A circuit board defect detection method and system using image processing technology

Abstract

The present invention discloses a circuit board defect detection method and system using image processing technology, and relates to the technical field of circuit boards. The system performs multi-dimensional collection and recording of the surface of a printed circuit board and the inner wall of a through hole through multi-angle image capture, and at the same time identifies the temperature of solder joints at pins through a colorimetric card, to help the system detect temperature-related defects such as cold welding, and combines multi-dimensional data information to learn and analyze a defect evaluation index Qxzs, and through intelligent comparison results, automatically determines the qualified state of the current printed circuit board for the system. This process reduces the intervention of subjective consciousness in traditional methods, and further improves detection efficiency and stability. The defect evaluation index Qxzs deepens the multi-party detection of the coating layer on the inner wall of the through hole through comprehensive consideration of multiple parameters, further accurately reflects the defect status of the circuit board, makes the evaluation more scientific and reliable, and helps to ensure the quality of the printed circuit board.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit boards, and in particular to a circuit board defect detection method and system using image processing technology. Background Art

[0002] In the context of current technological development, image processing technology has gradually become one of the indispensable tools in various fields. Among them, the field of electronic manufacturing, as one of the important fields of application of image processing technology, continues to usher in innovation and improvement. The production of circuit boards is one of the core links in the manufacture of electronic products. Its quality directly affects the performance and reliability of the final product. In addition, the design and manufacture of circuit boards have become increasingly complex, especially the printed circuit board, referred to as multi-layer PCB.

[0003] Printed circuit boards are the basic components of electronic products. They are covered with wires and components to connect and support electronic components to form a complete circuit. The key component is the through hole in the printed circuit board, and the inner wall of the through hole is usually coated with conductive material by electroplating to ensure electrical connection between different layers.

[0004] However, there are a series of problems with traditional PCB defect detection methods. First, manual detection is subjective and easily affected by the operator's skill level and fatigue, resulting in unstable detection results. Second, detailed detection and analysis of the inner wall state of the through hole is often neglected, resulting in signal transmission interruption between different layers, increased interlayer resistance, and signal distortion even if the surface of the PCB is in good condition, affecting the performance and stability of the circuit. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a circuit board defect detection method and system using image processing technology, which solves the problems in the above-mentioned background technology.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a circuit board defect detection system using image processing technology, including a multi-faceted imaging module, an image preprocessing module, a multi-point data acquisition module, a circuit board comprehensive defect analysis module and a quality monitoring module;

[0009] The multi-faceted imaging module is used to capture and shoot the multi-faceted orthographic projection of multiple groups of components on the surface of the printed circuit board and the copper cladding layer using an imaging device to obtain a component layout image, and at the same time identify and locate the through holes in the printed circuit board, capture the coating layer state of the inner wall of the through holes, and obtain a coating layer image;

[0010] The image preprocessing module is used to preprocess the coating layer image and the component layout image, remove noise and smooth the image, and generate a printed circuit board status data atlas by evenly dividing the coating layer image into a plurality of groups of square detection areas, marking the plurality of groups of square detection areas, setting a plurality of groups of data points in the square detection areas, and improving the image quality according to the image enhancement technology;

[0011] The multi-point data acquisition module is used to perform data recognition and monitoring on the coating layer image and the component layout image, respectively obtain relevant through-hole coating state data information and relevant component data information, and identify whether the component pin solder point temperature is qualified according to the colorimetric card;

[0012] The circuit board comprehensive defect analysis module is used to use convolutional neural network learning technology to extract features from relevant through-hole coating state data information and relevant component data information to obtain the thickness difference factor Hcyz, the number of pits Ktsz, the edge spacing Byjj and the number of cold solder joints Xhsz of the coating layer in the through-hole, and obtain the coating state coefficient Tfxs and the component state coefficient Yjxs through deep machine learning calculation, and associate the coating state coefficient Tfxs with the component state coefficient Yjxs, and fit and calculate the defect evaluation index Qxzs. The defect evaluation index Qxzs is obtained by the following formula:

[0013]

[0014] Wherein, Dc represents the solder joint diameter difference, Pjdc represents the offset angle difference, w1, w2, w3 and w4 represent the preset proportional coefficients of the coating state coefficient Tfxs, the component state coefficient Yjxs, the solder joint diameter difference Dc and the offset angle difference Pjdc, respectively, and P represents the first correction constant;

[0015] The quality monitoring module is used to pre-set the evaluation threshold A, and compare and analyze it with the defect evaluation index Qxzs to comprehensively determine the qualified state of the printed circuit board.

[0016] Preferably, the multi-faceted imaging module comprises a multi-faceted capturing unit;

[0017] The multi-surface capture unit is used to simultaneously perform top-down and side-view orthographic projection photography of multiple groups of components on the copper cladding layer, and to simultaneously perform top-down and side-view orthographic projection photography of the coating layer passing through the inner wall of the hole, and to mark component layout images and coating layer images taken from different directions.

[0018] Preferably, the image preprocessing module includes a processing unit and a segmentation unit;

[0019] The processing unit is used to utilize wavelet denoising technology to decompose data information in a plurality of coating layer images and component layout images through wavelet transform, remove noise components in the wavelet domain, and use histogram equalization to adjust brightness, stretch image range, and adjust image contrast;

[0020] The segmentation unit is used to segment the coating layer image into a plurality of groups of square detection areas, which are respectively marked as the first area L1, the second area L2, the third area L3, ..., the Nth area Ln, and distribute a plurality of groups of data points randomly into each square detection area.

[0021] Preferably, the multi-point data acquisition module includes a card color comparison unit, a surface acquisition unit and an in-hole acquisition unit;

[0022] The card color comparison unit is used to capture the image at the solder joint using the multi-faceted imaging module, place the colorimetric card in the image at the solder joint, ensure that the color of the colorimetric card corresponds to the actual colorimetric card, perform calibration, and simultaneously identify the solder joint position, extract color information around the solder joint, map the temperature information of the solder joint to the image at the solder joint using the temperature information calibrated by the colorimetric card, visualize the temperature information on the printed circuit board status data atlas, identify the qualified temperature status of the component pin solder joint, and obtain the number of cold solder joints Xhsz;

[0023] The surface acquisition unit is used to use the component layout image to identify and detect the component data information related to the printed circuit board surface, including the number of bent pins Yjws, component spacing Yjjz and the edge spacing Byjj between the component and the printed circuit board frame in each component;

[0024] The in-hole acquisition unit is used to use the coating layer image to identify and detect the through-hole coating status data information related to the inner wall of the through-hole in the printed circuit board, including the thickness mean Hjz in the square detection area, the total area mean Zqjz, the thickness difference factor Hcyz, the crack length Lwcd, the crack depth Lwsd, the number of pits Ktsz, the bubble density Qpmd, the pit area Kdmj and the uncoated area Wgmj.

[0025] Preferably, the circuit board comprehensive defect analysis module includes a through-hole area analysis unit and a copper-clad layer component analysis unit;

[0026] The through-hole area analysis unit includes a layer thickness distribution subunit, a defect subunit and a troubleshooting subunit;

[0027] The layer thickness distribution subunit is used to analyze the thickness mean Hjz in the square detection area and the total area mean Zqjz to obtain the thickness difference factor Hcyz. The thickness difference factor Hcyz is obtained by the following formula:

[0028]

[0029] Where N is the number of segmented square detection areas.

[0030] Preferably, the defect subunit is used to associate the thickness difference factor Hcyz with the number of pits Ktsz, and obtain the coating state coefficient Tfxs after dimensionless processing. The coating state coefficient Tfxs is obtained by the following formula:

[0031]

[0032] Wherein, Lwcd represents the crack length, Lwsd represents the crack depth, Kdmj represents the pit area, Wgmj represents the uncoated area, Qpmd represents the bubble density, h, c, s, k and q represent the preset proportional coefficients of the thickness difference factor Hcyz, the crack length Lwcd, the crack depth Lwsd, the pit number Ktsz and the bubble density Qpmd respectively, j represents the preset proportional coefficient of the sum of the pit area Kdmj and the uncoated area Wgmj, and C represents the second correction constant.

[0033] Preferably, the troubleshooting subunit is used to pre-set a standard threshold value V, and compare and analyze the coating state coefficient Tfxs with the standard threshold value V to determine whether the coating layer currently passing through the inner wall of the hole affects the electrical connection between layers;

[0034] If the coating state coefficient Tfxs is greater than or equal to the standard threshold value V, that is, Tfxs ≥ V, it means that the coating layer currently passing through the inner wall of the hole affects the electrical connection between layers, and rework is required at this time;

[0035] If the coating state coefficient Tfxs is less than the standard threshold value V, that is, Tfxs<V, it means that the coating layer currently passing through the inner wall of the hole does not affect the electrical connection between layers.

[0036] Preferably, the copper clad layer component analysis unit is used to obtain the component state coefficient Yjxs by associating the edge spacing Byjj with the number of cold solder joints Xhsz and performing dimensionless processing. The component state coefficient Yjxs is obtained by the following formula:

[0037]

[0038] Wherein, Yjjz represents the component spacing, Yjws represents the number of pin bending, a1, a2, a3 and a4 represent the preset proportional coefficients of the edge spacing Byjj, the number of cold solder joints Xhsz, the component spacing Yjjz and the number of pin bending Yjws respectively.

[0039] Preferably, the quality monitoring module compares and analyzes the defect evaluation index Qxzs with the evaluation threshold A to comprehensively determine the qualified state of the printed circuit board;

[0040] If the defect evaluation index Qxzs ≥ the evaluation threshold A, it means that the current printed circuit board is in an unqualified state. At this time, manual inspection will be introduced for the detected defects, local repair and replacement of unqualified components will be carried out, and the manufacturing process will be reviewed to find out the root cause of the defects;

[0041] If the defect evaluation index Qxzs is less than the evaluation threshold value A, it indicates that the current printed circuit board is in a qualified state. At this time, the production of the current printed circuit board will continue, and real-time monitoring will continue to be performed to ensure that no quality problems occur during the production process.

[0042] A circuit board defect detection method using image processing technology comprises the following steps:

[0043] Step 1: First, use imaging equipment to capture and shoot multiple groups of components and inner walls of the through-holes on the surface of the printed circuit board, and obtain component layout images and coating layer images respectively;

[0044] Step 2: Preprocess the coating layer image and component layout image to remove noise and smooth the image, evenly divide the coating layer image into several groups of square detection areas, set several groups of data points in the square detection areas, and improve the image quality based on image enhancement technology to generate a printed circuit board status data atlas;

[0045] Step 3: Then, data recognition and monitoring are performed on the coating layer image and the component layout image, and relevant through-hole coating status data information and relevant component data information are obtained respectively, and the temperature of the component pin solder point is identified to be qualified according to the colorimetric card;

[0046] Step 4: In addition, the convolutional neural network learning technology is used to extract the features of the relevant through-hole coating state data information and the relevant component data information, and through deep machine learning calculation, the coating state coefficient Tfxs, the component state coefficient Yjxs and the defect evaluation index Qxzs are obtained;

[0047] Step 5: Finally, the evaluation threshold A is preset, and compared with the defect evaluation index Qxzs to comprehensively determine the qualified state of the printed circuit board.

[0048] (III) Beneficial effects

[0049] The present invention provides a circuit board defect detection method and system using image processing technology, which has the following beneficial effects:

[0050] (1) The system captures images from multiple angles to collect and record the surface of the printed circuit board and the inner wall of the through hole in multiple dimensions. At the same time, the temperature of the solder joint at the pin is identified through a colorimetric card to help the system detect temperature-related defects such as cold solder joints. Combining multi-dimensional data information, the system learns and analyzes the defect assessment index Qxzs, and through intelligent comparison results, the system automatically determines the qualified status of the current printed circuit board. This process reduces the intervention of subjective consciousness in traditional methods and further improves detection efficiency and stability. The defect assessment index Qxzs comprehensively considers multiple parameters and deepens the multi-faceted detection of the coating layer on the inner wall of the through hole, further accurately reflecting the defect status of the circuit board, making the evaluation more scientific and reliable, and helping to ensure the quality of printed circuit boards and improve the performance and reliability of final electronic products.

[0051] (2) At the same time, the colorimetric card is combined with the image, and the color information extracted from the circuit board image is mapped to the corresponding temperature value using the calibration relationship of the colorimetric card. The corresponding temperature can be found by comparing the color value with the color value calibrated by the colorimetric card, thereby automatically capturing the temperature state at the solder joint and detecting whether there is a cold solder joint.

[0052] (3) At the same time, the coating status of the inner wall of the hole in the printed circuit board is collected in multiple dimensions and analyzed in each area in order to understand the thickness distribution of the coating layer in more detail and indirectly analyze its information connection between the layers of the board. In short, the system not only improves the accuracy and stability of detection, but also accelerates the production process, which has strongly promoted the development of the electronic manufacturing industry in a more efficient and reliable direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of a circuit board defect detection system using image processing technology according to the present invention;

[0054] Figure 2 The present invention is a schematic diagram of the steps of a circuit board defect detection method using image processing technology. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0056] Example 1

[0057] See also Figure 1, the present invention provides a circuit board defect detection system using image processing technology, a multi-surface imaging module, an image preprocessing module, a multi-point data acquisition module, a circuit board comprehensive defect analysis module and a quality monitoring module;

[0058] The multi-faceted imaging module is used to capture and shoot the multi-faceted orthographic projection of multiple groups of components on the surface of the printed circuit board and the copper cladding layer using an imaging device to obtain a component layout image, and at the same time identify and locate the through holes in the printed circuit board, capture the coating layer state of the inner wall of the through holes, and obtain a coating layer image;

[0059] The image preprocessing module is used to preprocess the coating layer image and the component layout image, remove noise and smooth the image, effectively improve the image quality, provide a clear and reliable data basis for subsequent analysis, and reduce the possibility of misjudgment. The coating layer image is evenly divided into several groups of square detection areas, and several groups of square detection areas are marked, and several groups of data points are set in the square detection areas. The image quality is improved according to the image enhancement technology, and a printed circuit board state data atlas is generated, which is conducive to subsequent analysis;

[0060] The multi-point data acquisition module is used to perform data recognition and monitoring on the coating layer image and the component layout image, respectively obtain relevant through-hole coating state data information and relevant component data information, and identify whether the component pin solder point temperature is qualified according to the colorimetric card;

[0061] The circuit board comprehensive defect analysis module is used to use convolutional neural network learning technology to extract features from relevant through-hole coating state data information and relevant component data information to obtain the thickness difference factor Hcyz, the number of pits Ktsz, the edge spacing Byjj and the number of cold solder joints Xhsz of the coating layer in the through-hole, and obtain the coating state coefficient Tfxs and the component state coefficient Yjxs through deep machine learning calculation, and associate the coating state coefficient Tfxs with the component state coefficient Yjxs, and fit and calculate the defect evaluation index Qxzs. The defect evaluation index Qxzs is obtained by the following formula:

[0062]

[0063] Wherein, Dc represents the solder joint diameter difference, Pjdc represents the offset angle difference, w1, w2, w3 and w4 represent the preset proportional coefficients of the coating state coefficient Tfxs, the component state coefficient Yjxs, the solder joint diameter difference Dc and the offset angle difference Pjdc, respectively, wherein 0.12≤w1≤0.30, 0.16≤w2≤0.37, 0.11≤w3≤0.22, 0.02≤w4≤0.11, and 0.50≤w1+w2+w3+w4≤1.0, and P represents the first correction constant;

[0064] The quality monitoring module is used to pre-set the evaluation threshold A, and compare and analyze it with the defect evaluation index Qxzs to comprehensively determine the qualified state of the printed circuit board.

[0065] During the operation of this system, the multi-faceted imaging module captures and shoots through orthographic projection to achieve all-round imaging of multiple groups of components on the surface of the printed circuit board and the inner wall of the through-hole, providing a more comprehensive component layout image, which helps to fully understand the status of components in the circuit board; the multi-point data acquisition module identifies the temperature of the component pin solder joints through the colorimetric card, provides real-time monitoring of key temperature information, and helps detect temperature-related defects such as cold welding; the circuit board comprehensive defect analysis module uses convolutional neural network learning technology to conduct in-depth learning of the through-hole coating status and component data, extract multiple defect parameters, and comprehensively evaluate the circuit board status through the defect evaluation index Qxzs. By comparing and analyzing it with the evaluation threshold A, it realizes automatic judgment of the qualified status of the printed circuit board, reduces human subjective intervention, and improves detection efficiency and stability.

[0066] Example 2

[0067] Please refer to Figure 1 ,Specifically: the multi-faceted imaging module includes a multi-faceted capturing unit;

[0068] The multi-surface capture unit is used to simultaneously perform top-down and side-view orthographic projection photography of multiple groups of components on the copper cladding layer, and to simultaneously perform top-down and side-view orthographic projection photography of the coating layer through the inner wall of the hole, and to mark component layout images and coating layer images taken from different directions for easy distinction.

[0069] The image preprocessing module includes a processing unit and a segmentation unit;

[0070] The processing unit is used to utilize wavelet denoising technology to decompose data information in a plurality of coating layer images and component layout images through wavelet transform, effectively reducing interference in the image, improving image clarity, removing noise components in the wavelet domain, and using histogram equalization to adjust brightness, stretch image range, and adjust image contrast;

[0071] The segmentation unit is used to segment the coating layer image into several groups of square detection areas, and mark them as the first area L1, the second area L2, the third area L3,..., the Nth area Ln respectively, and distribute several groups of data points in each square detection area in a random manner, providing clear image area division and marking information for subsequent analysis.

[0072] In this embodiment, the multi-faceted capture unit in the multi-faceted imaging module is responsible for taking top and side orthographic projection photos of multiple groups of components on the copper clad layer, and taking top and side orthographic projection photos of the coating layer through the inner wall of the hole. This design allows the system to simultaneously obtain component layout images and coating layer images from multiple angles, providing more comprehensive and three-dimensional image information, which is helpful for all-round analysis of the state of the circuit board. The processing unit uses wavelet transform denoising technology, which can decompose the image in the frequency domain, remove noise components more accurately, retain useful signal information, and improve image quality and analysis accuracy; the segmentation unit divides the coating layer image into several groups of square detection areas and marks them, which is helpful for regional analysis of the circuit board. The data points distributed in each square detection area are randomly distributed, making subsequent data collection more random and representative.

[0073] Example 3

[0074] Please refer to Figure 1 , specifically: the multi-point data acquisition module includes a card color comparison unit, a surface acquisition unit and an in-hole acquisition unit;

[0075] The card color comparison unit is used to capture the image at the solder joint using the multi-faceted imaging module, place the colorimetric card in the image at the solder joint, ensure that the color of the colorimetric card corresponds to the actual colorimetric card, perform calibration, and identify the solder joint position at the same time, extract color information around the solder joint, and map the temperature information of the solder joint to the image at the solder joint using the temperature information calibrated by the colorimetric card. The temperature of the solder joint can be displayed in the image by different colors, marks or numbers, and the temperature information can be visualized on the printed circuit board status data atlas to identify the qualified temperature status of the component pin solder joint to obtain the number of cold solder joints Xhsz, so that the operator can intuitively understand the temperature distribution of the solder joint, which can help quickly discover potential cold solder joint problems or other temperature-related defects;

[0076] The surface acquisition unit is used to use the component layout image to identify and detect the component data information related to the printed circuit board surface, including the number of bent pins Yjws, component spacing Yjjz and the edge spacing Byjj between the component and the printed circuit board frame in each component;

[0077] The in-hole acquisition unit is used to use the coating layer image to identify and detect the through-hole coating status data information related to the inner wall of the through-hole in the printed circuit board, including the thickness mean Hjz in the square detection area, the total area mean Zqjz, the thickness difference factor Hcyz, the crack length Lwcd, the crack depth Lwsd, the number of pits Ktsz, the bubble density Qpmd, the pit area Kdmj and the uncoated area Wgmj.

[0078] In this embodiment, by using the card color comparison unit, the system can capture images at the solder joints and use the colorimetric card for color calibration. By placing the colorimetric card in the image at the solder joint, calibrating and mapping the color information to the temperature information, the system can visualize the temperature information of the solder joints on the printed circuit board status data atlas, which enables the operator to intuitively understand the temperature distribution of the solder joints, and facilitates the rapid discovery of potential cold welding problems or other temperature-related defects. The surface acquisition unit uses the component layout image to identify and detect the component data information related to the surface of the printed circuit board, which helps the system obtain the geometry and layout information of the surface components, and provides key pin connection status and distance from the edge data; the in-hole acquisition unit uses the coating layer image to identify and detect the coating state data information related to the inner wall of the hole in the printed circuit board, which helps to fully understand the state of the coating through the hole, including the uniformity of the coating layer, thickness differences and possible defects, so that the system collects comprehensive data information in different aspects, which helps to comprehensively analyze the state of the circuit board and improve the accuracy and reliability of defect detection.

[0079] Example 4

[0080] Please refer to Figure 1 ,Specifically: the circuit board comprehensive defect analysis module includes a through hole area analysis unit and a copper clad layer component analysis unit;

[0081] The through-hole area analysis unit includes a layer thickness distribution subunit, a defect subunit and a troubleshooting subunit;

[0082] The layer thickness distribution subunit is used to analyze the thickness mean Hjz in the square detection area and the total area mean Zqjz to obtain the thickness difference factor Hcyz. The thickness difference factor Hcyz is obtained by the following formula:

[0083]

[0084] Wherein, N represents the number of segmented square detection areas, the total area mean Zqjz represents the average value of all thickness means Hjz in the entire coating layer image, and the thickness mean Hjz is achieved by adding the thickness values ​​of all data points in the square detection area and then dividing it by the number of data points.

[0085] In this embodiment, through the layer thickness distribution subunit, the system can analyze the thickness mean Hjz and the total area mean Zqjz in the square detection area to obtain the thickness difference factor Hcyz, which helps to understand the thickness distribution of the coating layer in more detail, especially the differences in different areas, and provides comprehensive thickness information, so that the system can identify the uniformity and consistency of the coating layer. The calculation formula of the thickness difference factor Hcyz is calculated based on the average value of the number of divided square detection areas N, the total area mean Zqjz and the thickness mean Hjz, which helps the system to accurately describe the thickness distribution of the coating layer in a mathematical way, so that the operator can more intuitively understand the uniformity and potential problems of the coating layer. Through the analysis of the layer thickness distribution subunit, the system can obtain specific thickness information in different square detection areas and calculate the overall state of the coating layer. This provides a comprehensive analysis method for the system, allowing the operator to have a deeper understanding of the situation of the through-hole coating during the detection process, which helps to accurately determine whether there are defects such as thickness differences.

[0086] Example 5

[0087] Please refer to Figure 1 Specifically, the defect subunit is used to associate the thickness difference factor Hcyz with the number of potholes Ktsz, and obtain the coating state coefficient Tfxs after dimensionless processing. The coating state coefficient Tfxs is obtained by the following formula:

[0088]

[0089] Wherein, Lwcd represents the crack length, Lwsd represents the crack depth, Kdmj represents the pit area, Wgmj represents the uncoated area, Qpmd represents the bubble density, h, c, s, k and q represent the preset proportional coefficients of the thickness difference factor Hcyz, the crack length Lwcd, the crack depth Lwsd, the number of pits Ktsz and the bubble density Qpmd respectively, j represents the preset proportional coefficient of the sum of the pit area Kdmj and the uncoated area Wgmj, wherein 0.15≤h≤0.31, 0.06≤c≤0.11, 0.05≤s≤0.11, 0.03≤k≤0.15, 0.06≤q≤0.14, 0.07≤j≤0.18, and 0.55≤h+c+s+j+k+q≤1.0, and C represents the second correction constant.

[0090] The above-mentioned crack length Lwcd and crack depth Lwsd are obtained by capturing images using a high-resolution camera and measuring the length and depth of the crack using image processing software.

[0091] The number of potholes Ktsz and the area of ​​potholes Kdmj can be identified and calculated by using a surface detection instrument or an image processing system.

[0092] The bubble density Qpmd can be collected and obtained by using ultrasonic testing equipment.

[0093] The uncoated area Wgmj uses image processing technology to acquire an image of the printed circuit board surface through a camera, and then identifies and measures the uncoated area.

[0094] The troubleshooting subunit is used to pre-set a standard threshold value V, and compare and analyze the coating state coefficient Tfxs with the standard threshold value V to determine whether the coating layer currently passing through the inner wall of the hole affects the electrical connection between layers;

[0095] If the coating state coefficient Tfxs is greater than or equal to the standard threshold value V, that is, Tfxs ≥ V, it means that the coating layer currently passing through the inner wall of the hole affects the electrical connection between layers, and rework is required at this time;

[0096] If the coating state coefficient Tfxs is less than the standard threshold value V, that is, Tfxs<V, it means that the coating layer currently passing through the inner wall of the hole does not affect the electrical connection between layers.

[0097] In this embodiment, the defect sub-unit obtains the coating state coefficient Tfxs by calculation. This process combines the influencing factors of different defects and quantitatively describes the state of the coating layer by mathematical means, which helps to systematically and comprehensively analyze the defects of the coating layer. At the same time, the coating state coefficient Tfxs is compared with the standard threshold V to help determine whether the coating layer passing through the inner wall of the hole affects the electrical connection between layers, so that the operator can promptly judge the impact of the coating layer and take corresponding measures.

[0098] Example 6

[0099] Please refer to Figure 1 Specifically: the copper clad layer component analysis unit is used to obtain the component state coefficient Yjxs by associating the edge spacing Byjj with the number of cold solder joints Xhsz and performing dimensionless processing. The component state coefficient Yjxs is obtained by the following formula:

[0100]

[0101] Wherein, Yjjz represents the component spacing, Yjws represents the number of bent pins, a1, a2, a3 and a4 represent the preset proportional coefficients of the edge spacing Byjj, the number of cold solder joints Xhsz, the component spacing Yjjz and the number of bent pins Yjws, respectively, wherein 0.12≤a1≤0.22, 0.10≤a2≤0.35, 0.11≤a3≤0.18, 0.14≤a4≤0.25, and 0.55≤a1+a2+a3+a4≤1.0.

[0102] The above-mentioned edge spacing Byjj can be measured by a laser rangefinder, a vision system or a mechanical sensor;

[0103] The number of cold solder joints Xhsz uses an image processing system to obtain images of the solder joint area through a camera or other imaging device, and then uses an image processing algorithm to identify the number of cold solder joints.

[0104] Component SpacingYjjz uses imaging equipment to capture component layout images, and then calculates the spacing between components through image processing software.

[0105] The number of bent pins, Yjws, is detected by using a mechanical sensor or a vision system.

[0106] In this embodiment, the calculation formula of the component state coefficient Yjxs takes into account factors such as edge spacing Byjj, number of cold solder joints Xhsz, component spacing Yjjz and number of pin bends Yjws, so that the system can comprehensively reflect factors such as component layout, pin welding quality and spacing between components, which is helpful to comprehensively evaluate the installation and quality status of components; by analyzing the component state coefficient Yjxs, the rationality of component layout and the goodness of welding quality can be understood, providing an important basis for subsequent quality evaluation.

[0107] Example 7

[0108] Please refer to Figure 1 Specifically: the quality monitoring module compares and analyzes the defect evaluation index Qxzs with the evaluation threshold A to comprehensively determine the qualified state of the printed circuit board;

[0109] If the defect evaluation index Qxzs ≥ the evaluation threshold A, it means that the current printed circuit board is in an unqualified state. The system will promptly discover and record the detected defects. At this time, manual inspection will be introduced for the detected defects, and local repairs or replacement of unqualified components will be carried out. The parameters that may cause defects in the current production process will be adjusted to reduce the defect rate. At the same time, it will help to reduce the unqualified rate, improve the production process, review the manufacturing process, and find out the root cause of the defects, which may involve equipment, materials or human factors;

[0110] If the defect assessment index Qxzs is less than the assessment threshold A, it indicates that the current printed circuit board is in a qualified state, which provides real-time quality certification for production. The qualified state is determined based on a comprehensive consideration of various detection parameters, ensuring a comprehensive assessment of the overall quality of the circuit board. At this time, the production of the current printed circuit board will continue, and real-time monitoring will continue to be carried out to ensure that no quality problems occur during the production process.

[0111] In this embodiment, by comparing and analyzing the defect evaluation index Qxzs with the evaluation threshold A, the system can accurately determine the qualified state of the printed circuit board. In the qualified state, the system will continue to monitor the production process of the current printed circuit board, and ensure that no quality problems will occur during the production process through real-time monitoring, which helps to improve production efficiency, reduce quality risks, and ensure that the produced circuit boards meet high quality standards and meet customer needs. Not only limited to handling unqualified states, the system can also review the parameters that may cause defects in the production process based on the detected defect information, and make suggestions for adjustment, which helps to continuously optimize the production process and improve manufacturing efficiency and product quality.

[0112] Example 8

[0113] Please refer to Figure 1 and Figure 2 Specifically: A circuit board defect detection method using image processing technology includes the following steps:

[0114] Step 1: First, use imaging equipment to capture and shoot multiple groups of components and inner walls of the through-holes on the surface of the printed circuit board, and obtain component layout images and coating layer images respectively;

[0115] Step 2: Preprocess the coating layer image and component layout image to remove noise and smooth the image, evenly divide the coating layer image into several groups of square detection areas, set several groups of data points in the square detection areas, and improve the image quality based on image enhancement technology to generate a printed circuit board status data atlas;

[0116] Step 3: Then, data recognition and monitoring are performed on the coating layer image and the component layout image, and relevant through-hole coating status data information and relevant component data information are obtained respectively, and the temperature of the component pin solder point is identified to be qualified according to the colorimetric card;

[0117] Step 4: In addition, the convolutional neural network learning technology is used to extract the features of the relevant through-hole coating state data information and the relevant component data information, and through deep machine learning calculation, the coating state coefficient Tfxs, the component state coefficient Yjxs and the defect evaluation index Qxzs are obtained;

[0118] Step 5: Finally, the evaluation threshold A is preset, and compared with the defect evaluation index Qxzs to comprehensively determine the qualified state of the printed circuit board.

[0119] In this embodiment, the method uses a multi-faceted imaging module to capture image information of the surface and interior of a printed circuit board, removes noise and smoothes the image through an image preprocessing module, and then obtains component and coating status data through a multi-point data acquisition module. The circuit board comprehensive defect analysis module uses convolutional neural network learning technology to calculate the defect evaluation index Qxzs through deep machine learning, thereby achieving accurate identification and evaluation of printed circuit board defects.

[0120] Example: A certain inspection workshop has introduced a circuit board defect detection method and system using image processing technology. The following is an example of a certain inspection workshop:

[0121] Data collection: thickness mean Hjz is 3.2; total area mean Zqjz is 2.45; the number of divided square inspection areas N is 12; crack length Lwcd is 1.6; crack depth Lwsd is 0.15; number of pits Ktsz is 6; bubble density Qpmd is 48%; pit area Kdmj is 2.1; uncoated area Wgmj is 3.2;

[0122] Preset proportional coefficients: h is 0.17; c is 0.08; s is 0.10; k is 0.05; q is 0.09; j is 0.10; the second correction constant C is 0.23;

[0123] The edge spacing Byjj is 0.2; the number of cold solder joints Xhsz is 23; the component spacing Yjjz is 2.6; the number of pin bends Yjws is 14;

[0124] Preset proportional coefficients: a1 is 0.15; a2 is 0.20; a3 is 0.12; a4 is 0.16;

[0125] The weld diameter difference Dc is 0.14; the offset angle difference Pjdc is 24;

[0126] Preset proportional coefficients: w1 is 0.16; w2 is 0.20; w3 is 0.14; w4 is 0.05; the first correction constant P is 0.57;

[0127] With the above data, the following calculations can be performed:

[0128] Thickness Difference Factor

[0129] Coating state coefficient:

[0130]

[0131] If the standard threshold V is 2.5, the coating state coefficient Tfxs is greater than the standard threshold V, indicating that the coating layer currently passing through the inner wall of the hole affects the electrical connection between layers, and rework is required at this time;

[0132] Component status coefficient

[0133] Defect Assessment Index

[0134] If the evaluation threshold A is 4.0, when the defect evaluation index Qxzs is greater than the evaluation threshold A, it means that the current printed circuit board is in an unqualified state. At this time, manual inspection will be introduced for the detected defects, local repairs and replacement of unqualified components will be carried out, and the manufacturing process will be reviewed to find out the root cause of the defects.

[0135] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A circuit board defect detection system using image processing technology, characterized in that: It includes multi-surface imaging module, image preprocessing module, multi-point data acquisition module, circuit board comprehensive defect analysis module and quality monitoring module; The multi-faceted imaging module is used to capture and shoot the multi-faceted orthographic projection of multiple groups of components on the surface of the printed circuit board and the copper cladding layer using an imaging device to obtain a component layout image, and at the same time identify and locate the through holes in the printed circuit board, capture the coating layer status of the inner wall of the through holes, and obtain the coating layer image; The image preprocessing module is used to preprocess the coating layer image and the component layout image, remove noise and smooth the image, and generate a printed circuit board status data atlas by evenly dividing the coating layer image into a plurality of groups of square detection areas, marking the plurality of groups of square detection areas, setting a plurality of groups of data points in the square detection areas, and improving the image quality according to the image enhancement technology; The multi-point data acquisition module is used to perform data recognition and monitoring on the coating layer image and the component layout image, respectively obtain relevant through-hole coating state data information and relevant component data information, and identify whether the component pin solder point temperature is qualified according to the colorimetric card; The circuit board comprehensive defect analysis module is used to use convolutional neural network learning technology to extract features from relevant through-hole coating state data information and relevant component data information to obtain the thickness difference factor Hcyz, the number of pits Ktsz, the edge spacing Byjj and the number of cold solder joints Xhsz of the coating layer in the through-hole, and obtain the coating state coefficient Tfxs and the component state coefficient Yjxs through deep machine learning calculation, and associate the coating state coefficient Tfxs with the component state coefficient Yjxs, and fit and calculate the defect evaluation index Qxzs. The defect evaluation index Qxzs is obtained by the following formula: Wherein, Dc represents the solder joint diameter difference, Pjdc represents the offset angle difference, w1, w2, w3 and w4 represent the preset proportional coefficients of the coating state coefficient Tfxs, the component state coefficient Yjxs, the solder joint diameter difference Dc and the offset angle difference Pjdc, respectively, and P represents the first correction constant; The quality monitoring module is used to pre-set an evaluation threshold value A, and compare and analyze it with the defect evaluation index Qxzs to comprehensively determine the qualified state of the printed circuit board; The circuit board comprehensive defect analysis module includes a through hole area analysis unit and a copper clad layer component analysis unit; The through-hole area analysis unit includes a layer thickness distribution subunit, a defect subunit and a troubleshooting subunit; The layer thickness distribution subunit is used to analyze the thickness mean Hjz in the square detection area and the total area mean Zqjz to obtain the thickness difference factor Hcyz. The thickness difference factor Hcyz is obtained by the following formula: In the formula, N represents the number of segmented square detection areas; The defect subunit is used to associate the thickness difference factor Hcyz with the number of potholes Ktsz, and obtain the coating state coefficient Tfxs after dimensionless processing. The coating state coefficient Tfxs is obtained by the following formula: Wherein, Lwcd represents the crack length, Lwsd represents the crack depth, Kdmj represents the pit area, Wgmj represents the uncoated area, Qpmd represents the bubble density, h, c, s, k and q represent the preset proportional coefficients of the thickness difference factor Hcyz, the crack length Lwcd, the crack depth Lwsd, the pit number Ktsz and the bubble density Qpmd, respectively, j represents the preset proportional coefficient of the sum of the pit area Kdmj and the uncoated area Wgmj, and C represents the second correction constant; The troubleshooting subunit is used to pre-set a standard threshold value V, and compare and analyze the coating state coefficient Tfxs with the standard threshold value V to determine whether the coating layer currently passing through the inner wall of the hole affects the electrical connection between layers; If the coating state coefficient Tfxs is greater than or equal to the standard threshold value V, that is, Tfxs ≥ V, it means that the coating layer currently passing through the inner wall of the hole affects the electrical connection between layers, and rework is required at this time; If the coating state coefficient Tfxs is less than the standard threshold value V, that is, Tfxs<V, it means that the coating layer currently passing through the inner wall of the hole does not affect the electrical connection between the layers; The copper clad layer component analysis unit is used to obtain the component state coefficient Yjxs by associating the edge spacing Byjj with the number of cold solder joints Xhsz and performing dimensionless processing. The component state coefficient Yjxs is obtained by the following formula: Wherein, Yjjz represents the component spacing, Yjws represents the number of pin bending, a1, a2, a3 and a4 represent the preset proportional coefficients of the edge spacing Byjj, the number of cold solder joints Xhsz, the component spacing Yjjz and the number of pin bending Yjws respectively.

2. A circuit board defect detection system using image processing technology according to claim 1, characterized in that: The multi-faceted imaging module includes a multi-faceted capture unit; The multi-surface capture unit is used to simultaneously perform top-down and side-view orthographic projection photography of multiple groups of components on the copper cladding layer, and to simultaneously perform top-down and side-view orthographic projection photography of the coating layer passing through the inner wall of the hole, and to mark component layout images and coating layer images taken from different directions.

3. A circuit board defect detection system using image processing technology according to claim 2, characterized in that: The image preprocessing module includes a processing unit and a segmentation unit; The processing unit is used to utilize wavelet denoising technology to decompose data information in a plurality of coating layer images and component layout images through wavelet transform, remove noise components in the wavelet domain, and use histogram equalization to adjust brightness, stretch image range, and adjust image contrast; The segmentation unit is used to segment the coating layer image into a plurality of groups of square detection areas, which are respectively marked as the first area L1, the second area L2, the third area L3, ..., the Nth area Ln, and distribute a plurality of groups of data points randomly into each square detection area.

4. The circuit board defect detection system using image processing technology according to claim 3 is characterized in that: The multi-point data acquisition module includes a card color comparison unit, a surface acquisition unit and an in-hole acquisition unit; The card color comparison unit is used to capture the image at the solder joint using the multi-faceted imaging module, place the colorimetric card in the image at the solder joint, ensure that the color of the colorimetric card corresponds to the actual colorimetric card, perform calibration, and simultaneously identify the solder joint position, extract color information around the solder joint, map the temperature information of the solder joint to the image at the solder joint using the temperature information calibrated by the colorimetric card, visualize the temperature information on the printed circuit board status data atlas, identify the qualified temperature status of the component pin solder joint, and obtain the number of cold solder joints Xhsz; The surface acquisition unit is used to use the component layout image to identify and detect the component data information related to the printed circuit board surface, including the number of bent pins Yjws, component spacing Yjjz and the edge spacing Byjj between the component and the printed circuit board frame in each component; The in-hole acquisition unit is used to use the coating layer image to identify and detect the through-hole coating status data information related to the inner wall of the through-hole in the printed circuit board, including the thickness mean Hjz in the square detection area, the total area mean Zqjz, the thickness difference factor Hcyz, the crack length Lwcd, the crack depth Lwsd, the number of pits Ktsz, the bubble density Qpmd, the pit area Kdmj and the uncoated area Wgmj.

5. The circuit board defect detection system using image processing technology according to claim 1, characterized in that: The quality monitoring module compares and analyzes the defect evaluation index Qxzs with the evaluation threshold A to comprehensively determine the qualified state of the printed circuit board; If the defect evaluation index Qxzs ≥ the evaluation threshold A, it means that the current printed circuit board is in an unqualified state. At this time, manual inspection will be introduced for the detected defects, local repair and replacement of unqualified components will be carried out, and the manufacturing process will be reviewed to find out the root cause of the defects; If the defect evaluation index Qxzs is less than the evaluation threshold value A, it indicates that the current printed circuit board is in a qualified state. At this time, the production of the current printed circuit board will continue, and real-time monitoring will continue to be performed to ensure that no quality problems occur during the production process.

6. A circuit board defect detection method using image processing technology, comprising a circuit board defect detection system using image processing technology as claimed in any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: First, use imaging equipment to capture and shoot multiple groups of components and inner walls of the through-holes on the surface of the printed circuit board, and obtain component layout images and coating layer images respectively; Step 2: Preprocess the coating layer image and component layout image to remove noise and smooth the image, evenly divide the coating layer image into several groups of square detection areas, set several groups of data points in the square detection areas, and improve the image quality based on image enhancement technology to generate a printed circuit board status data atlas; Step 3: Then, data recognition and monitoring are performed on the coating layer image and the component layout image, and relevant through-hole coating status data information and relevant component data information are obtained respectively, and the temperature of the component pin solder point is identified to be qualified according to the colorimetric card; Step 4: In addition, the convolutional neural network learning technology is used to extract the features of the relevant through-hole coating state data information and the relevant component data information, and through deep machine learning calculation, the coating state coefficient Tfxs, the component state coefficient Yjxs and the defect evaluation index Qxzs are obtained; Step 5: Finally, the evaluation threshold A is preset, and compared with the defect evaluation index Qxzs to comprehensively determine the qualified state of the printed circuit board.

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