding disc and generates a detection scheme according to the grinding disc characteristic data; obtains a number of historical grinding data and generates a grinding disc detection signal according to the historical grinding data; if a grinding disc detection signal is generated, the detection scheme is obtained and the grinding disc is detected according to the detection scheme. At the same time, during the detection process, the working temperature of the line laser is obtained in real time, and a calibration scheme is generated in a targeted manner according to the working temperature. Finally, a corresponding detection result is generated according to the scanning data obtained by the detection. Through the above-mentioned operation method, the grinding disc is detected in a targeted manner to ensure the accuracy of the grinding disc flatness detection.

[0026] Preferably, generating a grinding disc detection signal according to historical grinding data comprises: Extracting grinding time and grinding object characteristic data from a number of historical grinding data; the grinding object characteristic data is the characteristics of the grinding object that affect the wear of the grinding disc, such as the hardness and surface structure of the grinding object; inputting the grinding time and the grinding object characteristic data into the influence coefficient score generation model to obtain the corresponding influence coefficient score; the influence coefficient score generation model is obtained by training an artificial intelligence model; Obtain the influence coefficient score corresponding to the grinding object feature in each historical grinding data, accumulate and sum each influence coefficient score to obtain the total influence coefficient score, and determine whether the total influence coefficient score is greater than the set influence coefficient score threshold. If so, generate a grinding disc detection signal; if not, update the total influence coefficient score when the next grinding work is completed, and determine again whether the total influence coefficient score is greater than the set influence coefficient score threshold; the influence coefficient score threshold is set according to expert experience. When the total influence coefficient exceeds the influence coefficient score threshold, it means that the wear degree of the grinding disc may affect the subsequent grinding effect, that is, the grinding disc needs to be detected at this time.

[0027] This embodiment analyzes the working condition of the grinding disc after each grinding work, which can also be understood as the wear condition of the grinding disc during the current grinding work based on the last flatness detection; when the analysis result reaches the set requirement, the flatness detection of the grinding disc is performed, and the grinding disc is detected in time to ensure the yield rate of the grinding disc.

[0028] The impact coefficient scoring generation model is obtained through artificial intelligence model training, including: A number of historical grinding data and their corresponding impact coefficient scores are obtained through a database; the grinding time and grinding object characteristic data in the historical grinding data, as well as the impact coefficient score are integrated into a number of training data and test data; the grinding time is the time for this grinding work; the impact coefficient score used to train the impact coefficient score generation model is the score given by the expert on the impact of this grinding work on the wear of the grinding disc based on the grinding object characteristic data and the corresponding grinding time. The higher the impact coefficient score, the more serious the wear of the grinding disc caused by this grinding work; The artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data. Specifically, the grinding time and grinding object characteristic data in the test data are input into the trained artificial intelligence model, and the influence coefficient score is output to determine whether the difference between the influence coefficient score and the influence coefficient score recorded in the test data is within an acceptable range; if so, it means that this group of test data has passed the test, and the next group of test data test will be continued; if not, it is necessary to adjust the relevant parameters of the artificial intelligence model, and continue to use this group of test data for testing; until a set proportion of test data passes the test; finally, an influence coefficient score generation model is obtained with the grinding time and grinding object characteristic data as input and the influence coefficient score as output, wherein the artificial intelligence model includes a BP neural network model and a RBF neural network model.

[0029] See also Figure 3 , generating a detection scheme according to the grinding disc characteristic data, including: dividing the grinding disc into a plurality of detection areas, the specific division method can be divided according to the characteristics of the grinding disc, if the surface structure and material of the grinding disc are the same, then it is equally divided into a plurality of detection areas along the circumferential direction; if the surface structure and material of the grinding disc are different, then it is divided into a plurality of detection areas according to the areas where the different surface structures or materials are located; it can be understood that, in this embodiment, the grinding disc is divided into a plurality of detection areas along the circumferential direction, for example; extracting the regional material characteristic data and the regional structure characteristic data from the grinding disc characteristic data corresponding to each detection area, Extracting material hardness and material heat resistance scores from regional material features, as well as bump quantity, bump size, and bump height from regional structural feature data; Generate a preset scanning speed for the corresponding inspection area based on material hardness, number of bumps, bump size, and bump roundness; Generate a preset scanning intensity for the corresponding detection area according to the material heat resistance score, the number of bumps, the size of the bumps and the roundness of the bumps; generate a scanning area ranking table according to the preset scanning intensity of each detection area; The detection plan is integrated according to the scanning area ranking table and the preset scanning speed corresponding to each detection area.

[0030] This embodiment generates scanning speeds and scanning intensities for different regions in a targeted manner according to regional material feature data and regional structural feature data of different regions, so as to ensure that the scanning accuracy of each region meets the corresponding accuracy requirements, while avoiding the influence of laser scanning on the grinding disc.

[0031] Generate a preset scanning speed for the corresponding detection area according to the material hardness, the number of convex points, the size of the convex points and the roundness of the convex points, including: marking the material hardness as CY, the number of convex points as TS, the size of the convex points as TC, and the roundness of the convex points as TR; the roundness of the convex points is the roundness of the convex points, the greater the roundness of the convex points, the smoother the change of the convex point periphery, and the flatter the convex points; the smaller the roundness of the convex points, the steeper the change of the convex point periphery, and the sharper the convex points; By formula The scanning accuracy requirement score SJP is calculated; among them, α1, β1 and β2 are all proportional coefficients, α1 is used to adjust the range of the entire formula, β1 and β2 are used to adjust the ratio of the bump size and bump roundness to the detection accuracy, and their specific values ​​are set according to expert experience; ε1 is an adjustment coefficient, which is used to adjust the distribution of the detection accuracy requirements of different material hardnesses, and its specific values ​​are set according to expert experience. It can be understood that the adjustment coefficient has a corresponding unit, which is used to remove the dimension of the material hardness; DTC is the set unit bump size; TRY is the set roundness threshold; the unit bump size and roundness threshold can be used to remove the dimension, as well as to adjust the distribution of the detection accuracy requirements of different bump sizes and different bump roundness ranges, and its specific values ​​are set according to expert experience; By formula The preset scanning speed YMS is calculated, where α2 is the proportional coefficient, 0<α2<1; ZJP is the maximum accuracy requirement score; XMS is the scanning speed limit, and the scanning speed limit is the scanning speed that can meet the basic scanning accuracy requirements.

[0032] The more the number of bumps, the smaller the size of the bumps, and the smaller the roundness of the bumps, the more complex the surface structure of the corresponding area of ​​the grinding disc is, and the higher the scanning accuracy required. By setting a lower scanning speed, more scanning data can be obtained in the same area to improve the scanning accuracy. The greater the material hardness, the less wear the corresponding area suffers, and the greater the possibility of maintaining its original structural characteristics, which is roughly consistent with the scanning accuracy required by the original structure; the smaller the material hardness, the more severe the wear the corresponding area suffers, and the less likely it is to maintain its original structural characteristics. In order to ensure the accuracy of the scanning results, the scanning accuracy should be appropriately improved while maintaining the scanning accuracy required by the original structure.

[0033] Generate a preset scanning intensity for the corresponding detection area according to the material heat resistance score, the number of bumps, the size of the bumps and the roundness of the bumps, including: marking the material heat resistance score as NP; the heat-resistant material score is an evaluation of the material based on the heat resistance related parameters of the material type, such as the thermal expansion coefficient and the melting point; when the material type is less affected by heat, that is, the lower its thermal expansion coefficient and the higher its melting point, the smaller the impact when it is irradiated by high-power laser, and the higher the corresponding material heat resistance score; By formula The demand intensity impact score XQP is calculated; among them, α3, β3 and β4 are all proportional coefficients; α3 is used to adjust the influence range of material heat resistance score, bump number, bump size and bump roundness on scanning intensity demand, and 0<α3<1; β3 and β4 are used to adjust the proportional relationship between bump size and bump roundness, and are also used to adjust the range of influence of bump size and bump roundness on scanning intensity demand into a numerical range, and the specific values ​​are set according to expert experience; ε2 is an adjustment coefficient, which is used to adjust the distribution of laser scanning intensity requirements for different material heat resistance scores, and the specific values ​​are set according to expert experience. It can be understood that the adjustment coefficient has a corresponding unit, which is used to remove the dimension of material hardness; MNP is the maximum material heat resistance score; XMQ is the scanning intensity limit, and the scanning intensity limit is the scanning intensity corresponding to the maximum heat resistance score.

[0034] When the size of the bump is smaller and its corresponding roundness is smaller, that is, the bump is small and sharp, when a high-intensity laser is scanned on it, it may cause damage to the bump, thereby affecting the subsequent grinding effect of the grinding disc; when the number of bumps is greater, the above-mentioned damage area is larger. At the same time, when the heat resistance corresponding to the grinding disc material is lower, it is more affected by the high-intensity laser. Therefore, in order to reduce the damage to the grinding disc during laser scanning, this embodiment adjusts the scanning intensity of the line laser according to the material properties and structural properties of the grinding disc through the above formula; when the material properties and structural properties are easily affected by the intensity of the line laser, the scanning intensity of the line laser on the area is reduced to reduce the damage to the grinding disc during laser scanning.

[0035] It is worth noting that this embodiment divides the grinding wheel into zones and then generates the scanning intensity and scanning speed of each zone according to the characteristics of each zone; the flatness detection effect of grinding wheels with different structures or different materials on the same grinding wheel is better and the accuracy is higher.

[0036] Generate a scanning area sorting table according to the preset scanning intensity of each detection area, including: marking the detection area with preset scanning intensity greater than the low intensity threshold as a high intensity scanning area, and marking the remaining detection areas as low intensity scanning areas; arranging the high intensity scanning areas in the order of preset scanning intensity from large to small to obtain a high intensity scanning area sorting table, arranging the low intensity scanning areas in the order of preset scanning intensity from small to large to obtain a low intensity scanning sequence table, and cross-arranging the high intensity scanning area sorting table and the low intensity scanning area sorting table in the order from high to low to form a new scanning area sorting table. To ensure that each high intensity scanning area and low intensity scanning area can appear alternately, if the next scanning area after the high intensity scanning area appears is still a high intensity scanning area, then it is necessary to set a waiting time when the previous high intensity scanning area scans, and scan the next high intensity scanning area when the waiting time is reached, to ensure that there will not be two high intensity scanning areas scanning continuously, so that the line laser can work intermittently at high intensity when working.

[0037] A calibration scheme is generated according to the working temperature, including: obtaining the working temperature, and determining whether the working temperature is greater than a set safety temperature threshold; the safety temperature threshold is set according to expert experience, and when the temperature exceeds the safety temperature threshold, the scanning effect of the line laser cannot meet the set requirements; if yes, the current scanning intensity is obtained, and the scanning intensity is reduced according to the set step size, and the reduced scanning intensity is marked as the calibration scanning intensity, and a calibration scheme is generated according to the calibration scanning intensity; if no, the working temperature at the next moment is continuously obtained, and it is determined whether the corresponding working temperature is greater than the set safety temperature threshold.

[0038] Due to the long-term, high-intensity operation of the laser, the temperature of some parts of the laser may rise, that is, the operating temperature increases; due to the higher operating temperature, the parameters of some electronic components of the line laser may drift, resulting in distortion of the scanning signal, thereby reducing the detection effect. Therefore, by reducing the scanning intensity, that is, reducing the laser emission power of the line laser, the temperature can be reduced to enhance the detection effect.

[0039] It is worth noting that the step size of the scan intensity reduction can be a fixed setting or can be set according to the value of the operating temperature exceeding the safety temperature threshold. The larger the value exceeded, the longer the step size and the greater the corresponding scan intensity reduction, so as to alleviate the high temperature situation as soon as possible.

[0040] Generate the detection result of the corresponding grinding disc according to the plurality of scanning data, including: obtaining the scanning data of each detection area, extracting the three-dimensional point cloud data in the scanning data, obtaining the distance between each adjacent scanning point in the three-dimensional point cloud data; judging whether there is a distance exceeding the distance threshold set for the corresponding detection area; the distance threshold is set according to the expert experience If yes, the ratio of the number of distances exceeding the distance threshold set for the corresponding sector to the number of distances between all adjacent scanning points is obtained and recorded as the sparse ratio, and it is determined whether the sparse ratio is greater than the set re-inspection threshold. The re-inspection threshold is set according to the scanning accuracy requirements of each area. The higher the scanning accuracy requirement, the smaller the corresponding re-inspection threshold is set. In this embodiment, the re-inspection threshold is set by the scanning speed. The slower the scanning speed, the smaller the corresponding re-inspection threshold is set. Then, the detection area is set as the re-inspection area, and the re-inspection area is re-inspected according to the sparse ratio, and the re-inspected scanning data of the area is combined with the first scanning data for subsequent analysis. If no, the detection area is set as the scanning completion area. If no, the detection area is set as the scan completion area; Re-inspecting the re-inspection area according to the sparse ratio, including: obtaining the sparse ratio XS and the preset scanning speed YMS corresponding to the re-inspection area; The re-inspection scanning speed CMS is calculated; wherein α4 is a proportionality coefficient, and a specific value is set based on experience; the re-inspection area is re-inspected based on the re-inspection scanning speed and the predicted scanning intensity of the re-inspection area.

[0041] This embodiment uses the above formula to specifically reduce the scanning speed during the re-inspection process, which can increase the number of scanning points accordingly, thereby increasing the scanning accuracy; Acquire the three-dimensional point cloud data of each scanned area, acquire the standard grinding disc scanning surface, map each scanning point in the three-dimensional point cloud data to the three-dimensional space of the standard grinding disc scanning surface to form a detection surface; acquire the closest distance between each scanning point in the detection surface and the standard grinding disc scanning surface and mark it as the error distance, and judge whether the error distance is less than the set error distance standard, which is set according to expert experience; if yes, mark the scanning point as a normal scanning point; if no, mark the scanning point as an abnormal scanning point; The ratio of abnormal scanning points to normal scanning points is set as the detection score; the abnormal scanning points and their corresponding error distances are marked on the abnormal scanning points corresponding to the detection surface to generate a detection result map; and the detection score and the detection result map are integrated into the detection result.

[0042] This embodiment determines whether the particle density of each area meets the particle density standard of the corresponding area, that is, whether the distance between each scanning point is greater than the set distance threshold, and whether the sparse ratio is greater than the corresponding re-inspection threshold, to determine whether the scanning result of the area is in compliance. If it is in compliance, subsequent operations are performed; if it is not in compliance, the area is rescanned to increase the accuracy of the final detection result.

[0043] It is understandable that the present application can process the acquired scan data of each area through S3-S4 recorded in the invention patent with publication number CN114659474B to increase the accuracy of the final flatness detection.

[0044] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0045] How this application works: This embodiment obtains grinding disc characteristic data of the grinding disc and generates a detection scheme according to the grinding disc characteristic data; obtains a number of historical grinding data and generates a grinding disc detection signal according to the historical grinding data; if a grinding disc detection signal is generated, then obtains the detection scheme and detects the grinding disc according to the detection scheme. Meanwhile, during the detection process, the working temperature of the line laser is obtained in real time, and a calibration scheme is generated in a targeted manner according to the working temperature. After all areas are detected, the scanning data of each area is analyzed to determine whether re-inspection is needed; finally, a corresponding detection result is generated according to the scanning data obtained by the detection. Through the above-mentioned operation method, the grinding disc is detected in a targeted manner to ensure the accuracy of the grinding disc flatness detection.

[0046] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. A detection system based on the flatness of a grinding machine, comprising: Data acquisition module, detection signal generation module, detection scheme generation module, line laser detection module and database; characterized in that, The data acquisition module is used to acquire scanning data and the working temperature of the line laser through a data acquisition device connected thereto; The detection signal generating module is configured to obtain a number of historical grinding data through a database connected thereto, and to generate a grinding disc detection signal according to the historical grinding data; The detection scheme generating module is used to obtain the grinding disc characteristic data of the grinding disc through the database; and to generate a detection scheme according to the grinding disc characteristic data; The line laser detection module: obtains a detection plan, and performs grinding disc detection according to the detection plan; obtains the working temperature in real time, generates a calibration plan according to the working temperature, and adjusts the line laser according to the calibration plan; and obtains a number of scanning data, and generates a corresponding grinding disc detection result according to the number of scanning data.

2. A grinding machine flatness detection system according to claim 1, characterized in that: The method of generating a grinding disc detection signal according to historical grinding data comprises: Extracting grinding time and grinding object characteristic data from a number of historical grinding data; inputting the grinding time and grinding object characteristic data into an influence coefficient score generation model to obtain a corresponding influence coefficient score; the influence coefficient score generation model is obtained by training an artificial intelligence model; Obtain the influence coefficient score corresponding to the grinding object feature in each historical grinding data, add up the influence coefficient scores to get the total influence coefficient score, and judge whether the total influence coefficient score is greater than the set influence coefficient score threshold. If so, generate a grinding disc detection signal; if not, update the total influence coefficient score when the next grinding work is completed, and judge again whether the total influence coefficient score is greater than the set influence coefficient score threshold.

3. A grinding machine flatness detection system according to claim 2, characterized in that: The impact coefficient scoring generation model is obtained through artificial intelligence model training, including: Obtain a number of historical grinding data and their corresponding impact coefficient scores through a database; integrate the grinding time and grinding object feature data in the historical grinding data, as well as the impact coefficient scores, into a number of training data and test data; The training data is used to train the artificial intelligence model, and the test data is used to test the trained artificial intelligence model, and finally an influence coefficient score generation model is obtained, whose input is grinding time and grinding object characteristic data, and output is influence coefficient score, wherein the artificial intelligence model includes BP neural network model and RBF neural network model.

4. A grinding machine flatness detection system according to claim 1, characterized in that: The generating of the detection scheme according to the grinding disc characteristic data comprises: The grinding disc is divided into several detection areas, and the regional material feature data and regional structure feature data in the grinding disc feature data corresponding to each detection area are extracted. Extracting material hardness and material heat resistance scores from regional material features, as well as bump quantity, bump size, and bump height from regional structural feature data; Generate a preset scanning speed for the corresponding inspection area based on material hardness, number of bumps, bump size, and bump roundness; Generate a preset scanning intensity for the corresponding inspection area based on the material heat resistance score, bump quantity, bump size, and bump roundness; Generate a scanning area ranking table according to the preset scanning intensity of each detection area; The detection plan is integrated according to the scanning area ranking table and the preset scanning speed corresponding to each detection area.

5. A grinding machine flatness detection system according to claim 4, characterized in that: The method of generating a preset scanning speed for a corresponding detection area according to the material hardness, the number of bumps, the size of the bumps and the roundness of the bumps includes: The material hardness is marked as CY, the number of bumps is marked as TS, the bump size is marked as TC, and the bump roundness is marked as TR; By formula The scanning accuracy requirement score SJP is calculated; where α1, β1 and β2 are all proportional coefficients; ε1 is the adjustment coefficient; DTC is the set unit bump size; TRY is the set roundness threshold; By formula The preset scanning speed YMS is calculated, where α2 is the proportional coefficient, 0<α2<1; ZJP is the maximum accuracy requirement score; and XMS is the scanning speed limit.

6. A grinding machine flatness detection system according to claim 4, characterized in that: The method of generating a preset scanning intensity of a corresponding detection area according to the material heat resistance score, the number of bumps, the size of the bumps, and the roundness of the bumps includes: The material heat resistance score is marked as NP; By formula The demand intensity impact score XQP is calculated; among them, α3, β3 and β4 are proportional coefficients; ε2 is the adjustment coefficient; MNP is the maximum material heat resistance score; XMQ is the scanning intensity limit.

7. A grinding machine flatness detection system according to claim 4, characterized in that: The step of generating a scanning area ranking table according to the preset scanning intensity of each detection area includes: The detection areas with preset scanning intensity greater than the low-intensity threshold are marked as high-intensity scanning areas, and the remaining detection areas are marked as low-intensity scanning areas; the high-intensity scanning areas are arranged in order from large to small according to the preset scanning intensity to obtain a high-intensity scanning area sorting table, and the low-intensity scanning areas are arranged in order from small to large according to the preset scanning intensity to obtain a low-intensity scanning sequence table, and the high-intensity scanning area sorting table and the low-intensity scanning area sorting table are cross-arranged in order from high to low to form a new scanning area sorting table.

8. A grinding machine flatness detection system according to claim 1, characterized in that: The step of generating a calibration scheme according to the operating temperature comprises: Get the working temperature and determine whether the working temperature is greater than the set safety temperature threshold; if yes, get the current scanning intensity, reduce the scanning intensity according to the set step size, mark the reduced scanning intensity as the calibration scanning intensity, and generate a calibration scheme based on the calibration scanning intensity; if no, continue to get the working temperature at the next moment, and determine whether the corresponding working temperature is greater than the set safety temperature threshold.

9. A grinding machine flatness detection system according to claim 1, characterized in that: The method of generating a detection result of a corresponding grinding disc according to a plurality of scanning data includes: Obtain scanning data of each detection area, extract three-dimensional point cloud data from the scanning data, and obtain the distance between each adjacent scanning point in the three-dimensional point cloud data; determine whether there is a distance exceeding a distance threshold set for the corresponding detection area; If yes, the ratio of the number of distances exceeding the distance threshold set for the corresponding sector to the number of distances between all adjacent scanning points is obtained and recorded as the sparse ratio. It is determined whether the sparse ratio is greater than the set re-inspection threshold, the detection area is set as the re-inspection area, and the re-inspection area is re-inspected according to the sparse ratio; if no, the detection area is set as the scanned area; If no, the detection area is set as the scan completion area; Acquire the three-dimensional point cloud data of each scanned area, acquire the standard grinding disc scanning surface, map each scanning point in the three-dimensional point cloud data to the three-dimensional space of the standard grinding disc scanning surface to form a detection surface; acquire the closest distance between each scanning point in the detection surface and the standard grinding disc scanning surface and mark it as the error distance, and judge whether the error distance is less than the set error distance standard; if yes, mark the scanning point as a normal scanning point; if no, mark the scanning point as an abnormal scanning point; The ratio of abnormal scanning points to normal scanning points is set as the detection score; the abnormal scanning points and their corresponding error distances are marked on the abnormal scanning points corresponding to the detection surface to generate a detection result map; and the detection score and the detection result map are integrated into the detection result.

10. A grinding machine flatness detection system according to claim 9, characterized in that: The re-inspecting the re-inspected area according to the sparse ratio includes: Obtaining a sparse ratio XS and a preset scanning speed YMS corresponding to the re-inspection area; By formula The re-inspection scanning speed CMS is calculated, wherein α4 is a proportionality coefficient; and the re-inspection area is re-inspected according to the re-inspection scanning speed and the predicted scanning intensity of the re-inspection area.

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