Method, system and computer storage medium for defect detection of prepreg layup

Abstract

The application provides a kind of prepreg layer defect detection method, system and computer storage medium, defect detection method includes to the prepreg layer to be detected variable frequency thermal excitation, after its infrared phase-locked diagram is converted into infrared phase-locked gray diagram is collected;Infrared phase-locked gray diagram is carried out boundary identification, and then carries out image segmentation with gray threshold as constraint condition, obtains the infrared phase-locked gray diagram of division gray area;Gray area greater than gray threshold is carried out shape identification, and the area containing singular point and / or edge curve is obtained, and then the defect type and the total area of defect area of the prepreg layer to be detected are obtained.The application uses linearly increasing variable frequency infrared excitation source to excite the prepreg layer, can excite the defect information of different depth, makes the infrared signal radiated by near surface and deeper layer defect can be detected by external detector, solves the problem that infrared phase-locked method is difficult to detect deep defect in the prior art.

Description

Technical Field

[0001] This invention belongs to the field of prepreg production and layup technology, specifically relating to a method, system and computer storage medium for detecting defects in prepreg layup. Background Technology

[0002] Fiber-reinforced composite prepreg refers to reinforcing fibers (such as carbon fiber, glass fiber, aramid fiber, etc.) that have been impregnated with a resin matrix, and the resin has been partially cured (stage B) in preparation for subsequent final molding. Prepregs are commonly used to manufacture high-performance composite parts and are widely used in aerospace, automotive, sporting goods, and other industries. During the production process, different resin systems and reinforcing fibers require different process parameters, such as temperature, pressure, and time. These parameters need to be adjusted according to specific circumstances; improper adjustments can lead to defects such as bubbles, delamination, and separation. The quality of the prepreg directly affects the structure and performance of the final composite product; therefore, rigorous inspection of the prepreg is necessary before production and use to identify potential defects.

[0003] Common prepreg defects and their identification methods include: First, visual inspection: visually inspecting for obvious damage, cracks, stains, or foreign objects; and touching the prepreg surface to check for roughness or unevenness. Second, thickness measurement: using an ultrasonic or contact thickness gauge to measure the prepreg thickness to confirm if it meets design requirements. Third, resin content determination: weighing the prepreg and calculating its density to determine if the resin content is within the specified range. Fourth, infrared spectroscopy: analyzing resin content using infrared spectroscopy, which provides more accurate data. Fifth, vacuum suction: assessing the porosity of the prepreg through vacuum suction. Sixth, focused X-ray CT scan or X-ray imaging: using X-ray computed tomography to observe internal porosity; or using X-ray imaging to detect defects in the internal structure of the prepreg. Seventh, density method: calculating the fiber volume fraction by measuring density and combining it with theoretical values. Eighth, microscopic observation: observing fiber distribution using an optical or electron microscope. The ninth method is mechanical property testing, which involves tensile testing of the prepreg to assess its strength and modulus of elasticity; and bending testing to test its rigidity and toughness. The tenth method is thermal analysis and thermogravimetric analysis (TGA), which uses differential scanning calorimetry (DSC) to analyze the thermal properties of the prepreg, such as the onset temperature of the curing reaction and the amount of heat released; and TGA to assess the thermal stability of the prepreg, especially its performance changes at high temperatures. The eleventh method is non-destructive testing (NDE), which uses ultrasonic testing to detect defects such as delamination and cracks in the prepreg.

[0004] Among the analytical methods mentioned above, the more important non-destructive testing methods include ultrasonic testing, X-ray imaging, and infrared imaging. However, ultrasonic testing suffers from interference and clutter in the near-surface region due to scattering interference, resulting in blurred near-surface images. Since prepreg products are often thin, ultrasonic testing has limitations and is not suitable for detecting defects in prepreg products. While X-ray imaging can accurately reveal the characteristics and distribution of defects within materials, its radioactivity and high investment costs make it inconvenient for production line setup. The application of infrared detection technology in prepreg defect detection is a continuously developing field, with many research institutions and companies exploring and developing new technologies and methods. Lock-in infrared (LIT) is an advanced non-destructive testing technique, particularly suitable for detecting minute internal defects in materials. This method uses periodic heating signals to excite the material and captures the changes in surface temperature over time using an infrared camera. By analyzing these temperature fluctuations, the location, size, and nature of defects within the material can be identified. Phase-locked infrared (PLI) detection is based on the principle of thermal waves, which involves periodically heating a material using an external heating source (infrared light source or laser) and then recording the temperature changes on the material's surface using an infrared camera. Defects within the material (such as cracks, delamination, and pores) can affect the propagation of thermal waves, causing localized temperature responses to differ from normal areas. Therefore, by using Fourier transform or other signal processing techniques, frequency components related to these defects can be extracted from the temperature data.

[0005] While infrared phase-locked loop (ILL) methods offer numerous advantages for prepreg defect detection, they also have certain limitations. First, depth limitation: IIL is better suited for detecting surface or near-surface defects (based on the thermal diffusion equation). For deep defects, thermal attenuation can weaken the signal, making it difficult to detect subtle, deep defects. Second, limited penetration depth: The heating source has limited penetration capability. For thicker materials or materials with high thermal conductivity, higher energy input or longer detection times may be required, potentially affecting detection efficiency. Third, material properties: Prepregs are composite structures with different thermophysical properties than traditional metals. Thermal conductivity, heat capacity, and other thermophysical properties can all affect the detection results. Fourth, absorption coefficient: The absorption coefficient of the material surface significantly impacts the heating effect. Different surface treatments or coatings can affect the detection results. Fifth, environmental interference: Background temperature fluctuations and changes in ambient temperature can affect the detection results; therefore, detection needs to be performed in a stable environment. Sixth, complex data processing: IIL detection requires complex signal processing, such as Fourier transforms, to extract useful information, increasing data processing time and difficulty. Seventh, result interpretation. Even after signal processing, expert knowledge is sometimes required to correctly interpret the test results, especially in the case of complex defects.

[0006] Patent CN114577814A proposes a detection scheme based on phase-locked infrared (LLI) technology. However, this method struggles to pinpoint the precise spatial location of defects in multilayer metal ICs. Composite prepregs, whose thermal conductivity is not metallic, are insensitive to depth information, making LII unsuitable for direct detection. Patent CN103149217A provides a method for detecting surface and subsurface defects in optical components; however, its application varies due to the limitations of infrared light transmission capabilities. For fiber-reinforced prepregs, this algorithm cannot be directly applied to detect internal defects. Patent CN117092163A discloses a dual-pulse nondestructive testing method for sandwich composite materials, which excites the material with sinusoidally varying thermal waves, but still fails to address the aforementioned seven problems.

[0007] Therefore, developing a defect detection method that can elicit defect information at different depths of prepreg layup has become an urgent problem for those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to solve the problems existing in the prior art and to provide a method, system and computer storage medium for detecting defects in prepreg layup. Starting from the control parameters of the infrared phase-locked loop method, and taking into account the characteristics of prepreg defect detection, a corresponding data processing method is used to realize the extraction and identification of defect information.

[0009] One of the objectives of this invention is to provide a method for detecting defects in prepreg layup, including...

[0010] S1: Variable frequency thermal excitation is applied to the prepreg ply to be tested, and infrared phase-locked image of the prepreg ply to be tested is acquired;

[0011] S2: Convert the infrared phase-locked image into an infrared phase-locked grayscale image, perform boundary recognition on the infrared phase-locked grayscale image, and obtain an infrared phase-locked grayscale image containing boundary lines;

[0012] S3: Perform image segmentation on the infrared phase-locked grayscale image containing boundary lines using a grayscale threshold as a constraint to obtain an infrared phase-locked grayscale image divided into grayscale regions.

[0013] S4: Perform shape recognition on grayscale regions greater than the grayscale threshold to obtain regions containing singular points and / or edge curves; based on the singular points and / or edge curves, obtain the defect type of the prepreg layup to be detected and the total area of ​​the defect region.

[0014] In a preferred embodiment of the present invention, in step S1,

[0015] The frequency of the infrared excitation heat source is as shown in equation (1).

[0016] f = f1 + f2T (Equation 1)

[0017] In equation (1), f is the frequency of the infrared excitation heat source in Hz; f1 is the initial frequency of the infrared excitation heat source in Hz; f2 is the time coefficient of the infrared excitation heat source in Hz / s; and T is the excitation time of the frequency conversion thermal excitation in s.

[0018] In a preferred embodiment of the present invention, in step S1,

[0019] The value of f1 ranges from 0.1 to 1000 Hz, preferably from 0.1 to 100 Hz; and / or,

[0020] The value of f2 ranges from 0.1 to 100 Hz / s, preferably from 0.1 to 20 Hz / s; and / or,

[0021] The value of T ranges from 0 to 30 s, preferably from 0 to 10 s.

[0022] In a preferred embodiment of the present invention, in step S2,

[0023] The infrared phase-locked image is converted into an infrared phase-locked grayscale image using a weighted average method; and / or,

[0024] Wavelet analysis was used to identify the boundaries of the infrared phase-locked grayscale image.

[0025] In a preferred embodiment of the present invention, in step S4,

[0026] An edge detection algorithm is used to identify the shape of grayscale regions that are greater than the grayscale threshold.

[0027] In a preferred embodiment of the present invention, in step S4,

[0028] If the region larger than the grayscale threshold has continuously distributed non-zero values ​​and its size is within a preset range, it is identified as a bubble defect, and the area of ​​the bubble defect is obtained based on the area of ​​the singular points; and / or,

[0029] If the area larger than the grayscale threshold constitutes a connected region of grayscale features, then it is determined whether the edge curve contains the boundary line of the prepreg ply to be detected; if the edge curve does not contain the boundary line of the prepreg ply to be detected, it is identified as a local debonding defect, and the area of ​​the connected region is the area of ​​the local debonding defect; and / or, if the edge curve contains the boundary line of the prepreg ply to be detected, it is identified as a delamination defect, and the area of ​​the connected region is the area of ​​the local debonding defect.

[0030] In a preferred embodiment of the present invention, the defect detection method for the prepreg layup further includes the following steps:

[0031] S5: Based on the infrared phase-locked grayscale image containing the boundary lines, obtain the total area of ​​the prepreg ply to be tested; based on the total area of ​​the defective region in the prepreg ply to be tested and the total area of ​​the prepreg ply to be tested, obtain the product evaluation result.

[0032] In a preferred embodiment of the present invention, the defect detection method for the prepreg layup further includes the following steps:

[0033] S6: Based on the region containing singularities and / or edge curves obtained in step S4, adjust the infrared phase-locked grayscale image containing boundary lines; the adjustment of the infrared phase-locked grayscale image containing boundary lines includes at least one of increasing contrast, strengthening grayscale values, and reducing grayscale threshold.

[0034] The second objective of this invention is to provide a defect detection system for prepreg plies, comprising a frequency conversion thermal excitation and acquisition module, a grayscale conversion and boundary recognition module, a grayscale region segmentation module, a shape recognition module, and a defect type judgment and calculation module, which are sequentially connected in communication. The frequency conversion thermal excitation and acquisition module is used to perform frequency conversion thermal excitation on the prepreg ply to be detected and acquire an infrared phase-locked image of the prepreg ply. The grayscale conversion and boundary recognition module is used to convert the infrared phase-locked image into an infrared phase-locked grayscale image and perform boundary recognition on the infrared phase-locked grayscale image to obtain an infrared phase-locked grayscale image containing boundary lines. The grayscale region segmentation module is used to perform image segmentation on the infrared phase-locked grayscale image containing boundary lines using a grayscale threshold as a constraint condition to obtain an infrared phase-locked grayscale image divided into grayscale regions. The shape recognition module is used to perform shape recognition on grayscale regions larger than the grayscale threshold to obtain regions containing singularities and / or edge curves. The defect type judgment and calculation module is used to obtain the defect type and the total area of ​​the defect region of the prepreg ply to be detected based on the singularities and / or the edge curves.

[0035] A third objective of this invention is to provide a computer-readable storage medium storing at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the prepreg layup defect detection method as described in one objective of this invention.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. The defect detection method for prepreg ply of the present invention, before acquiring the infrared phase-locked image of the prepreg ply, excites the prepreg ply in advance with a frequency-converting infrared excitation source. The frequency of the frequency-converting infrared excitation source increases linearly, so that the thermal excitation process of the prepreg ply is different. Therefore, under this thermal excitation, the infrared signals radiated by near-surface and relatively deep defects can be detected by an external detector (i.e., defect information at different depths can be excited), making the detection depth of the defect detection method more optimal. This solves the problem that the infrared phase-locked image method in the prior art is more suitable for detecting surface or near-surface defects and is difficult to detect deep defects. Finally, the detection results obtained by the defect detection method are more accurate.

[0038] 2. The defect detection method for prepreg layup in this invention uses a grayscale variation method to segment the defect image, and uses image recognition to analyze areas with obvious defects; for areas where grayscale variation is not limited, a wavelet algorithm for regional grayscale integration is used to improve the defect recognition capability. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the defect detection method for prepreg layup according to the present invention;

[0040] Figure 2 This is a schematic diagram of the defect detection method for prepreg layup according to the present invention;

[0041] Figure 3 This is a schematic diagram of the frequency of the linearly modulated signal source of the present invention;

[0042] Figure 4 A schematic diagram for identifying the shape of bubble defects;

[0043] Figure 5 A schematic diagram for identifying the shape of debonding defects;

[0044] Figure 6 A schematic diagram for identifying the shape of layered defects. Detailed Implementation

[0045] The present invention will now be described in further detail with reference to the accompanying drawings:

[0046] Example 1

[0047] like Figure 1 and Figure 2 As shown, the present invention provides a method for detecting defects in prepreg layup, comprising the following steps:

[0048] Step S1: Perform frequency conversion thermal excitation on the prepreg ply to be tested and acquire the infrared phase-locked image of the prepreg ply to be tested.

[0049] This invention employs an infrared thermal imager equipped with a frequency-converting infrared excitation source to acquire infrared phase-locked image (PLA) images of the prepreg layers to be inspected. A frequency-converting infrared excitation source is configured around the infrared detection camera to achieve frequency conversion control of the infrared light waves. First, infrared detection cameras are deployed on the prepreg production line, with the cameras perpendicular to the prepreg. Specifically, the infrared detection cameras are located on top of the prepreg and connected to a guide rail, allowing for controlled movement on the rail. The fixed frame of the infrared detection camera needs to have sufficient rigidity to prevent significant changes in the acquired images during camera movement. Preferably, the infrared camera is an electrically cooled infrared camera; and / or, the infrared camera uses a near-focal-length lens; and / or, the focal length range of the infrared camera is 0.2–2 meters.

[0050] In a preferred embodiment of the present invention, the frequency range of the infrared excitation heat source is configured according to formula (1) to be suitable for the resin and fibers inside the prepreg with different thermal conductivity.

[0051] f = f1 + f2T (Equation 1)

[0052] In equation (1), f is the frequency of the infrared excitation heat source, in Hz; f1 is the initial frequency of the infrared excitation heat source, in Hz; f2 is the time coefficient of the infrared excitation heat source, in Hz / s; and T is the excitation time of the frequency conversion thermal excitation, in s. Preferably, the value of f1 is in the range of 0.1 to 1000 Hz, more preferably 0.1 to 100 Hz; and / or, the value of f2 is in the range of 0.1 to 100 Hz / s, more preferably 0.1 to 20 Hz / s; and / or, the value of T is in the range of 0 to 30 s, more preferably 0 to 10 s, and most preferably 3 to 10 s; and / or, the frequency f of the infrared excitation heat source is in the range of 10-1000 Hz. It should be noted that for thicker prepreg layers, the time coefficient f2 of the infrared excitation heat source can be increased.

[0053] During the acquisition of the infrared phase-locked image (IRL-LOC) of the prepreg layup under test, the frequency of the infrared excitation source increases linearly, thus resolving richer information. When a linearly modulated infrared excitation heat source is used instead of a fixed-frequency ω infrared excitation heat source for thermal excitation of the prepreg layup under test, the frequency of the signal source increases linearly after modulation, such as... Figure 3 As shown. Assuming the duration of thermal excitation of the linearly modulated signal source is r, and the signal broadening of the linearly modulated signal source is B, then the signal of thermal wave excitation on the surface of the prepreg layer to be detected is as shown in equation (2):

[0054]

[0055] In equation (2), Q(x=0, t) is the surface temperature of the prepreg ply to be tested (at x=0 and time t), in °C; Q0 is the initial surface temperature of the prepreg ply to be tested, in °C; j is the imaginary unit (j 2 =-1); f is the frequency of the infrared excitation heat source in Hz; t is the time in seconds; B is the signal broadening of the linearly modulated signal source in seconds; r is the duration of the thermal excitation of the linearly modulated signal source in seconds.

[0056] If we replace the thermal wave with a fixed frequency ω with the excitation thermal wave of equation (2) as the signal source, then the heat distribution function on the semi-infinite plate is as shown in equations (3) to (5):

[0057]

[0058]

[0059]

[0060] In equations (3) to (5), T(x, t) is the temperature distribution function with respect to distance and time (at position x and time t); T0 is the ambient temperature in °C; and a is the thermal diffusivity in m³.2 / s; k is the phase angle of the temperature fluctuation, in rad.

[0061] Therefore, under the excitation state of thermal wave in equation (2), the thermal diffusion length of the prepreg layer to be tested is as shown in equation (6):

[0062]

[0063] In equation (6), μ fm The thermal diffusion length is expressed in meters (m). It should be noted that B / r in equation (6) is actually the mode modulation factor, which follows linear modulation characteristics and is an important parameter for controlling the linear change of frequency with time. Establishing the correlation coefficient between the thermal diffusion length and the frequency mode adjustment helps the detection system detect deep defects in the material. If the parameter B / r is zero, the above equation degenerates into a steady-state diffusion form with a fixed frequency ω.

[0064] When the prepreg layup to be tested is a homogeneous, defect-free sample, its radiant heat wavelength under the aforementioned modal thermal excitation is λ, and the expression for the radiant heat wavelength is as shown in equation (7):

[0065]

[0066] As can be seen from equation (7), as the frequency of the infrared excitation source increases linearly, the deeper defects will also have a more significant phase difference, thus making them easier to be captured by a high-sensitivity infrared camera.

[0067] Step S2: Convert the infrared phase-locked image into an infrared phase-locked grayscale image, and perform wavelet analysis on the infrared phase-locked grayscale image to obtain an infrared phase-locked grayscale image containing boundary lines.

[0068] This step is a preliminary processing of the infrared phase-locked image, specifically including grayscale change processing and boundary recognition processing of the infrared phase-locked image to provide boundary conditions for subsequent processing. For example, grayscale image conversion can be achieved using a weighted average method, that is, the weighted sum of the three color components is used as the grayscale value, i.e.

[0069] Grayscale = 0.299 × R + 0.587 × G + 0.114 × B (Equation 8)

[0070] The infrared phase-locked image is converted using the method in equation (8) to obtain an infrared phase-locked grayscale image. It should be noted that the grayscale value of the pixel in the infrared phase-locked grayscale image is related to the temperature of the pixel in the infrared phase-locked image acquired in step S1. The temperature is related to the frequency of the frequency-converted infrared excitation source, which is related to the detection depth. Therefore, the frequency change of the frequency-converted infrared excitation source will cause the temperature change. The rate of temperature change reflects the superimposed signal at different detection depths. After processing, the detection signals at different depths can be obtained.

[0071] The wavelet analysis method involves performing wavelet decomposition on the infrared phase-locked grayscale image and extracting high-frequency coefficients. By calculating the gradient of these high-frequency coefficients, the rate of change of grayscale values ​​between adjacent pixels and the fluctuation range of the rate of change in a specific direction can be obtained. Based on this, the boundaries of the infrared image are determined (boundaries typically correspond to areas with high grayscale value change rates). The following example illustrates "areas with high grayscale value change rates." The grayscale values ​​within the detection range are normalized to obtain the maximum grayscale fluctuation range (Gray). For areas where the local rate of change reaches greater than 50% Gray, especially greater than 70%, these are identified as boundary regions. It should be noted that performing wavelet analysis on images for boundary identification is a conventional method in this field and will not be elaborated upon here.

[0072] Step S3: Perform image segmentation on the infrared phase-locked grayscale image containing boundary lines using a grayscale threshold as a constraint to obtain an infrared phase-locked grayscale image (i.e., a binary image) divided into grayscale regions; wherein, the infrared phase-locked grayscale image of the grayscale region includes grayscale regions greater than the grayscale threshold and grayscale regions not greater than the grayscale threshold.

[0073] This step involves further processing of the infrared phase-locked image. First, a grayscale threshold is determined based on the characteristics of the overall grayscale values ​​of the image. Then, this grayscale threshold is used as a constraint to divide the grayscale regions, ultimately obtaining an infrared phase-locked image with divided grayscale regions. Those skilled in the art know that for prepreg layups containing defects of varying depths, defects closer to the surface of the prepreg layup show more pronounced grayscale values ​​at their edges during detection, while relatively deep defects in the prepreg layup show insignificant grayscale values ​​at their edges during detection. When determining the grayscale threshold, a prepreg layup with a thickness of approximately 3–5 mm can be fabricated, with defects of different depths (hollow rings or other clips / films) pre-embedded during the fabrication process. The method of this invention is used to find defects with a pre-embedded fixed shape (pre-set shape, such as a ring or the shape of a film), extract the edge gray values ​​of these defects in the infrared phase-locked grayscale image, form the edge gray value range of the defects (including all depths), and extract the gray values ​​with 75-85% confidence in this range as the gray value threshold. This is "determining the gray value threshold based on the characteristics of the overall gray value of the image".

[0074] Regions with grayscale values ​​greater than the grayscale threshold are considered defective. Regions with grayscale values ​​less than the threshold generally show no significant temperature changes, indicating the presence of actual physical connections and not defective areas; however, these regions may also be system noise. For products with high quality requirements for the prepreg layup, regions with grayscale values ​​less than the threshold can be further analyzed. Because this invention uses variable-frequency thermal excitation on the prepreg layup to be tested, image difference analysis can be performed at different time lengths. If regions with grayscale values ​​less than the threshold are present in all differential images at a fixed location, these regions are presumed to be unidentified minor defects, further improving the accuracy of the defect detection method.

[0075] Step S4: Perform shape recognition on the grayscale regions exceeding the grayscale threshold to obtain regions containing singular points and / or edge curves; based on the singular points and / or edge curves, obtain the defect type of the prepreg layup to be detected and the total area of ​​the defect region. It should be noted that "performing shape recognition on the grayscale regions exceeding the grayscale threshold" mainly uses conventional edge detection algorithms to obtain continuous boundaries, such as the Canny algorithm in patent CN117092163A. This shape recognition method will not be elaborated here. If some regions are discontinuous, continuous boundaries can be obtained by interpolating the regions exceeding the grayscale threshold. Preferably, the area of ​​the region containing the edge curve is obtained by integrating the coordinate sequence of the edge curve. Specifically, in this embodiment, the area of ​​the region within the edge curve also represents the number of pixels within the edge curve.

[0076] The following explains "singularities" and "edge curves" separately. A singularity refers to a closed small region, not a single point, but rather a region whose structural size is within a certain range, where all pixels within this closed region have grayscale values ​​higher than a grayscale threshold. An "edge curve" has at least 70% of its pixels within its continuous boundary having grayscale values ​​higher than a grayscale threshold; pixels outside the edge curve have grayscale values ​​no higher than the grayscale threshold, while pixels on the edge curve have grayscale values ​​higher than the grayscale threshold. This step includes three cases. In the first case, shape recognition is performed on the grayscale regions exceeding the grayscale threshold, yielding only regions containing singularities. The total area of ​​the defective region in the prepreg layup to be inspected can then be obtained based on these singularities. In the second case, shape recognition is performed on the grayscale regions exceeding the grayscale threshold, yielding only regions containing edge curves. The total area of ​​the defective region in the prepreg layup to be inspected can then be obtained by integrating the area of ​​the coordinate sequence of the edge curves. In the third case, shape recognition is performed on the grayscale region that is greater than the grayscale threshold to obtain the region that contains both singular points and / or edge curves. In this case, the area of ​​the coordinate sequence of the edge curve is integrated and combined with the area of ​​the singular points to obtain the total area of ​​the defective region in the prepreg layer to be detected.

[0077] This invention relates to three typical defects: bubbles, localized debonding, and delamination. In regions containing singularities and / or edge curves, different defect types exhibit different mathematical characteristics. For example, ... Figure 4 As shown, for suspected bubble defects, during shape recognition in areas exceeding a grayscale threshold, these areas are all continuously distributed non-zero values ​​("non-zero value" refers to grayscale values). Simultaneously, the structural dimensions of these continuously distributed non-zero values ​​are below a certain range (e.g., below 1 square centimeter). Therefore, if the areas exceeding the grayscale threshold are all continuously distributed non-zero values ​​and their dimensions are within a preset range, the bubble defect type can be output. If the infrared phase-locked image has a high pixel count, the area of ​​the identified bubble defect can be essentially locked through the pixel singularities. Since the infrared phase-locked image in this embodiment has a high pixel count, the area of ​​the identified bubble defect pixel singularity is the bubble defect area. As mentioned earlier, "singularity refers to a closed small region," and the bubble defect area can be obtained by integrating the coordinate sequence of this closed small region. Figure 2 Solving for singularities in the equation.

[0078] like Figure 5 As shown, for suspected local debonding defects, during shape recognition in areas with grayscale values ​​greater than the grayscale threshold, these areas form connected regions with grayscale features, i.e., regions composed of pixels with the same grayscale features and adjacent positions, without being interrupted by pixels with different grayscale features. Figure 6 As shown, for suspected local delamination defects, during shape recognition in areas exceeding the grayscale threshold, a first edge curve and a second edge curve are included. These two edge curves together constitute a connected region of grayscale features. The first edge curve is part of the boundary line in step S2. If the "area exceeding the grayscale threshold" constitutes a connected region of grayscale features, it is further determined whether the "edge curve" contains the boundary line of the prepreg ply to be detected. If the edge curve does not contain the boundary line of the prepreg ply to be detected, it is identified as a local debonding defect; if the edge curve contains the boundary line of the prepreg ply to be detected, it is identified as a delamination defect. Whether the edge curve contains the boundary line of the prepreg ply to be detected can be determined specifically by the coordinates of the edge curve and the coordinates of the boundary line.

[0079] In a preferred embodiment of the present invention, the coordinate sequence of the edge curve is integraled according to equations (9) and (10).

[0080]

[0081] p(x,y)=xy

[0082] Q(x,y)=x 2 +y 2 Equation (10)

[0083] In equation (2), L is the edge curve of the closed region D; x and y are the coordinates of the pixel points in the edge curve.

[0084] If the grayscale image formed by the points at the pixel positions of the edge curve L represents a local debonding defect, then the coordinates of the corresponding pixel point, which are the (x, y) values ​​of the L curve, represent the location of the debonding defect. The area of ​​the local debonding defect can be obtained through calculations using equations (9) and (10).

[0085] In a preferred embodiment of the present invention, the edge curves include a first edge curve and a second edge curve, and the coordinate sequences of the first edge curve and the second edge curve are integrally applied according to equations (11) and (12).

[0086]

[0087] p(x,y)=xy

[0088] Q(x,y)=x+y Equation (12)

[0089] In equation (11), L1 is the first edge curve of the closed region D (which is part of the boundary line in step S2); L2 is the second edge curve of the closed region D; x and y are the coordinates of the pixel points in the first edge curve and the second edge curve.

[0090] It should be noted that Equation (11) is for verifying the solution. The integral solution of area and length is not performed in this loop. Therefore, in order to facilitate the solution of partial derivatives, the function equation is set as shown in Equation (12).

[0091] As can be seen from the above, defect identification is based on the characteristic of "areas greater than the grayscale threshold". If the "areas greater than the grayscale threshold" are continuously distributed non-zero values ​​and the size of the "areas greater than the grayscale threshold" is within a preset range, it is identified as a bubble defect, and the area of ​​the bubble defect is obtained based on the area of ​​the pixel singular region. If the "areas greater than the grayscale threshold" constitute a connected region of grayscale features, it is further determined whether the "edge curve" contains the boundary line of the prepreg ply to be detected; if the edge curve does not contain the boundary line of the prepreg ply to be detected, it is identified as a local debonding defect, and the area of ​​the local debonding defect is obtained by calling formula (9) and formula (10); if the edge curve contains the boundary line of the prepreg ply to be detected, it is identified as a delamination defect, and the area of ​​the delamination defect is obtained by calling formula (11) and formula (12).

[0092] In a preferred embodiment of the present invention, the defect detection method for the prepreg layup further includes the following steps:

[0093] Step S5: Obtain the total area of ​​the prepreg ply to be tested based on the infrared phase-locked grayscale image containing the boundary lines; obtain the product evaluation result based on the total area of ​​the defective region in the prepreg ply to be tested and the total area of ​​the prepreg ply to be tested. It should be noted that the product area also represents the total number of pixels within that area. The infrared phase-locked grayscale image containing the boundary lines has already been obtained in the aforementioned step S2, therefore the total number of pixels within the boundary lines is the product area.

[0094] In a preferred embodiment of the present invention, the defect detection method for the prepreg layup further includes the following steps:

[0095] S6: Based on the regions containing singularities and / or edge curves obtained in step S4, adjust the infrared phase-locked grayscale image containing boundary lines. After adjustment, repeat steps S3 and S4. Preferably, the adjustment method includes at least one of increasing contrast, strengthening grayscale values, and decreasing the grayscale threshold. Increasing contrast refers to adjusting the degree of difference between different grayscale value regions in the "infrared phase-locked grayscale image containing boundary lines," making the difference in grayscale values ​​more obvious; strengthening grayscale values ​​refers to enhancing the grayscale value of each pixel in the "infrared phase-locked grayscale image containing boundary lines." Specifically, for regions greater than the grayscale threshold (i.e., regions considered to have defects), increase contrast and / or strengthen grayscale values, and / or decrease the grayscale threshold, and re-segment the infrared phase-locked grayscale image containing boundary lines to obtain an infrared phase-locked grayscale image divided into grayscale regions. This can further improve the accuracy of typical defect type identification. For example, histogram equalization can be used to improve the contrast of the region with a gray value greater than the gray threshold, and gray-scale transformation can be used to enhance the region with a gray value greater than the gray threshold; these two methods are conventional methods for improving contrast and enhancing gray values, and will not be described in detail here.

[0096] In practical applications, in some cases, when performing image segmentation on the infrared phase-locked image containing boundary lines using a grayscale threshold as a constraint, some areas in a normal adhesive state may have grayscale values ​​greater than the grayscale threshold. This may be due to differences in the adhesive coating process conditions, resulting in slight variations in the adhesive layer thickness. In other cases, when performing image segmentation on the infrared phase-locked image containing boundary lines using a grayscale threshold as a constraint, the resulting grayscale value change is not significant. However, this does not mean there are no defects; there may still be slight defects. In other words, in this case, step S3 involves missed detections caused by an inappropriate threshold setting. Therefore, methods such as increasing contrast and decreasing the grayscale threshold can be used.

[0097] For example, before performing steps S4 and S5, contrast enhancement can be applied to the "regions not exceeding the grayscale threshold" to complete image segmentation before proceeding to steps S4 and S5. If, after contrast enhancement, connected but disconnected regions are still found in step S4 (i.e., pixels belonging to the same object are divided into two or more parts due to image segmentation, and these parts are spatially discontinuous), the grayscale threshold is lowered, and image segmentation is performed on the infrared phase-locked grayscale image containing boundary lines using the lowered grayscale threshold as a constraint. After image segmentation is completed, step S4 is performed again.

[0098] Example 2

[0099] like Figure 2 As shown, this invention provides a defect detection method for prepreg layups, including an acquisition module, a grayscale conversion module, a boundary segmentation module, a grayscale region segmentation module, a shape recognition module, a calculation module, and a judgment module. These four modules will be described in detail below.

[0100] The variable frequency thermal excitation and acquisition module is used to perform variable frequency thermal excitation on the prepreg ply to be tested and to acquire the infrared phase-locked image of the prepreg ply to be tested. The specific method is as shown in step S1 of Example 1, and will not be repeated here.

[0101] The grayscale conversion and boundary recognition module is communicatively connected to the frequency conversion thermal excitation and acquisition module. It is used to convert the infrared phase-locked image into an infrared phase-locked grayscale image and perform boundary recognition on the infrared phase-locked grayscale image to obtain an infrared phase-locked grayscale image containing boundary lines. The specific method is as shown in step S2 of Embodiment 1, and will not be repeated here.

[0102] The grayscale region segmentation module, communicatively connected to the grayscale conversion and boundary recognition module, is used to segment the infrared phase-locked grayscale image containing boundary lines using a grayscale threshold as a constraint, thereby obtaining an infrared phase-locked grayscale image divided into grayscale regions. The specific method is shown in step S3 of Embodiment 1, and will not be repeated here.

[0103] The shape recognition module, communicatively connected to the grayscale region segmentation module, is used to perform shape recognition on grayscale regions larger than the grayscale threshold to obtain regions containing singularities and / or edge curves. The specific method is as shown in step S4 of Embodiment 1, and will not be repeated here.

[0104] The defect type determination and calculation module is communicatively connected to the grayscale region segmentation module. It is used to obtain the defect type and total area of ​​the defect region of the prepreg layer to be inspected based on the singular points and / or the edge curves. The specific method is shown in step S4 of Example 1, and will not be repeated here.

[0105] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0106] In the description of this invention, unless otherwise stated, the terms "upper," "lower," "left," "right," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0107] The above technical solution is only one embodiment of the present invention. For those skilled in the art, based on the principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the technical solutions described in the specific embodiments of the present invention. Therefore, the above description is only preferred and has no limiting significance.

Claims

1. A method for detecting defects in prepreg layup, characterized in that, include S1: Apply linearly increasing frequency-modulated thermal excitation to the prepreg ply to be tested and acquire the infrared phase-locked loop diagram of the prepreg ply to be tested; wherein, the frequency of the infrared excitation heat source is as shown in equation (1). Equation (1) In equation (1), ƒ is the frequency of the infrared excitation heat source, in Hz; The initial frequency of the infrared excitation heat source is expressed in Hz. is the time coefficient of the infrared excitation heat source, in Hz / s; T is the excitation time of the frequency conversion thermal excitation, in s; S2: The infrared phase-locked image is converted into an infrared phase-locked grayscale image using a weighted average method, and the infrared phase-locked grayscale image is subjected to boundary identification using wavelet analysis to obtain an infrared phase-locked grayscale image containing boundary lines. S3: Perform image segmentation on the infrared phase-locked grayscale image containing boundary lines using a grayscale threshold as a constraint to obtain an infrared phase-locked grayscale image divided into grayscale regions. S4: An edge detection algorithm is used to identify the shape of grayscale regions greater than a grayscale threshold, obtaining regions containing singularities and edge curves; based on the singularities and edge curves, the defect type and total area of ​​the prepreg layup to be detected are obtained; if the region greater than the grayscale threshold is a continuously distributed non-zero value and its size is within a preset range, it is identified as a bubble defect, and the area of ​​the bubble defect is obtained based on the area of ​​the singularities; if the region greater than the grayscale threshold constitutes a connected region of grayscale features, it is determined whether the edge curve contains the boundary line of the prepreg layup to be detected; if the edge curve does not contain the boundary line of the prepreg layup to be detected, it is identified as a local debonding defect, and the area of ​​the connected region is the area of ​​the local debonding defect; if the edge curve contains the boundary line of the prepreg layup to be detected, it is identified as a delamination defect, and the area of ​​the connected region is the area of ​​the delamination defect.

2. The method for detecting defects in prepreg layup according to claim 1, characterized in that, In step S1, The value range is 0.1~1000Hz; The value range is 0.1~100Hz / s; T The value range is 0~30s.

3. The method for detecting defects in prepreg layup according to claim 2, characterized in that, In step S1, The value range is 0.1~100Hz; The value range is 0.1~20Hz / s; T The value range is 0~10s.

4. The method for detecting defects in prepreg layup according to claim 1, characterized in that, The defect detection method for the prepreg layup also includes the following steps: S5: Based on the infrared phase-locked grayscale image containing the boundary lines, obtain the total area of ​​the prepreg ply to be tested; based on the total area of ​​the defective region in the prepreg ply to be tested and the total area of ​​the prepreg ply to be tested, obtain the product evaluation result.

5. The method for detecting defects in prepreg layup according to claim 1, characterized in that, The defect detection method for the prepreg layup also includes the following steps: S6: Based on the region containing singularities and edge curves obtained in step S4, adjust the infrared phase-locked grayscale image containing boundary lines; the adjustment of the infrared phase-locked grayscale image containing boundary lines includes at least one of increasing contrast, strengthening grayscale values, and reducing grayscale threshold.

6. A defect detection system for prepreg layup, characterized in that, It includes a frequency conversion thermal excitation and acquisition module, a grayscale conversion and boundary recognition module, a grayscale region segmentation module, a shape recognition module, and a defect type judgment and calculation module, which are connected in sequence via communication. The variable frequency thermal excitation and acquisition module is used to perform variable frequency thermal excitation on the prepreg ply to be tested and to acquire the infrared phase-locked image of the prepreg ply to be tested; wherein, the frequency of the infrared excitation heat source is as shown in equation (1). Equation (1) In equation (1), ƒ is the frequency of the infrared excitation heat source, in Hz; The initial frequency of the infrared excitation heat source is expressed in Hz. is the time coefficient of the infrared excitation heat source, in Hz / s; T is the excitation time of the frequency conversion thermal excitation, in s; The grayscale conversion and boundary recognition module is used to convert the infrared phase-locked image into an infrared phase-locked grayscale image, and to perform boundary recognition on the infrared phase-locked grayscale image to obtain an infrared phase-locked grayscale image containing boundary lines. The grayscale region segmentation module is used to segment the infrared phase-locked grayscale image containing boundary lines using a grayscale threshold as a constraint condition, to obtain an infrared phase-locked grayscale image divided into grayscale regions. The shape recognition module is used to perform shape recognition on grayscale regions that are larger than the grayscale threshold, and to obtain regions containing singular points and edge curves. The defect type judgment and calculation module is used to obtain the defect type and total area of ​​the defect region of the prepreg ply to be detected based on the singular points and the edge curves. If the area greater than the grayscale threshold is a continuously distributed non-zero value and the size is within a preset range, it is identified as a bubble defect, and the area of ​​the bubble defect is obtained based on the area of ​​the singular points. If the area greater than the grayscale threshold constitutes a connected region of grayscale features, it is determined whether the edge curve contains the boundary line of the prepreg ply to be detected. If the edge curve does not contain the boundary line of the prepreg ply to be detected, it is identified as a local debonding defect, and the area of ​​the connected region is the area of ​​the local debonding defect. If the edge curve contains the boundary line of the prepreg ply to be detected, it is identified as a delamination defect, and the area of ​​the connected region is the area of ​​the delamination defect.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the defect detection method for prepreg layup as described in any one of claims 1 to 5.

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