A method for preparing a sample and characterizing the three-dimensional deformation inside the sample

Abstract

The present invention provides a method for preparing a sample and characterizing the three-dimensional deformation inside the sample. First, multiple first digital particle models are generated; then, multiple first volume images are generated according to the center point position of the speckle particles simulated by each first digital particle model; then, according to all the first volume images, the simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample are determined; thus, the target sample can be prepared according to the simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample. By generating and processing and calculating multiple first digital particle models in a numerical simulation manner, the distribution parameters of the speckle particles in the target sample are obtained. Compared with the prior art, the speckle structure inside the prepared target sample is more stable, which is conducive to improving the accuracy of analyzing the internal state of the sample using the DVC method.

Description

Technical Field

[0001] The present application relates to the field of mechanical measurement technology, and in particular to a method for preparing a sample and a method for characterizing the three-dimensional deformation inside the sample. Background Art

[0002] Accurate preparation of complex structure rock samples and accurate measurement of the three-dimensional deformation inside complex structure rocks are of great significance for studying the mechanism of rock disasters. These studies can provide important reference for safe mining and disaster prevention and control in mines.

[0003] At present, the preparation of complex structure rock samples in the laboratory is mainly through mechanical cutting and grinding of natural rock materials, and the characterization of the three-dimensional deformation inside complex structure rocks is mainly based on CT scanning and digital volume correlation (DVC). CT scanning uses natural rock materials to make complex structure rock samples. Due to the presence of holes, cracks and various mineral components inside natural rocks, the density and X-ray absorption capacity of structural rock samples at different positions are different. During CT imaging, these differences will appear as changes in grayscale information on the image, and then form a natural speckle structure inside the rock. Using the digital image correlation method, the position changes of the speckle structure inside the sample in the image can be tracked and calculated, thereby characterizing the three-dimensional deformation inside the sample.

[0004] However, due to the heterogeneity and discontinuity of complex structured rocks, mechanical cutting and grinding methods are difficult to accurately and repeatedly prepare samples with consistent structures, and the sample preparation process takes a long time. In addition, the speckle structure inside natural rock material samples is random, and some samples even lack speckle structures suitable for analysis, which makes it difficult to accurately obtain the state changes inside the sample during the experiment. At the same time, since the internal structures of different samples are inevitably different, it is difficult to summarize the experimental results in a regular manner after repeated experiments using different samples. Therefore, it is a key technical problem that needs to be solved urgently to develop a sample preparation method with high stability, good repeatability, and short production cycle, and accurately characterize the three-dimensional deformation inside the prepared sample. Summary of the invention

[0005] In view of this, one of the technical problems solved by the present application is to provide a sample preparation method and a method for characterizing the three-dimensional deformation inside the sample, so as to overcome the problems in the prior art that the sample preparation period is long using natural rock materials, the internal complex structures of different specimens are inconsistent, and the speckle structure inside the sample has a certain randomness, which affects the accuracy of analyzing the internal state of the sample using the DVC method.

[0006] In a first aspect, an embodiment of the present application discloses a sample preparation method, the method comprising: generating a plurality of first digital particle models; wherein the voxel regions simulated by all the first digital particle models have the same size, and the particle size values ​​and / or volume fraction values ​​of the simulated speckle particles are different;

[0007] Generate a plurality of first volume images according to the center point position of each speckle particle simulated by the first digital particle model; wherein the first volume image is a Gaussian speckle volume image;

[0008] Determining, based on all the first volume images, a simulated optimal particle size value and a simulated optimal volume fraction value corresponding to the speckle particles included in the target sample;

[0009] The target sample is prepared according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample.

[0010] Optionally, determining the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample according to all the first volume images includes:

[0011] Performing translation transformation processing on all the first volume images respectively, and calculating and obtaining a root mean square error value corresponding to each of the first volume images;

[0012] Performing surface fitting processing on the root mean square error values, the particle size values ​​of the speckle particles, and the volume fraction values ​​of the speckle particles corresponding to all the first volume images to obtain a first fitting surface;

[0013] The simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample are determined according to the minimum value of the root mean square error value in the first fitting surface.

[0014] Optionally, performing translation transformation processing on all the first volume images respectively and calculating a root mean square error value corresponding to each first volume image includes:

[0015] Using the formula , calculate and obtain the root mean square error value corresponding to each of the first volume images;

[0016] in, is the number of speckle particles in the first digital particle model; is the calculated displacement value of the sth speckle particle calculated using the DVC method; is the subvoxel displacement value of the first volume image.

[0017] Optionally, determining the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample according to all the first volume images includes:

[0018] Calculate and obtain an average grayscale gradient value corresponding to each of the first volume images;

[0019] Performing surface fitting processing on the average grayscale gradient values, the particle size values ​​of the speckle particles, and the volume fraction values ​​of the speckle particles corresponding to all the first volume images to obtain a second fitting surface;

[0020] The simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample are determined according to the maximum value of the average grayscale gradient value in the second fitting surface.

[0021] Optionally, the method further comprises:

[0022] Scanning the test sample using a target imaging device to obtain a second volume image; wherein speckle particles of various particle size values ​​are embedded in the matrix of the test sample;

[0023] According to the second volume image, a particle equivalent speckle size value and a noise equivalent speckle size value corresponding to the speckle particles of various particle size values ​​included in the test sample are calculated;

[0024] Obtaining a physical optimum particle size value corresponding to the target imaging device according to particle equivalent speckle size values ​​and noise equivalent speckle size values ​​corresponding to speckle particles of various particle size values ​​included in the test sample;

[0025] Correspondingly, the preparing the target sample according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample includes:

[0026] The target sample is prepared according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample and the physical optimal particle size value corresponding to the target imaging device.

[0027] Optionally, the preparing the target sample according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample and the physical optimal particle size value corresponding to the target imaging device comprises:

[0028] generating a second digital particle model according to a preset shape of the target sample, and a simulation optimal particle size value and a simulation optimal volume fraction value corresponding to the speckle particles included in the target sample;

[0029] According to the physical optimal particle size value corresponding to the target imaging device, scaling the second digital particle model to obtain a three-dimensional digital volume model;

[0030] The target sample is prepared to have the same shape and size as the three-dimensional digital body model.

[0031] Optionally, the preparing the target sample having the same shape and size as the three-dimensional digital body model comprises:

[0032] A third volume image is generated according to the center point positions of all the speckle particles simulated by the three-dimensional digital volume model and the physical optimal particle size value corresponding to the target imaging device; wherein, in the third volume image, the area where the speckle particles are located and the area where the matrix is ​​located are distinguished by binarization;

[0033] According to the third body image, the target sample having the same shape and size as the three-dimensional digital body model is prepared by 3D printing.

[0034] Optionally, the step of calculating, according to the second volume image, particle equivalent speckle size values ​​and noise equivalent speckle size values ​​corresponding to speckle particles of various particle size values ​​included in the test sample comprises:

[0035] performing segmentation processing on the second volume image to obtain volume image regions corresponding to the speckle particles of various particle size values ​​included in the test sample;

[0036] After Fourier transform processing is performed on the volume image regions corresponding to the speckle particles of each particle size value, autocorrelation analysis is performed to obtain autocorrelation curves corresponding to the speckle particles of each particle size value;

[0037] The curve width when the autocorrelation coefficient in the autocorrelation curve corresponding to the speckle particles of each particle size value is equal to the preset coefficient value is determined as the particle equivalent speckle size value corresponding to the speckle particles of each particle size value.

[0038] Optionally, the step of calculating, according to the second volume image, particle equivalent speckle size values ​​and noise equivalent speckle size values ​​corresponding to speckle particles of various particle size values ​​included in the test sample comprises:

[0039] Segmenting the second volume image to obtain volume image regions corresponding to the speckle particles of various particle size values ​​included in the test sample; wherein the volume image regions corresponding to the speckle particles of all the particle size values ​​have the same size, and each of the volume image regions only includes an image of speckle particles of one particle size value;

[0040] Calculate the equivalent speckle size value of the matrix corresponding to the matrix in the volume image area corresponding to the speckle particles of each particle size value;

[0041] According to the matrix equivalent speckle size value corresponding to the matrix, the noise equivalent speckle size value corresponding to the speckle particles of each particle size value is obtained.

[0042] A second aspect of an embodiment of the present application discloses a method for characterizing three-dimensional deformation inside a sample, the method comprising:

[0043] Using a target imaging device to perform scanning imaging processing on the target sample prepared according to the above method to obtain a first scanned image;

[0044] Performing a loading test on the target sample so that the target sample is deformed;

[0045] Using a target imaging device to perform scanning and imaging processing on the deformed target sample to obtain a second scanned image;

[0046] The displacement field and / or strain field inside the target sample is calculated based on the first scanning image and the second scanning image.

[0047] In the embodiment of the present invention, a plurality of first digital particle models are first generated; then a plurality of first volume images are generated according to the center point position of the speckle particles simulated by each first digital particle model; then the simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample are determined according to all the first volume images; thus, the target sample can be prepared according to the simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample. The embodiment of the present invention generates and processes and calculates a plurality of first digital particle models by numerical simulation to obtain the distribution parameters of the speckle particles in the target sample. Compared with the prior art, the speckle structure inside the prepared target sample is more stable, which is conducive to improving the accuracy of analyzing the internal state of the sample by the DVC method. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a schematic diagram of a flow chart of a sample preparation method disclosed in Example 1 of the present application;

[0050] Figure 2 is a schematic diagram of the effect of generating a fourth volume image and a first volume image according to the first digital particle model;

[0051] Figure 3 It is a schematic diagram of the effect of the first fitting surface;

[0052] Figure 4 It is a schematic diagram of the effect of the second fitting surface;

[0053] Figure 5 It is a schematic diagram of a flow chart of a sample preparation method disclosed in Example 2 of the present application;

[0054] Figure 6 It is a schematic diagram of the effect of scanning the test sample to generate a second body image;

[0055] Figure 7 It is a flow chart of a method for characterizing three-dimensional deformation inside a sample disclosed in Example 3 of the present application. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] It should be noted that the terms "first", "second", "third" and "fourth" in the specification and claims of the present application are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present application and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0058] Embodiment 1

[0059] like Figure 1 As shown, Figure 1 This is a schematic flow chart of a sample preparation method disclosed in Example 1 of the present application, and the sample preparation method includes:

[0060] Step S101: generating a plurality of first digital particle models.

[0061] In this embodiment, the first digital particle model is a model generated by computer simulation software, and is used to simulate a sample of a predetermined shape and size. Each simulated sample includes a matrix and a plurality of speckle particles of the same or different shapes and sizes embedded in the matrix, that is, the particle size values ​​of all the speckle particles simulated by each first digital particle model may be the same or different, which is not limited in this embodiment.

[0062] The shapes and sizes of the sample and speckle particles simulated by the first digital particle model are not limited and can be reasonably selected according to actual application requirements. For example, under the premise of ensuring the accuracy of subsequent calculation results, it is preferred that the entire first digital particle model is a voxel area of ​​100×100×100; in order to facilitate the preparation of speckle particles and the corresponding calculation and processing of the volume image area corresponding to the speckle particles, it is preferred that the shape of the speckle particles simulated by the first digital particle model is spherical.

[0063] In this embodiment, the voxel regions simulated by all the first digital particle models have the same size, but the particle size values ​​and / or volume fraction values ​​of the speckle particles simulated by different first digital particle models are different. That is, compared with other first digital particle models, the particle size value and / or volume fraction value of the speckle particles simulated by each first digital particle model is different.

[0064] Step S102 : generating a plurality of first volume images according to the center point position of the speckle particles simulated by each first digital particle model.

[0065] In this embodiment, the first volume image is a Gaussian distribution speckle volume image. According to the center point positions of all speckle particles simulated by each first digital particle model, a corresponding first volume image can be generated. The first volume image can be a volume image with Gaussian white noise added or a volume image without Gaussian white noise added, which is not limited in this embodiment.

[0066] Alternatively, see Figure 2 In order to make the subsequent simulation calculation results closer to the actual results, it is preferred that step S102 includes the following sub-steps S102a and S102b:

[0067] Sub-step S102a: generating a plurality of fourth volume images according to the center point position of each speckle particle simulated by the first digital particle model.

[0068] The fourth volume image is an image directly generated according to the center point position of the speckle particles simulated by each first digital particle model, and is a volume image without adding Gaussian white noise.

[0069] Sub-step S102b, adding noise to all fourth volume images according to a preset Gaussian white noise adding rule to obtain a plurality of first volume images.

[0070] There is no limit to the specific Gaussian white noise addition rule, and there is no limit to the specific method of adding noise to all fourth-body images, which can be reasonably selected according to actual application requirements. For example, in order to perform noise addition processing more easily, it is preferred to add Gaussian white noise with a mean of 0 and a standard deviation of 4% to each fourth-body image; in order to make the first-body image after the noise addition processing more accurately represent the physical state, Gaussian noise estimation can be performed on the physical sample to determine the mean and standard deviation of the Gaussian white noise added to each fourth-body image.

[0071] Optionally, in order to reduce the complexity of calculation and improve the DVC calculation accuracy, it is preferred that the first volume image is a volume image that has not been subjected to noise addition processing.

[0072] Step S103 : determining the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample according to all the first volume images.

[0073] In this embodiment, the target sample is the sample that needs to be prepared in the end, that is, the sample that needs to be used in the loading experiment. The distribution parameters of the speckle particles in the target sample can be determined according to all the first volume images, so that the target sample can be further prepared according to the distribution parameters of the speckle particles.

[0074] The distribution parameters of the speckle particles in the target sample include the optimal simulated particle size value and the optimal simulated volume fraction value corresponding to the speckle particles. The specific method for determining the optimal simulated particle size value and the optimal simulated volume fraction value corresponding to the speckle particles included in the target sample is not limited and can be reasonably selected according to actual application requirements.

[0075] Optionally, in order to quickly and accurately determine the distribution parameters of the speckle particles in the target sample, step S103 may include the following sub-steps S103a to S103c:

[0076] Sub-step S103a, performing translation transformation processing on all first volumetric images respectively, and calculating and obtaining a root mean square error value corresponding to each first volumetric image.

[0077] The translation transformation processing is performed in the same manner on all first volume images. Performing translation transformation processing on all first volume images is equivalent to applying the same displacement value to the samples simulated by all first volume images. The DVC method can be used to calculate the displacement calculation values ​​corresponding to several positions of each first volume image before and after the translation transformation processing, especially the displacement calculation values ​​corresponding to the speckle particles before and after the translation transformation processing.

[0078] In addition, the specific calculation method of the root mean square error value corresponding to each first volume image is not limited and can be reasonably selected according to actual application requirements.

[0079] Sub-step S103b, performing surface fitting processing on the root mean square error values, the particle size values ​​of the speckle particles and the volume fraction values ​​of the speckle particles corresponding to all the first volume images to obtain a first fitting surface.

[0080] The first fitting surface is used to characterize the corresponding relationship between the root mean square error value, the particle size value of the speckle particles, and the volume fraction value of the speckle particles. The specific function type used to fit the first fitting surface is not limited and can be reasonably selected according to actual application requirements.

[0081] The effect diagram of the first fitting surface can be found in Figure 3 , Figure 3 The RMSE in the formula corresponds to the root mean square error, the Radius corresponds to the particle size of the speckle particles, and the Volume fraction corresponds to the volume fraction of the speckle particles.

[0082] Sub-step S103c, determining the optimal simulation particle size value and the optimal simulation volume fraction value corresponding to the speckle particles included in the target sample according to the minimum value of the root mean square error value in the first fitting surface.

[0083] Among them, after determining the position of the minimum value of the root mean square error value in the first fitting surface, the particle size value and the volume fraction value of the speckle particles corresponding to the minimum value can be further determined, and these two values ​​are determined as the simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample.

[0084] Furthermore, in order to more accurately calculate the root mean square error value corresponding to each first volumetric image, sub-step S103a may further include:

[0085] Using the formula , calculate and obtain the root mean square error value corresponding to each first volume image.

[0086] in, is the number of speckle particles in the first digital particle model; is the calculated displacement value of the sth speckle particle calculated using the DVC method; is the subvoxel displacement value of the first volume image.

[0087] In addition, it should be noted that, in the above formula, all the speckle particles simulated by the first digital particle model may be selected to calculate the root mean square error value corresponding to each first volume image, or part of the speckle particles simulated by the first digital particle model may be selected to calculate the root mean square error value corresponding to each first volume image, which is not limited in this embodiment.

[0088] Optionally, in order to quickly and accurately determine the distribution parameters of the speckle particles in the target sample, step S103 may further include the following sub-steps S103d to S103f:

[0089] Sub-step S103d: Calculate and obtain the average grayscale gradient value corresponding to each first volume image.

[0090] Sub-step S103e: performing surface fitting processing on the average grayscale gradient values, the particle size values ​​of the speckle particles and the volume fraction values ​​of the speckle particles corresponding to all the first volume images to obtain a second fitting surface.

[0091] The second fitting surface is used to characterize the correspondence between the average grayscale gradient value, the particle size value of the speckle particles, and the volume fraction value of the speckle particles. The specific function type used to fit the second fitting surface is not limited and can be reasonably selected according to actual application requirements.

[0092] The effect diagram of the second fitting surface can be found in Figure 4 , Figure 4 The MIG in it corresponds to the average gray gradient value, the Radius corresponds to the particle size value of the speckle particles, and the Volume fraction corresponds to the volume fraction value of the speckle particles.

[0093] Sub-step S103f, determining the simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample according to the maximum value of the average grayscale gradient value in the second fitting surface.

[0094] Among them, after determining the position of the maximum value of the average grayscale gradient value in the second fitting surface, the particle size value and the volume fraction value of the speckle particles corresponding to the maximum value can be further determined, and these two values ​​are determined as the simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample.

[0095] In addition, it should be noted that, in this embodiment, sub-steps S103a-sub-step S103c and sub-steps S103d-sub-steps S103f can be implemented one by one, or both can be implemented to mutually verify the accuracy of the calculation of the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles.

[0096] Step S104 , preparing the target sample according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample.

[0097] In this embodiment, the specific material and method for preparing the target sample are not limited and can be reasonably selected according to actual application requirements, but it is necessary to ensure that the materials of the speckle particles and the matrix included in the target sample are different.

[0098] In addition, if radiation is used to image the target sample during the sample process, it is also necessary to make the speckle particles and the matrix materials included in the target sample have different absorption rates for radiation. For example, in order to make the two materials have a larger difference in the absorption rates for radiation, it is preferred that the matrix is ​​made of Vero clear material and the speckle particles are made of Radiomatrix material.

[0099] As can be seen from the above embodiments of the present invention, the present embodiment generates a plurality of first digital particle models first; then generates a plurality of first volume images according to the center point position of the speckle particles simulated by each first digital particle model; then determines the simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample according to all the first volume images; thus, the target sample can be prepared according to the simulation optimal particle size value and the simulation optimal volume fraction value corresponding to the speckle particles included in the target sample. The embodiment of the present invention generates and processes and calculates a plurality of first digital particle models by numerical simulation to obtain the distribution parameters of the speckle particles in the target sample. Compared with the prior art, the speckle structure inside the prepared target sample is more stable, which is conducive to improving the accuracy of analyzing the internal state of the sample using the DVC method.

[0100] Embodiment 2

[0101] like Figure 5 As shown, Figure 5 This is a schematic flow chart of a sample preparation method disclosed in Example 2 of the present application, and the sample preparation method includes:

[0102] Step S201, scanning the test sample using a target imaging device to obtain a second volume image.

[0103] In this embodiment, the target imaging device is used to image the matrix and speckle structure of the target sample during the entire experiment to obtain a corresponding volume image. The specific type and performance parameters of the target imaging device are not limited and can be reasonably selected according to actual application requirements.

[0104] In this embodiment, the test sample is a pre-prepared physical sample, and the matrix of the test sample is embedded with speckle particles of various particle sizes. The number and particle size of the speckle particles in the test sample are not limited and can be reasonably selected according to actual application requirements. The effect diagram of the test sample and the second body image can be seen in Figure 6 .

[0105] For example, in order to ensure the accuracy of subsequent calculation results and reduce the amount of data calculation, it is preferred that speckle particles with 100 different particle size values ​​are embedded in the matrix of the test sample, and the particle size values ​​of all speckle particles are distributed in an arithmetic progression.

[0106] For another example, in order to reduce the amount of data calculation, it may be preferred that the number of speckle particles of each particle size value is 1.

[0107] For another example, in order to facilitate the preparation of the test sample and ensure the imaging effect, the particle size range of the speckle particles in the test sample may preferably be 0.01 mm-1 mm.

[0108] Step S202: according to the second volume image, the particle equivalent speckle size value and the noise equivalent speckle size value corresponding to the speckle particles of various particle size values ​​included in the test sample are calculated.

[0109] In this embodiment, the particle equivalent speckle size value is used to characterize the equivalent size of the speckle particles in the second volume image, and the noise equivalent speckle size value is used to characterize the equivalent size of the matrix in the second volume image. In the experimental study, the volume image region where the speckle particles are located is the main research object, and the volume image region of the matrix without speckle particles will interfere with the research object. Therefore, in order to obtain a better imaging effect, the volume image region where the matrix is ​​located can be regarded as the noise volume image region, and the noise equivalent speckle size value is calculated in step S202.

[0110] In this embodiment, in order to determine the particle size of the speckle particles with the best imaging effect using the target imaging device from the speckle particles with various particle size values, the particle equivalent speckle size value and the noise equivalent speckle size value corresponding to the speckle particles of each particle size value can be calculated respectively.

[0111] The specific calculation method of the particle equivalent speckle size value and the noise equivalent speckle size value corresponding to the speckle particles of each particle size value is not limited and can be reasonably selected according to the actual application requirements. For example, after segmenting the second volume image, the particle equivalent speckle size value and the noise equivalent speckle size value corresponding to the speckle particles of each particle size value can be calculated according to the segmented volume image area; or the particle equivalent speckle size value and the noise equivalent speckle size value corresponding to the speckle particles of each particle size value can be obtained by overall calculation using a specific algorithm without segmenting the second volume image.

[0112] Optionally, in order to more accurately and reasonably determine the equivalent speckle size value of the particles corresponding to the speckle particles of each particle size value, step S202 may further include the following sub-steps S202a to S202c:

[0113] Sub-step S202a, segmenting the second volume image to obtain volume image regions corresponding to speckle particles of various particle size values ​​included in the test sample.

[0114] In order to objectively compare the imaging effects of the target imaging device on speckle particles of different particle size values, the volume image areas corresponding to the speckle particles of each particle size value are the same, that is, the shapes and sizes of all the segmented volume image areas are the same.

[0115] Furthermore, in order to reduce the amount of data calculation, it is preferred that each volume image region includes only one speckle particle.

[0116] Sub-step S202b, after Fourier transform processing is performed on the volume image regions corresponding to the speckle particles of each size value, autocorrelation analysis is performed to obtain autocorrelation curves corresponding to the speckle particles of each size value.

[0117] Among them, Fourier transform processing is used to convert the volume image from the spatial domain to the frequency domain.

[0118] Sub-step S202c, determining the curve width when the autocorrelation coefficient in the autocorrelation curve corresponding to the speckle particles of each particle size value is equal to the preset coefficient value as the particle equivalent speckle size value corresponding to the speckle particles of each particle size value.

[0119] The specific value of the preset coefficient value is not limited and can be reasonably selected according to actual application requirements. For example, in order to more accurately and objectively determine the particle equivalent speckle size value corresponding to the speckle particles of each particle size value, the preset coefficient value can be preferably 0.5.

[0120] Optionally, in order to more accurately and reasonably determine the noise equivalent speckle size value corresponding to the speckle particles of each particle size value, step S202 may further include the following sub-steps S202d to S202f:

[0121] Sub-step S202d: segment the second volume image to obtain volume image regions corresponding to the speckle particles of various particle size values ​​included in the test sample.

[0122] The volume image regions corresponding to the speckle particles of all particle size values ​​are of the same size, and each volume image region only includes images of speckle particles of one particle size value. In addition, in order to reduce the amount of data calculation, it is preferred that each volume image region only includes one speckle particle.

[0123] Sub-step S202e, calculating the matrix equivalent speckle size value corresponding to the matrix in the volume image region corresponding to the speckle particles of each particle size value.

[0124] The calculation method of the substrate equivalent speckle size value corresponding to the substrate is not limited and can be reasonably selected according to actual application requirements. For example, the volume image area at several positions of the substrate in the volume image area can be calculated respectively, and the substrate equivalent speckle size value corresponding to the substrate can be determined according to the calculation results.

[0125] Furthermore, in order to obtain a more accurate calculation result, it is preferred that the substrate equivalent speckle size value corresponding to the substrate is calculated by respectively calculating the volume image regions at multiple positions of the substrate, and determining the average value of the calculation results as the substrate equivalent speckle size value corresponding to the substrate.

[0126] Sub-step S202f, obtaining the noise equivalent speckle size value corresponding to the speckle particles of each particle size value according to the matrix equivalent speckle size value corresponding to the matrix.

[0127] The noise equivalent speckle size value corresponding to the speckle particles of each particle size value in sub-step S202f may be the same as or different from the matrix equivalent speckle size value corresponding to the matrix in sub-step S202e, which is not limited in this embodiment. For example, the matrix equivalent speckle size value corresponding to the matrix may be directly used as the noise equivalent speckle size value corresponding to the speckle particles; or the noise equivalent speckle size value corresponding to the speckle particles may be obtained by multiplying the matrix equivalent speckle size value corresponding to the matrix by a preset safety factor value.

[0128] Furthermore, in order to more accurately measure the interference caused by the volume image area of ​​the matrix to the image area of ​​the studied speckle particles, sub-step S202f may include: taking the product of the matrix equivalent speckle size value corresponding to the matrix and the preset safety factor value as the noise equivalent speckle size value corresponding to the speckle particles of each particle size value.

[0129] The preset safety factor value is usually required to be greater than 1, and the specific value is not limited and can be reasonably selected according to actual application requirements. For example, in order to obtain a more accurate and reasonable calculation result, the preset safety factor value can be preferably greater than or equal to 1.1 and less than or equal to 1.5.

[0130] Step S203, obtaining a physical optimum particle size value corresponding to the target imaging device according to the particle equivalent speckle size values ​​and the noise equivalent speckle size values ​​corresponding to the speckle particles of various particle size values ​​included in the test sample.

[0131] In this embodiment, the imaging effect of the volume image area corresponding to the speckle particles of different particle size values ​​can be measured together according to the particle equivalent speckle size value and the noise equivalent speckle size value. The specific method for determining the physical optimal particle size value corresponding to the target imaging device is not limited and can be reasonably selected according to actual application requirements.

[0132] For example, according to the ratio of the particle equivalent speckle size value to the noise equivalent speckle size value, the particle size value of the speckle particle with the smallest ratio among the ratios whose values ​​are greater than or equal to 1 can be determined as the physically optimal particle size value corresponding to the target imaging device; or, the particle equivalent speckle size values ​​and the noise equivalent speckle size values ​​corresponding to the speckle particles of all particle size values ​​can be fitted, and the physically optimal particle size value corresponding to the target imaging device can be determined according to the curve obtained by fitting.

[0133] Optionally, in order to determine the physical optimal particle size value corresponding to the target imaging device more simply and reasonably, step S203 may include the following sub-steps S203a and S203b:

[0134] In sub-step S203a, the speckle particle with a ratio of the particle equivalent speckle size value to the noise equivalent speckle size value greater than or equal to 1 and with the smallest ratio is determined as the optimal speckle particle.

[0135] Sub-step S203b, obtaining the physical optimal particle size value corresponding to the target imaging device according to the particle size value of the optimal speckle particles.

[0136] The particle size value of the optimal speckle particles can be directly used as the physical optimal particle size value corresponding to the target imaging device; or the value obtained by multiplying the particle size value of the optimal speckle particles by a preset optimization coefficient value can be used as the physical optimal particle size value corresponding to the target imaging device, which is not limited in this embodiment.

[0137] Step S204: generating a plurality of first digital particle models.

[0138] In this embodiment, step S204 is substantially the same as or similar to step S101 in the aforementioned first embodiment, and will not be described in detail herein.

[0139] Step S205 : generating a plurality of first volume images according to the center point position of the speckle particles simulated by each first digital particle model.

[0140] In this embodiment, step S205 is substantially the same as or similar to step S102 in the aforementioned first embodiment, and will not be described in detail herein.

[0141] Step S206 : determining the optimal simulated particle size value and the optimal simulated volume fraction value corresponding to the speckle particles included in the target sample according to all the first volume images.

[0142] In this embodiment, step S206 is substantially the same as or similar to step S103 in the aforementioned first embodiment, and will not be described in detail herein.

[0143] Step S207 , preparing the target sample according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample, and the physical optimal particle size value corresponding to the target imaging device.

[0144] In this embodiment, considering that not only different types of imaging devices may have certain differences in imaging effects on the target sample, but also the same type of imaging devices may have different imaging effects on the same target sample due to differences in device performance parameters, in order to better record the state change of the target sample during the experiment, after determining the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample, the speckle particle distribution parameters of the target sample can be determined in combination with the physical optimal particle size value corresponding to the target imaging device, and the target sample can be further prepared according to the distribution parameters of the speckle particles.

[0145] Optionally, in order to obtain a better sample preparation effect, a three-dimensional digital body model of the target sample may be generated first, and then the target sample may be prepared according to the three-dimensional digital body model. Specifically, step S207 may also include the following sub-steps S207a to S207d:

[0146] Sub-step S207a, generating a second digital particle model according to the preset shape of the target sample, and the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample.

[0147] The preset shape of the target sample can be set according to actual application requirements, and this embodiment does not specifically limit it. For example, in order to facilitate processing and obtain better imaging effects, the preset shape of the target sample can be preferably cylindrical.

[0148] In addition, the method for generating the second digital particle model in sub-step S207a is basically the same as the method for generating the first digital particle model in the aforementioned step S204, and the main difference is that the overall shapes and / or overall sizes of the two digital particle models may be different, but if speckle particles with the same size value and the same volume fraction value are simulated, the particle size values ​​and volume fraction values ​​corresponding to the speckle particles in the two models are the same.

[0149] Sub-step S207b: scaling the second digital particle model according to the physical optimal particle size value corresponding to the target imaging device to obtain a three-dimensional digital volume model.

[0150] The scaling process for the second digital particle model includes reducing, enlarging or maintaining the size of the second digital particle model unchanged. The specific processing is mainly determined by the physical optimal particle size value corresponding to the target imaging device, and this embodiment does not limit it here.

[0151] Sub-step S207c: preparing a target sample having the same shape and size as the three-dimensional digital body model.

[0152] Further, sub-step S207b may include: scaling the second digital particle model according to the physical optimal particle size value and the preset magnification value corresponding to the target imaging device to obtain a three-dimensional digital volume model.

[0153] In order to ensure the imaging effect of the target imaging device on the target sample, the preset magnification value may be preferably less than or equal to the minimum magnification value of the target imaging device.

[0154] Furthermore, in order to prepare a target sample containing speckle particles with good stability and repeatability, the target sample may be prepared by 3D printing. Specifically, sub-step S207c may also include the following sub-steps A and B:

[0155] Sub-step A: generating a third volume image according to the center point positions of all speckle particles simulated by the three-dimensional digital volume model and the physical optimal particle size value corresponding to the target imaging device.

[0156] In the third volume image, the area where the speckle particles are located and the area where the matrix is ​​located are distinguished by binarization. In addition, complex three-dimensional structures, such as complex crack structures, can also be added to the third volume image by image processing technology.

[0157] Sub-step B: preparing a target sample having the same shape and size as the three-dimensional digital body model by 3D printing according to the third body image.

[0158] It can be seen from the above embodiments of the present invention that, compared with the aforementioned embodiment 1, the present embodiment prepares the target sample according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles, and the physical optimal particle size value corresponding to the target imaging device, so that the imaging effect of the target sample using the target imaging device can be significantly improved; the target sample is prepared by 3D printing, which not only shortens the sample preparation cycle, but also can obtain the target sample containing speckle particles with good stability and repeatability, which is conducive to improving the experimental effect and accuracy.

[0159] Embodiment 3

[0160] Embodiment 3 of the present application provides a method for characterizing the three-dimensional deformation inside a sample. Figure 7 This is a schematic flow chart of a method for characterizing three-dimensional deformation of a sample disclosed in Example 3 of the present application. The sample preparation method includes:

[0161] Step S301, using a target imaging device to perform scanning imaging processing on a target sample to obtain a first scanned image.

[0162] In this embodiment, the target sample is a sample prepared by any optional implementation of the aforementioned embodiment 1 and embodiment 2.

[0163] Step S302: performing a loading test on the target sample to cause the target sample to deform.

[0164] In this embodiment, the specific method of performing the loading experiment on the target sample is not limited, and is mainly determined according to the experimental requirements. For example, it can be an in-situ loading, an ex-situ loading, or other loading methods.

[0165] Step S303: Use a target imaging device to perform scanning and imaging processing on the deformed target sample to obtain a second scanned image.

[0166] Step S304: Calculate and obtain the displacement field and / or strain field inside the target sample according to the first scanning image and the second scanning image.

[0167] It can be seen from the above embodiments of the present invention that this embodiment uses a target sample prepared by any optional implementation method in the aforementioned embodiment one and embodiment two to carry out a loading experiment, and uses a target imaging device to image the target sample during the experiment. The state change inside the target sample during the experiment can be further accurately obtained through the DVC method, and the distribution evolution law of the displacement field and strain field inside the target sample can be obtained.

[0168] So far, specific embodiments of the present application have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recorded in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing can be advantageous.

[0169] The present application is described with reference to the flowcharts and / or block diagrams of the methods according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0171] Those skilled in the art should understand that the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0172] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0173] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A sample preparation method, characterized in that: The method comprises: generating a plurality of first digital particle models; wherein the voxel regions simulated by all the first digital particle models have the same size, and the particle size values ​​and / or volume fraction values ​​of the simulated speckle particles are different; Generate a plurality of first volume images according to the center point position of each speckle particle simulated by the first digital particle model; wherein the first volume image is a Gaussian speckle volume image; Performing translation transformation processing on all the first volume images respectively, and calculating and obtaining a root mean square error value corresponding to each of the first volume images; Performing surface fitting processing on the root mean square error values, the particle size values ​​of the speckle particles, and the volume fraction values ​​of the speckle particles corresponding to all the first volume images to obtain a first fitting surface; Determining a simulation optimal particle size value and a simulation optimal volume fraction value corresponding to the speckle particles included in the target sample according to the minimum value of the root mean square error value in the first fitting surface; The target sample is prepared according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample.

2. The method according to claim 1, characterized in that The performing translation transformation processing on all the first volume images respectively and calculating the root mean square error value corresponding to each first volume image comprises: Using the formula , calculate and obtain the root mean square error value corresponding to each of the first volume images; in, is the number of speckle particles in the first digital particle model; is the calculated displacement value of the sth speckle particle calculated using the DVC method; is the subvoxel displacement value of the first volume image.

3. The method according to claim 1, characterized in that The method further comprises: Scanning the test sample using a target imaging device to obtain a second volume image; wherein speckle particles of various particle size values ​​are embedded in the matrix of the test sample; According to the second volume image, a particle equivalent speckle size value and a noise equivalent speckle size value corresponding to the speckle particles of various particle size values ​​included in the test sample are calculated; Obtaining a physical optimum particle size value corresponding to the target imaging device according to particle equivalent speckle size values ​​and noise equivalent speckle size values ​​corresponding to speckle particles of various particle size values ​​included in the test sample; Correspondingly, the preparing the target sample according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample includes: The target sample is prepared according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample and the physical optimal particle size value corresponding to the target imaging device.

4. The method according to claim 3, characterized in that The preparing the target sample according to the simulated optimal particle size value and the simulated optimal volume fraction value corresponding to the speckle particles included in the target sample and the physical optimal particle size value corresponding to the target imaging device comprises: generating a second digital particle model according to a preset shape of the target sample, and a simulation optimal particle size value and a simulation optimal volume fraction value corresponding to the speckle particles included in the target sample; scaling the second digital particle model according to the physical optimal particle size value corresponding to the target imaging device to obtain a three-dimensional digital volume model; The target sample is prepared to have the same shape and size as the three-dimensional digital body model.

5. The method according to claim 4, characterized in that The step of preparing the target sample having the same shape and size as the three-dimensional digital body model comprises: A third volume image is generated according to the center point positions of all the speckle particles simulated by the three-dimensional digital volume model and the physical optimal particle size value corresponding to the target imaging device; wherein, in the third volume image, the area where the speckle particles are located and the area where the matrix is ​​located are distinguished by binarization; According to the third body image, the target sample having the same shape and size as the three-dimensional digital body model is prepared by 3D printing.

6. The method according to claim 3, characterized in that The step of calculating, according to the second volume image, the particle equivalent speckle size values ​​and the noise equivalent speckle size values ​​corresponding to the speckle particles of various particle size values ​​included in the test sample comprises: performing segmentation processing on the second volume image to obtain volume image regions corresponding to the speckle particles of various particle size values ​​included in the test sample; After Fourier transform processing is performed on the volume image regions corresponding to the speckle particles of each particle size value, autocorrelation analysis is performed to obtain autocorrelation curves corresponding to the speckle particles of each particle size value; The curve width when the autocorrelation coefficient in the autocorrelation curve corresponding to the speckle particles of each particle size value is equal to the preset coefficient value is determined as the particle equivalent speckle size value corresponding to the speckle particles of each particle size value.

7. The method according to claim 3, characterized in that The step of calculating, according to the second volume image, the particle equivalent speckle size values ​​and the noise equivalent speckle size values ​​corresponding to the speckle particles of various particle size values ​​included in the test sample comprises: Segmenting the second volume image to obtain volume image regions corresponding to the speckle particles of various particle size values ​​included in the test sample; wherein the volume image regions corresponding to the speckle particles of all the particle size values ​​have the same size, and each of the volume image regions only includes an image of speckle particles of one particle size value; Calculate the equivalent speckle size value of the matrix corresponding to the matrix in the volume image area corresponding to the speckle particles of each particle size value; According to the matrix equivalent speckle size value corresponding to the matrix, the noise equivalent speckle size value corresponding to the speckle particles of each particle size value is obtained.

8. A method for characterizing the three-dimensional deformation inside a sample, characterized in that: The method comprises: Using a target imaging device to perform scanning imaging processing on the target sample obtained by the preparation method according to any one of claims 1 to 7 to obtain a first scanned image; Performing a loading test on the target sample so that the target sample is deformed; Using a target imaging device to perform scanning and imaging processing on the deformed target sample to obtain a second scanned image; The displacement field and / or strain field inside the target sample is calculated based on the first scanning image and the second scanning image.

Images