Carbon fiber defect detection method, system, equipment and medium

Abstract

The invention relates to the technical field of carbon fiber production, in particular to a carbon fiber defect detection method, system and equipment and a medium, and the method comprises the following steps: arranging a double-roller structure in a camera detection area, and applying set tension to carbon fiber yarns; collecting a carbon fiber yarn image, and identifying a carbon fiber yarn contour in the image; calculating a minimum enclosing rectangle for each carbon fiber yarn contour, and calculating an included angle between a long side of the minimum enclosing rectangle and an image side; an angle threshold range is set, and yarn contours which do not conform to the angle threshold range are screened out; dividing the screened yarn contour into a pure black area and a light black area through a double-threshold segmentation method; whether the yarn contour area occupied by the light black area exceeds a set value or not is judged, and if yes, interference is judged; and if not, judging that defects exist. According to the method, false alarms caused by shaking of the carbon fiber tows can be effectively reduced, and the accuracy and reliability of defect detection are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon fiber production, and in particular to a carbon fiber defect detection method, system, equipment and medium. Background Art

[0002] Carbon fiber has a series of excellent properties such as high specific strength, high specific modulus, fatigue resistance, creep resistance, corrosion resistance and small thermal expansion coefficient. Therefore, it is widely used as the reinforcement of carbon fiber reinforced resin-based composites (CFRP) in cutting-edge defense technologies such as aerospace and aviation. It is also a new material for the upgrading of civilian industries.

[0003] During the production process of carbon fiber, defects such as "hair balls", "hairy fibers", "broken fibers" and joints may appear. Relying solely on manual visual methods to detect defects in the carbon fiber production process will inevitably result in missed detections. Therefore, it is necessary to rely on a visual inspection system to conduct real-time online monitoring of carbon fiber defects that appear in the carbon fiber production process.

[0004] During the carbon fiber production process, problems such as the shaking and swaying of the tow often cause certain shadows on the image, resulting in false alarms in visual inspection and affecting production.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a carbon fiber defect detection method, system, equipment and medium, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a carbon fiber defect detection method, comprising the following steps: A double roller structure is set in the camera detection area to apply a set tension to the carbon fiber yarn; Collect carbon fiber yarn images and identify the contours of the carbon fiber yarns in the images; Calculating a minimum circumscribed rectangle for each of the carbon fiber yarn contours, and calculating an angle between a long side of the minimum circumscribed rectangle and an edge of the image; Setting an angle threshold range and screening out yarn profiles that do not meet the angle threshold range; The screened yarn contours are divided into pure black area and light black area by using a double threshold segmentation method; Determine whether the light black area occupies the yarn contour area exceeding the set value. If so, it is judged as interference; if not, it is judged as a defect.

[0008] Furthermore, the double-roller structure is provided in the camera detection area, including: a pressure roller and a pulling roller are provided in the camera image acquisition area to apply tension to the carbon fiber yarn.

[0009] Furthermore, the identifying of the carbon fiber yarn profile in the image comprises the following steps: Convert the carbon fiber yarn image to a grayscale image; Performing threshold segmentation on the grayscale image to extract pixel points whose grayscale values ​​are less than a set value; Connected domains are extracted from the extracted pixel points, and are screened using an area threshold to remove connected domains whose areas are smaller than the area threshold, thereby obtaining the carbon fiber yarn profile.

[0010] Furthermore, the method of dividing the screened yarn contour into a pure black area and a light black area by a double threshold segmentation method comprises the following steps: By setting the pure black gray value threshold, the pure black pixels in the extracted carbon fiber yarn contour are extracted; Perform connected domain extraction on the extracted pure black pixel points to obtain a pure black area; For each carbon fiber yarn profile, the complement of the pure black region therein is calculated to obtain the light black region of each carbon fiber yarn profile.

[0011] Furthermore, the pure black grayscale threshold is: The average grayscale value of each yarn profile in the image that meets the angle threshold range is calculated, and the average of the calculated average grayscale values ​​is calculated, and the obtained average value is the pure black grayscale threshold.

[0012] Further, in the process of judging whether the area of ​​the yarn contour occupied by the light black region exceeds a set value, the set value is 50%.

[0013] Further, the step of judging whether the area of ​​the light black region occupied by the yarn contour exceeds a set value, and if so, judging it as interference, further comprises: It is determined whether the average grayscale value of the light black area is greater than the average grayscale value of the pure black area plus 20. If it is greater, it is determined to be interference.

[0014] The present invention also includes a carbon fiber defect detection system, using the above method, the detection system includes: A tension unit, which is used to set a double roller structure in the camera detection area to apply a set tension to the carbon fiber yarn; An image acquisition unit, used for acquiring carbon fiber yarn images and identifying the contours of the carbon fiber yarns in the images; An angle calculation unit, used for calculating the minimum circumscribed rectangle for each of the carbon fiber yarn contours, and calculating the angle between the long side of the minimum circumscribed rectangle and the image side; An angle judgment unit, used to set an angle threshold range and filter out yarn profiles that do not meet the angle threshold range; A double-threshold segmentation unit is used to divide the screened yarn profile into a pure black area and a light black area by a double-threshold segmentation method; The judging unit is used to judge whether the area of ​​the light black area occupied by the yarn contour exceeds the set value. If it exceeds, it is judged as interference; if it does not exceed, it is judged as a defect.

[0015] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0016] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0017] The beneficial effects of the present invention are as follows: the present invention calculates the area ratio of the light black area in the yarn profile. If the area ratio of the light black area exceeds a preset threshold, it is judged as interference, that is, shadow interference caused by shaking. If the area ratio of the light black area does not exceed the set value, the area is judged to be a real defect. Through the above steps, false alarms caused by shaking of carbon fiber tows can be effectively reduced, and the accuracy and reliability of defect detection can be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a structural schematic diagram of the system of the present invention; Figure 3 It is a structural schematic diagram of the computer device of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0021] like Figure 1 As shown: A carbon fiber defect detection method comprises the following steps: A double roller structure is set in the camera detection area to apply a set tension to the carbon fiber yarn; Collect carbon fiber yarn images and identify the contours of the carbon fiber yarns in the images; Calculate the minimum circumscribed rectangle for each carbon fiber yarn contour, and calculate the angle between the long side of the minimum circumscribed rectangle and the image side; Set the angle threshold range to filter out the yarn profiles that do not meet the angle threshold range; The screened yarn contours are divided into pure black area and light black area by using a double threshold segmentation method; Determine whether the light black area occupies the yarn contour area exceeding the set value. If so, it is judged as interference; if not, it is judged as a defect.

[0022] In the detection area of ​​the camera, a double roller structure is set to apply a set tension to the carbon fiber yarn to reduce false alarms caused by yarn shaking and shaking. This step stabilizes the motion state of the yarn by mechanical means to reduce the impact of shaking on detection accuracy. An industrial camera is used to collect real-time images of carbon fiber yarns, and the image processing algorithm is used to identify the yarn contour in the image. This step is used to separate the yarn from the background and form contour data for analysis.

[0023] For each identified carbon fiber yarn profile, calculate its minimum circumscribed rectangle. Then, further calculate the angle between the long side of the minimum circumscribed rectangle and the image boundary. Through this calculation, the deflection of the yarn caused by shaking can be captured. According to the angle distribution of normal yarns (usually 0 degrees or 180 degrees), set an angle threshold range (for example, ± a few degrees). Through this threshold range, filter out yarn profiles that do not meet the angle requirements, which may deviate from the normal angle due to shaking.

[0024] For the abnormal yarn profiles that have been screened out, the double threshold segmentation method is used to divide them into pure black areas and light black areas. The pure black area corresponds to normal carbon fiber yarns, while the light black area may be a shadow or defect caused by shaking. Calculate the area ratio of the light black area in the yarn profile. If the area ratio of the light black area exceeds the preset threshold, it is judged as interference, that is, shadow interference caused by shaking. If the area ratio of the light black area does not exceed the set value, the area is judged to be a real defect.

[0025] Through the above steps, false alarms caused by shaking of carbon fiber tows can be effectively reduced, and the accuracy and reliability of defect detection can be significantly improved.

[0026] In this embodiment, a double-roller structure is provided in the camera detection area, including: a pressing roller and a pulling roller are provided in the camera image acquisition area to apply tension to the carbon fiber yarn.

[0027] By setting the pressure roller and the pulling roller, the carbon fiber yarn can be effectively controlled in the detection area, the vibration amplitude of the yarn can be reduced, and thus the false alarm caused by shaking can be reduced. This can improve the accuracy of the visual inspection system, ensure that the yarn remains stable during the image acquisition process, and help to better identify actual defects.

[0028] The process of identifying the contour of the carbon fiber yarn in the image includes the following steps: Convert the carbon fiber yarn image to a grayscale image; Perform threshold segmentation on the grayscale image and extract the pixels whose grayscale value is less than the set value; The connected domains of the extracted pixels are extracted and screened by the area threshold, and the connected domains with an area smaller than the area threshold are removed to obtain the carbon fiber yarn profile.

[0029] First, the collected carbon fiber yarn image is converted into a grayscale image. Grayscale images can simplify image processing, reduce computational complexity, and make the difference between the brightness of the yarn and the background more obvious. Threshold segmentation is performed on the grayscale image. A grayscale threshold is set and pixels with grayscale values ​​less than the set value are extracted. This step can effectively separate the darker yarn area from the brighter background area.

[0030] Connected domain analysis is performed on the extracted pixels to identify groups of pixels that are connected to each other in the image and form independent regions. This step separates the various parts of the yarn from the background by analyzing the connectivity of the pixels. The extracted connected domains are screened by area. An area threshold is set and connected domains with an area smaller than the threshold are removed. This step is used to filter out noise and other possible false detection areas, retaining only connected domains with larger areas, and finally obtaining the outline of the carbon fiber yarn.

[0031] The method uses a double threshold segmentation method to divide the screened yarn contour into a pure black area and a light black area, including the following steps: By setting the pure black gray value threshold, the pure black pixels in the extracted carbon fiber yarn contour are extracted; Perform connected domain extraction on the extracted pure black pixel points to obtain a pure black area; For each carbon fiber yarn profile, the complement of the pure black region therein is calculated to obtain the light black region of each carbon fiber yarn profile.

[0032] First, a pure black grayscale value threshold is set for the carbon fiber yarn profile. This threshold is used to distinguish very dark pixels from other pixels in the yarn. In the extracted carbon fiber yarn profile, for all pixels, the pixels with grayscale values ​​lower than the set pure black grayscale value threshold are screened out. In this way, the pixels of the pure black part of the yarn can be extracted.

[0033] The extracted pure black pixels are subjected to connected domain analysis, and the interconnected pure black pixels are combined to form one or more independent pure black regions. The purpose of this step is to ensure that each pure black region is a continuous dark region in the yarn and filter out isolated noise points.

[0034] For each carbon fiber yarn profile, the complement of the pure black area is calculated. The complement refers to the yarn pixels that do not belong to the pure black area. These pixels usually have a slightly brighter grayscale value than the pure black area, so they are defined as the light black area. The light black area is calculated by subtracting the pixels of the pure black area from the total pixels of the yarn profile, and the remaining pixels are the light black area. The contour of the carbon fiber yarn is divided into the pure black area and the light black area, which provides a basis for the subsequent judgment of whether the yarn has defects or interference.

[0035] In this embodiment, the pure black grayscale threshold is: The average gray value of each yarn profile in the image that meets the angle threshold range is calculated, and the average of the calculated average gray values ​​is calculated, and the obtained average value is the pure black gray threshold.

[0036] For each screened carbon fiber yarn profile that meets the angle threshold range, the average grayscale value of all pixels in the profile is calculated to obtain the average grayscale value of each yarn profile. For example, for yarn profiles A, B, and C, their average grayscale values ​​g_A, g_B, and g_C are calculated respectively.

[0037] Sum the average grayscale values ​​of all yarn profiles that meet the angle threshold range and calculate their average value. Assuming that there are n yarn profiles that meet the conditions, the calculation formula for their total average grayscale value g_avg is: ; The pure black grayscale threshold calculated by this method can be adaptively determined according to the normal yarn contour brightness in the image, thereby accurately segmenting the pure black area in the carbon fiber yarn. This dynamic threshold setting can improve the reliability and adaptability of detection.

[0038] As a preferred embodiment of the above, in determining whether the light black region occupies an area of ​​the yarn contour exceeding a set value, the set value is 50%.

[0039] Among them, judging whether the area of ​​the light black area in the yarn outline exceeds the set value, if it exceeds, it is judged as interference, and also includes: It is determined whether the average grayscale value of the light black area is greater than the average grayscale value of the pure black area plus 20. If it is greater, it is determined to be interference.

[0040] Calculate the grayscale value of the pixels in the light black area to get the average grayscale value G of the light black area 淡黑At the same time, calculate the average gray value G of the pure black area 纯黑 , compared with G 淡黑 and G 纯黑 + Relationship between 20: If G 淡黑 >G 纯黑 +20, the area is judged as an interference area, because the grayscale of the light black area is significantly higher than that of the pure black area, indicating that the area is affected by interference factors such as jitter and smear. By combining the dual judgment of area ratio and grayscale difference, the accuracy of identifying interference factors in carbon fiber detection can be effectively improved, avoiding false alarms caused by jitter, shadows and other problems.

[0041] like Figure 2 As shown, this embodiment also includes a carbon fiber defect detection system. Using the above method, the detection system includes: A tension unit, which is used to set a double roller structure in the camera detection area to apply a set tension to the carbon fiber yarn; An image acquisition unit, used for acquiring carbon fiber yarn images and identifying the contours of the carbon fiber yarns in the images; An angle calculation unit, used to calculate the minimum circumscribed rectangle for each carbon fiber yarn contour, and calculate the angle between the long side of the minimum circumscribed rectangle and the image side; An angle judgment unit, used to set an angle threshold range and filter out yarn profiles that do not meet the angle threshold range; A double-threshold segmentation unit is used to divide the screened yarn profile into a pure black area and a light black area by a double-threshold segmentation method; The judging unit is used to judge whether the area of ​​the light black area occupied by the yarn contour exceeds the set value. If it exceeds, it is judged as interference; if it does not exceed, it is judged as a defect.

[0042] See also Figure 3 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0043] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0044] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0045] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0046] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0048] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0049] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0050] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0051] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0052] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A carbon fiber defect detection method, characterized in that: The steps include: A double roller structure is set in the camera detection area to apply a set tension to the carbon fiber yarn; Collect carbon fiber yarn images and identify the contours of the carbon fiber yarns in the images; Calculating a minimum circumscribed rectangle for each of the carbon fiber yarn contours, and calculating an angle between a long side of the minimum circumscribed rectangle and an edge of the image; Setting an angle threshold range and screening out yarn profiles that do not meet the angle threshold range; The screened yarn contours are divided into pure black area and light black area by using a double threshold segmentation method; Determine whether the light black area occupies the yarn contour area beyond the set value. If so, it is judged as interference; If it does not exceed the limit, it is considered a defect.

2. The carbon fiber defect detection method according to claim 1, characterized in that: The double-roll structure is arranged in the camera detection area, including: a pressing roller and a pulling roller are arranged in the camera image acquisition area to apply tension to the carbon fiber yarn.

3. The carbon fiber defect detection method according to claim 1, characterized in that: The method of identifying the carbon fiber yarn profile in the image comprises the following steps: Convert the carbon fiber yarn image to a grayscale image; Performing threshold segmentation on the grayscale image to extract pixel points whose grayscale values ​​are less than a set value; Connected domains are extracted from the extracted pixel points, and are screened using an area threshold to remove connected domains whose areas are smaller than the area threshold, thereby obtaining the carbon fiber yarn profile.

4. The carbon fiber defect detection method according to claim 3, characterized in that: The method of dividing the screened yarn profile into a pure black area and a light black area by a double threshold segmentation method comprises the following steps: By setting the pure black gray value threshold, the pure black pixels in the extracted carbon fiber yarn contour are extracted; Perform connected domain extraction on the extracted pure black pixel points to obtain a pure black area; For each carbon fiber yarn profile, the complement of the pure black region therein is calculated to obtain the light black region of each carbon fiber yarn profile.

5. The carbon fiber defect detection method according to claim 4, characterized in that: The pure black grayscale threshold is: The average grayscale value of each yarn profile in the image that meets the angle threshold range is calculated, and the average of the calculated average grayscale values ​​is calculated, and the obtained average value is the pure black grayscale threshold.

6. The carbon fiber defect detection method according to claim 1, characterized in that: In the process of judging whether the light black region occupies an area of ​​the yarn outline exceeding a set value, the set value is 50%.

7. The carbon fiber defect detection method according to claim 6, characterized in that: The method of judging whether the area of ​​the light black region occupied by the yarn outline exceeds a set value, and if so, judging it as interference, further includes: It is determined whether the average grayscale value of the light black area is greater than the average grayscale value of the pure black area plus 20. If it is greater, it is determined to be interference.

8. A carbon fiber defect detection system, characterized in that: Using the method according to any one of claims 1 to 7, the detection system comprises: A tension unit, which is used to set a double roller structure in the camera detection area to apply a set tension to the carbon fiber yarn; An image acquisition unit, used for acquiring carbon fiber yarn images and identifying the contours of the carbon fiber yarns in the images; An angle calculation unit, used for calculating the minimum circumscribed rectangle for each of the carbon fiber yarn contours, and calculating the angle between the long side of the minimum circumscribed rectangle and the image side; An angle judgment unit, used to set an angle threshold range and filter out yarn profiles that do not meet the angle threshold range; A double-threshold segmentation unit is used to divide the screened yarn profile into a pure black area and a light black area by a double-threshold segmentation method; The judging unit is used to judge whether the area of ​​the light black area occupied by the yarn contour exceeds the set value. If it exceeds, it is judged as interference; if it does not exceed, it is judged as a defect.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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