Three-dimensional imaging detection method for defects of fiber-reinforced polymer composites

Abstract

The present invention discloses a three-dimensional imaging detection method for defects in fiber-reinforced polymer composites. The fiber-reinforced polymer composites are immersed in a fluorescent nanoparticle dispersion solution with a concentration of 0.25 mg / mL to 5 mg / mL to obtain fiber-reinforced polymer composites in which the defects are selectively labeled by the fluorescent nanoparticles. After the above composites are cleaned and dried, they are placed under a microscope. The laser wavelength is adjusted to the position of the absorption peak of the fluorescent nanoparticles, and the composite material is moved along the laser propagation direction to scan and obtain optical sections at different thicknesses of the composite material. The defect structure information in the above optical sections is extracted, and three-dimensional reconstruction of the defect structure is carried out to obtain a three-dimensional image of the defects in the fiber-reinforced polymer composites. Optical sections are made on the three-dimensional image of the defects in the fiber-reinforced polymer composites along any plane in space to analyze the three-dimensional structure of the defects. The present invention can greatly improve the accuracy of defect detection and realize the analysis of the morphology, size and spatial distribution of the defects.

Description

Technical Field

[0001] The invention belongs to the technical field of polymer composite materials, and in particular to a three-dimensional imaging detection method for defects of fiber-reinforced polymer composite materials. Background Art

[0002] Fiber-reinforced polymer composites are key materials for achieving lightweight and strong protection in new energy vehicles, armored equipment, aerospace vehicles, etc. The development and application of fiber-reinforced polymer composites can not only improve equipment performance, but also significantly reduce energy consumption. Against the backdrop of increasingly tight energy resources, the large-scale promotion and application of advanced polymer composite components has important practical significance [Sun, X.; Huang, C.; Wang, L.; Liang, L.; Cheng, Y.; Fei, W.; Li, Y. Adv. Mater. 2021, 33, 2001105.]. At the same time, due to the multi-component and multi-phase characteristics of polymer composites, they are easily affected by external fields during the component forming process, resulting in different types of defects, such as voids, cracks, delamination, impurities, etc. [Zhang Chuhong; Chen Ning; Li Li; Chen Yinghong; Wang Qi. Polymer Materials Science and Engineering 2021, 37, 209-217]. The existence of defects in polymer composites brings unpredictable risks to the service process of equipment. Component failure caused by defects can cause huge casualties and economic losses. Therefore, in order to avoid the occurrence of defects during the molding process and component service as much as possible, it is necessary to perform accurate and non-destructive defect detection on fiber-reinforced polymer composites to quickly discover component defects and eliminate safety hazards [Richely, E.; Bourmaud, A.; Placet, V.; Guessasma, S.; Beaugrand, J. Prog. Mater. Sci. 2022, 124,100851.].

[0003] Traditional non-destructive testing methods, such as ultrasonic testing, are simple to operate and have a fast detection speed, enabling quick location of defect positions. However, this method has low resolution, making it difficult to detect sub-micron and micron-sized defects, and it can only obtain two-dimensional image information, unable to conduct a detailed analysis of defects [Segers, J.; Kersemans, M.; Hedayatrasa, S.; Calderon, J.; Paepegem, W. V. NDT&E Int. 2018, 98, 130-133.]. Therefore, further developing defect characterization methods, improving the detection accuracy of defects in fiber-reinforced polymer composite components, and achieving three-dimensional imaging of defect morphology and distribution are important research topics in the field of polymer composites. Three-dimensional imaging of defects in fiber-reinforced polymer composites can not only detect component defects earlier and eliminate potential safety hazards but also provide a new perspective for studying the evolution process of defects in fiber-reinforced polymer composites, guiding the forming and processing of fiber-reinforced polymer composites.

[0004] Recently, many breakthroughs have been made in optical microscopy imaging technology. Laser confocal microscopy imaging technology uses laser to excite fluorescent molecules, and the energy level transition of fluorescent molecules generates new photons, enabling three-dimensional imaging with sub-micron spatial resolution and large penetration depth [Denk, W.; Strickler, J. H.; Webb, W. W. Science 1990, 248, 73.]. Therefore, laser confocal microscopy imaging technology is expected to improve the detection accuracy (sub-micron level) of defects in fiber-reinforced polymer composites, solve the bottleneck problem of 3D imaging of defect morphology and distribution, and has great application prospects in the field of polymer composites. However, currently, laser confocal microscopy imaging technology is mainly applied in the biomedical field, and applying it to the polymer composite system still requires solving the key scientific problem of defect imaging contrast. Summary of the Invention

[0005] To solve the problems existing in the prior art, the present invention provides a three-dimensional imaging method for defects in fiber-reinforced polymer composite parts. Through the concentration gradient of fluorescent nanoparticles and entropy drive at the defect, the fluorescent nanoparticles are selectively dispersed on the defect surface to obtain the imaging contrast of the defect; optical sections of the fiber-reinforced polymer composite parts at different thicknesses are obtained through optical sectioning, and finally, through the extraction of defect structure information in the optical sections and the three-dimensional reconstruction of the defect structure, a three-dimensional image of the defects in the fiber-reinforced polymer composite parts is obtained, greatly improving the defect detection accuracy and realizing the analysis of defect morphology, size, and spatial distribution.

[0006] To achieve the above object, the present invention provides the following technical solution: A three-dimensional imaging detection method for defects in fiber-reinforced polymer composites, the specific steps are as follows:

[0007] S1 Immerse the fiber-reinforced polymer composite in a fluorescent nanoparticle dispersion with a concentration of 0.25 mg / mL to 5 mg / mL to obtain a fiber-reinforced polymer composite in which the defects are selectively labeled with fluorescent nanoparticles;

[0008] S2 After cleaning and drying the fiber-reinforced polymer composite selectively labeled with fluorescent nanoparticles, place it under a microscope, adjust the laser wavelength to the position of the absorption peak of the fluorescent nanoparticles, and move the composite along the laser propagation direction to scan and obtain optical slices at different thicknesses of the composite;

[0009] S3 Extract the defect structure information in the optical slices at different thicknesses of the composite, perform three-dimensional reconstruction of the defect structure, and obtain a three-dimensional image of the defects in the fiber-reinforced polymer composite;

[0010] S4 Perform optical slicing on the three-dimensional image of the defects in the fiber-reinforced polymer composite along any plane in space to analyze the three-dimensional structure of the defects.

[0011] Further, in S1, the fluorescent nanoparticles include Nile red-modified silica nanoparticles, fluorescein-modified silica nanoparticles, carbon quantum dot-modified silica nanoparticles, or Nile blue-modified silica nanoparticles, and the particle size is 10 nm to 100 nm.

[0012] Further, in S1, the soaking time is 0.5 h to 8 h.

[0013] Further, in S1, the fiber-reinforced polymer composite includes films, plates, and profiles.

[0014] Further, in S2, a confocal fluorescence microscope is used to move the composite 1 μm to 110 μm along the laser propagation direction towards or away from the objective lens, and scan at intervals of 0.5 μm to 2 μm along the laser propagation direction to obtain optical slices at different thicknesses of the composite.

[0015] Further, in S3, perform light scattering and light absorption correction on the optical slices at different thicknesses of the composite, extract surface defects, internal defects, and delamination defects between fibers and the matrix, and perform three-dimensional reconstruction according to the position order of the optical slices.

[0016] Further, the threshold segmentation method is used to extract surface defects, internal defects, and delamination defects between fibers and the matrix of the composite.

[0017] Further, in S4, the analysis of the three-dimensional structure of the defect includes size measurement, volume calculation, shape factor analysis, and mathematical statistical analysis of the defect.

[0018] Further, in S1, the fibers in the fiber-reinforced polymer composite are short glass fibers, short carbon fibers, and natural fibers.

[0019] Further, in S3, during three-dimensional reconstruction:

[0020] For the defects on the surface of the fiber-reinforced polymer composite, the composite material except for the defects is presented by using a gray-scale palette and volume rendering;

[0021] For the defects inside the fiber-reinforced polymer composite, the defects inside the composite material are presented by using a red palette and surface rendering;

[0022] For the delamination defects between the fibers and the matrix of the fiber-reinforced polymer composite, the delamination defects are presented by using a red palette and volume rendering, and the fibers are presented by using a yellow palette and volume rendering.

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

[0024] The present invention provides a three-dimensional imaging detection method for defects in fiber-reinforced polymer composite parts. By immersing the fiber-reinforced polymer composite in a fluorescent nanoparticle dispersion liquid, driven by the concentration gradient of fluorescent nanoparticles inside the fiber-reinforced polymer composite and in the dispersion liquid, the fluorescent nanoparticles diffuse into the fiber-reinforced polymer composite. At the same time, since the surface free energy of the interface between the fluorescent nanoparticles and air is less than the sum of the surface free energy of the interface between the fluorescent nanoparticles and the fiber-reinforced polymer composite matrix and the surface free energy of the interface between the fiber-reinforced polymer composite matrix and air, the fluorescent nanoparticles will selectively diffuse towards the surface of the defect driven by entropy, thereby obtaining the imaging contrast of the defect, enabling the defects in the fiber-reinforced polymer composite to be imaged under a confocal fluorescence microscope. Optical slices of the fiber-reinforced polymer composite at different thicknesses are obtained by using the confocal fluorescence microscope, and further through the extraction and three-dimensional reconstruction of the defect structure information in the optical slices, a three-dimensional image of the defects in the fiber-reinforced polymer composite is obtained, improving the detection accuracy of the defects in the fiber-reinforced polymer composite from millimeters to sub-microns, an improvement of nearly 10 4 times;

[0025] The three-dimensional imaging detection method for defects of fiber-reinforced polymer composites of the present invention can obtain three-dimensional images of surface defects, internal defects, delamination defects between fibers and matrix, etc. without damaging the sample, and through measuring the size, calculating the volume, analyzing the shape factor and performing mathematical statistical analysis on the defects in the three-dimensional image, the three-dimensional morphology, three-dimensional size, spatial distribution, volume size and volume fraction of the defects are analyzed. This has significant advantages compared with the existing ultrasonic detection method that can only obtain two-dimensional images and cannot quantitatively analyze the defect structure;

[0026] The detection method of the present invention can be used for the research on the formation and expansion mechanism of defects in fiber-reinforced polymer composites, can be used for the defect detection of fiber-reinforced polymer composites in service, and can also be used to guide the structural design and product R & D process of fiber-reinforced polymer composites and guide the industrial production of fiber-reinforced polymer composites. Brief Description of the Drawings

[0027] Figure 1 It is a schematic diagram of the surface defect of the glass fiber-reinforced polylactic acid composite material in Example 1 of the present invention, Figure 1 where a is the three-dimensional image of the surface defect, Figure 1 b in Figure 1 and Figure 1 c in xz are respectively the optical sections of the yz plane and the Figure 1 plane in a in Figure 1 ; d in z is the change of the transverse dimensions of the four surface defects I, II, III and IV in c in

[0028] Figure 2 It is a schematic diagram of the surface defect of the glass fiber-reinforced polypropylene composite material in Example 2 of the present invention;

[0029] Figure 3 It is a schematic diagram of the internal defect of the carbon fiber-reinforced polylactic acid composite material in Example 3 of the present invention, Figure 3 where a is the three-dimensional image of the internal defect; Figure 3 b in

[0030] Figure 4 is the volume distribution of the internal defect of the material; Figure 4 It is a schematic diagram of the delamination defect of the carbon fiber-reinforced polylactic acid composite material in Example 4 of the present invention, Figure 4 where a is the three-dimensional image of the delamination defect, Figure 4 b in Figure 4 is the enlarged view of a single carbon fiber and the delamination defect around the carbon fiber in the dashed box in a in

[0031] Figure 5Fluorescence microscope photograph of the surface defects of the glass fiber-reinforced polylactic acid composite material in Comparative Example 1 of the present invention, the scale bar is 100 μm;

[0032] Figure 6 Optical microscope photograph of the surface defects of the glass fiber-reinforced polylactic acid composite material in Comparative Example 2 of the present invention, the scale bar is 100 μm;

[0033] Figure 7 Scanning electron microscope photograph of the internal defects of the carbon fiber-reinforced polylactic acid composite material in Comparative Example 3 of the present invention, the scale bar is 20 μm. Detailed implementation manners

[0034] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation manners. It is necessary to point out here that the following embodiments are only used to further illustrate the present invention and should not be construed as limiting the protection scope of the present invention. Some non-essential improvements and adjustments made by those skilled in the art based on the above-mentioned inventive content still fall within the protection scope of the present invention.

[0035] In addition, it should be noted that the materials in the following embodiments are all in parts by weight.

[0036] A three-dimensional imaging detection method for defects of a fiber-reinforced polymer composite material of the present invention specifically comprises the following steps:

[0037] (1) Preparation of the fluorescent nanoparticle dispersion: Ultrasonically disperse fluorescent nanoparticles with a particle size of 10 nm to 100 nm in an organic solvent, with an ultrasonic power of 100 W ~500 W , and an ultrasonic dispersion time of 0.5 h to 2 h to prepare a fluorescent nanoparticle dispersion with a concentration of 0.25 mg / mL to 5 mg / mL.

[0038] (2) Selective labeling of the fiber-reinforced polymer composite material with fluorescent nanoparticles: Immerse the entire fiber-reinforced polymer composite material in the fluorescent nanoparticle dispersion, and select an immersion time of 0.5 h to 8 h according to the wall thickness of the workpiece. The greater the wall thickness, the longer the immersion time; under the entropy drive of the fluorescent nanoparticle concentration gradient and the defect, selectively disperse the fluorescent nanoparticles on the surface of the defect to obtain a fiber-reinforced polymer composite material with defects selectively labeled with fluorescent nanoparticles.

[0039] (3) Three-dimensional imaging of the defects of the fiber-reinforced polymer composite material: Place the fiber-reinforced polymer composite material under a confocal fluorescence microscope, adjust the laser wavelength to near the absorption peak wavelength of the fluorescent nanoparticles, so that the fluorescent nanoparticles generate a fluorescence signal, and image the defects of the workpiece;

[0040] Specifically, the laser propagation direction is defined as zDirection, perpendicular z The plane in the xy direction is defined as z plane. Move the composite material along the z direction towards or away from the objective lens by a distance of 1 μm to 110 μm, and scan at intervals of 0.5 μm to 2 μm along the xy direction to obtain optical slices at different thicknesses of the workpiece.

[0041] (4) Three-dimensional image reconstruction of defects in fiber-reinforced polymer composites: Perform light scattering and light absorption correction on the optical slices of the fiber-reinforced polymer composites at different thicknesses in the xy plane. Use the threshold segmentation method to extract structural information such as surface defects, internal defects, and delamination defects between fibers and the matrix, and perform three-dimensional reconstruction according to the position order of the optical slices.

[0042] (5) Perform optical slicing on the three-dimensional image of the defects in the fiber-reinforced polymer composites along any plane in space for the analysis of the three-dimensional structure of the defects; and perform size measurement, volume calculation, shape factor analysis, and mathematical statistical analysis on the defects to obtain structural parameters such as the three-dimensional size, shape, and volume fraction of the defects.

[0043] Preferably, in step (1), the fluorescent nanoparticles have the property of photoluminescence, including but not limited to Nile red-modified silica nanoparticles, fluorescein-modified silica nanoparticles, carbon quantum dot-modified silica nanoparticles, and Nile blue-modified silica nanoparticles.

[0044] Preferably, in step (2), the fibers in the fiber-reinforced polymer composites include but not limited to chopped glass fibers, chopped carbon fibers, natural fibers, etc.;

[0045] Preferably, in step (2), the fiber-reinforced polymer composites include films, plates, profiles, etc.

[0046] The three-dimensional imaging detection method of the present invention can selectively present the surface defects, internal defects, and delamination defects between fibers and the matrix of the fiber-reinforced polymer composites, intuitively reflect the shape and spatial distribution of the defects in the fiber-reinforced polymer composites, and can analyze and obtain the three-dimensional size and its size distribution, the number of defects, and the volume fraction of the defects in the fiber-reinforced polymer composites. When the distribution of the defects in the composite material is too concentrated or the number and volume fraction are too large, stop the service of the composite material in time to eliminate potential safety hazards.

[0047] Preferably, during three-dimensional reconstruction, for the defects on the surface of the material, select a grayscale color palette and volume rendering method to present the composite material except for the defects;

[0048] For the defects inside the material, a red color palette and surface rendering are selected to present the defects inside the material;

[0049] For the delamination defects between fibers and polymer matrix materials, a red color palette and volume rendering are selected for presentation. For the fibers, a yellow color palette and volume rendering are used for presentation, and 3D images of the delamination defects and fibers of fiber-reinforced polymer composites are obtained simultaneously.

[0050] Example 1

[0051] (1) Preparation of Nile red-modified silica nanoparticle dispersion: 20 mg of Nile red-modified silica nanoparticles with a particle size of 10 nm were ultrasonically dispersed into 20 mL of N,N-dimethylacetamide to prepare a 1 mg / mL fluorescent nanoparticle dispersion with an ultrasonic power of 500 W , and the ultrasonic dispersion time was 2 h.

[0052] (2) Selective labeling of glass fiber-reinforced polylactic acid composites with Nile red-modified silica nanoparticles: The glass fiber-reinforced polylactic acid composites were placed into the Nile red-modified silica nanoparticle dispersion and left for 0.5 h to allow the fluorescent nanoparticles to diffuse to the surface defects of the glass fiber-reinforced polylactic acid composites.

[0053] (3) 3D imaging of the surface defects of glass fiber-reinforced polylactic acid composites: The glass fiber-reinforced polylactic acid composites labeled with fluorescent nanoparticles were washed with distilled water, dried, placed under a confocal fluorescence microscope, the laser wavelength was adjusted to 543 nm, and the composites were imaged; the sample was moved towards the objective lens along the laser propagation direction by a distance of 40 μm, and optical slices at different thicknesses of the composites were obtained by scanning every 0.5 μm. xy Planar optical sections.

[0054] (4) 3D image reconstruction of the surface defects of glass fiber-reinforced polylactic acid composites: The optical slices at different thicknesses of the glass fiber-reinforced polylactic acid composites were corrected for light scattering and light absorption. The threshold segmentation method was used to extract the structural information of the composites except for the surface defects. 3D reconstruction was performed according to the order of the optical slice positions. A gray color palette and volume rendering were selected to present the composites except for the surface defects, and 3D images of the surface defects of the glass fiber-reinforced polylactic acid composites were obtained; and optical slices of the 3D map of the surface defects of the glass fiber-reinforced polylactic acid composites were made along xz and yz planes for the analysis of the surface defect structure.

[0055] Example 2

[0056] (1) Preparation of fluorescein-modified silica nanoparticle dispersion: 10 mg of fluorescein-modified silica nanoparticles with a particle size of 30 nm were ultrasonically dispersed in 20 mL of p-xylene to prepare a fluorescent nanoparticle dispersion with a concentration of 0.5 mg / mL. The ultrasonic power was 300 W , and the ultrasonic dispersion time was 1 h.

[0057] (2) Selective labeling of glass fiber-reinforced polypropylene composite with fluorescein-modified silica nanoparticles: The glass fiber-reinforced polypropylene composite was placed in the fluorescein-modified silica nanoparticle dispersion for 2 h to allow the fluorescent nanoparticles to diffuse to the surface defects of the glass fiber-reinforced polypropylene composite.

[0058] (3) Three-dimensional imaging of surface defects of glass fiber-reinforced polypropylene composite: The glass fiber-reinforced polypropylene composite labeled with fluorescent nanoparticles was washed with distilled water, dried, and placed under a confocal fluorescence microscope. The laser wavelength was adjusted to 495 nm to image the composite; the sample was moved away from the objective lens along the laser propagation direction by a distance of 110 μm, and optical slices at different thicknesses of the composite were obtained by scanning at intervals of 2.0 μm. xy Planar optical slices.

[0059] (4) Three-dimensional image reconstruction of surface defects of glass fiber-reinforced polypropylene composite: The optical slices at different thicknesses of the glass fiber-reinforced polypropylene composite were corrected for light scattering and light absorption. The structural information of the composite except for the defects was extracted by the threshold segmentation method. Three-dimensional reconstruction was performed according to the position order of the optical slices. The composite except for the defects was presented by selecting a grayscale color palette and volume rendering to obtain a three-dimensional image of the surface defects of the glass fiber-reinforced polypropylene composite.

[0060] Example 3

[0061] (1) Preparation of carbon quantum dot-modified silica nanoparticle dispersion: 5 mg of carbon quantum dot-modified silica nanoparticles with a particle size of 50 nm were ultrasonically dispersed in 20 mL of N,N-dimethylacetamide to prepare a fluorescent nanoparticle dispersion with a concentration of 0.25 mg / mL. The ultrasonic power was 200 W , and the ultrasonic dispersion time was 1.5 h.

[0062] (2) Selective labeling of carbon fiber-reinforced polylactic acid composite with carbon quantum dot-modified silica nanoparticles: The carbon fiber-reinforced polylactic acid composite was placed in the carbon quantum dot-modified silica nanoparticle dispersion for 6 h to allow the fluorescent nanoparticles to diffuse to the internal defects of the carbon fiber-reinforced polylactic acid composite.

[0063] (3)Three-dimensional imaging of internal defects in carbon fiber reinforced polylactic acid composites: Selectively label the carbon fiber reinforced polylactic acid composites with carbon quantum dot-modified silica nanoparticles, wash with distilled water, dry, place under a confocal fluorescence microscope, adjust the laser wavelength to 450 nm, and image the composites; Move the sample towards the objective lens along the laser propagation direction, with a moving distance of 52 μm, and scan every 1.0 μm to obtain xy plane optical sections.

[0064] (4)Three-dimensional image reconstruction of internal defects in carbon fiber reinforced polylactic acid composites: Perform light scattering and light absorption correction on the optical sections at different thicknesses of the carbon fiber reinforced polylactic acid composites, extract the image information of internal defects through threshold segmentation method, perform three-dimensional reconstruction according to the position order of the optical sections, select the red color palette and surface rendering method to present the internal defects of the composites, and obtain the three-dimensional image of the internal defects in the carbon fiber reinforced polylactic acid composites; And statistically analyze the volume of each internal defect and calculate the volume fraction of the internal defects in the composites.

[0065] Example 4

[0066] (1)Preparation of Nile blue-modified silica nanoparticle dispersion: Ultrasonically disperse 100 mg of carbon quantum dot-modified silica nanoparticles with a particle size of 100 nm into 20 mL of N,N-dimethylacetamide to prepare a 5 mg / mL fluorescent nanoparticle dispersion, with an ultrasonic power of 100 W and an ultrasonic dispersion time of 0.5 h.

[0067] (2)Selective labeling of carbon fiber reinforced polylactic acid composites with Nile blue-modified silica nanoparticles: Place the carbon fiber reinforced polylactic acid composites into the Nile blue-modified silica nanoparticle dispersion and leave for 8 h to allow the fluorescent nanoparticles to diffuse into the fiber-matrix delamination defects of the carbon fiber reinforced polylactic acid composites.

[0068] (3)Three-dimensional imaging of fiber-matrix delamination defects in carbon fiber reinforced polylactic acid composites: Place the carbon fiber reinforced polylactic acid composite film sample under a confocal fluorescence microscope, adjust the laser wavelength to 450 nm, and image the composites; Move the sample towards the objective lens along the laser propagation direction, with a moving distance of 80 μm, and scan every 1.5 μm to obtain xy plane optical sections.

[0069] (4)Three-dimensional image reconstruction of delamination defects between fibers and matrix in carbon fiber reinforced polylactic acid composites: Optical slices at different thicknesses of carbon fiber reinforced polylactic acid composites were corrected for light scattering and light absorption. The structural information of fibers and delamination defects was extracted using the threshold segmentation method. Three-dimensional reconstruction was performed according to the order of optical slice positions. The delamination defects were presented using a red color palette and volume rendering, and the carbon fibers were presented using a yellow color palette and volume rendering to obtain a three-dimensional image of delamination defects between fibers and matrix in carbon fiber reinforced polylactic acid composites; and the volume and distribution of delamination defects in carbon fiber reinforced polylactic acid composites were quantitatively analyzed.

[0070] Comparative Example 1

[0071] The glass fiber reinforced polylactic acid composite selectively labeled with fluorescent nanospheres prepared in Example 1 was placed under a fluorescence microscope for observation to obtain a two-dimensional fluorescence image of surface defects of the glass fiber reinforced polylactic acid composite for comparison.

[0072] Comparative Example 2

[0073] The glass fiber reinforced polylactic acid composite selectively labeled with fluorescent nanospheres prepared in Example 1 was placed under an optical microscope for observation to obtain an optical microscope image of surface defects of the glass fiber reinforced polylactic acid composite for comparison.

[0074] Comparative Example 3

[0075] The carbon fiber reinforced polylactic acid composite selectively labeled with fluorescent nanoparticles in Example 3 was frozen in liquid nitrogen for 1 hour, and then the composite was quenched with tweezers to obtain a flat surface; a layer of gold particles was sprayed on the quenched surface of the specimen using an ion sputtering instrument and placed under a scanning electron microscope for observation to obtain a scanning electron microscope image of internal defects of the carbon fiber reinforced polylactic acid composite for comparison.

[0076] In Example 1 of the present invention, a three-dimensional image of surface defects of the glass fiber reinforced polylactic acid composite was obtained, as shown in Figure 1 a of the appendix. In the three-dimensional image, it can be intuitively observed that there are many pit-shaped surface defects on the surface of the glass fiber reinforced polylactic acid composite. These surface defects vary in size and are randomly distributed on the surface of the glass fiber reinforced polylactic acid composite, and the surface defects extend into the material interior with a certain depth of extension;

[0077] From xz and yz planar sectional views ( Figure 1 b of Figure 1 and Figure 1 c of yzThe transverse dimensions of the four surface defects I, II, III, and IV on the plane were statistically analyzed. The results in the dimension direction of the x-axis are as shown in Figure 1 d. It can be intuitively obtained the structural changes when the defects extend into the composite material. As the four surface defects extend deeper into the composite material, their transverse dimensions all decrease. Among them, defects I and II extend to about 7 μm and 14 μm from the lower surface into the composite material respectively, while defects III and IV penetrate through the entire glass fiber-reinforced polylactic acid composite material.

[0078] Similar to Example 1, in Example 2 of the present invention, three-dimensional images of the surface defects of the glass fiber-reinforced polypropylene composite material were also obtained, as shown in Figure 2 . It can be intuitively observed the surface defects that meander and extend on the material surface. Through the analysis of the surface defects, the three-dimensional dimensions and the extension depth information of the surface defects can also be obtained. Furthermore, the surface defects of the composite material can be evaluated to confirm its service safety.

[0079] Furthermore, in Example 3 of the present invention, three-dimensional images of the internal defects of the carbon fiber-reinforced polylactic acid composite material were obtained. As can be seen from Figure 3 a, there are 12 defects with different sizes, shapes, and randomly distributed in the composite material. Through the statistical analysis of the volumes of the internal defects in the carbon fiber-reinforced polylactic acid composite material, the volumes of the internal defects (internal defects No. 1 to No. 12) of the composite material can be obtained, as shown in Figure 3 b. The volume of internal defect No. 1 is the largest, about 1800 μm 3 , and the volume of internal defect No. 12 is the smallest, about 1 μm 3 . The volume distribution of the internal defects is relatively wide and the shapes are very complex. The volume of the internal defects accounts for 0.2% of the total volume of the carbon fiber-reinforced polylactic acid composite material. Furthermore, the internal defects of the composite material can be evaluated to ensure its service safety.

[0080] Furthermore, in Example 4 of the present invention, three-dimensional images of the fibers and the delamination defects between the fibers and the matrix of the carbon fiber-reinforced polylactic acid composite material were obtained. As shown in Figure 4 a, the carbon fibers in the carbon fiber-reinforced polylactic acid composite material tend to be distributed parallel to the xy plane without obvious orientation. There are delamination defects between the carbon fibers and the polylactic acid matrix. Further magnifying the delamination defects, as shown in Figure 4 b, the delamination defects closely surround the carbon fibers and show a discontinuous distribution. Through the volume analysis of the delamination defects, it can be seen that there are a large number of delamination defects with relatively small volumes (<1000 μm 3 ) in the carbon fiber-reinforced polylactic acid composite material, and there are fewer delamination defects with large volumes ( Figure 4In c), the delamination defect accounts for 0.7% of the carbon fiber reinforced polylactic acid composite film sample. Furthermore, the delamination defect of the composite material can be evaluated to ensure its service safety.

[0081] From the three-dimensional imaging results of the composite material defects above, it can be understood that the surface defects, internal defects and delamination defects of the composite material all have characteristics such as complex three-dimensional shapes, relatively random spatial distributions, wide three-dimensional sizes and volume distributions. However, the Figure 5 and Figure 6 obtained in Comparative Example 1 and Comparative Example 2 can only show the two-dimensional images of the surface defects. Analyzing the two-dimensional images can only obtain the sizes and shapes of the surface defects at the surface, and cannot obtain the information of the surface defects extending into the material interior, let alone analyze the depths of the surface defects and the sizes at different extension depths. Therefore, the two-dimensional images of Comparative Example 1 and Comparative Example 2 cannot well analyze the surface defects of the composite material, while Examples 1 and 2 of the present invention truly reflect the three-dimensional shapes and sizes of the surface defects of the composite material, proving the significant advantages of the present invention.

[0082] The Figure 7 obtained in Comparative Example 3 of the present invention can only show the structural information of a specific cross-section of the internal defects, and can only analyze the shapes and sizes of the internal defects at the cross-section. Similarly, it cannot obtain the three-dimensional shapes and sizes, spatial distribution conditions and other structural information of the internal defects, and cannot comprehensively analyze the internal defects of the composite material. However, in Example 3 of the present invention, the three-dimensional images of each internal defect can be directly obtained, the volumes of each defect can be quantitatively analyzed, and the internal defects can be visually reflected. This also shows that the three-dimensional imaging detection method for the defects of the fiber reinforced polymer composite material of the present invention has obvious advantages in the structural analysis of the composite material defects.

Claims

1. A three-dimensional imaging detection method for defects of fiber-reinforced polymer composites, characterized in that, The specific steps are as follows: S1 Immerse the fiber-reinforced polymer composite material in a fluorescent nanoparticle dispersion solution with a concentration of 0.25 mg / mL to 5 mg / mL to obtain a fiber-reinforced polymer composite material in which the defects are selectively labeled by fluorescent nanoparticles; S2 Clean and dry the fiber-reinforced polymer composite material in which the defects are selectively labeled by fluorescent nanoparticles, then place it under a microscope. Adjust the laser wavelength to the absorption peak position of the fluorescent nanoparticles, and move the composite material along the laser propagation direction to scan and obtain optical sections at different thicknesses of the composite material; S3 Extract the defect structure information in the optical sections at different thicknesses of the composite material, perform three-dimensional reconstruction of the defect structure, and obtain a three-dimensional image of the defects in the fiber-reinforced polymer composite material; S4 Perform optical sectioning on the three-dimensional image of the defects in the fiber-reinforced polymer composite material along any plane in space to analyze the three-dimensional structure of the defects; In S1, the fluorescent nanoparticles include Nile red-modified silica nanoparticles, fluorescein-modified silica nanoparticles, carbon quantum dot-modified silica nanoparticles, or Nile blue-modified silica nanoparticles, and the particle size is 10 nm to 100 nm; In S2, use a confocal fluorescence microscope to move the composite material 1 μm to 110 μm along the laser propagation direction towards or away from the objective lens, and scan every 0.5 μm to 2 μm along the laser propagation direction to obtain optical sections at different thicknesses of the composite material; In S3, perform light scattering and light absorption correction on the optical sections at different thicknesses of the composite material, extract surface defects, internal defects, and delamination defects between fibers and the matrix, and perform three-dimensional reconstruction according to the position order of the optical sections; In S3, when performing three-dimensional reconstruction: For the defects on the surface of the fiber-reinforced polymer composite material, use a gray color palette and volume rendering method to present the composite material except for the defects; For the internal defects of the fiber-reinforced polymer composite material, use a red color palette and surface rendering method to present the internal defects of the composite material; For the delamination defects between the fibers and the matrix of the fiber-reinforced polymer composite material, use a red color palette and volume rendering method to present the delamination defects, and use a yellow color palette and volume rendering method to present the fibers; 2. The three-dimensional imaging detection method for defects of a fiber-reinforced polymer composite material according to claim 1, wherein In S1, the soaking time is 0.5 h to 8 h; 3. The three-dimensional imaging detection method for defects of a fiber-reinforced polymer composite material according to claim 1, characterized in that, In S1, the fiber-reinforced polymer composite material includes films, plates, and profiles; 4. A three-dimensional imaging detection method for defects of a fiber-reinforced polymer composite material according to claim 1, characterized in that, Use the threshold segmentation method to extract the surface defects, internal defects, and delamination defects between fibers and the matrix of the composite material; 5. The three-dimensional imaging detection method for defects of a fiber-reinforced polymer composite material according to claim 1, characterized in that, In S4, the analysis of the three-dimensional structure of the defects includes measuring the size of the defects, calculating the volume, analyzing the shape factor, and performing mathematical statistical analysis; 6. The three-dimensional imaging detection method for defects of a fiber-reinforced polymer composite material according to claim 1, characterized in that, In S1, the fibers in the fiber-reinforced polymer composite material are short glass fibers, short carbon fibers, or natural fibers.

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