A method for evaluating the welding quality of skin skeleton structure

Abstract

The present invention discloses a method for evaluating the assembly and welding quality of a skin skeleton structure. The method uses a laser three-dimensional scanner to perform 3D scanning to obtain measured point cloud data, and comprehensively evaluates the assembly quality and welding quality of the skin skeleton through the point cloud data. This method replaces traditional manual inspection, greatly improving inspection efficiency, inspection accuracy, and welding quality, reducing scrap rates, and reducing production costs. In addition, the welding quality of the component can be inspected and evaluated immediately after welding, thereby improving production efficiency. The assembly and welding quality evaluation of a variety of different skin skeleton structures is highly adaptable. A non-contact inspection method is adopted, and the skin with lower rigidity is not contacted during the inspection process, thereby avoiding damage to the component.

Description

Technical Field

[0001] The invention relates to the technical field of welding quality detection, and in particular to a method for evaluating the welding quality of a skin skeleton structure. Background Art

[0002] Skin-frame structures mainly refer to complex components with skin and frame as the main parts. In the actual production and manufacturing process, skin-frame structures usually cover the skin sheet on the frame structure and weld them together. The assembly quality of the skin and frame seriously affects the final welding quality. The skin is fixed on the frame, and ensuring the assembly quality of the skin on the frame is the key to ensuring the accuracy of the skin shape. Therefore, its production and assembly precision requirements are very strict. At present, manual inspection is mostly used for the assembly dimension inspection of skin frames. However, the inspection efficiency of manual inspection of assembly quality is low, the measurement accuracy is not high, and it is easily affected by external environmental factors, which increases the final scrap rate, reduces production efficiency, and increases production costs.

[0003] The welding production of skin skeleton structures needs to improve efficiency, and intelligent production has become a trend. Therefore, high-precision intelligent assembly quality inspection is required before welding to meet the assembly size inspection requirements of skin skeleton welding. With the continuous development of current optoelectronic technology, image processing technology and computer technology, optical non-contact measurement methods are highly accurate and fast, and have gradually become the preferred measurement method in the industrial field. This patent proposes a method for a skin skeleton structure that uses a three-dimensional laser scanner to obtain measured component point cloud data, and obtains assembly quality through calculation and comparison of point cloud data, replacing traditional manual measurement, and performing intelligent detection of its welding quality, thereby improving detection efficiency and detection accuracy and reducing production costs. Summary of the Invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a method for evaluating the welding quality of skin skeleton structures to solve some or all of the above-mentioned problems existing in the prior art.

[0005] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a method for evaluating the welding quality of a skin skeleton structure, comprising the following steps:

[0006] S1. Grab the skeleton and place it on the processing platform, and clamp it in place using a tooling;

[0007] S2. Scan the front and back of the skeleton using a laser 3D scanner to obtain and save point cloud data of the front and back of the skeleton;

[0008] S3. The front skin is sucked and placed on the frame for assembly. After assembly is completed, the fixture above the skin falls and clamps the skin frame tightly. The reverse skin is assembled and fixed using the same method;

[0009] S4. Scan the front and back of the assembly in S3 using a laser 3D scanner to obtain and save the measured point cloud data of the skin skeleton after the front and back skins are assembled;

[0010] S5. Point cloud data processing: noise reduction is performed on the acquired point cloud data to remove clutter caused by environmental interference, interference objects attached to the parts themselves, or part tooling, and point cloud repair is performed to streamline the point cloud data volume;

[0011] S6. Analyze the measured point cloud data to determine the assembly gap, compare it with a preset threshold, and evaluate whether the assembly quality meets subsequent welding requirements;

[0012] If the welding requirements are met, use laser to perform spot welding on the joints between the front and back skins and the frame lock bottom; if the welding requirements are not met, remove the skins and repeat S3-S5;

[0013] S7. After spot welding is completed, open the front and back skin fixtures and use laser to perform internal lap penetration welding on the front and back sides and full lock bottom butt welding around the perimeter;

[0014] S8. After welding is completed, a laser 3D scanner is used to scan the front and back of the welded assembly to obtain point cloud data. After processing the point cloud data in S5, it is matched and compared with the theoretical digital model point cloud data to comprehensively evaluate whether the welding quality is qualified.

[0015] Preferably, two assembly clearances are calculated in S6:

[0016] (1) Calculate and evaluate the assembly gap of the skin-frame lock-bottom butt weld: extract the skin point cloud coordinates of the front and back skin and the frame lock-bottom joint obtained in S4, and segment the point clouds on different lines. Then, use the least squares method to fit the outer contour of the lock-bottom butt weld of the skin scan part, and thus obtain the skin outer contour line equation; use the same method to extract the skeleton point cloud coordinates of the frame-skin joint, segment the point clouds on different lines, and calculate the length of the perpendicular line from these points to the corresponding skin contour line to obtain the assembly gap of each point of the skin-frame lock-bottom butt weld;

[0017] (2) Calculate and evaluate the assembly gap at the skin-skeleton lap penetration weld: extract the point cloud coordinates of the upper surface of the front and back skins obtained in S4, segment the point clouds in different planes, and then fit the point clouds on different planes into planes to obtain the surface contour equation of the skin upper surface; use the same method, based on the skeleton scanning point cloud obtained in S2, extract the upper surface point cloud coordinates of the skeleton ribs, segment the point clouds in different planes, calculate the length of the vertical line of these points from the corresponding skin upper surface contour to obtain the assembly gap at each point of the skin-skeleton lap penetration weld.

[0018] Preferably, in S8, the measured three-dimensional point cloud of the processed assembly after welding is matched and compared with the theoretical digital model point cloud, the line profile error and the surface profile error of the surface are calculated, and the surface morphology and surface quality are evaluated to see whether they are qualified.

[0019] Preferably, the surface morphology to be evaluated includes flatness and symmetry, and the evaluation method adopts the least square method and the minimum area method for comprehensive evaluation;

[0020] Flatness detection: Match the measured point cloud data with the theoretical point cloud data, unify the coordinate system, and segment the theoretical point cloud data based on whether the point clouds are on the same plane. Use the least squares method to fit the theoretical point cloud data on each segmented plane to obtain the plane equation Aix+Biy+Ciz+Di=0 as the ideal reference plane. Multiple planes can be determined using the measured point cloud coordinates and the normal of the ideal reference plane. The distances of these planes from the ideal reference plane are calculated. Among all the measured point cloud measurement points, the distance from each point to the ideal reference plane must have a maximum or minimum value. The difference between the maximum and minimum values ​​is the flatness degree.

[0021] Symmetry detection: Match the measured point cloud data with the theoretical point cloud data. After unifying the coordinate system, the theoretical central symmetry plane of the upper and lower surface skins is obtained from the theoretical point cloud data as the reference plane. The distance from the points on the measured upper and lower surface skin point clouds to the reference plane is calculated respectively. The difference in the upper and lower distances multiplied by 2 is the degree of symmetry.

[0022] Preferably, the matching of measured point cloud data and theoretical point cloud data is divided into coarse registration and fine registration;

[0023] The coarse registration is performed by manually selecting at least three pairs of equivalent points in the measured point cloud and the theoretical point cloud to preliminarily align the point clouds;

[0024] Precise registration is based on the least squares principle. It matches points with the same name and iterates continuously until the error between two iterations is less than a given threshold, thereby obtaining the best result of point cloud data matching.

[0025] Preferably, the number of scans performed by the laser three-dimensional scanner is not less than 3 times each time.

[0026] The beneficial effects of the present invention are:

[0027] The present invention discloses a method for evaluating the assembly and welding quality of a skin skeleton structure. The method uses a laser three-dimensional scanner to perform 3D scanning on the assembly and welding results of a skin skeleton structure to obtain point cloud data of the component, and comprehensively evaluates the assembly quality and welding quality of the skin skeleton structure through the point cloud data. This method replaces traditional manual inspection, greatly improving inspection efficiency, inspection accuracy, and welding quality, reducing scrap rate, and reducing production costs. In addition, the welding quality of the component can be inspected and evaluated immediately after welding, thereby improving production efficiency. The assembly and welding quality evaluation of a variety of different skin skeleton structures is highly adaptable. A non-contact inspection method is adopted, and the skin with lower rigidity is not contacted during the inspection process, thereby avoiding damage to the component. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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 or the description of the prior art. 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.

[0029] Figure 1 It is a technical flow chart of the present invention.

[0030] Figure 2 This is a schematic diagram of a skin skeleton structure.

[0031] Figure 3 for Figure 2 Explosion diagram.

[0032] Figure 4 Schematic diagram of the welds and gaps of the skin skeleton structure.

[0033] Figure 5 This is the front point cloud data of the skeleton.

[0034] Figure 6 Point cloud data for the front face of the skin skeleton assembly.

[0035] Figure 7 It is the point cloud data of the reverse side of the skeleton.

[0036] Figure 8 Provides point cloud data for the reverse side of the skin skeleton assembly.

[0037] Figure 9 This is the point cloud data of the front face of the skin skeleton component after welding.

[0038] Description of reference numerals:

[0039] 1-Skeleton, 2-Skin. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Example:

[0042] The present embodiment provides a method for evaluating the quality of welding of a skin skeleton structure. Specifically, the method includes the following steps:

[0043] S1. Grab the skeleton 1 and place it on the processing platform, and clamp it in place using a tool;

[0044] S2. Scan the front and back of the skeleton 1 using a laser 3D scanner to obtain and save the point cloud data of the front and back of the skeleton 1;

[0045] S3. The front skin 2 is sucked and placed on the frame 1 for assembly. After assembly is completed, the fixture 2 above the skin falls and clamps the skin frame. The reverse skin 2 is assembled and fixed using the same method;

[0046] S4. Scan the front and back of the assembly in S3 using a laser 3D scanner to obtain and save the measured point cloud data of the skin skeleton after the front and back skins are assembled;

[0047] S5. Point cloud data processing: noise reduction is performed on the acquired point cloud data to remove clutter caused by environmental interference, interference objects attached to the parts themselves, or part tooling, and point cloud repair is performed to streamline the point cloud data volume;

[0048] S6. Analyze the measured point cloud data to determine the assembly gap, compare it with a preset threshold, and evaluate whether the assembly quality meets subsequent welding requirements;

[0049] If the welding requirements are met, use laser to perform spot welding on the joints between the front and back skins and the frame lock bottom; if the welding requirements are not met, remove the skins and repeat S3-S6;

[0050] S7. After spot welding is completed, open the front and back skin fixtures and use laser to perform internal lap penetration welding on the front and back sides and full lock bottom butt welding around the perimeter;

[0051] S8. After welding is completed, a laser 3D scanner is used to scan the front and back of the welded assembly to obtain point cloud data. After processing the point cloud data in S5, it is matched and compared with the theoretical digital model point cloud data to comprehensively evaluate whether the welding quality is qualified.

[0052] In a further embodiment, two assembly gaps are analyzed and calculated in S6. The skin of the skin skeleton structure is a bent part with three planes, and the outer contour of the skin lock bottom butt weld consists of five straight lines. The calculation method is as follows:

[0053] (1) Calculate and evaluate the assembly gap at the skin frame lock bottom butt weld (such as Figure 4 (As shown in C, where A is the weld): Extract the skin point cloud coordinates at the joint between the front and back skins and the skeleton lock bottom obtained in S4, segment the point clouds on different lines, and then use the least squares method to fit the outer contour of the skin scan to obtain a total of 5 straight line equations, and obtain the spatial unit direction vector of each straight line equation. The same method is used to extract the coordinates of the skeleton point cloud at the junction of the skeleton and the skin, and the point coordinates Ai(xi, yi, zi) are obtained. The point clouds on five different straight lines are segmented, and a point Mi(x0, y0, z0) on each straight line of the skin outer contour is randomly selected to calculate the straight line distance from each point at the skeleton lock bottom junction to the corresponding skin outer contour. This will obtain the assembly gap at each location of the skin frame lock bottom butt weld.

[0054] (2) Calculate and evaluate the assembly gap at the skin frame lap penetration weld (such as Figure 4 (As shown in B): Extract the point cloud coordinates of the upper surface of the front and back skins obtained in S4, segment the point clouds in different planes, and then fit the point clouds on different planes into a total of three planes Aix+Biy+Ciz+Di=0 (i=1, 2, 3) to obtain the contour equation of the upper surface of the skin. Using the same method, based on the skeleton scanning point cloud obtained in S2, extract the upper surface point cloud coordinates Ni(xi, yi, zi) of the skeleton ribs, and segment the point clouds in three different planes to calculate the length of the perpendicular line from the skeleton point cloud to the skin surface equation. Obtain the assembly clearance at each location of the skin frame lap penetration welding;

[0055] In a further embodiment, in S8, the measured three-dimensional point cloud of the assembly after welding is matched and compared with the theoretical digital model point cloud, the line profile error and the surface profile error of the curved surface are calculated, and the surface morphology and surface quality are evaluated to see whether they are qualified.

[0056] In a further embodiment, the surface morphology to be evaluated includes flatness and symmetry. The evaluation method adopts the least square method and the minimum area method for comprehensive evaluation, and the calculation evaluation method is:

[0057] (1) Surface flatness detection: Match the measured point cloud data with the theoretical point cloud data with high precision and unify the coordinate system. Divide the theoretical point cloud data into three planes according to whether the point clouds are on the same plane. Use the least squares method to fit the theoretical point cloud data on each divided plane to obtain the plane equation Aix+Biy+Ciz+Di=0 as the ideal reference plane. Using the measured point cloud coordinates (xi, yi, zi) and the normal of the ideal reference plane, we can determine each plane Aix+Biy+Ciz+Dj=0, and calculate the distance between these planes and the ideal reference plane. Among all the measured point cloud measurement points, the distance from each point to the ideal reference plane must have a maximum value Dmax and a minimum value Dmin (the distance is positive if it is in the same direction as the normal, and negative if it is not). The value of Dmax-Dmin is the flatness.

[0058] (2) Symmetry detection: The measured point cloud data and the theoretical point cloud data are matched with high precision and the coordinate system is unified. The theoretical central symmetry plane Aix+Biy+Ciz+Di=0 of the upper and lower skin surfaces is obtained from the theoretical point cloud data and used as the reference plane. The distances Di and Dj from the points on the skin point cloud corresponding to the measured upper and lower surfaces to the reference plane are calculated respectively. |Di-Dj|×2 is the degree of symmetry.

[0059] In a further embodiment, the matching of measured point cloud data and theoretical point cloud data is divided into coarse registration and fine registration. Coarse registration manually selects at least three pairs of equivalent points in the measured point cloud and the theoretical point cloud to preliminarily align the point clouds. Fine registration is based on the least squares principle and matches points of the same name. Through continuous iteration, until the error value of two iterations is less than a given threshold, the optimal result of point cloud data matching is obtained.

[0060] In a further embodiment, the scanning accuracy of the laser 3D scanner is 0.01 mm.

[0061] In a further embodiment, the three-dimensional laser scanner scans the component five times each time.

[0062] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for evaluating the welding quality of a skin skeleton structure, characterized in that: The steps include: S1. Grab the skeleton and place it on the processing platform, then clamp it in place using a tool. S2. Scan the front and back of the skeleton using a laser 3D scanner to obtain and save point cloud data of the front and back of the skeleton; S3. The front skin is sucked and placed on the frame for assembly. After assembly is completed, the clamp above the skin falls and clamps the skin frame firmly. The reverse skin is assembled and fixed using the same method. S4. Scan the front and back of the assembly in S3 using a laser 3D scanner to obtain and save the measured point cloud data of the skin skeleton after the front and back skins are assembled; S5. Point cloud data processing: noise reduction is performed on the acquired point cloud data to remove clutter caused by environmental interference, interference objects attached to the parts themselves, or part tooling, and point cloud repair is performed to streamline the point cloud data volume; S6. Analyze the measured point cloud data to determine the assembly gap, compare it with a preset threshold, and evaluate whether the assembly quality meets subsequent welding requirements; If the welding requirements are met, use laser to perform spot welding on the joints between the front and back skins and the frame lock bottom; if the welding requirements are not met, remove the skins and repeat S3-S5; S7. After spot welding is complete, open the front and back skin fixtures and use laser to perform internal lap penetration welding on the front and back sides, as well as full-length butt welding around the perimeter. S8. After welding is completed, a laser 3D scanner is used to scan the front and back of the welded assembly to obtain point cloud data. After processing the point cloud data in S5, it is matched and compared with the theoretical digital model point cloud data to comprehensively evaluate whether the welding quality is qualified; Among them, in S8, the measured three-dimensional point cloud of the processed assembly after welding is matched and compared with the theoretical digital model point cloud, the line profile error and the surface profile error of the surface are calculated, and the surface morphology and surface quality are evaluated. The surface morphology that needs to be evaluated includes flatness and symmetry, and the evaluation method adopts the least square method and the minimum area method for comprehensive evaluation; Flatness detection: Match the measured point cloud data with the theoretical point cloud data, unify the coordinate system, and segment the theoretical point cloud data based on whether the point clouds are on the same plane. Use the least squares method to fit the theoretical point cloud data on each segmented plane to obtain the plane equation Aix+Biy+Ciz+Di=0 as the ideal reference plane. Multiple planes can be determined using the measured point cloud coordinates and the normal of the ideal reference plane. The distances of these planes from the ideal reference plane are calculated. Among all the measured point cloud measurement points, the distance from each point to the ideal reference plane must have a maximum or minimum value. The difference between the maximum and minimum values ​​is the flatness degree. Symmetry detection: Match the measured point cloud data with the theoretical point cloud data. After unifying the coordinate system, the theoretical central symmetry plane of the upper and lower surface skins is obtained from the theoretical point cloud data as the reference plane. The distance from the points on the measured upper and lower surface skin point clouds to the reference plane is calculated respectively. The difference in the upper and lower distances multiplied by 2 is the degree of symmetry.

2. The method for evaluating the welding quality of a skin skeleton structure according to claim 1, wherein: Two types of assembly clearances are calculated in S6: (1) Calculate and evaluate the assembly gap of the skin-frame lock-bottom butt weld: extract the skin point cloud coordinates of the front and back skin and the frame lock-bottom joint obtained in S4, and segment the point clouds on different lines. Then, use the least squares method to fit the outer contour of the lock-bottom butt weld of the skin scan part, and thus obtain the skin outer contour line equation; use the same method to extract the skeleton point cloud coordinates of the frame-skin joint, segment the point clouds on different lines, and calculate the length of the perpendicular line from these points to the corresponding skin contour line to obtain the assembly gap of each point of the skin-frame lock-bottom butt weld; (2) Calculate and evaluate the assembly gap at the skin-frame lap penetration weld: extract the point cloud coordinates of the upper surface of the front and back skins obtained in S4, segment the point clouds in different planes, and then fit the point clouds on different planes into planes to obtain the contour equation of the skin upper surface; use the same method, based on the skeleton scanning point cloud obtained in S2, extract the point cloud coordinates of the upper surface of the skeleton ribs, segment the point clouds in different planes, calculate the length of the vertical line of these points from the corresponding skin upper surface contour to obtain the assembly gap at each point of the skin-frame lap penetration weld.

3. The method for evaluating the welding quality of a skin skeleton structure according to claim 1, wherein: The matching of measured point cloud data and theoretical point cloud data is divided into coarse registration and fine registration; The coarse registration is performed by manually selecting at least three pairs of equivalent points in the measured point cloud and the theoretical point cloud to preliminarily align the point clouds; Precise registration is based on the least squares principle. It matches points with the same name and iterates continuously until the error between two iterations is less than a given threshold, thereby obtaining the best result of point cloud data matching.

4. The method for evaluating the welding quality of a skin skeleton structure according to claim 1, wherein: The number of scans performed by the laser 3D scanner is not less than 3 times each time.

Images