A global continuum modeling approach for realistic microstructures of composite materials

Abstract

The present invention discloses a global continuous modeling method for the real microstructure of a composite material. The microstructure morphology of the composite material is obtained by a scanning electron microscope and saved as a digital image; the digital image is decolorized by an image processing software, and the minimum and maximum values ​​of the output brightness value are set to increase the contrast; a geometric model of the corresponding size of the digital image coverage area is established; based on the decolorization map and the material parameters of each single phase or component of the composite material, a spatial distribution function of the material parameters in the entire domain of the composite material is established, and the material parameter items of the numerical model are assigned; according to conventional simulation means, grid division, physical field definition, boundary condition and initial condition setting and numerical solution are completed in sequence. The present invention can not only extract the microstructure of the material with higher resolution, but also automatically define the material parameter values ​​corresponding to different phases or components in the entire domain at one time based on the real microstructure, significantly reducing the error rate and distortion rate during modeling.

Description

Technical Field

[0001] The invention relates to the field of material numerical simulation modeling, and in particular to a global continuous modeling method for real microstructure of composite materials. Background Art

[0002] A composite material is a material composed of multiple different components or phases. The inhomogeneity or anisotropy of the spatial distribution of its components causes one or more of its physical, mechanical, chemical and other properties to change along the spatial distribution or in a certain direction to adapt to different environments and special functional requirements.

[0003] From a microscopic perspective, the presence of microcracks, capillaries, and second phases will cause the inhomogeneity of composite materials; from a macroscopic perspective, macrocracks, notches, and adhesive joints will also cause the inhomogeneity of composite materials. Material inhomogeneity is advantageous in some cases. For example: fiber-reinforced resin-based composites for special valves, aluminum-based composites (MMC) widely used in the automotive industry, concrete materials widely used in the construction industry, and boron / aluminum composites widely used in aerospace equipment. Material inhomogeneity is disadvantageous in some cases, such as: YSZ (yttria-stabilized zirconia) electrodes for solid oxide fuel cells and thermal barrier coatings for thermal insulation of hot end components of gas turbines may leave randomly distributed pores and microcracks inside during spray preparation, causing them to fail prematurely.

[0004] In recent years, driven by the strong demand in the fields of nano, biology, materials, energy, aerospace, etc., the non-uniformity and anisotropy of composite materials indicate that they are materials with micro / mesostructure or micro defects, and their damage and destruction mechanisms are significantly different from those of ordinary materials. Therefore, when studying their destruction behavior and mechanisms through numerical simulation, their micro / mesostructure or defects must be considered. Traditional microstructure modeling methods based on digital images are generally discontinuous methods, that is, in the numerical model established containing the microstructure of composite materials, each unconnected structure or defect is treated as an independent geometric body. In the subsequent steps of material parameter definition, initial condition and boundary condition setting, it is necessary to manually select each geometric body in turn, which is very cumbersome and prone to problems such as missing selection and wrong selection. Sometimes, in order to complete the above-mentioned parameter definition and boundary condition setting steps, some tiny and sharp organizational structures have to be rounded, thereby losing some details and reducing resolution. Summary of the invention

[0005] In view of the technical problems of the existing non-continuous digital image microstructure modeling methods, such as the high probability of missing or wrong selection of each geometric body in manual selection, the easy loss of some details, and the reduction of resolution, the present invention provides a global continuous modeling method for the real microstructure of composite materials. This method can not only extract the microstructure of the material with higher resolution, but also automatically and once define the material parameter values ​​corresponding to different phases or components in the whole domain based on the real microstructure.

[0006] To achieve the above process, the present invention adopts the following scheme:

[0007] A global continuous modeling method for real microstructure of composite materials, comprising the following steps:

[0008] S1. Use a scanning electron microscope to obtain the microstructure morphology of the corresponding composite material and save it as a digital image;

[0009] S2. Decolorizing the obtained digital image using image processing software to obtain a decolorized image, and setting the minimum and maximum output brightness values, that is, narrowing the brightness value output range to increase the contrast of different phases or components;

[0010] S3. Establishing a geometric model of the size corresponding to the area covered by the digital image;

[0011] S4. Based on the decolorization image and the material parameters of each single phase or component of the composite material, a spatial distribution function of the material parameters in the entire composite material domain is established, and the spatial distribution function is assigned to the material parameter items in the numerical model;

[0012] S5. According to the simulation method, complete the meshing, physical field definition, boundary condition and initial condition setting and numerical solution in sequence.

[0013] Furthermore, in S1, before obtaining the microstructure morphology of the composite material, it is repeatedly polished until it is smooth.

[0014] Furthermore, the specific method of S2 is:

[0015] In the image processing software, the minimum output brightness value is set to the peak brightness value of the pixels in a certain phase or component area in the image, and the maximum output brightness value is set to the peak brightness value of the pixels in another phase or component area, thereby narrowing the output range of the image brightness value. At this time, the two different phases or components of the composite material are displayed in black and white, respectively, with the brightness value of black being 0 and the brightness value of white being 255, while the area near the interface gradually transitions from black to white.

[0016] Furthermore, the specific method of step S3 is:

[0017] Refer to the ruler of the SEM image to measure the specific length and width of the image coverage area;

[0018] Set reasonable measurement units in the finite element modeling software and build a two-dimensional geometric model of corresponding dimensions.

[0019] Furthermore, the specific method of step S4 is:

[0020] The brightness value corresponding to each pixel in the decolorized image Divide uniformly by 255 to get a two-dimensional distribution function , which is in the form of the following formula (I):

[0021] (I),

[0022] Based on the above two-dimensional distribution function and material parameters of each single phase or component in the composite material M 0. M 1. Derivation and establishment of the spatial distribution function of material parameters in the entire composite material domain , which is in the form of the following formula (II):

[0023] (II),

[0024] in M are material parameters including density, specific heat, thermal conductivity, coefficient of thermal expansion, elastic modulus, Poisson's ratio or fracture energy, M 0 is the material parameter of the phase or component corresponding to the black area, M 1The white area corresponds to the material parameters of the phase or component;

[0025] In the finite element software, the user-defined function Assign values ​​to material parameters in the numerical model.

[0026] The beneficial effects of the present invention are:

[0027] The present invention can not only extract the microstructure of composite materials with higher resolution, but also automatically and once define the material parameter values ​​corresponding to different phases or components in the entire domain based on the real microstructure, thereby significantly reducing the error rate and distortion rate during modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the flow chart of the modeling method of the present invention;

[0029] Figure 2 This is a microstructure image of the composite material A (aluminum-based composite material reinforced with silicon carbide particles for automobile brake discs) obtained using a scanning electron microscope in an embodiment of the present invention;

[0030] Figure 3 This is a decolorized image A obtained based on the microstructure image processing of the composite material A in an embodiment of the present invention;

[0031] Figure 4 is a two-dimensional distribution function obtained by calculation based on the decolorized image A in the embodiment of the present invention g(x,y) of cloud maps;

[0032] Figure 5 In the embodiment of the present invention, based on g(x,y) Spatial distribution of thermal conductivity of composite material A defined by function;

[0033] Figure 6 In the embodiment of the present invention, based on g(x,y) Spatial distribution diagram of thermal expansion coefficient of composite material A defined by function;

[0034] Figure 7 In the embodiment of the present invention, based on g ( x,y ) A three-dimensional display diagram of the spatial distribution of thermal conductivity of the composite material A defined by the function;

[0035] Figure 8 The internal temperature distribution diagram of the composite material A obtained by numerical simulation in the embodiment of the present invention;

[0036] Fig. 9 This is a diagram showing the internal thermal stress distribution of the composite material A obtained by numerical simulation in an embodiment of the present invention;

[0037] Fig.10 This is a microstructure image of composite material B (carbon fiber reinforced resin-based composite material for valve body) obtained by scanning electron microscope in an embodiment of the present invention;

[0038] Fig.11 The decolorized image B is obtained based on the microstructure image processing of the composite material B in the embodiment of the present invention;

[0039] Fig.12 is a two-dimensional distribution function obtained by calculation based on the decolorized image B in the embodiment of the present invention g(x,y) of cloud maps;

[0040] Fig.13 In the embodiment of the present invention, based on g(x,y) Spatial distribution of thermal conductivity of composite material B defined by function;

[0041] Fig.14 In the embodiment of the present invention, based on g(x,y) The spatial distribution diagram of the thermal expansion coefficient of the composite material B defined by the function;

[0042] Fig.15In the embodiment of the present invention, based on g ( x,y ) a three-dimensional display diagram of the spatial distribution of the thermal expansion coefficient of the composite material B defined by the function;

[0043] Fig.16 The internal temperature distribution diagram of the composite material B obtained by numerical simulation in an embodiment of the present invention;

[0044] Fig.17 Graph showing the internal thermal stress distribution of composite material B obtained by numerical simulation in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described in detail below with reference to specific embodiments, but the present invention is not limited thereto. Example 1

[0046] Step S1. Use a scanning electron microscope to obtain the microstructure morphology of composite material A (silicon carbide particle reinforced aluminum matrix composite material for automobile brake disc) and save it as a digital image, such as Figure 2 shown.

[0047] Step S2. Decolorizing the obtained digital image using image processing software to obtain a decolorized image, and Photoshop is selected as the image processing software;

[0048] In Photoshop, set the minimum and maximum output brightness values. Figure 2 , the minimum brightness value is 117, and the maximum brightness value is 216. In this way, the brightness value output range is reduced to 117-216, such as Figure 3 shown. Figure 2 The pixels with brightness values ​​of 117 and 216 are in Figure 3 The brightness values ​​will be changed to 0 (black) and 255 (white) respectively, thereby increasing the contrast between different phases or components;

[0049] Step S3. Establishing a geometric model of the size corresponding to the area covered by the digital image;

[0050] Comparison Figure 2 The scale in Figure 2 The coverage area is measured and its size is 500×343 μm. A two-dimensional rectangular geometric model is established in the numerical simulation software, and its length and width are 500×343 μm. The numerical simulation software used in this embodiment is COMSOL (it is one of many numerical simulation software, and other numerical simulation software can also be used).

[0051] Step S4. Based on the decolorized image and the material parameters of each single phase or component of the composite material, a spatial distribution function of the material parameters in the entire composite material domain is established, and assigned to the material parameter items in the numerical model.

[0052] The brightness value corresponding to each pixel in the decolorized image Divide uniformly by 255 to get a two-dimensional distribution function , which has the following form:

[0053] ,

[0054] The distribution cloud diagram of the function is as follows Figure 4 shown.

[0055] Based on the above two-dimensional distribution function and material parameters of each single phase or component in the composite material M 0. M 1. Derivation and establishment of the spatial distribution function of material parameters in the entire composite material domain , which has the following form:

[0056] ,

[0057] in M are material parameters, such as density, specific heat, thermal conductivity, thermal expansion coefficient, elastic modulus, Poisson's ratio, fracture energy, etc. M 0 is the material parameter of the phase or component corresponding to the black area, M 1White areas correspond to material parameters of phases or components.

[0058] The material parameters of the single component of the composite material A in this embodiment are as follows:

[0059]

[0060] In numerical simulation software, the user-defined function Assigned to the material parameter item in the numerical model. In this embodiment, the thermal conductivity of the silicon carbide particle reinforced aluminum matrix composite material The spatial distribution of Figure 5 As shown, the thermal expansion coefficient The spatial distribution of Figure 6 As shown, thermal conductivity Spatially distributed stereoscopic display such as Figure 7 As shown, through Figures 5 to 7 It can be seen that the spatial distribution of material parameters corresponds well to its microstructure. For example, the thermal conductivity of the region where the silicon carbide particles are located is about 400 W / (m·K), and that of the region occupied by the aluminum alloy is 150 W / (m·K). Figure 7It can be seen that its spatial distribution is continuous.

[0061] Step S5. According to conventional simulation methods, complete the grid division, physical field definition, initial condition and boundary condition setting and numerical solution in sequence.

[0062] In this embodiment, the grid is a quadrilateral grid with a maximum cell size of 0.001 mm;

[0063] The physical fields are steady-state solid heat transfer and solid mechanics, and plane strain problems;

[0064] The initial condition is the initial temperature T0 = 293.15 K;

[0065] The boundary conditions are as follows: vertices A, B, C, and D are shown in the following table. Figure 4 .

[0066]

[0067] The temperature distribution inside the composite material under the above conditions obtained by simulation is as follows Figure 8 As shown, the Mises stress distribution is Fig. 9 As shown. Figure 8 It can be seen that the isotherms are not parallel straight lines, that is, the spatial distribution of the temperature gradient is inconsistent. This is because the thermal conductivity of silicon carbide particles and aluminum alloys is different. The temperature gradient is smaller in areas with high thermal conductivity. The simulation results show these phenomena. Fig. 9 It can be seen that due to the difference in thermal expansion coefficient, elastic modulus and other parameters of the two materials, large stress will be generated in the area near the particles when the temperature changes. The stress distribution state in the simulation results is strongly related to the distribution position of the particles, which well demonstrates these phenomena. Therefore, the above simulation model can well simulate the physical laws such as heat conduction and stress evolution inside the real microstructure composite material. Example 2

[0068] Step S1. Use a scanning electron microscope to obtain the microstructure morphology of composite material B (carbon fiber reinforced resin matrix composite material for valve body) and save it as a digital image, such as Fig.10 shown.

[0069] Step S2. Decolorizing the obtained digital image using image processing software to obtain a decolorized image, and Photoshop is selected as the image processing software;

[0070] In Photoshop, set the minimum and maximum output brightness values. Fig.10 , the minimum brightness value is 135, and the maximum brightness value is 161. In this way, the output range of the brightness value is reduced to 135-161, such as Fig.11 shown. Fig.10 The pixels with brightness values ​​of 135 and 161 are in Fig.11 The brightness values ​​will be changed to 0 (black) and 255 (white) respectively, thereby increasing the contrast between different phases or components;

[0071] Step S3. Establishing a geometric model of the size corresponding to the area covered by the digital image;

[0072] Comparison Fig.10 The scale in Fig.10 The coverage area is measured and its size is 95×82 μm. A two-dimensional rectangular geometric model is established in the numerical simulation software, and its length and width are 95×82 μm. The numerical simulation software used in this embodiment is COMSOL.

[0073] Step S4. Based on the decolorized image and the material parameters of each single phase or component of the composite material, a spatial distribution function of the material parameters in the entire composite material domain is established, and assigned to the material parameter items in the numerical model.

[0074] The brightness value corresponding to each pixel in the decolorized image Divide uniformly by 255 to get a two-dimensional distribution function , which has the following form:

[0075] ,

[0076] The distribution cloud diagram of the function is as follows Fig.12 shown.

[0077] Based on the above two-dimensional distribution function and material parameters of each single phase or component in the composite material M 0. M 1. Derivation and establishment of the spatial distribution function of material parameters in the entire composite material domain , which has the following form:

[0078] ,

[0079] in M are material parameters, such as density, specific heat, thermal conductivity, thermal expansion coefficient, elastic modulus, Poisson's ratio, fracture energy, etc. M 0 is the material parameter of the phase or component corresponding to the black area, M 1White areas correspond to material parameters of phases or components.

[0080] The material parameters of the single component of the composite material B in this embodiment are as follows:

[0081]

[0082] In numerical simulation software, the user-defined function Assigned to the material parameter item in the numerical model. In this embodiment, the thermal conductivity of the fiber reinforced ceramic matrix composite material The spatial distribution of Fig.13 As shown, the thermal expansion coefficient The spatial distribution of Fig.14 As shown, the thermal expansion coefficient Spatially distributed stereoscopic display such as Fig.15 As shown, through Figures 13 to 15 It can be seen that the spatial distribution of material parameters and its microstructure can correspond well. For example, the thermal expansion coefficient of the area where the carbon fiber is located is about 7×10 -5 1 / K, the area occupied by the resin is 2.7×10 -5 1 / K, by Fig.15 It can be seen that its spatial distribution is continuous.

[0083] Step S5. According to conventional simulation methods, complete the grid division, physical field definition, initial condition and boundary condition setting and numerical solution in sequence.

[0084] In this embodiment, the grid is a quadrilateral grid with a maximum cell size of 0.0004 mm;

[0085] The physical fields are steady-state solid heat transfer and solid mechanics, and plane strain problems;

[0086] The initial condition is the initial temperature T0 = 293.15 K;

[0087] The boundary conditions are as follows: vertices A, B, C, and D are shown in the following table. Fig.12 .

[0088]

[0089] The temperature distribution inside the composite material under the above conditions obtained by simulation is as follows Fig.16 As shown, the Mises stress distribution is Fig.17 As shown. Fig.16 It can be seen that the isotherms are not parallel straight lines, that is, the spatial distribution of the temperature gradient is inconsistent. This is because the thermal conductivity of silicon carbide particles and aluminum alloys is different. The temperature gradient is smaller in areas with high thermal conductivity. The simulation results show these phenomena. Fig.17 It can be seen that due to the difference in thermal expansion coefficient, elastic modulus and other parameters of the two materials, large stress will be generated in the area near the particles when the temperature changes. The stress distribution state in the simulation results is strongly related to the distribution position of the particles, which well demonstrates these phenomena. Therefore, the above simulation model can well simulate the physical laws such as heat conduction and stress evolution inside the real microstructure composite material.

Claims

1. A global continuous modeling method for the real microstructure of composite materials, characterized in that The steps include: S1. Use a scanning electron microscope to obtain the microstructure morphology of the corresponding composite material and save it as a digital image; S2. Decolorizing the obtained digital image using image processing software to obtain a decolorized image, and setting the minimum and maximum output brightness values, that is, narrowing the brightness value output range to increase the contrast of different phases or components; S3. Establishing a geometric model of the size corresponding to the area covered by the digital image; S4. Based on the decolorization image and the material parameters of each single phase or component of the composite material, a spatial distribution function of the material parameters in the entire composite material domain is established, and the spatial distribution function is assigned to the material parameter items in the numerical model; S5. According to the simulation method, complete the meshing, physical field definition, boundary condition and initial condition setting and numerical solution in sequence; The specific method of S2 is: In the image processing software, the minimum value of the output brightness value is set to the peak value of the brightness value of the pixels in a certain phase or component area in the image, and the maximum value of the output brightness value is set to the peak value of the brightness value of the pixels in another phase or component area, thereby narrowing the output range of the image brightness value. At this time, the two different phases or components of the composite material are displayed in black and white respectively, the brightness value of black is 0, the brightness value of white is 255, and the brightness value near the interface gradually transitions from black to white; The specific method of S4 is: The brightness value corresponding to each pixel in the decolorized image Divide uniformly by 255 to get a two-dimensional distribution function , which is in the form of the following formula (I): （I）， Based on the above two-dimensional distribution function and material parameters of each single phase or component in the composite material M 0. M 1. Derive and establish the spatial distribution function of material parameters in the entire composite material domain , which is in the form of the following formula (II): (II), in M are material parameters including density, specific heat, thermal conductivity, coefficient of thermal expansion, elastic modulus, Poisson's ratio or fracture energy, M 0 is the material parameter of the phase or component corresponding to the black area, M 1 is the material parameter of the phase or component corresponding to the white area; In the finite element software, the user-defined function Assign values ​​to material parameters in the numerical model.

2. The global continuous modeling method for the real microstructure of composite materials according to claim 1, characterized in that: In S1, before obtaining the microstructure morphology of the composite material, the composite material is repeatedly polished until it is smooth.

3. The global continuous modeling method for the real microstructure of composite materials according to claim 1, characterized in that: The specific methods of S3 are: Refer to the ruler of the SEM image to measure the specific length and width of the image coverage area; Set reasonable measurement units in the finite element modeling software and build a two-dimensional geometric model of corresponding dimensions.

Images