Skin defect detection method

Abstract

The present application provides a skin defect detection method, comprising the following steps: collecting dent defect data and surrounding structure feature data of a skin, the surrounding structure feature data being feature data of reference objects surrounding a dent; acquiring structure parameters of the skin from a preset theoretical model of the skin, the skin represented by the theoretical model comprising the reference objects and having no defect; and on the basis of the dent defect data, the surrounding structure feature data and the structure parameters, performing calculation to obtain defect feature data, the defect feature data comprising at least one of the position of the dent, the depth of the dent, the radius of the dent, the profile of the dent, the degree of smoothness of the dent, and the distance from the dent to a rivet line. The described skin defect detection method is used for improving the efficiency and accuracy of skin defect detection, thereby improving the efficiency and accuracy of skin repair.

Description

Skin Defect Detection Method Cross-referencing

[0001] This application claims priority to Chinese application No. 2024118373486, filed on December 12, 2024. The contents of the aforementioned application are incorporated herein by reference. Technical Field

[0002] This application relates to the field of nondestructive testing technology, and in particular to a method for detecting skin defects. Background Technology

[0003] In the manufacturing, use, and maintenance of aerospace, automotive, shipbuilding, wind power, construction, rail transportation, and defense industries, skin panels are prone to pitting defects due to external forces or other reasons. The conventional method for dealing with skin pitting is to use manual or semi-automatic methods to detect and evaluate the defects, thereby formulating appropriate concession acceptance or maintenance plans.

[0004] The purely manual method typically involves measuring the dent's shape, depth, radius, and distance from the dent's center to the connecting nail wire using tools such as translucent paper and rulers. The measurement data is then submitted to the design department for evaluation, leading to the development of a repair or acceptance plan. The semi-automated method usually involves manufacturing or maintenance field personnel using manual methods or semi-automated tools such as scanners to measure the dent's characteristic data. This measurement data is then transmitted to the design department or a semi-automated evaluation system for rapid assessment, ultimately leading to the development of a repair or acceptance plan.

[0005] However, both manual measurement and semi-automatic scanning require manual input of multiple characteristic parameters of the pits, which are then passed to designers or input into the evaluation system for defect assessment. This process is cumbersome and inefficient. Furthermore, manual measurement or input of defect feature data results in low standardization and the risk of measurement or recording errors. Therefore, this application provides a skin defect detection method to solve the above problems. Summary of the Invention

[0006] Technical issues

[0007] The technical problem to be solved by this application is to provide a skin defect detection method that can improve the efficiency and accuracy of skin defect detection, thereby improving the efficiency and accuracy of skin repair.

[0008] Technical solution

[0009] To address the aforementioned technical problems, according to embodiments of this application, a skin defect detection method is provided, comprising the following steps: collecting pit defect data and surrounding structural feature data, wherein the surrounding structural feature data is feature data of a reference object surrounding the pit; obtaining structural parameters of the skin from a preset theoretical model of the skin, wherein the theoretical model represents a skin that includes the reference object and is free of defects; and calculating defect feature data based on the pit defect data, the surrounding structural feature data, and the structural parameters, wherein the defect feature data includes at least one of pit location, pit depth, pit radius, pit contour, pit smoothness, and distance of the pit from the nail line.

[0010] Further, calculating defect feature data based on the pit defect data, the surrounding structural feature data, and the structural parameters may include: generating a skin model based on the pit defect data and the surrounding structural feature data; aligning the skin model with the theoretical model to obtain a reference model; and using the reference model to calculate the defect feature data based on the pit defect data, the surrounding structural feature data, and the structural parameters.

[0011] Furthermore, the skin defect detection method may also include: after calculating defect feature data based on the pit defect data, the surrounding structural feature data, and the structural parameters, obtaining strength assessment conditions, the strength assessment conditions including several preset limit values, the limit values ​​being used to indicate the data boundaries for which a strength assessment of the pit defect is required; determining whether a strength assessment is required based on the defect feature data and the strength assessment conditions; and determining the pit defect handling method based on the strength assessment results.

[0012] Furthermore, the skin defect detection method may also include: after aligning the skin model with a preset theoretical model to obtain a reference model, calculating the alignment degree between the skin model and the theoretical model; determining whether the alignment degree is within the evaluable range; and if the alignment degree is within the evaluable range, continuing the detection; if the alignment degree is not within the evaluable range, prompting the regeneration of the skin model.

[0013] Further, determining whether a strength assessment is needed based on the defect feature data and the strength assessment conditions may include: comparing the defect feature data and the strength assessment conditions; if the defect feature data meets the strength assessment conditions, a strength assessment result indicating that a strength assessment is needed is obtained; if the defect feature data does not meet the strength assessment conditions, a strength assessment result indicating that a strength assessment is not needed is obtained.

[0014] Furthermore, comparing the defect feature data with the strength assessment conditions may include: comparing the defect feature data with the strength assessment conditions to obtain several judgment results; and judging whether the defect feature data meets the strength assessment conditions based on the several judgment results.

[0015] Furthermore, comparing the defect feature data with the strength assessment conditions yields several judgment results, which may include: if the defect feature data meets the strength assessment conditions, the judgment result is 1; if the defect feature data does not meet the strength assessment conditions, the judgment result is 0.

[0016] Further, determining whether the defect feature data meets the strength assessment conditions based on several judgment results may include: filtering several judgment results; when several judgment results are all 0, the defect feature data does not meet the strength assessment conditions, otherwise the defect feature data meets the strength assessment conditions.

[0017] Furthermore, determining the pit defect handling method based on the strength assessment results may include: when a strength assessment is required, sending the defect feature data to the terminal device of the strength assessment personnel to enable the strength assessment personnel to perform the strength assessment; when a strength assessment is not required, sending the defect feature data directly to the terminal device of the on-site maintenance personnel to prompt the on-site maintenance personnel to handle the pit defect.

[0018] Furthermore, collecting pit defect data and surrounding structural feature data of the skin may include: scanning the pit with a scanner to obtain point cloud data of the pit; and scanning reference objects around the pit with a scanner to obtain surrounding structural feature data, wherein the reference objects include at least one of mounting holes on the skin, edges on the skin, and corners on the skin.

[0019] Beneficial effects

[0020] Compared with the prior art, the technical solution of this application can achieve at least the following beneficial effects:

[0021] 1. This application collects pit defect data and surrounding structural feature data of the skin, obtains the structural parameters of the skin from the preset theoretical model of the skin, and then calculates the defect feature data based on the pit defect data, surrounding structural feature data and structural parameters, thereby completing the measurement of pit defects and improving the efficiency and accuracy of skin defect detection and processing.

[0022] 2. This application reduces manual measurement and input operations in the data calculation and recording process, effectively avoids human input errors, improves the efficiency and accuracy of skin defect detection, and thus improves the efficiency and accuracy of skin repair. Attached Figure Description

[0023] Figure 1 is an overall flowchart of a skin defect detection method according to this application.

[0024] Figure 2 is a schematic diagram illustrating the application of a skin defect detection method of this application.

[0025] Figure 3 is a top view of the skin in Figure 2.

[0026] Figure 4 is a cross-sectional view of Figure 2 (AA).

[0027] Figure 5 is a flowchart of the detection and processing of skin depressions according to an embodiment of this application.

[0028] Explanation of reference numerals in the attached figures:

[0029] 1. Scanner; 2. Recess; 3. Mounting hole; 4. Skin. Embodiments of the present invention

[0030] To make the technical problems, technical solutions, and beneficial effects to be solved by the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art to which this application pertains. The words "comprising" and similar terms used herein mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but does not exclude other elements or objects.

[0031] This embodiment provides a method for detecting skin defects. Referring to Figures 1 and 2, the method for detecting skin defects includes the following steps:

[0032] S1: Collect the pit 2 defect data and surrounding structural feature data of skin 4. The surrounding structural feature data is the feature data of the reference objects around pit 2.

[0033] When a defect is found in the skin 4, image data of the pit 2 is first acquired and recorded. Based on this image data, a preliminary assessment of the skin 4 defect is made to determine if it is within the scope of the on-site maintenance personnel's capabilities. If the defect is within their capabilities, it is handled by the on-site maintenance personnel, who can choose to directly repair it or continue testing to obtain more data before further repair. If the defect is beyond their capabilities, further testing is conducted to obtain the data required for strength assessment and send it to the strength assessment personnel. When further testing is required, the inspectors scan the pit 2 using an optical scanner 1 to obtain the pit 2 defect data, i.e., the point cloud data of the pit 2. They then scan the surrounding reference objects using the optical scanner 1 to obtain surrounding structural feature data. These reference objects include at least one of the following: mounting holes 3 on the skin 4, edges on the skin 4, and corners on the skin 4.

[0034] S2: Obtain the structural parameters of skin 4 from the preset theoretical model of skin 4. The theoretical model represents skin 4, which includes a reference object and has no defects.

[0035] During the design and manufacturing of skin 4, its structural parameters are recorded and saved in the theoretical model. These parameters include the material thickness and dimensional specifications of skin 4. The structural parameters of skin 4 are obtained from the pre-set theoretical model as reference data for subsequent testing. The theoretical model represents skin 4, which includes a reference object and is free of defects.

[0036] S3: Defect feature data is calculated based on the pit 2 defect data, surrounding structural feature data and structural parameters. The defect feature data includes at least one of the following: pit 2 location, pit 2 depth, pit 2 radius, pit 2 outline, pit 2 smoothness and pit 2 distance from the rivet line. The distance between pit 2 and the rivet line is the closest distance between the edge of pit 2 and the rivet. The rivet is used to pass through the mounting hole on the skin to fix the skin to the frame.

[0037] Referring to Figures 3 and 4, a skin 4 model is generated based on the pit 2 defect data and surrounding structural feature data. The skin 4 model is aligned with the theoretical model to obtain a reference model. The alignment degree between the skin 4 model and the theoretical model is calculated, and it is determined whether the alignment degree is within the evaluable range. If the alignment degree is within the evaluable range, the detection continues; if the alignment degree is not within the evaluable range, a prompt is made to regenerate the skin 4 model. Using the reference model, based on the pit 2 defect data, surrounding structural feature data, and structural parameters, defect feature data is calculated. This defect feature data is stored and recorded for subsequent strength assessment by personnel.

[0038] S4: Obtain strength assessment conditions, which include several preset limit values. The limit values ​​are used to indicate the data boundaries for strength assessment of pit 2 defect. Determine whether strength assessment is required based on the defect feature data and strength assessment conditions. Determine the treatment method for pit 2 defect based on the strength assessment results.

[0039] Referring to Figures 3 and 5, after obtaining the defect feature data, the strength assessment conditions are then acquired. These conditions include several preset limit values, which indicate the data boundaries for which a strength assessment of the pit 2 defect is required. The defect feature data is compared with the strength assessment conditions to obtain several judgment results. If the defect feature data meets the strength assessment conditions, the judgment result is 1; otherwise, it is 0. The judgment results are then used to determine whether the defect feature data meets the strength assessment conditions. These results are then filtered. If all judgment results are 0, the defect feature data does not meet the strength assessment conditions; otherwise, it does. If the defect feature data meets the strength assessment conditions, a strength assessment result is obtained where a strength assessment is required; otherwise, a strength assessment result is obtained where no strength assessment is required. When a strength assessment is required, the defect feature data is sent to the strength assessment personnel's terminal device for assessment. When no strength assessment is required, the defect feature data is sent directly to the on-site maintenance personnel's terminal device to prompt them to address the pit 2 defect.

[0040] The following example, using the detection of a dent on skin 4, will be explained in detail with reference to Figures 2 and 5:

[0041] During the manufacturing process, a dent 2 was created in the wall panel of the skin 4 due to an impact. The dent is now being inspected and evaluated.

[0042] Step 1: Collect data on pit 2 defects in skin 4 and surrounding structural features.

[0043] First, the inspector uses an optical scanner 1, such as a structured light scanner 1, to scan the wall panel of the skin 4 to obtain the point cloud data of the pit 2 defect. Then, the inspector scans the point cloud data of the reference objects around the pit 2 defect, such as the point cloud data of the mounting holes 3, edges, corners, etc. on the skin 4 around the pit 2 defect, for subsequent defect feature extraction.

[0044] Step 2: Obtain the structural parameters of skin 4 from the pre-set theoretical model of skin 4.

[0045] When designing and manufacturing skin 4, the structural parameters of skin 4 are recorded and saved in the theoretical model. The structural parameters of skin 4 are obtained from the preset theoretical model of skin 4 as reference data for subsequent testing.

[0046] The third step is to calculate the defect feature data based on the pit 2 defect data, surrounding structural feature data, and structural parameters.

[0047] Referring to Figures 3 and 4, a skin 4 model is generated based on the collected pit 2 defect data and surrounding structural feature data. The skin 4 model is then aligned with the theoretical model. In this embodiment, four key holes (P1-P4) are used as features for alignment between the skin 4 model and the theoretical model. The alignment degree between the skin 4 model and the theoretical model is calculated, and it is determined whether the alignment degree is within an evaluable range. If the alignment degree is within an evaluable range, the detection continues; if the alignment degree is not within an evaluable range, a prompt is made to regenerate the skin 4 model. After the skin 4 model is aligned with the theoretical model, a reference model is obtained. Defect feature data is obtained by calculating based on the pit 2 defect data, surrounding structural feature data, and structural parameters using the reference model.

[0048] In this embodiment, the minimum envelope circle diameter D of pit 2, the distance l of pit 2 from the nail connection positioning line, and the depth d of pit 2 are calculated using the reference model based on the pit 2 defect data, surrounding structural feature data, and structural parameters. The pit 2 depth d includes the pit 2 depth at different positions. Based on the pit 2 depth at different positions, it can be determined whether wrinkles, cracks, or holes appear in the pit.

[0049] Step 4: Obtain strength assessment conditions. Strength assessment conditions are used to indicate the data range that needs to be assessed for the strength of pit 2. Based on the defect characteristic data and strength assessment conditions, determine whether a strength assessment is needed. Based on the strength assessment results, determine the treatment method for pit 2.

[0050] In this embodiment, the strength assessment conditions are specifically as follows:

[0051] Condition 1: Depth d of pit 2 > 2mm;

[0052] Condition 2: The minimum envelope circle diameter D of pit 2 is greater than 100 mm;

[0053] Condition 3: The distance l from the pit 2 to the nail line is less than 2D;

[0054] Condition 4: Any one of the following appears: sharp wrinkles, cracks, or holes.

[0055] Based on condition 1, if the depth d of pit 2 is greater than 2mm, then condition 1 is met, and the result is 1; otherwise, the result is 0. Based on condition 2, if the minimum envelope circle diameter D of pit 2 is greater than 100mm, then condition 2 is met, and the result is 1; otherwise, the result is 0. Based on condition 3, if the distance l from pit 2 to the nail line is less than 2D, then condition 3 is met, and the result is 1; otherwise, the result is 0. Based on condition 4, if any of the following appears: sharp wrinkles, cracks, or holes, then condition 4 is met, and the result is 1; otherwise, the result is 0. Thus, four results are obtained. These four results are then filtered. If several results are all 0, the defect feature data does not meet the strength assessment conditions; otherwise, the defect feature data does meet the strength assessment conditions. If the defect feature data meets the above strength assessment conditions, it is sent to the strength assessment personnel's terminal device for strength assessment. The strength assessment personnel complete the defect assessment and issue a maintenance plan, which is then transmitted to the on-site maintenance personnel for maintenance. If the defect characteristic data does not meet the above strength assessment conditions, it will be directly sent to the terminal equipment of the on-site maintenance personnel to prompt them to handle the pit 2 defect. The data report generated after handling will be archived and the maintenance record will be updated.

[0056] The beneficial effects of this application are:

[0057] 1. This application collects pit 2 defect data and surrounding structural feature data of skin 4, obtains structural parameters of skin 4 from the preset theoretical model of skin 4, and calculates defect feature data based on pit 2 defect data, surrounding structural feature data and structural parameters, thereby completing the measurement of pit 2 defect, improving the efficiency and accuracy of skin 4 defect detection and processing.

[0058] 2. This application reduces manual measurement and input operations in the data calculation and recording process, effectively avoids human input errors, improves the efficiency and accuracy of skin 4 defect detection, and thus improves the efficiency and accuracy of skin 4 repair.

[0059] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0060] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] While the embodiments of this application have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations fall within the scope and spirit of this application as described herein. Furthermore, the application described herein may have other embodiments and can be implemented or carried out in various ways.

Claims

1. A method for detecting skin defects, comprising: Data on pit defects and surrounding structural features of the skin are collected, wherein the surrounding structural features are feature data of reference objects around the pit. The structural parameters of the skin are obtained from a pre-defined theoretical model of the skin, wherein the theoretical model represents a skin that includes a reference object and is free of defects; as well as The defect feature data is calculated based on the pit defect data, the surrounding structural feature data, and the structural parameters. The defect feature data includes at least one of the following: pit location, pit depth, pit radius, pit outline, pit smoothness, and distance of pit from the nail line.

2. The skin defect detection method as described in claim 1, wherein, The step of calculating defect feature data based on the pit defect data, the surrounding structural feature data, and the structural parameters includes: A skin model is generated based on the pit defect data and the surrounding structural feature data; Aligning the skinned model with the theoretical model yields a reference model; and The reference model is used to calculate the defect feature data based on the pit defect data, the surrounding structural feature data, and the structural parameters.

3. The skin defect detection method as described in claim 1 further includes: After calculating the defect feature data based on the pit defect data, the surrounding structural feature data, and the structural parameters, Obtain strength assessment conditions, which include several preset limit values, and the limit values ​​are used to indicate the data boundaries for which the pit defect needs to be assessed for strength. Determine whether a strength assessment is needed based on the defect characteristic data and the strength assessment conditions; and The treatment method for pit defects is determined based on the strength assessment results.

4. The skin defect detection method as described in claim 2 further includes: After aligning the skin model with the preset theoretical model to obtain the reference model, Calculate the alignment between the skin model and the theoretical model; Determine whether the alignment is within an evaluable range; as well as If the alignment is within the evaluable range, the detection continues; if the alignment is outside the evaluable range, the system prompts the user to regenerate the skin model.

5. The skin defect detection method as described in claim 3, wherein, The step of determining whether a strength assessment is needed based on the defect feature data and the strength assessment conditions includes: By comparing the defect feature data with the strength assessment conditions, if the defect feature data meets the strength assessment conditions, a strength assessment result requiring strength assessment is obtained; and If the defect feature data does not meet the strength assessment conditions, then a strength assessment result is obtained that does not require strength assessment.

6. The skin defect detection method as described in claim 5, wherein comparing the defect feature data and the strength evaluation conditions includes: Several judgment results are obtained by comparing the defect feature data with the strength assessment conditions; as well as Based on several judgment results, determine whether the defect feature data meets the strength assessment conditions.

7. The skin defect detection method as described in claim 6, wherein, The step of comparing the defect feature data with the strength assessment conditions to obtain several judgment results includes: if the defect feature data meets the strength assessment conditions, the judgment result is 1; if the defect feature data does not meet the strength assessment conditions, the judgment result is 0.

8. The skin defect detection method as described in claim 7, wherein, The step of determining whether the defect feature data meets the strength assessment conditions based on a plurality of judgment results includes: Filter several of the aforementioned judgment results; and If several of the judgment results are all 0, then the defect feature data does not meet the strength evaluation conditions; otherwise, the defect feature data meets the strength evaluation conditions.

9. The skin defect detection method as described in claim 3 further includes: The treatment method for pit defects is determined based on the strength assessment results. When a strength assessment is required, the defect feature data is sent to the terminal device of the strength assessment personnel so that the strength assessment personnel can perform the strength assessment; When no strength assessment is required, the defect feature data is directly sent to the terminal device of the on-site maintenance personnel to prompt them to handle the pitting defect.

10. The skin defect detection method as described in claim 1, wherein, The data collected on the pitting defects and surrounding structural features of the skin include: The pit is scanned using a scanner to obtain point cloud data of the pit; and By scanning reference objects around the pit with a scanner, data on the surrounding structural features are obtained. The reference objects include at least one of the following: mounting holes on the skin, edges on the skin, and corners on the skin.

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