An industrial part production nondestructive testing system based on 3D point cloud matching

Abstract

The application discloses an industrial part production nondestructive testing system based on 3D point cloud matching, comprising a sensor data acquisition module, a data information preprocessing module, a data cutting processing module, a data feature acquisition module, a part basic model, a feature matching processing module, an error analysis calculation module and a quality analysis report module; the three-dimensional model is scanned and processed to obtain three-dimensional point cloud data of the three-dimensional model, the acquired three-dimensional point cloud data is processed, three-dimensional point cloud data is filtered through voxel grid filtering, the accuracy of the three-dimensional model is improved through point cloud registration, the establishment of the part three-dimensional model is facilitated, the cutting processing of the three-dimensional model is performed, the calculation pressure is reduced, the matching of the detected three-dimensional point cloud data and the basic model data of the part is improved, the error threshold between the matching is used to determine whether the part has defects, and the nondestructive testing of the industrial part production is completed.

Description

Technical Field

[0001] The present invention relates to the technical field of parts production detection, and in particular to a non-destructive detection system for industrial parts production based on 3D point cloud matching. Background Art

[0002] In the fiercely competitive global economy, parts processing companies of all types are exploring diverse methods to ensure the quality of their products. Among these methods, shape and dimensional inspection of manufactured parts is a crucial component of this quality assurance process. Currently, in parts manufacturing industries with intensive processing demands and high quality requirements, such as aircraft parts manufacturing, parts inspection relies entirely on coordinate measuring machines (CMMs). However, these machines are complex to operate, and not only do their inspection speeds fall short of meeting the requirements of online, real-time testing, but their accuracy also depends on the operator's proficiency. Consequently, as industrial inspection requirements continue to rise, existing inspection methods (such as CMMs, theodolites / total stations, and other industrial measurement systems) are struggling to meet these demands. This is the direct reason for the research and development of visual measurement methods. The research and development of visual measurement methods and systems can address the shortcomings of current inspection methods and further address practical challenges faced by industrial inspection sectors. Although visual measurement technology has reached a practical stage, with numerous products already in use, it is still in its developmental stages. Therefore, independent research on appropriate visual measurement methods and the development of practical systems with high performance-price ratios and ease of operation are both necessary and urgent.

[0003] A 3D point cloud is a data structure used to represent objects or scenes in three-dimensional space. In its most basic form, it is a collection of three-dimensional coordinate points (X, Y, Z). These points are obtained by discretely sampling the surface of a real-world object or scene. Therefore, a point cloud can be considered a discrete representation of the scene surface in a given coordinate system.

[0004] Compared with traditional conventional measurement technology, measurement technology based on 3D point cloud (3D Point Cloud) has many advantages such as fast speed, strong penetration, non-contact, full digital, high precision, and high density.

[0005] In parts inspection, the applications of 3D point cloud matching technology include:

[0006] Defect detection: Identify defects or deviations on the part surface by comparing the actual point cloud with the point cloud of the design model.

[0007] Quality control: Real-time monitoring of parts quality during the production process to reduce defective rates.

[0008] Helps improve production efficiency and product quality.

[0009] In defect detection, 3D point cloud matching technology can be applied in the following ways:

[0010] Surface defect identification: Compare point cloud data with a reference model to detect surface defects such as cracks, pits, or wear.

[0011] Geometric deviation detection: Detect the deviation between the geometric dimensions of the part and the design model, and identify errors in manufacturing.

[0012] However, the existing 3D point cloud matching non-destructive testing system for industrial parts production has problems such as large workload and large amount of data, which cannot achieve effective and fast calculation and processing. In addition, the three-dimensional model is large, and direct processing is relatively complex, which is not conducive to fast calculation and analysis processing. In addition, the matching between features cannot accurately determine defects.

[0013] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0014] In response to the problems in the related art, the present invention proposes a non-destructive testing system for industrial parts production based on 3D point cloud matching to overcome the above-mentioned technical problems existing in the existing related art.

[0015] To this end, the specific technical solutions adopted in the present invention are as follows:

[0016] A non-destructive testing system for industrial parts production based on 3D point cloud matching, including a sensor data acquisition module, a data information preprocessing module, a data cutting processing module, a data feature acquisition module, a part basic model, a feature matching processing module, an error analysis and calculation module, and a quality analysis and reporting module:

[0017] The sensor data acquisition module is used to collect data on industrial parts through sensors, that is, to scan the industrial parts multiple times through scanning equipment to obtain three-dimensional point cloud data of the industrial parts;

[0018] The data information preprocessing module is used to preprocess the three-dimensional point cloud data collected by the sensor, wherein the data information preprocessing module includes de-noising, data alignment, point cloud cropping and point cloud registration of the data information to obtain a three-dimensional model of the industrial part;

[0019] The data cutting processing module is used to cut the collected three-dimensional model into several small model parts;

[0020] The data feature acquisition module is used to perform feature acquisition processing on the several small model parts after cutting, and obtain a feature set of each small model part;

[0021] The part basic model is used to input the manufacturing model parameters of the industrial part and obtain the basic data information and basic feature information of the industrial part;

[0022] The feature matching processing module is used to match the basic feature information of the industrial parts with the information in the collected feature set;

[0023] The error analysis and calculation module is used to calculate the matching error between the matched basic feature information and the information in the collected feature set, and detect surface defects and geometric errors of industrial parts by calculating the distance or geometric error between the three-dimensional point cloud data and the basic model;

[0024] The quality analysis report module is used to generate a report based on the error analysis results to obtain a quality inspection report for industrial parts.

[0025] As a preferred embodiment, the sensor used in the sensor data acquisition module includes a laser scanner, a structured light scanner or an optical measurement system. The laser scanner, the structured light scanner or the optical measurement system scans the produced industrial parts to obtain detailed three-dimensional point cloud data.

[0026] As a preferred embodiment, the point cloud registration includes initial registration and fine registration;

[0027] The initial registration is calculated using the SIFT algorithm, and the SIFT algorithm locates the extreme points through Gaussian blur calculation to perform preliminary alignment on the three-dimensional point cloud data of multiple scans;

[0028] The calculation formula is as follows:

[0029] L(x,y,z)=G(x,y,z)*I(n,m);

[0030] Among them, L(x, y, z) is the positioning extreme point in the calculated three-dimensional point cloud data, G(x, y, z) is the Gaussian function, and I(n, m) is the light intensity during scanning.

[0031] As a preferred embodiment, the fine registration uses the ICP algorithm to perform fine registration on the three-dimensional point cloud data, specifically:

[0032] Assume that the midpoint of the first scanned 3D point cloud data is a i , then the midpoint of the second scanned 3D point cloud data is b i , find a in the first scanned 3D point cloud data in the second scanned 3D point cloud data i The closest point b i ;

[0033] Calculate the distance between two scanned points of 3D point cloud data. The formula is:

[0034] d(a i ,b i )=||a i -b i ||。

[0035] As a preferred embodiment, the denoising in the data information preprocessing module adopts voxel grid filtering;

[0036] The size V of the voxel is set to 2, and the entire point cloud space is divided into a plurality of voxel units of the same size;

[0037] A data point is randomly selected from the three-dimensional point cloud data, and the data point is set as (x, y, z). According to the data point in the voxel, the integer is taken down, and the obtained data points are aggregated by mean value;

[0038] The mean value aggregation is expressed as:

[0039]

[0040] Where (x', y', z') is the representative point position of the data points in the voxel, (x n , y n , z n ) is the coordinate of the nth data point in the voxel, and N is the total number of data points in the voxel;

[0041] According to the calculated representative point of the data points in the voxel, the filtered three-dimensional point cloud data is generated.

[0042] As a preferred embodiment, the data alignment matches the three-dimensional point cloud data of multiple scans, so that the points of the same three-dimensional point cloud data of multiple scans can match each other;

[0043] Suppose the point of the three-dimensional point cloud data of the first scan is a i =(x i ,y i ,z i ), and the point of the three-dimensional point cloud data of the second scan is b i =(x j ,y j ,z j );

[0044] By calculating the rotation matrix R and the displacement vector t corresponding to the three-dimensional point cloud data, the points of the transformed three-dimensional point cloud data of the first scan and the points of the three-dimensional point cloud data of the second scan are matched as much as possible;

[0045] The calculation formula is as follows:

[0046]

[0047] in, is the midpoint between two points, and the midpoint is used to rigidly transform the two points to minimize the distance between them.

[0048] As a preferred embodiment, the data cutting processing module adopts bounding box segmentation:

[0049] By calculating the minimum bounding box of the model, the bounding box is divided into multiple parts along the x, y or z axis;

[0050] Formula: Bounding box coordinates:

[0051] Min x ≤x≤Max x ,

[0052] Min y ≤y≤Max y ,,

[0053] Min z ≤z≤Max z ,

[0054] Among them, Min x 、Min y 、Min z The minimum value of the boundary on the x-axis, y-axis, and z-axis, Max x Max y Max z They are the maximum values ​​of the boundaries on the x-axis, y-axis, and z-axis respectively. By determining the size of the bounding box, the three-dimensional point cloud can be cut.

[0055] As a preferred embodiment, the data feature acquisition module is used to acquire features in the cut 3D model and calculate the distribution and discreteness of the 3D point cloud data of the 3D model through statistical features, including:

[0056] The mean calculation formula is as follows:

[0057]

[0058] The formula for calculating variance is as follows:

[0059]

[0060] The formula for calculating the standard deviation is as follows:

[0061]

[0062] The features in the 3D model are collected and processed through the distribution and discreteness of the 3D point cloud data.

[0063] As a preferred embodiment, the part base model is used to record production data information of the part, and the feature matching processing module matches the features in the three-dimensional model with the base feature information recorded by the part base model, and the matching calculation formula is as follows:

[0064]

[0065] Wherein, (x i ,y i ,z i ) is the collected three-dimensional point cloud data, (x o ,y o ,z o ) is the data of the part base model, and the distance between the two points is calculated to realize feature matching.

[0066] As a preferred embodiment, the calculation formula of the error analysis calculation module is as follows:

[0067] C=|E i-1 -E i |,

[0068] Wherein, E i is the error value of the i-th iteration, and C is the change amount of error;

[0069] In each iteration, the error is calculated, and it is determined whether the detection error gradually decreases to below the preset threshold value. Usually, the algorithm will stop iteration when the error change is less than a certain threshold value. When the change amount of C tends to zero, and the error tends to the threshold value, it is determined that the industrial part has a production error.

[0070] The beneficial effects of the present application are:

[0071] 1. The present application scans the industrial parts through the sensor, realizes the acquisition of the three-dimensional point cloud data of the parts, and in order to improve the accurate description of the three-dimensional model of the parts, the industrial parts are scanned and collected at multiple angles and multiple times, and the data information obtained by scanning is analyzed and processed through the data information preprocessing module, so as to improve the accuracy of the data information, and the point cloud alignment of the data information is realized, the three-dimensional model is generated, and the unnecessary point cloud part is removed through point cloud cutting, reducing the calculation burden;

[0072] 2. In use, the accuracy of the three-dimensional model is improved through point cloud registration, and the initial registration and fine registration are included in the point cloud registration, the point cloud data obtained at different scanning angles can be processed, and the accuracy of the generated three-dimensional model is improved;

[0073] 3. When the present invention is in use, the data cutting processing module cuts the 3D model to reduce its size, so that each cut part can be processed by a separate feature matching processing module. That is, the 3D model is cut by a bounding box, so that the 3D model can be formed into a specific range size, which is convenient for matching with the basic model of the part, realizing matching detection, and facilitating the completion of defect identification of the part.

[0074] 4. When the present invention is used, it filters the three-dimensional point cloud data through voxel grid filtering to remove clutter in the three-dimensional point cloud data. The calculated voxel representative points are used to generate a filtered point cloud. Each voxel unit will replace all points in the voxel with one point, thereby significantly reducing the number of points in the point cloud. The voxel grid filtering is used to remove or filter out unnecessary points, thereby obtaining a more accurate or more satisfactory point cloud data set. The point at the center of the voxel is used to represent all points in the voxel, thereby removing unnecessary points, reducing the amount of point cloud data, and making the processing more efficient.

[0075] 5. When the present invention is in use, the feature matching processing module completes the matching of the data information of the production part inspection and the basic data of the part. The distribution and discreteness of the three-dimensional point cloud data information are used to facilitate the collection and processing of the three-dimensional point cloud data information features. The error is calculated in each iteration to determine whether the detection error gradually decreases below a preset threshold. Generally, the algorithm stops iterating when the error change is less than a certain threshold. When the change in C approaches zero and the error approaches the threshold, it is determined that the part has a production error.

[0076] The present invention has the advantages of obtaining three-dimensional point cloud data of the three-dimensional model by scanning the three-dimensional model, and processing the collected three-dimensional point cloud data, thereby facilitating the establishment of a three-dimensional model of the part, and reducing the calculation pressure by cutting the three-dimensional model, improving the matching of the detected three-dimensional point cloud data with the basic model data of the part, and determining whether the part has defects through the error threshold between the matches, thereby completing non-destructive testing of the production of industrial parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0078] Figure 13D point cloud matching-based nondestructive testing system for industrial parts production according to an embodiment of the present invention. DETAILED DESCRIPTION

[0079] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. By referring to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0080] According to an embodiment of the present invention, a non-destructive testing system for industrial parts production based on 3D point cloud matching is provided.

[0081] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, a nondestructive testing system for industrial parts production based on 3D point cloud matching according to an embodiment of the present invention includes a sensor data acquisition module, a data information preprocessing module, a data cutting processing module, a data feature acquisition module, a part basic model, a feature matching processing module, an error analysis calculation module, and a quality analysis report module:

[0082] The sensor data acquisition module is used to collect data on industrial parts through sensors, that is, to scan the industrial parts multiple times through scanning equipment to obtain three-dimensional point cloud data of the industrial parts;

[0083] The data information preprocessing module is used to preprocess the three-dimensional point cloud data collected by the sensor, wherein the data information preprocessing module includes de-noising, data alignment, point cloud cropping and point cloud registration of the data information to obtain a three-dimensional model of the industrial part;

[0084] The data cutting processing module is used to cut the collected three-dimensional model into several small model parts;

[0085] The data feature acquisition module is used to perform feature acquisition processing on the several small model parts after cutting, and obtain a feature set of each small model part;

[0086] The part basic model is used to input the manufacturing model parameters of the industrial part and obtain the basic data information and basic feature information of the industrial part;

[0087] The feature matching processing module is used to match the basic feature information of the industrial parts with the information in the collected feature set;

[0088] The error analysis and calculation module is used to calculate the matching error between the matched basic feature information and the information in the collected feature set, and detect surface defects and geometric errors of industrial parts by calculating the distance or geometric error between the three-dimensional point cloud data and the basic model;

[0089] The quality analysis report module is used to generate a report based on the error analysis results to obtain a quality inspection report for industrial parts.

[0090] As a preferred embodiment, the sensor used in the sensor data acquisition module includes a laser scanner, a structured light scanner or an optical measurement system. The laser scanner, the structured light scanner or the optical measurement system scans the produced parts to obtain detailed point cloud data.

[0091] It should be noted that non-destructive scanning of industrial parts is completed by using a laser scanner, structured light scanner or optical measurement system to obtain three-dimensional point cloud data information of the parts.

[0092] As a preferred embodiment, the steps of the sensor scanning the production parts are as follows:

[0093] Check equipment: ensure that the laser scanner, the structured light scanner, or the optical measurement system is in good condition, including calibration, cleaning, and normal operation; check the power supply and software version of the scanner;

[0094] Prepare the part: Clean the surface of the part to remove dirt or grease that may affect the scan. If the surface of the part is smooth and highly reflective, spray it with scanning powder to improve the scan quality.

[0095] Set up the scanning environment: Choose an appropriate scanning environment to avoid the effects of strong light, shadows, and temperature changes on the scanning results; ensure that the scanning area is stable and vibration-free;

[0096] Multiple scans: Aim the scanner at the part and perform a comprehensive scan. Depending on the type of scanner and the complexity of the part, perform multiple-angle and multi-position scans to obtain complete 3D data. Ensure that the scanner and part remain stable during the scanning process to avoid movement of the part or scanner. The collected data should be dense enough to ensure high resolution.

[0097] It should be noted that in order to achieve accurate scanning and processing of parts, the scanning equipment and parts need to be processed to improve the accuracy of scanning parts, and multiple scanning processes should be performed on the parts at different angles to facilitate the acquisition of complete three-dimensional data. The collected data should be dense enough to ensure high resolution, and then the three-dimensional establishment of the parts is completed through matching between the data.

[0098] As a preferred embodiment, the point cloud registration includes initial registration and fine registration;

[0099] The initial registration is calculated by using a SIFT algorithm, and the SIFT algorithm calculates the positioning extreme points by using a Gaussian blur to preliminarily align the three-dimensional point cloud data of multiple scans;

[0100] The calculation formula is as follows:

[0101] L(x, y, z) = G(x, y, z) * I(n, m);

[0102] Wherein, L(x, y, z) is the positioning extreme point in the calculated three-dimensional point cloud data, G(x, y, z) is a Gaussian function, and I(n, m) is the light intensity during scanning.

[0103] The point cloud data obtained at different scanning angles is aligned by the initial registration, and the preliminary alignment of the three-dimensional point cloud in the first scan and the second scan is completed.

[0104] As a preferred embodiment, the fine registration uses an ICP algorithm to finely register the three-dimensional point cloud data, specifically:

[0105] Let the midpoint of the three-dimensional point cloud data of the first scan be a i , and the midpoint of the three-dimensional point cloud data of the second scan be b i . Find the nearest point b i of a i in the three-dimensional point cloud data of the second scan.

[0106] The distance between the points of the two scans of the three-dimensional point cloud data is calculated, and the formula is represented as:

[0107] d(a i , b i ) = ||a i - b i ||;

[0108] It should be noted that the ICP algorithm is used to improve the registration accuracy of the three-dimensional point cloud, so as to improve the recognition degree between the first scan and the second scan.

[0109] As a preferred embodiment, the noise removal in the data information preprocessing module uses a voxel grid filter.

[0110] The size V of the voxel is set to 2, and the entire point cloud space is divided into a plurality of voxel units of the same size.

[0111] A data point is randomly selected from the three-dimensional point cloud data, and the data point is set as (x, y, z). The integer is taken down according to the data point in the voxel, and the obtained data points are aggregated by mean value.

[0112] The mean aggregation is expressed as:

[0113]

[0114] Among them, (x', y', z') is the representative point position of the data point in the voxel, (x n ,y n , z n ) is the coordinate of the nth data point within the voxel, and N is the total number of data points within the voxel;

[0115] The filtered three-dimensional point cloud data is generated according to the calculated representative points of the data points within the voxel.

[0116] It should be noted that all the 3D point cloud information of parts is processed through voxel grid filtering to facilitate the reduction of 3D point cloud information, that is, each voxel unit will replace all the points in the voxel with one point, thereby significantly reducing the number of points in the point cloud, and then filtering out abnormal clutter data information, thereby improving the accuracy of data information.

[0117] As a preferred embodiment, the data alignment matches the three-dimensional point cloud data scanned multiple times, so that points of the same three-dimensional point cloud data scanned multiple times can match each other;

[0118] Let the point of the first scanned 3D point cloud data be a i =(x i ,y i ,z i ), then the point of the 3D point cloud data scanned for the second time is b i =(x j ,y j ,z j );

[0119] By calculating the rotation matrix R and displacement vector t corresponding to the three-dimensional point cloud data, the points of the transformed three-dimensional point cloud data of the first scan are matched as closely as possible with the points of the three-dimensional point cloud data of the second scan;

[0120] The calculation formula is as follows:

[0121]

[0122] in, is the midpoint between two points, and the midpoint is used to rigidly transform the two points to minimize the distance between them.

[0123] It should be noted that in order to achieve matching of three-dimensional point cloud data collected by multiple scans, improve the accuracy of three-dimensional model establishment, and match between two points, the matching between the two points is determined by rigid transformation, and in order to improve the matching between the two points, the center point between the two points is used as the origin for rigid transformation adjustment.

[0124] As a preferred embodiment, the point cloud cropping removes or filters out unnecessary points through voxel grid filtering, thereby obtaining a more accurate or more demand-oriented point cloud data set, by dividing the point cloud data into voxels of a fixed size, and the voxels are cubic grids with a side length of 2, and using the point at the center of the voxel to represent all the points in the voxel, thereby removing unnecessary points, reducing the amount of point cloud data, and making processing more efficient.

[0125] It should be noted that in order to reduce the collected 3D point cloud, the point at the center of the voxel is used to represent all the points in the voxel, thereby removing unnecessary points, reducing the amount of point cloud data, making processing more efficient, facilitating calculation processing, improving calculation efficiency and reducing calculation pressure.

[0126] As a preferred embodiment, the data cutting processing module adopts bounding box segmentation:

[0127] By calculating the minimum bounding box of the model, the bounding box is divided into multiple parts along the x, y or z axis;

[0128] Formula: Bounding box coordinates:

[0129] Min x ≤x≤Max x ,

[0130] Min y ≤y≤Max y ,,

[0131] Min z ≤z≤Max z ,

[0132] Among them, Min x 、Min y 、Min z The minimum value of the boundary on the x-axis, y-axis, and z-axis, Max x Max y Max z They are the maximum values ​​of the boundaries on the x-axis, y-axis, and z-axis respectively. By determining the size of the bounding box, the three-dimensional point cloud can be cut.

[0133] It should be noted that the setting of the minimum bounding box facilitates the cutting and segmentation of the three-dimensional model, facilitates the calculation of each segmented feature, and improves the calculation efficiency and reduces the calculation pressure.

[0134] As a preferred embodiment, the data feature acquisition module is used to collect features in the cut 3D point cloud, because the data information collected by scanning is used to describe the distribution and discreteness of the data through statistical features.

[0135] The mean calculation formula is as follows:

[0136]

[0137] The formula for calculating variance is as follows:

[0138]

[0139] The formula for calculating the standard deviation is as follows:

[0140]

[0141] The distribution and discreteness of 3D point cloud data information make it easier to collect and process the information features of 3D point cloud data.

[0142] It should be noted that in order to realize feature extraction of data information, the distribution and discreteness of the data are described by statistical features, which facilitates feature collection of the data and facilitates feature matching of the data.

[0143] As a preferred embodiment, the part base model is used to input the production data information of the part, and the features of the 3D cloud data information are matched with the feature information inputted by the part base model through the search feature matching processing module, and the matching calculation formula is as follows:

[0144]

[0145] Among them, (x i ,y i ,z i ) is the collected 3D point cloud data, (x o ,y o ,z o ) is the data of the basic model of the part, and the features are matched by calculating the distance between the two points.

[0146] It should be noted that by calculating and analyzing the distance between feature points, it is convenient to complete the matching of two points and to achieve feature matching.

[0147] As a preferred embodiment, the calculation formula of the error analysis calculation module is as follows:

[0148] C=|E i-1 -E i |,

[0149] Among them, E i is the error value of the i-th iteration, and C is the change in error;

[0150] The error is calculated in each iteration to determine whether the detection error gradually decreases below the preset threshold. Usually, the algorithm stops iterating when the error change is less than a certain threshold. When the change in C approaches zero and the error approaches the threshold, it is determined that the part has a production error.

[0151] It should be noted that, through the error analysis calculation module, that is, through iterative error calculation processing, the control error gradually reaches below the threshold, so as to determine that the production of the part is qualified and there is no error. If the error amount is in the iterative process, when the change in C tends to zero, the error tends to the threshold, then it is determined that the part has a production error.

[0152] In summary, the present invention has the advantages of obtaining three-dimensional point cloud data of the three-dimensional model by scanning and processing the three-dimensional model, and processing the collected three-dimensional point cloud data, thereby facilitating the establishment of a three-dimensional model of the part, and reducing the calculation pressure by cutting the three-dimensional model, improving the matching of the detected three-dimensional point cloud data with the basic model data of the part, and determining whether the part has defects through the error threshold between the matches, thereby completing non-destructive testing of the production of industrial parts.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A non-destructive testing system for industrial parts production based on 3D point cloud matching, characterized in that: It includes sensor data acquisition module, data information preprocessing module, data cutting processing module, data feature acquisition module, part basic model, feature matching processing module, error analysis calculation module and quality analysis report module: The sensor data acquisition module is used to collect data on industrial parts through sensors, that is, to scan the industrial parts multiple times through scanning equipment to obtain three-dimensional point cloud data of the industrial parts; The data information preprocessing module is used to preprocess the three-dimensional point cloud data collected by the sensor, wherein the data information preprocessing module includes de-noising, data alignment, point cloud cropping and point cloud registration of the data information to obtain a three-dimensional model of the industrial part; The data cutting processing module is used to cut the collected three-dimensional model into several small model parts; The data feature acquisition module is used to perform feature acquisition processing on the several small model parts after cutting, and obtain a feature set of each small model part; The part basic model is used to input the manufacturing model parameters of the industrial part and obtain the basic data information and basic feature information of the industrial part; The feature matching processing module is used to match the basic feature information of the industrial parts with the information in the collected feature set; The error analysis and calculation module is used to calculate the matching error between the matched basic feature information and the information in the collected feature set, and detect surface defects and geometric errors of industrial parts by calculating the distance or geometric error between the three-dimensional point cloud data and the basic model; The quality analysis report module is used to generate a report based on the error analysis results to obtain a quality inspection report for industrial parts.

2. The industrial parts production nondestructive testing system based on 3D point cloud matching according to claim 1 is characterized in that: The sensors used in the sensor data acquisition module include laser scanners, structured light scanners or optical measurement systems. The laser scanners, structured light scanners or optical measurement systems scan the produced industrial parts to obtain detailed three-dimensional point cloud data.

3. The non-destructive testing system for industrial parts production based on 3D point cloud matching according to claim 1, characterized in that: The point cloud registration includes initial registration and fine registration; The initial registration is calculated using the SIFT algorithm, and the SIFT algorithm locates the extreme points through Gaussian blur calculation to perform preliminary alignment on the three-dimensional point cloud data of multiple scans; The calculation formula is as follows: L(x,y,z)=G(x,y,z)*I(n,m); Among them, L(x, y, z) is the extreme point of positioning in the calculated three-dimensional point cloud data, G(x, y, z) is the Gaussian function, and I(n, m) is the light intensity during scanning.

4. The industrial parts production nondestructive testing system based on 3D point cloud matching according to claim 3 is characterized in that: The fine registration uses the ICP algorithm to perform fine registration on the three-dimensional point cloud data, specifically: Assume that the midpoint of the first scanned 3D point cloud data is a i , then the midpoint of the second scanned 3D point cloud data is b i , find a in the first scanned 3D point cloud data in the second scanned 3D point cloud data i The closest point b i ; Calculate the distance between two scanned points of 3D point cloud data. The formula is: d(a i ,b i )=||a i -b i ||。 5. The industrial parts production nondestructive testing system based on 3D point cloud matching according to claim 1 is characterized in that: The denoising in the data information preprocessing module adopts voxel grid filtering; Set the voxel size V to 2, and the entire point cloud space will be divided into multiple voxel units of the same size; Randomly select a data point from the 3D point cloud data, set the data point to (x, y, z), round down the integer within the voxel according to the data point, and average the obtained data points; The mean aggregation is expressed as: Among them, (x', y', z') is the representative point position of the data point in the voxel, (x n ,y n , z n ) is the coordinate of the nth data point within the voxel, and N is the total number of data points within the voxel; The filtered three-dimensional point cloud data is generated according to the calculated representative points of the data points within the voxel.

6. The industrial parts production nondestructive testing system based on 3D point cloud matching according to claim 1 is characterized in that: The data alignment matches the three-dimensional point cloud data scanned multiple times so that points of the same three-dimensional point cloud data scanned multiple times can match each other; Let the point of the first scanned 3D point cloud data be a i =(x i ,y i ,z i ), then the point of the 3D point cloud data scanned for the second time is b i =(x j ,y j ,z j ); By calculating the rotation matrix R and displacement vector t corresponding to the three-dimensional point cloud data, the points of the transformed three-dimensional point cloud data of the first scan are matched as closely as possible with the points of the three-dimensional point cloud data of the second scan; The calculation formula is as follows: in, is the midpoint between two points, and the midpoint is used to rigidly transform the two points to minimize the distance between them.

7. The industrial parts production nondestructive testing system based on 3D point cloud matching according to claim 1 is characterized in that: The data cutting processing module adopts bounding box segmentation, including: By calculating the minimum bounding box of the 3D model, the bounding box is divided into multiple parts along the x, y or z axis; Formula: Bounding box coordinates: Among them, Min x 、Min y 、Min z The minimum value of the boundary on the x-axis, y-axis, and z-axis, Max x Max y Max z They are the maximum values ​​of the boundaries on the x-axis, y-axis, and z-axis respectively. By determining the size of the bounding box, the three-dimensional model can be cut.

8. The industrial parts production nondestructive testing system based on 3D point cloud matching according to claim 6, characterized in that: The data feature acquisition module is used to collect features in the cut 3D model and calculate the distribution and discreteness of the 3D point cloud data of the 3D model through statistical features, including: The mean calculation formula is as follows: The formula for calculating variance is as follows: The formula for calculating the standard deviation is as follows: The features in the 3D model are collected and processed through the distribution and discreteness of the 3D point cloud data.

9. The industrial parts production nondestructive testing system based on 3D point cloud matching according to claim 1, characterized in that: The part basic model is used to input the production data information of the part, and the feature matching processing module matches the features in the three-dimensional model with the basic feature information inputted by the part basic model, and the matching calculation formula is as follows: Among them, (x i ,y i ,z i ) is the collected 3D point cloud data, (x o ,y o ,z o ) is the data of the basic model of the part, and the features are matched by calculating the distance between the two points.

10. The industrial parts production nondestructive testing system based on 3D point cloud matching according to claim 1, characterized in that: The calculation formula of the error analysis calculation module is as follows: C=|E i-1 -AND i |, Among them, E i is the error value of the i-th iteration, and C is the change in error; The error is calculated in each iteration to determine whether the detection error gradually decreases below a preset threshold. Usually, the algorithm stops iterating when the error change is less than a certain threshold. When the change in C approaches zero and the error approaches the threshold, it is determined that there is a production error in the industrial part.

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