Method, apparatus and medium for testing the wettability of an electronic-grade glass fiber cloth

Abstract

The application discloses a kind of electronic grade glass fibre cloth wettability test method, equipment and medium, it is related to glass fibre infiltration detection technical field, including, according to multilayer test environment, fluorescence microsphere displacement image is collected, and resin penetration front profile morphology is extracted to calculate front fractal dimension;According to interlayer uneven position of infiltration extraction physical coordinates, cut micro area sample is extracted, fluorescence lifetime imaging method is used to collect fluorescence decay signal, and fluorescence lifetime is determined;According to the spatial distribution of fluorescence lifetime, calculate spatial standard deviation, and the deviation comparison of wetting heat integral value is carried out respectively, and output infiltration defect diagnosis conclusion and infiltration quality feedback information.The application is carried out by box counting fractal analysis to resin penetration front, realizes the quantitative characterization of front profile, and with gradient mutation detection identifies abnormal area, automatically outputs the physical coordinates of interlayer uneven infiltration, improves positioning stability and repeatability, provides navigation for micro area cutting and interface mechanism characterization.

Description

Technical Field

[0001] This invention relates to the field of wetting detection technology, specifically a method, equipment, and medium for testing the wetting properties of electronic-grade glass fiber cloth. Background Technology

[0002] Electronic-grade glass fiber cloth serves as a reinforcement in printed circuit boards and encapsulation laminates. Its resin wetting behavior affects dielectric consistency, interlayer adhesion, and porosity formation. Wetting tests have evolved from static contact angle and dynamic penetration observation to multimodal methods such as tracer imaging, flow front analysis, and thermal characterization, supporting quantitative assessment of wetting.

[0003] However, existing methods mainly rely on single-layer and macroscopic average indicators, which makes it difficult to quantitatively describe the complex morphology of the permeation front under multi-layer pressure stacking conditions and output the coordinates of abnormal areas. This results in a lack of reliable spatial guidance for locating the uneven interlayer wetting and characterizing the micro-region mechanism, which restricts the fine optimization of process parameters. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for testing the wettability of electronic-grade glass fiber cloth, which solves the problems of uneven interlayer wettability of electronic-grade glass fiber cloth under multilayer stacking conditions, making it difficult to quantitatively locate and characterize the mechanism.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for testing the wettability of electronic-grade glass fiber cloth, comprising: constructing a multi-layer stacked sample based on electronic-grade glass fiber cloth, distributing fluorescent microspheres at the interfaces of adjacent layers, applying controllable interlayer pressure to form a multi-layer testing environment; acquiring images of fluorescent microsphere displacement based on the multi-layer testing environment, and extracting the contour morphology of the resin penetration front to calculate the fractal dimension of the front; constructing a fractal dimension spatial distribution based on the fractal dimension of the front, and locating the interlayer wetting non-uniformity position based on the local abrupt change region in the fractal dimension spatial distribution; extracting physical coordinates to cut micro-area samples based on the interlayer wetting non-uniformity position, acquiring fluorescence decay signals using fluorescence lifetime imaging, and measuring fluorescence lifetime; constructing a fluorescence lifetime spatial distribution based on fluorescence lifetime, acquiring interface temperature images and measuring interface wetting heat through infrared thermal imaging, and generating a wetting heat integral value; calculating the spatial standard deviation based on the fluorescence lifetime spatial distribution, comparing the spatial standard deviation with the wetting heat integral value, and outputting a wetting defect diagnosis conclusion and wetting quality feedback information.

[0008] As a preferred embodiment of the electronic-grade glass fiber cloth wettability test method of the present invention, the multilayer stacked sample refers to a layered structure composed of at least two single layers of electronic-grade glass fiber cloth stacked sequentially along the thickness direction.

[0009] As a preferred embodiment of the electronic-grade glass fiber cloth wettability testing method of the present invention, the specific steps for forming a multi-layer test environment are as follows:

[0010] Single-layer samples were extracted from electronic-grade glass fiber cloth, and multiple layers of cloth were oriented and stacked to obtain an initial stacked structure.

[0011] Based on the initial stacking structure, fluorescent microspheres are distributed at specific points on the interfaces of adjacent layers to obtain a labeled interface stack.

[0012] By applying interlayer pressure that varies continuously along the thickness direction to the marked interface stack, a multilayer test environment is obtained.

[0013] As a preferred embodiment of the wettability testing method for electronic-grade glass fiber cloth described in this invention, the specific steps for extracting the resin penetration front profile morphology and calculating the fractal dimension of the front are as follows.

[0014] Based on a multi-layer test environment, fluorescent microsphere displacement images were acquired during resin infiltration, and the positions of fluorescent microspheres in adjacent frames were matched to obtain a set of microsphere displacement trajectories.

[0015] Based on the set of microsphere displacement trajectories, spatial mapping of displacement amplitude is performed to generate a displacement amplitude distribution field. Contour lines are extracted from the displacement amplitude distribution field to obtain the contour morphology of the resin penetration front.

[0016] Based on the profile morphology of the resin penetration front, box-count fractal analysis was used to perform multi-scale coverage statistics to obtain the fractal dimension of the front.

[0017] As a preferred embodiment of the wettability testing method for electronic-grade glass fiber cloth described in this invention, the specific steps for locating uneven wetting locations between layers are as follows.

[0018] Based on the contour morphology of the resin penetration front, local window fractal analysis is performed in the neighborhood of the contour to generate a set of fractal dimension values, and spatial interpolation is performed to generate the spatial distribution of fractal dimension.

[0019] Based on the fractal dimension spatial distribution, the gradient magnitude detection method is used to calculate the local rate of change and obtain the gradient distribution field.

[0020] The abrupt change regions are screened based on the gradient distribution field, and local abrupt change regions are identified and the locations of uneven interlayer wetting are output.

[0021] As a preferred embodiment of the electronic-grade glass fiber cloth wettability testing method of the present invention, the specific steps for determining the fluorescence lifetime are as follows:

[0022] Coordinate information is extracted from local abrupt regions in the fractal dimension spatial distribution, and the location of uneven interlayer wetting is determined to obtain the physical coordinates of the micro-region.

[0023] Based on the physical coordinates of the micro-regions, micro-regions are peeled off from multi-layer stacked samples to obtain independent micro-region samples;

[0024] Fluorescence lifetime imaging was used to collect fluorescence decay data from independent micro-region samples, generating a time series of fluorescence decay signals. Multi-exponential fitting analysis was then performed to obtain the fluorescence lifetime.

[0025] As a preferred embodiment of the electronic-grade glass fiber cloth wettability testing method of the present invention, the specific steps for generating the wetting heat integral value are as follows:

[0026] The spatial distribution of fluorescence lifetime is obtained by constructing a spatial location mapping.

[0027] Infrared thermal imaging was used to collect temperature field data between layers of independent micro-region samples, obtain interface temperature images, and perform heat flux integral measurement to generate wetting heat integral values.

[0028] As a preferred embodiment of the electronic-grade glass fiber cloth wettability testing method of the present invention, the specific steps for outputting the wettability defect diagnosis conclusion and wettability quality feedback information are as follows.

[0029] The fluorescence lifetime values ​​at each location in the spatial distribution of fluorescence lifetime are extracted, and the spatial discreteness is quantified to obtain the spatial standard deviation of fluorescence lifetime.

[0030] Based on the spatial standard deviation of fluorescence lifetime and the integral value of wetting heat, dimensionless deviation calculation and cross-correlation are performed to obtain the characteristic deviation vector of wetting defects.

[0031] Based on the feature deviation vector of the infiltration defect, multidimensional feature matching and category classification are performed to obtain the diagnostic conclusion of the infiltration defect and the feedback information of the infiltration quality.

[0032] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the electronic-grade glass fiber cloth wettability test method as described in the first aspect of the present invention.

[0033] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the electronic-grade glass fiber cloth wettability test method as described in the first aspect of the present invention.

[0034] The beneficial effects of this invention are as follows: by performing box-count fractal analysis on the resin penetration front, quantitative characterization of the front morphology is achieved, and abnormal regions are identified by gradient mutation detection. The physical coordinates of uneven interlayer wetting are automatically output, improving positioning stability and repeatability, and providing navigation for micro-area cutting and interface mechanism characterization. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of the wettability test method for electronic-grade glass fiber cloth.

[0037] Figure 2 A flowchart for constructing a multi-layer testing environment and extracting fractal dimension.

[0038] Figure 3 A flowchart for locating unevenly infiltrated areas and analyzing micro-area samples.

[0039] Figure 4 A flowchart for generating immersion quality diagnosis and feedback. Detailed Implementation

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0041] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0043] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for testing the wettability of electronic-grade glass fiber cloth, comprising the following steps:

[0044] S1: Multilayer stacked samples were constructed based on electronic-grade glass fiber cloth, and fluorescent microspheres were distributed at the interface of adjacent layers to apply controllable interlayer pressure, thus forming a multilayer testing environment.

[0045] S1.1: A multilayer stacked sample refers to a layered structure consisting of at least two single layers of electronic-grade glass fiber cloth stacked sequentially along the thickness direction.

[0046] Single-layer samples were extracted from electronic-grade glass fiber cloth, and multiple layers of cloth were oriented and stacked to obtain an initial stacked structure.

[0047] Furthermore, electronic-grade glass fiber cloth rolls are cut to obtain single layers of electronic-grade glass fiber cloth. The warp and weft directions of the single layers of electronic-grade glass fiber cloth are marked. At least two single layers of electronic-grade glass fiber cloth are aligned according to the warp and weft marks and then stacked sequentially along the thickness direction. The surfaces of adjacent single layers of electronic-grade glass fiber cloth are in contact to form a layered structure composed of single layers of electronic-grade glass fiber cloth, i.e., the initial stacked structure.

[0048] S1.2: Based on the initial stacking structure, fluorescent microspheres are distributed at specific points on the interfaces of adjacent layers to obtain a labeled interface stack.

[0049] Furthermore, based on the initial stacked structure, spatial coordinate mapping is performed on the interfaces between adjacent layers of the initial stacked structure to obtain physical coordinates. Based on the physical coordinates, fluorescent microspheres are distributed at specific points on the interfaces between adjacent layers to form a spatial distribution of fluorescent microspheres, thus obtaining a labeled interface stack.

[0050] Specifically, spatial coordinate mapping refers to establishing a correspondence between the pixel coordinates in the digital image and data domain and the actual physical location (physical coordinates) of the multilayer stacked sample during the resin penetration test.

[0051] Spatial calibration of the interlayer interface of the initial stacked structure is performed, and the two-dimensional pixel position of the interface in the imaging is converted into two-dimensional physical coordinates (X, Y) in the interface plane. The X-axis is aligned with the warp direction of the electronic-grade glass fiber cloth, and the Y-axis is aligned with the weft direction. The acquired fluorescent microsphere displacement image, resin penetration front contour, and fractal dimension distribution data are all mapped and anchored to the corresponding physical positions. Image processing information (displacement trajectory and fractal dimension abrupt change point are traced back to the actual spatial position of the sample) is used to obtain the alignment from the virtual data space to the physical entity space.

[0052] The fluorescent microsphere fixed-point distribution processing is based on the physical coordinates obtained by spatial coordinate mapping of the adjacent layer interfaces of the initial stacked structure. Fluorescent microspheres are placed at the interface positions, forming a spatial position information distribution pattern between the layers, and a marked interface stack is obtained. The processing uses physical coordinates as the positioning reference. The displacement trajectory of each fluorescent microsphere during resin infiltration can be traced back to the original spatial position, providing a spatial marking basis for extracting the microsphere displacement trajectory set, generating the displacement amplitude distribution field, and identifying the contour morphology of the resin infiltration front.

[0053] S1.3: Apply interlayer pressure that varies continuously along the thickness direction to the marked interface stack to obtain a multilayer test environment.

[0054] Furthermore, interlayer pressure that varies continuously along the thickness direction is applied to the marked interface stack, forming a pressure gradient distribution from high to low and from low to high at the interfaces of adjacent layers of the marked interface stack. During the resin infiltration process, the interlayer pressure distribution is dynamically adjusted according to the change in the displacement amplitude distribution field of the microsphere displacement trajectory set in the fluorescent microsphere displacement image. The pressure gradient inside the marked interface stack remains continuous and stable, thus obtaining a multilayer test environment.

[0055] It should be noted that the amplitude difference between the high and low quantile regions in the displacement amplitude distribution field is used as the feedback basis for pressure adjustment. When the amplitude difference is greater than the difference corresponding to the initial state of the displacement amplitude distribution field, the local pressure is adjusted in steps of 0.05 to 0.2 MPa along the gradient descent direction. The interval between two adjacent adjustments is not less than 5 seconds to adapt to the viscoelastic response of the resin flow. When the amplitude difference fluctuation range obtained from three consecutive samplings converges to a smaller proportion of the difference corresponding to the initial state, the pressure distribution is determined to have reached a continuous stable state.

[0056] The resin infiltration process refers to the physicochemical process in which liquid resin, driven by pressure, gradually impregnates and fills the interlayer interface of a multilayer stacked structure of electronic-grade glass fiber cloth from the outside in.

[0057] During resin permeation, resin molecules, under the combined action of dynamic forces and externally applied gradient pressure, advance towards the interlayer interface along the microporous channel network formed by the interlacing of the fiber cloth, forming a permeation front with spatial irregularity. The advancement of the front causes the fluorescent microspheres distributed at the interface to shift, and the set of trajectories maps the spatial distribution characteristics of the resin flow. The complexity of the front profile can be quantitatively characterized by fractal dimension.

[0058] The penetration process is accompanied by the release of interfacial wetting heat (originating from the energy conversion of the interfacial interaction between the resin and the fiber surface). At the same time, the degree of resin wetting of the fiber surface directly affects the polarity change of the local microenvironment and modulates the attenuation characteristics of the fluorescent probe.

[0059] The decay characteristic refers to the decay law of fluorescence intensity after excitation over time. It is manifested as a time series of fluorescence decay signals formed by arranging fluorescence intensity measurements in chronological order, showing the temporal evolution characteristics.

[0060] A multilayer test environment refers to a test environment consisting of a layered structure (i.e., a multilayer stacked sample) composed of at least two single layers of electronic-grade glass fiber cloth stacked sequentially along the thickness direction. After the fluorescent microspheres at the interfaces of adjacent layers are marked with fixed distributions, interlayer pressure that varies continuously along the thickness direction is applied. In the test environment, pressure gradients are formed at the interfaces of adjacent layers, with pressure gradients from high to low and from low to high. During the resin permeation process, the interlayer pressure is dynamically adjusted according to the changes in the displacement amplitude distribution field of the fluorescent microsphere displacement trajectory set, so as to keep the internal pressure gradient continuous and stable, simulating the resin wetting behavior in a multilayer pressurized stacked state under actual working conditions.

[0061] S2: Based on the multi-layer test environment, acquire images of the displacement of fluorescent microspheres and extract the contour morphology of the resin penetration front to calculate the fractal dimension of the front.

[0062] S2.1: Based on the multi-layer test environment, fluorescent microsphere displacement images are acquired during the resin infiltration process, and the positions of fluorescent microspheres in adjacent frames are matched to obtain a set of microsphere displacement trajectories.

[0063] Furthermore, during the resin infiltration process, continuous time-series acquisition of the multilayer test environment is performed to obtain a sequence of fluorescent microsphere displacement images. The spatial positions of the fluorescent microspheres are extracted from adjacent frames of the fluorescent microsphere displacement image sequence. The displacement distance is calculated based on the spatial positions of the fluorescent microspheres in adjacent frames. The position of the fluorescent microsphere with the smallest displacement distance is used for position matching. The spatial positions of the fluorescent microspheres that have completed position matching are connected in time sequence to form a single microsphere displacement trajectory. Multiple single microsphere displacement trajectories are summarized to form a set of microsphere displacement trajectories.

[0064] S2.2: Based on the set of microsphere displacement trajectories, perform spatial mapping of displacement amplitude to generate a displacement amplitude distribution field, and extract contour lines from the displacement amplitude distribution field to obtain the profile morphology of the resin penetration front.

[0065] Furthermore, based on the microsphere displacement trajectory set, the spatial coordinate position and displacement information of each fluorescent microsphere during the resin permeation process are extracted. The displacement amplitude is calculated to obtain the displacement amplitude. The displacement amplitude is spatially mapped to the spatial coordinate position to obtain the discrete spatial distribution of the displacement amplitude. The discrete spatial distribution of the displacement amplitude is spatially interpolated to generate the displacement amplitude distribution field. The contour lines of the displacement amplitude distribution field are extracted to obtain the contour morphology of the resin permeation front.

[0066] Specifically, displacement amplitude spatial mapping is a data processing technique that binds the displacement amplitude (i.e., the length and modulus of the displacement trajectory) generated by the fluorescent microspheres during resin permeation to the original spatial coordinate position.

[0067] Extract the spatial coordinates of the endpoint and starting point of each fluorescent microsphere from the set of microsphere displacement trajectories, calculate the distance to obtain the displacement amplitude, and map the displacement amplitude to the spatial coordinates corresponding to the initial position of the microsphere and the midpoint of the trajectory to form a mapping relationship from spatial position to displacement amplitude.

[0068] Contour extraction refers to identifying and connecting spatial locations with the same displacement amplitude from the displacement amplitude distribution field to form a continuous curve to characterize the profile of the resin penetration front.

[0069] The resin penetration front profile refers to the two-dimensional geometric profile of the spatial boundary between the wetted and unwetted areas during the resin penetration process along the interlayer interface of multi-layer stacked electronic-grade glass fiber cloth. The two-dimensional geometric profile is obtained by constructing a displacement amplitude distribution field through the set of fluorescent microsphere displacement trajectories, and extracting contour lines from spatial locations with the same displacement amplitude in the distribution field. This directly characterizes the irregular spatial expansion morphology caused by differences in local pore structure, fiber orientation, and pressure gradient when the resin advances in the interwoven microporous network of the fiber cloth.

[0070] S2.3: Based on the profile of the resin penetration front, box-count fractal analysis is used to perform multi-scale coverage statistics to obtain the fractal dimension of the front.

[0071] Furthermore, based on the resin penetration front contour morphology, when performing multi-scale coverage statistics using the box-count fractal analysis method, the resin penetration front contour morphology is represented as a two-dimensional contour image. Square grids with successively decreasing grid sizes are used to cover the two-dimensional contour image. The total number of square grids intersecting with the resin penetration front contour morphology under each grid size is counted, and the grid size sequence and the sequence of the total number of intersecting square grids are obtained.

[0072] A linear fit is performed with the logarithm of the grid size as the x-axis and the logarithm of the total number of intersecting square grids as the y-axis. The absolute value of the slope of the straight line obtained by the linear fit is the front fractal dimension.

[0073] It should be noted that the box-count fractal analysis method refers to the means of statistically quantifying the geometric complexity of the resin penetration front contour morphology through multi-scale coverage. The resin penetration front contour morphology is converted into a two-dimensional contour image, and a set of square grids with successively decreasing grid sizes are used to cover the two-dimensional contour image layer by layer.

[0074] For example, first cover the entire image with a square grid with a side length of 100 micrometers and count the total number of grids intersecting the contour. Then, use a finer grid with a side length of 50 micrometers to cover and count again, and so on down to smaller scales such as 10 micrometers and 5 micrometers. Obtain the grid size sequence arranged from largest to smallest and the corresponding sequence of the total number of intersecting square grids. Take the logarithm of each grid size as the x-axis value and the logarithm of the corresponding total number of intersecting square grids as the y-axis value. Form a data point set in the coordinate system and perform linear fitting. The absolute value of the slope of the straight line obtained by linear fitting is defined as the front fractal dimension.

[0075] Linear fitting refers to a mathematical method that uses the logarithm of the grid size as the abscissa and the logarithm of the total number of intersecting square grids as the ordinate to fit a straight line. The method aims to minimize the sum of squared deviations along the ordinate and obtain the absolute value of the slope of the straight line, which is the front fractal dimension.

[0076] Logarithmic values ​​of different grid sizes are used as x-axis data points, and the logarithmic value of the total number of corresponding intersecting square grids is used as y-axis data points, forming several coordinate point pairs. A straight line is calculated using the least squares criterion. The line is the fitted line when the sum of the squares of the deviations between the theoretical coordinate values ​​and the actual data points in the y-axis direction is minimized.

[0077] For example, when the grid size sequence is 100 micrometers, 50 micrometers, and 25 micrometers, the logarithmic values ​​are 2.00, 1.70, and 1.40, respectively; the corresponding sequence of the total number of intersecting grids is 15, 28, and 52, with logarithmic values ​​of 1.18, 1.45, and 1.72, respectively; by performing linear fitting on the three sets of data points (2.00, 1.18), (1.70, 1.45), and (1.40, 1.72), the absolute value of the slope of the line obtained is the front fractal dimension.

[0078] S3: Construct the fractal dimension spatial distribution based on the frontier fractal dimension, and locate the uneven interlayer infiltration location based on the local abrupt change regions in the fractal dimension spatial distribution.

[0079] S3.1: Based on the contour morphology of the resin penetration front, perform local window fractal analysis in the contour neighborhood to generate a set of fractal dimension values, and perform spatial interpolation to generate the spatial distribution of fractal dimension.

[0080] Furthermore, based on the contour morphology of the resin penetration front, multiple local windows are divided within the contour neighborhood for local window fractal analysis. The contour morphology of the resin penetration front within the coverage area of ​​each local window is statistically analyzed using box counting fractal analysis to obtain fractal dimension values. The fractal dimension values ​​corresponding to each local window are arranged according to the center position of the local window to form a set of fractal dimension values. Spatial interpolation is then performed to generate the spatial distribution of fractal dimension.

[0081] It should be noted that spatial interpolation is a data processing method that fills in the values ​​at known spatial locations in the discrete spatial distribution of displacement amplitude and the fractal dimension numerical set according to the spatial positional relationship, generating a continuous distribution field covering the entire spatial region.

[0082] After the spatial mapping of displacement amplitude is completed, displacement amplitude is obtained only at the actual spatial coordinates of the fluorescent microspheres, forming a discrete spatial distribution of displacement amplitude. Since there are spatial gaps in the distribution of microspheres, spatial interpolation is needed to fill the gap regions numerically, and reasonable numerical estimates are obtained for the unknown positions between discrete points, generating a continuous displacement amplitude distribution field. Similarly, after obtaining the set of fractal dimension values ​​through local window fractal analysis, each fractal dimension value corresponds only to the center position of the local window. Spatial interpolation is used to extend the fractal dimension values ​​to the transition region between windows according to the spatial positional relationship, generating a complete spatial distribution of fractal dimension.

[0083] S3.2: Based on the fractal dimension spatial distribution, the gradient magnitude detection method is used to calculate the local rate of change and obtain the gradient distribution field.

[0084] Furthermore, based on the fractal dimension value of each spatial location in the fractal dimension spatial distribution, the gradient magnitude detection method is used to calculate the difference between the fractal dimension values ​​of the location and the horizontally adjacent locations to obtain the horizontal change. The difference between the fractal dimension values ​​of the location and the vertically adjacent locations is calculated to obtain the vertical change. The horizontal and vertical changes are squared and summed, and then square rooted to obtain the gradient magnitude. The gradient magnitudes corresponding to all spatial locations in the fractal dimension spatial distribution are arranged according to the original spatial positional relationship to form a gradient distribution field.

[0085] The formula for the gradient magnitude detection method is:

[0086] ;

[0087] in, The fractal dimension spatial distribution of the th The gradient magnitude at each spatial location represents the degree of drastic local change in the fractal dimension at that location. For spatial location index, Indexed by the direction dimension. For the first The directional dimension represents the contribution of the horizontal and vertical changes in equilibrium (with a value of 1). For the first The spatial location is at the first The change in fractal dimension calculated in each direction;

[0088] when for Time represents the change in the horizontal direction (the difference in fractal dimension between the current position and its horizontally adjacent position). for Time represents the change in the vertical direction (the difference in fractal dimension between the current position and the vertically adjacent position).

[0089] It should be noted that, , and The fractal dimension itself is a dimensionless scaling index. By squaring, summing, and then taking the square root of a dimensionless quantity, the dimension remains dimensionless during the calculation process, and the formula has a unified dimension.

[0090] The gradient magnitude detection method is based on the fractal dimension value of each spatial location in the fractal dimension spatial distribution. It calculates and obtains the gradient magnitude corresponding to the spatial location. The calculation is performed sequentially on all spatial locations in the fractal dimension spatial distribution to obtain the gradient magnitude corresponding to each spatial location. The gradient magnitudes are then arranged according to the spatial location to form a gradient distribution field.

[0091] S3.3: Based on the gradient distribution field, abrupt change regions are screened to identify local abrupt change regions and output the locations of uneven interlayer wetting.

[0092] Furthermore, based on the gradient distribution field, the local maximum value of the gradient magnitude is identified by comparing the gradient magnitude in the neighborhood as a local abrupt change region. The spatial coordinates of the local maximum value of the gradient magnitude are mapped to the physical coordinates corresponding to the fractal dimension spatial distribution, and the location of the interlayer infiltration inhomogeneity is output.

[0093] It should be noted that, for example, in the gradient distribution field corresponding to the fractal dimension spatial distribution, a 3×3 neighborhood window is constructed with the grid coordinates (25, 30) as the center for gradient magnitude comparison. The gradient magnitude at this location is 0.68, and the gradient magnitudes of the surrounding 8 neighboring points are 0.35, 0.41, 0.45, 0.43, 0.26, 0.22, 0.24, and 0.28, respectively. After comparing them one by one, it is confirmed that the gradient magnitude at the center location is greater than that of all neighboring points, and it is identified as a local gradient magnitude. The maximum value location, i.e. the local abrupt change region, is determined by aligning the origin of the grid coordinate system with the origin of the physical coordinate system of the multilayer stacked sample. The X-axis is aligned with the warp direction of the electronic-grade glass fiber cloth, and the Y-axis is aligned with the weft direction. The grid spacing is 10 micrometers. The grid coordinates (25, 30) are converted to physical coordinates. The displacement in the x-direction is 25 multiplied by 10 micrometers, which equals 250 micrometers. The displacement in the y-direction is 30 multiplied by 10 micrometers, which equals 300 micrometers. The output (250 micrometers, 300 micrometers) is used as the location of the interlayer wetting non-uniformity.

[0094] S4: Based on the uneven interlayer wetting location, the physical coordinates of the micro-area sample were extracted, and the fluorescence decay signal was acquired using the fluorescence lifetime imaging method, and the fluorescence lifetime was measured.

[0095] S4.1: Based on the local abrupt change regions in the fractal dimension spatial distribution, coordinate information is extracted and the location of uneven interlayer wetting is determined to obtain the physical coordinates of the micro-area.

[0096] Furthermore, based on the local abrupt change regions identified in the fractal dimension spatial distribution, the spatial coordinates of the local abrupt change regions are read. The spatial coordinates correspond to the spatial coordinates of the resin penetration front contour morphology. The spatial coordinates of the resin penetration front contour morphology correspond to the physical position of the multilayer stacked sample. The spatial coordinates are extracted as coordinate information. Based on the correspondence between the spatial coordinates and the physical position, the uneven wetting position between layers is located to obtain the physical coordinates of the micro-area.

[0097] It should be noted that the displacement amplitude distribution field, fractal dimension spatial distribution, and gradient distribution field are constructed based on the same spatial grid coordinate system. The grid coordinate system establishes a correspondence with the physical coordinates of the multi-layer stacked sample during the spatial coordinate mapping stage. The grid spacing is 5 micrometers to 50 micrometers, the grid origin coincides with the physical coordinate origin of the sample, and the X and Y axes are aligned with the warp and weft directions of the electronic-grade glass fiber cloth, respectively.

[0098] S4.2: Based on the physical coordinates of the micro-regions, perform micro-region peeling on the multi-layer stacked samples to obtain independent micro-region samples.

[0099] Furthermore, based on the physical coordinates of the micro-area, the uneven wetting position between layers is located on the multi-layer stacked sample. Mechanical peeling is then performed along the interface between adjacent single layers of electronic-grade glass fiber cloth to separate the local area corresponding to the physical coordinates of the micro-area, forming an independent micro-area sample.

[0100] S4.3: Fluorescence lifetime imaging method is used to collect fluorescence decay data of independent micro-region samples, generate time series of fluorescence decay signals, and perform multi-exponential fitting analysis to obtain fluorescence lifetime.

[0101] Furthermore, when using fluorescence lifetime imaging to collect fluorescence decay data of independent micro-region samples, the fluorescence decay signal of fluorescence intensity changing with time is recorded at the spatial location of the independent micro-region sample. The fluorescence intensity measurements corresponding to the spatial location are arranged in chronological order to form a time series of fluorescence decay signals.

[0102] Multi-exponential fitting analysis was performed on the time series of fluorescence decay signals to fit the time series into multiple exponential decay terms and extract the time constants corresponding to each exponential decay term. The average value was calculated based on the time constants to obtain the fluorescence lifetime.

[0103] Specifically, fluorescence lifetime imaging is a detection method that obtains characteristic parameters reflecting the physicochemical state of a local microenvironment and characterizes its spatial distribution by detecting the decay of fluorescence intensity over time after excitation of a fluorescent probe. Point-by-point scanning is performed on the surface of an independent micro-region sample, acquiring the entire process signal of fluorescence intensity decay from the excitation peak to the baseline at each spatial coordinate location, forming a fluorescence decay signal time series characterizing the decay process. Multi-exponential fitting analysis is used to analyze the time series, decomposing it into multiple exponential decay components with different decay rates. The time constant corresponding to each component is extracted, and the fluorescence lifetime value at each location is obtained by averaging the time constants.

[0104] Since fluorescence lifetime is sensitive to microenvironment polarity and intermolecular interactions, its spatial distribution can reflect the uniformity of resin wetting at the interface between fiber layers. By synchronously recording the spatial coordinates of each fluorescence lifetime value and filling in all spatial coordinates with their respective fluorescence lifetime values, a fluorescence lifetime spatial distribution characterizing the spatial heterogeneity of wetting quality can be constructed.

[0105] S5: Construct the spatial distribution of fluorescence lifetime based on fluorescence lifetime, acquire interface temperature images through infrared thermal imaging and measure the interface wetting heat, and generate the integrated value of wetting heat.

[0106] S5.1: Construct a spatial location mapping of fluorescence lifetimes to obtain the spatial distribution of fluorescence lifetimes.

[0107] Furthermore, when performing spatial coordinate mapping on fluorescence lifetime, the fluorescence lifetime imaging method simultaneously records the spatial coordinate positions corresponding to the fluorescence decay signal. Point-by-point scanning is performed on the surface of the independent micro-region sample, and the corresponding fluorescence lifetime is obtained for each spatial coordinate position through multi-exponential fitting analysis. All spatial coordinate positions are numerically filled with their respective corresponding fluorescence lifetime values ​​to form the spatial distribution of fluorescence lifetime.

[0108] S5.2: Temperature field is acquired between layers of independent micro-region samples using infrared thermal imaging to obtain interface temperature images, and heat flow integral is measured to generate wetting heat integral values.

[0109] Furthermore, the temperature field of the interlayer interface of the independent micro-region sample is acquired by infrared thermal imaging to obtain an interface temperature image. The interface temperature image represents the spatial temperature distribution of the interlayer interface of the independent micro-region sample. The heat flux integral is measured based on the spatial gradient of the temperature values ​​in the interface temperature image. The heat flux integral measurement integrates the heat flux in the space of the interlayer interface of the independent micro-region sample and integrates it within the time of the resin penetration process. The spatial integral information and the time integral information together generate the wetting heat integral value.

[0110] Specifically, the wetting heat integral value refers to the cumulative heat characterization obtained by acquiring the interlayer interface temperature field of an independent micro-region sample through infrared thermal imaging during the resin permeation process, calculating the heat flow based on the temperature spatial gradient, and performing double integration in the spatial and temporal domains.

[0111] The physical significance stems from the energy conversion released when resin molecules interact with the glass fiber surface. The more thorough the wetting process, the larger the contact area between the resin and fiber interface, the tighter the bond, and the more heat is released through the interfacial interaction, resulting in a corresponding increase in the wetting heat integral value. Conversely, if there are interfacial debonding and local dry spot defects, poor interfacial contact leads to a weakening of the interaction, and the wetting heat integral value decreases significantly.

[0112] S6: Calculate the spatial standard deviation based on the spatial distribution of fluorescence lifetime, compare the spatial standard deviation with the wettability heat integral value, and output the diagnosis conclusion of wetting defects and the feedback information of wetting quality.

[0113] S6.1: Extract the fluorescence lifetime values ​​at each location in the spatial distribution of fluorescence lifetime, perform spatial discreteness quantification, and obtain the spatial standard deviation of fluorescence lifetime.

[0114] Furthermore, fluorescence lifetime values ​​corresponding to all spatial locations are extracted from the fluorescence lifetime spatial distribution to form a fluorescence lifetime value set. The average value of the fluorescence lifetime value set is calculated. The difference between each fluorescence lifetime value in the fluorescence lifetime value set and the average value is calculated and squared to obtain a set of squared differences. The set of squared differences is summed and the summation result is divided by the total number of values ​​in the fluorescence lifetime value set to obtain the variance. The square root of the variance is then taken to obtain the spatial standard deviation of fluorescence lifetime.

[0115] S6.2: Based on the spatial standard deviation of fluorescence lifetime and the integral value of wetting heat, dimensionless deviation calculation and cross-correlation are performed to obtain the characteristic deviation vector of wetting defects.

[0116] Furthermore, fluorescence lifetime values ​​corresponding to each spatial location are extracted from the fluorescence lifetime spatial distribution to form a fluorescence lifetime value set. The average value of the fluorescence lifetime value set is calculated to obtain the average fluorescence lifetime. The first dimensionless deviation is obtained by quoting the spatial standard deviation of fluorescence lifetime with the average fluorescence lifetime.

[0117] Extract the wetting heat integral values ​​corresponding to each micro-region sample to form a wetting heat integral value set. Calculate the average value of the wetting heat integral value set to obtain the average wetting heat integral value. Calculate the quotient between the wetting heat integral value and the average wetting heat integral value to obtain the second dimensionless deviation. Cross-correlate the first dimensionless deviation and the second dimensionless deviation to form the wetting defect feature deviation vector.

[0118] The formula for constructing the feature deviation vector of infiltration defects is:

[0119] ;

[0120] in, For the first The comprehensive wetting defect feature deviation vector of each micro-region sample is used as the quantized output of the wetting defect feature deviation vector. For micro-area sample indexing, This is the first dimensionless deviation. This is the second dimensionless deviation. For the first The first dimensionless bias vector of each micro-region sample For the first The second dimensionless deviation vector of each micro-region sample.

[0121] It should be noted that, , , , and All are dimensionless quantities, the first dimensionless deviation Deviation from the second dimensionless quantity The original dimensions are eliminated by quotienting the average value. , Since the normalization coefficients are also dimensionless, the overall ratios retain the dimensionless characteristic, and the formulas have unified dimensions.

[0122] The wetting defect characteristic deviation vector is a two-dimensional characteristic vector composed of the first dimensionless deviation and the second dimensionless deviation.

[0123] S6.3: Based on the feature deviation vector of the infiltration defect, perform multi-dimensional feature matching and category classification to obtain the diagnostic conclusion of the infiltration defect and the feedback information of the infiltration quality.

[0124] Furthermore, the signs of the first dimensionless deviation and the second dimensionless deviation in the feature deviation vector of the infiltration defect are determined respectively to obtain the signs of the first dimensionless deviation and the second dimensionless deviation. The signs of the first dimensionless deviation and the second dimensionless deviation are combined to form a sign combination, and the sign combination is subjected to multi-dimensional feature matching. Based on the infiltration defect type determination corresponding to the sign combination, the category classification is determined, and the infiltration defect diagnosis conclusion and infiltration quality feedback information are output.

[0125] Specifically, multidimensional feature matching refers to comparing and associating the multiple dimensional feature parameters contained in the infiltration defect feature deviation vector with the infiltration defect features. The symbol combination formed by combining the sign of the first dimensionless deviation with the sign of the second dimensionless deviation is compared one by one with the symbol combination patterns corresponding to various defects stored in the infiltration defect features to identify the defect features that match the current symbol combination and establish a mapping relationship between measurement data and defect types.

[0126] Category classification refers to the decision-making process of classifying the current test sample into the category of wetting defect type based on the results of multi-dimensional feature matching. When the symbol combination successfully matches the defect in the feature, the current micro-area sample is classified into the defect type according to the corresponding wetting defect type definition (such as positive-positive symbol combination corresponding to interface debonding defect, negative-positive symbol combination corresponding to local dry spot defect). The wetting quality level feedback (excellent, good, qualified, and unqualified) is output in combination with the magnitude of the deviation, forming a wetting defect diagnosis conclusion and wetting quality evaluation.

[0127] The diagnostic conclusion of infiltration defects refers to the diagnostic information output by the infiltration defect characteristic deviation vector, which is composed of the first dimensionless deviation and the second dimensionless deviation calculated from the fluorescence lifetime spatial standard deviation and the wetting heat integral value, respectively. This is achieved through symbol combination matching and category classification. The diagnostic information includes the identification of the infiltration defect type (such as interface debonding, local dry spots and pressure distribution imbalance), the infiltration quality level (four levels: excellent, good, qualified and unqualified) according to the magnitude of the deviation amplitude, the physical coordinates of the micro-area corresponding to the uneven infiltration location between layers, and the quantitative indicators supporting the diagnosis (fluorescence lifetime spatial standard deviation, wetting heat integral value and dimensionless deviation vector).

[0128] The wetting quality feedback information refers to the set of information output based on the wetting defect diagnosis conclusion, including wetting quality level evaluation, defect location information, and process improvement information. The wetting quality level is divided into four levels: excellent, good, qualified, and unqualified, based on the magnitude of the first and second dimensionless deviations. The micro-region physical coordinates of the interlayer wetting non-uniformity location are used to locate the defect area. The wetting defect type is identified, such as interface debonding, local dry spots, and pressure distribution imbalance. The quantitative indicators include the fluorescence lifetime spatial standard deviation, wetting heat integral value, and dimensionless deviation vector.

[0129] This embodiment also provides a computer device applicable to the wettability test method for electronic-grade glass fiber cloth, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the wettability test method for electronic-grade glass fiber cloth as proposed in the above embodiment.

[0130] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0131] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for testing the wettability of electronic-grade glass fiber cloth as proposed in the above embodiments. The storage medium 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, magnetic disk, or optical disk.

[0132] In summary, this invention achieves quantitative characterization of the resin penetration front morphology by performing box-count fractal analysis on the resin penetration front, identifies abnormal regions by gradient mutation detection, and automatically outputs the physical coordinates of uneven interlayer wetting, thereby improving positioning stability and repeatability and providing navigation for micro-area cutting and interface mechanism characterization.

[0133] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for testing the wettability of electronic-grade glass fiber cloth, characterized in that: include, Multilayer stacked samples were constructed using electronic-grade glass fiber cloth, and fluorescent microspheres were distributed at the interfaces of adjacent layers to apply controllable interlayer pressure, thus forming a multilayer testing environment. Based on the multi-layer test environment, images of fluorescent microsphere displacement were acquired, and the morphology of the resin penetration front contour was extracted to calculate the fractal dimension of the front. Construct a fractal dimension spatial distribution based on the frontier fractal dimension, and locate the uneven interlayer wetting position based on the local abrupt change regions in the fractal dimension spatial distribution; Based on the location of uneven interlayer wetting, physical coordinates were extracted to cut micro-region samples. Fluorescence decay signals were acquired using fluorescence lifetime imaging, and fluorescence lifetime was measured. Based on fluorescence lifetime, a spatial distribution of fluorescence lifetime is constructed. Interface temperature images are acquired by infrared thermal imaging and interface wetting heat is measured to generate integrated wetting heat values. The spatial standard deviation is calculated based on the spatial distribution of fluorescence lifetime. The spatial standard deviation is compared with the wetting heat integral value to output the diagnosis conclusion of wetting defects and the feedback information of wetting quality.

2. The wettability test method for electronic-grade glass fiber cloth as described in claim 1, characterized in that: The multilayer stacked sample refers to a layered structure composed of at least two single layers of electronic-grade glass fiber cloth stacked sequentially along the thickness direction.

3. The wettability test method for electronic-grade glass fiber cloth as described in claim 2, characterized in that: The specific steps for forming a multi-layered testing environment are as follows. Single-layer samples were extracted from electronic-grade glass fiber cloth, and multiple layers of cloth were oriented and stacked to obtain an initial stacked structure. Based on the initial stacking structure, fluorescent microspheres are distributed at specific points on the interfaces of adjacent layers to obtain a labeled interface stack. By applying interlayer pressure that varies continuously along the thickness direction to the marked interface stack, a multilayer test environment is obtained.

4. The wettability test method for electronic-grade glass fiber cloth as described in claim 1, characterized in that: The fractal dimension of the resin penetration front is calculated by extracting the resin penetration front contour morphology. The specific steps are as follows: Based on a multi-layer test environment, fluorescent microsphere displacement images were acquired during resin infiltration, and the positions of fluorescent microspheres in adjacent frames were matched to obtain a set of microsphere displacement trajectories. Based on the set of microsphere displacement trajectories, spatial mapping of displacement amplitude is performed to generate a displacement amplitude distribution field. Contour lines are extracted from the displacement amplitude distribution field to obtain the contour morphology of the resin penetration front. Based on the profile morphology of the resin penetration front, box-count fractal analysis was used to perform multi-scale coverage statistics to obtain the fractal dimension of the front.

5. The wettability test method for electronic-grade glass fiber cloth as described in claim 1, characterized in that: The specific steps for identifying uneven wetting between the positioning layers are as follows. Based on the contour morphology of the resin penetration front, local window fractal analysis is performed in the neighborhood of the contour to generate a set of fractal dimension values, and spatial interpolation is performed to generate the spatial distribution of fractal dimension. Based on the fractal dimension spatial distribution, the gradient magnitude detection method is used to calculate the local rate of change and obtain the gradient distribution field. The abrupt change regions are screened based on the gradient distribution field, and local abrupt change regions are identified and the locations of uneven interlayer wetting are output.

6. The wettability test method for electronic-grade glass fiber cloth as described in claim 1, characterized in that: The specific steps for determining fluorescence lifetime are as follows. Coordinate information is extracted from local abrupt regions in the fractal dimension spatial distribution, and the location of uneven interlayer wetting is determined to obtain the physical coordinates of the micro-region. Based on the physical coordinates of the micro-regions, micro-regions are peeled off from multi-layer stacked samples to obtain independent micro-region samples; Fluorescence lifetime imaging was used to collect fluorescence decay data from independent micro-region samples, generating a time series of fluorescence decay signals. Multi-exponential fitting analysis was then performed to obtain the fluorescence lifetime.

7. The wettability test method for electronic-grade glass fiber cloth as described in claim 1, characterized in that: The specific steps for generating the wetting heat integral value are as follows: The spatial distribution of fluorescence lifetime is obtained by constructing a spatial location mapping. Infrared thermal imaging was used to collect temperature field data between layers of independent micro-region samples, obtain interface temperature images, and perform heat flux integral measurement to generate wetting heat integral values.

8. The wettability test method for electronic-grade glass fiber cloth as described in claim 1, characterized in that: The specific steps for outputting the diagnostic conclusions of wetting defects and the feedback information on wetting quality are as follows. The fluorescence lifetime values ​​at each location in the spatial distribution of fluorescence lifetime are extracted, and the spatial discreteness is quantified to obtain the spatial standard deviation of fluorescence lifetime. Based on the spatial standard deviation of fluorescence lifetime and the integral value of wetting heat, dimensionless deviation calculation and cross-correlation are performed to obtain the characteristic deviation vector of wetting defects. Based on the feature deviation vector of the infiltration defect, multidimensional feature matching and category classification are performed to obtain the diagnostic conclusion of the infiltration defect and the feedback information of the infiltration quality.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the electronic-grade glass fiber cloth wettability test method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the wettability test method for electronic-grade glass fiber cloth according to any one of claims 1 to 8.

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