Method and system for intelligently diagnosing welding spot nugget ultrasonic detection result through AI vision

Abstract

According to the AI vision intelligent diagnosis welding spot nugget ultrasonic detection result method and system, AI vision intelligent diagnosis and welding spot ultrasonic detection are combined, effective nugget area extraction and nugget detection circle automatic calibration are carried out on an ultrasonic detection result image through AI recognition, and the welding spot nugget ultrasonic detection result is obtained. The pixel proportion of the effective nugget area is calculated according to the pixel extraction information in the ultrasonic detection result image, the diameter of the effective nugget is calculated according to the preset algorithm, and finally the quality of the welding spot is judged according to the preset intelligent diagnosis rule, so that the risk of subjective misjudgment in the process of judging the qualification of the detection result image manually is avoided, and the detection accuracy is improved. The diagnosis reliability and the detection efficiency are improved, and the labor cost is saved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent automobile welding, and in particular to a method and system for AI visual intelligent diagnosis of weld nugget ultrasonic detection results. Background Art

[0002] In the current industrial inspection field, AI-based solder joint / weld seam / glue appearance inspection technology has formed a mature application system. Its technical implementation path mainly includes the following steps: first, digital modeling of qualified standard workpieces is carried out through 3D scanning technology to build a high-precision 3D model with features such as geometric morphology and dimensional accuracy; then, the standard model is converted into multi-view 2D image data and input into the AI ​​inspection system for deep learning training, and finally, the appearance quality judgment standard of solder joints / weld seams / glue is established based on image feature matching.

[0003] It should be pointed out that the traditional visual inspection method has essential technical limitations: there is no strict correlation between the surface morphology of the solder joint and its internal quality parameters (such as the effective diameter of the weld nugget, internal pore density, etc.). In order to break through this technical bottleneck, the industry generally uses ultrasonic detection technology for supplementary inspection. This technology can accurately quantify the internal structural characteristics of the solder joint through the sound wave reflection signal, significantly improving the comprehensiveness and accuracy of quality assessment.

[0004] However, the current ultrasonic testing system still has significant room for improvement: the C-scan images generated by the test need to rely on manual experience to calculate the nugget area and evaluate the pore density. This process has two defects: on the one hand, human subjective judgment can easily lead to standard execution deviation, and on the other hand, the efficiency of manual image analysis cannot meet the high-speed testing requirements of modern production lines. This technical breakpoint seriously restricts the overall reliability and versatility of the quality inspection system. Summary of the invention

[0005] In view of the defects in the prior art, the purpose of the present invention is to provide an AI visual intelligent diagnosis method and system for weld nugget ultrasonic detection results, which combines AI visual intelligence with weld nugget ultrasonic detection results, intelligently and accurately calculates the area ratio value according to the graphics and performs standard judgment, thereby avoiding the risk of subjective misjudgment or inaccuracy in manual judgment of weld nugget ultrasonic detection results, reducing the probability of escape of vehicle body welding quality risk problems, and improving the efficiency of quality inspection diagnosis.

[0006] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0007] According to a first aspect of the present invention, there is provided an AI visual intelligent diagnosis method for ultrasonic detection results of weld nuggets, comprising the following steps:

[0008] S1. Image generation and extraction of effective nugget area: Generate an image of the nugget ultrasonic detection result by ultrasonically scanning the weld point, and automatically identify and mark the effective nugget area in the image; the effective nugget area refers to the core area of ​​the weld point that meets the quality requirements;

[0009] S2. Automatic calibration of the nugget detection circle: The system extracts the contour of the effective nugget area, and applies a preset algorithm to fit a detection circle of the effective nugget at the geometric center of the effective nugget area, wherein the diameter d of the detection circle represents the nugget detection diameter, wherein the preset algorithm is a circular detection algorithm optimized for blurring of the effective nugget contour and noise interference;

[0010] S3. Calculation of pixel ratio of effective nugget area: The AI ​​intelligent diagnosis system counts the number of pixels in the effective nugget area within the detection circle and the total number of pixels in the detection circle, and calculates the effective nugget pixel ratio K; K = number of pixels in the effective nugget area / total number of pixels in the detection circle;

[0011] S4. Calculation of effective nugget diameter: The AI ​​intelligent diagnosis system calculates the effective nugget diameter D according to the following formula: D²=d 2 ×K, where d is the diameter of the weld nugget detection circle, and K is the effective weld nugget pixel ratio in step S3;

[0012] S5. Welding spot quality determination: The AI ​​intelligent diagnosis system compares the effective nugget diameter D with the preset welding spot standard diameter and minimum diameter to determine the welding spot quality. The determination rules are as follows:

[0013] (1) The weld with effective nugget diameter D larger than the standard diameter is a qualified weld;

[0014] (2) The weld with effective nugget diameter D in the range of standard diameter and minimum diameter is a weld with small nugget;

[0015] (3) The welds whose effective nugget diameter D is smaller than the minimum diameter are considered to be unqualified welds;

[0016] The standard diameter and the minimum diameter are preset calibration ranges of the spot diameter determined according to different spot welding material thicknesses.

[0017] Optionally, step S1 also includes using Gaussian filtering and median filtering methods to reduce noise interference on the ultrasonic detection result image.

[0018] Optionally, in step S2, an edge detection algorithm is used to extract the contour of the effective nugget area, and the edge detection algorithm includes: Canny algorithm or Sobel algorithm.

[0019] Optionally, the preset algorithm for fitting a detection circle of the effective nugget at the geometric center of the effective nugget area in step S2 includes: a least squares circle fitting algorithm or a Hough circle transform algorithm.

[0020] Optionally, the pixel ratio K of the effective nugget area within the detection circle area is accurate to four decimal places.

[0021] Optionally, the standard diameter is a standard value of a weld nugget diameter at a preset material thickness under Ford quality standards, and the minimum diameter is a lower limit threshold of a weld nugget diameter allowed at a preset material thickness under Ford quality standards.

[0022] According to a second aspect of the present invention, there is provided an AI visual intelligent diagnosis system for ultrasonic detection results of weld nuggets, which is used to implement the above-mentioned AI visual intelligent diagnosis method for ultrasonic detection results of weld nuggets.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention gives full play to the advantages of AI visual intelligence, combines AI visual intelligence with ultrasonic detection of welds, and uses AI intelligence to diagnose the quality of welds in ultrasonic detection result images according to preset algorithms and judgment rules, thereby effectively avoiding the risk of subjective misjudgment in the process of manually judging the eligibility of detection result images, improving diagnostic reliability and inspection efficiency, and saving labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0026] Figure 1 This is a flowchart of the method steps for AI visual intelligent diagnosis of weld nugget ultrasonic detection results described in the first embodiment;

[0027] Figure 2 This is an image of the weld nugget ultrasonic testing result described in the first embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative efforts are within the scope of protection of the present application. Furthermore, the descriptions involving "first", "second", etc. in the application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features.

[0030] First Embodiment

[0031] like Figure 1 As shown, this embodiment provides a method for AI visual intelligent diagnosis of weld nugget ultrasonic detection results, which combines AI visual intelligent diagnosis with weld ultrasonic detection, collects information on ultrasonic detection result images through AI recognition and calculates according to a preset algorithm, and finally determines the quality of the weld according to a preset intelligent diagnosis rule, thereby avoiding the risk of subjective misjudgment in the process of manually judging the eligibility of the detection result image. The specific steps include:

[0032] Step S1. Image recognition and extraction of effective weld nugget area.

[0033] By ultrasonic scanning the weld, Figure 2 The ultrasonic test result image of the weld nugget is shown. The AI ​​intelligent diagnosis system automatically identifies the "effective nugget" area (i.e. the core part of the weld that meets the quality requirements) in the ultrasonic test result image based on color features (such as the preset red, green, and blue ranges) and marks it with a specific color. Gaussian filtering, median filtering and other methods are used to reduce noise interference in the ultrasonic test result image.

[0034] Step S2: Automatic calibration of the nugget detection circle.

[0035] The AI ​​intelligent diagnosis system applies preset algorithms and uses edge detection algorithms such as Canny and Sobel to extract the contour of the solder joint, and fills the voids or smoothes the edges through expansion and corrosion. The extracted solder joint contour is fitted into a detection circle of an effective weld nugget using the least squares circle fitting algorithm or the Hough circle transform algorithm, and a detection circle is automatically generated at the geometric center of the weld nugget in the ultrasonic detection result image, that is, Figure 2 The dotted circle in the figure finally adjusts the radius and center coordinates of the circle adaptively according to the actual size and position of the weld spot to ensure the geometric matching between the detection circle and the actual contour of the effective weld nugget. The diameter of the detection circle represents the detection diameter d of the weld nugget.

[0036] Among them, the “least squares circle fitting method” and “Hough circle transform” are circle detection algorithms that are optimized for problems such as blurred weld nugget contour and noise interference.

[0037] Step S3: Calculate the pixel area ratio of the nugget region in the detection circle.

[0038] The AI ​​intelligent diagnosis system counts the number of pixels in the "effective nugget" area marked with a specific color and the total number of pixels in the detection circle within the generated detection circle, and calculates the pixel ratio K of the effective nugget area within the detection circle, with an accuracy of four decimal places (0.01%). In this process, it is necessary to filter the noise in the image to ensure the accuracy of the data. K = number of pixels in the effective nugget area / total number of pixels in the detection circle.

[0039] Step S4: Calculate the effective nugget diameter.

[0040] The AI ​​intelligent diagnosis system calculates the effective nugget diameter D of the measured weld point according to the following formula:

[0041] According to the formula: D²=4×S 圆 × K / π to get, D 2 =d 2 ×K, thus obtaining the effective nugget diameter D.

[0042] Among them, S 圆 is the total area of ​​the detection circle (calculated by the detection diameter d of the detection circle), and K is the ratio of the effective weld nugget to the total pixels of the detection circle area.

[0043] Step S5. Determine the quality of the solder joint according to the effective nugget diameter D and the intelligent diagnosis standard:

[0044] The AI ​​intelligent diagnosis system judges the quality of solder joints through the following diagnostic criteria:

[0045] (1) The weld with effective nugget diameter D larger than the standard diameter is a qualified weld;

[0046] (2) The weld with effective nugget diameter D in the range of standard diameter and minimum diameter is a weld with small nugget;

[0047] (3) A weld whose effective nugget diameter D is smaller than the minimum diameter is considered an unqualified weld.

[0048] Among them, the standard diameter and the minimum diameter are both preset calibration values. As shown in Table 1 below, the "standard diameter" is the qualified standard value of the weld nugget diameter under a specific material thickness under Ford's quality standards, and the "minimum diameter" is the lower limit threshold of the weld nugget diameter allowed under a specific material thickness under Ford's quality standards. That is, according to Ford's quality standards, under a certain material thickness, the same weld has a comparison calibration value of "standard diameter" and "minimum diameter". In this embodiment, the AI ​​intelligent diagnosis system automatically compares the effective nugget diameter of the weld under a specific material thickness with its corresponding "standard diameter" and "minimum diameter" to determine the quality of the weld, avoiding the risk of subjective misjudgment or inaccuracy in the manual judgment of the weld nugget ultrasonic detection results, reducing the probability of escape of body welding quality risk problems, and improving the quality inspection diagnosis efficiency by at least 30%.

[0049]

[0050] Table 1 Qualified standard diameter and minimum diameter of weld nugget for specific metal sheet thickness under Ford quality standard

[0051] Second Embodiment

[0052] This embodiment provides a system for AI visual intelligent diagnosis of weld nugget ultrasonic detection results, which is used to implement the method for AI visual intelligent diagnosis of weld nugget ultrasonic detection results described in the first embodiment.

[0053] The above describes the specific embodiments of the present invention. Based on the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention.

Claims

1. An AI visual intelligent diagnosis method for ultrasonic detection results of weld nuggets, characterized in that: The following steps are involved: S1. Image generation and extraction of effective nugget area: Generate an image of the nugget ultrasonic detection result by ultrasonically scanning the weld point, and automatically identify and mark the effective nugget area in the image; the effective nugget area refers to the core area of ​​the weld point that meets the quality requirements; S2. Automatic calibration of the nugget detection circle: The system extracts the contour of the effective nugget area, and applies a preset algorithm to fit a detection circle of the effective nugget at the geometric center of the effective nugget area, wherein the diameter d of the detection circle represents the nugget detection diameter, wherein the preset algorithm is a circular detection algorithm optimized for blurring of the effective nugget contour and noise interference; S3. Calculation of pixel ratio of effective nugget area: The AI ​​intelligent diagnosis system counts the number of pixels in the effective nugget area within the detection circle and the total number of pixels in the detection circle, and calculates the effective nugget pixel ratio K; K = number of pixels in the effective nugget area / total number of pixels in the detection circle; S4. Calculation of effective nugget diameter: The AI ​​intelligent diagnosis system calculates the effective nugget diameter D according to the following formula: D²=d 2 ×K, where d is the diameter of the weld nugget detection circle, and K is the effective weld nugget pixel ratio in step S3; S5. Welding spot quality determination: The AI ​​intelligent diagnosis system compares the effective nugget diameter D with the preset welding spot standard diameter and minimum diameter to determine the welding spot quality. The determination rules are as follows: (1) The weld with effective nugget diameter D larger than the standard diameter is a qualified weld; (2) The weld with effective nugget diameter D in the range of standard diameter and minimum diameter is a weld with small nugget; (3) The welds whose effective nugget diameter D is smaller than the minimum diameter are considered to be unqualified welds; The standard diameter and the minimum diameter are preset calibration ranges of the spot diameter determined according to different spot welding material thicknesses.

2. The AI ​​visual intelligent diagnosis method for ultrasonic detection results of weld nuggets according to claim 1 is characterized in that: Step S1 also includes using Gaussian filtering and median filtering methods to reduce noise interference on the ultrasonic detection result image.

3. The AI ​​visual intelligent diagnosis method for ultrasonic detection results of weld nuggets according to claim 1 is characterized in that: In step S2, an edge detection algorithm is used to extract the contour of the effective nugget area. The edge detection algorithm includes: Canny algorithm or Sobel algorithm.

4. The AI ​​visual intelligent diagnosis method for ultrasonic detection results of weld nuggets according to claim 1 is characterized in that: The preset algorithm for fitting a detection circle of the effective nugget at the geometric center of the effective nugget region in step S2 includes: a least squares circle fitting algorithm or a Hough circle transform algorithm.

5. The AI ​​visual intelligent diagnosis method for ultrasonic detection results of weld nuggets according to claim 1 is characterized in that: The pixel ratio K of the effective nugget area within the detection circle area is accurate to four decimal places.

6. The AI ​​visual intelligent diagnosis method for ultrasonic detection results of weld nuggets according to claim 1 is characterized in that: The standard diameter is the standard value of the weld nugget diameter at a preset material thickness under Ford quality standards, and the minimum diameter is the lower limit threshold of the weld nugget diameter allowed at a preset material thickness under Ford quality standards.

7. An AI visual intelligent diagnosis system for ultrasonic detection results of weld nuggets, characterized in that: A method for implementing the AI ​​visual intelligent diagnosis of weld nugget ultrasonic detection results as described in any one of claims 1 to 6.

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