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WO2014080622A1 - Procédé et dispositif pour le traitement d'image tridimensionnelle de charge fibreuse dans un matériau composite - Google Patents

Procédé et dispositif pour le traitement d'image tridimensionnelle de charge fibreuse dans un matériau composite Download PDF

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Publication number
WO2014080622A1
WO2014080622A1 PCT/JP2013/006806 JP2013006806W WO2014080622A1 WO 2014080622 A1 WO2014080622 A1 WO 2014080622A1 JP 2013006806 W JP2013006806 W JP 2013006806W WO 2014080622 A1 WO2014080622 A1 WO 2014080622A1
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Prior art keywords
composite material
filler
dimensional image
image processing
evaluation
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English (en)
Japanese (ja)
Inventor
亨 鈴木
寿夫 杉田
雅宏 瀬戸
昌 山部
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Panasonic Corp
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Panasonic Corp
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Priority to JP2014548459A priority Critical patent/JP5844921B2/ja
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
    • G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
    • G01N23/046—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material using tomography, e.g. computed tomography [CT]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2223/00—Investigating materials by wave or particle radiation
    • G01N2223/40—Imaging
    • G01N2223/419—Imaging computed tomograph
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2223/00—Investigating materials by wave or particle radiation
    • G01N2223/60—Specific applications or type of materials
    • G01N2223/615—Specific applications or type of materials composite materials, multilayer laminates

Definitions

  • the present invention relates to a three-dimensional image processing method and a three-dimensional image processing apparatus for fibrous filler in a composite material.
  • the warpage deformation of a molded product is caused by factors such as the shape of the molded product, the distribution of thermal history on the molded product, the distribution of thermal mechanical properties such as the linear expansion coefficient and Young's modulus in the molded product, and the thermal load on the molded product. Occur in combination. Warpage deformation occurs as a response of the molded product to a thermal load or the like, and depends on the distribution of thermal properties and mechanical properties of the molded product. These characteristic distributions depend on the distribution of the amount of fibrous filler at each point in the molded product and the distribution of the longitudinal direction of the fibrous filler (orientation distribution). Therefore, by grasping the distribution and orientation distribution of the fibrous filler in the molded product, the molding conditions and the mold can be optimized so that these distributions can be optimized, and warping deformation of the molded product can be suppressed. And can be controlled.
  • a three-dimensional image of the molded product is obtained using X-ray CT (computer tomography), and the three-dimensional image is analyzed to analyze the fiber morphology.
  • a method for obtaining and evaluating the distribution is known (for example, see Non-Patent Document 1). This evaluation method evaluates the tendency of fiber orientation by using as an index the degree of detour for a three-dimensional path formed by mutually complicated fibers.
  • the degree of detour is obtained by cutting out a layered rectangular area from a three-dimensional image, thinning the image of the fiber in the rectangular area, and regarding the path having both end points at both ends of the rectangular area, the shortest path among the paths between the end points.
  • the ratio between the path length and the linear distance between the two end points is obtained and defined by the ratio.
  • a method of directly and non-destructively extracting a wire sealed with resin in a semiconductor package and a three-dimensional coordinate of a blood vessel or a nervous system in a living body is known (for example, see Patent Document 1).
  • a plurality of tomographic images intersecting with a predetermined direction of the subject are obtained, and two constituent points obtained as corresponding to the cross-section of the linear element in the subject are obtained from each of the two adjacent images, Let it be the start point and end point of a vector representing a linear element.
  • a tomographic image is obtained by X-ray CT or MRI.
  • the constituent points in the tomographic image are determined by the center of gravity of the circle or ellipse image representing the cross section of the linear element, and two points that are the shortest distance between the adjacent images are selected as the pair of the start point and the end point.
  • Non-Patent Document 1 since the thinning process is performed on the image, there is a possibility that a noise portion in the image is extracted as a filler. . This is because the information on the thickness of the fiber is lost by the thinning process, and noise and the fiber cannot be distinguished. Moreover, since it is not the method of extracting each filler independently, only the qualitative information of the tendency of fiber orientation can be obtained.
  • the present invention solves the above-mentioned problem, and is a method for producing a fibrous filler in a composite material capable of quantitatively extracting orientation information of individual fillers from a three-dimensional image of the composite material including a fibrous filler in a base material. It is an object to provide a three-dimensional image processing method and a three-dimensional image processing apparatus.
  • the three-dimensional image processing method for a fibrous filler in a composite material obtains orientation information of the filler from a three-dimensional image of a composite material containing a fibrous filler in a base material.
  • an extraction process for extracting filler orientation information by fitting the shape model to the candidate area selected by the area selection process using the Monte Carlo method that randomly changes the parameters that change the shape and arrangement of the filler shape model.
  • the extraction step extracts the orientation information of one filler from the candidate area, and then removes the area involved in the extraction, to the candidate area after the removal
  • extraction by the Monte Carlo method may be repeated.
  • the method for processing a three-dimensional image of fibrous filler in a composite material includes a data input step of inputting a three-dimensional image as voxel data.
  • the voxel data is acquired by X-ray CT of the composite material, and each voxel of the voxel data is obtained. May have a value based on the X-ray intensity value as a data value.
  • the data input step includes an interpolation step of forming new voxel data having voxels whose data values are values obtained by linear interpolation of data values between the voxels. You may prepare.
  • the region selecting step compares the voxel data value with a predetermined threshold value based on the X-ray intensity value, and has a voxel having a data value larger than the threshold value.
  • a set of candidate regions may be used.
  • a group of voxels adjacent to each other may be generated from the voxels belonging to the candidate area, and the generated individual groups may be used as new candidate areas.
  • the extraction step uses a virtual cylinder as a shape model, changes parameters representing the shape and arrangement of the virtual cylinder in the candidate area, and the virtual cylinder and the candidate
  • the degree of fitting with a region may be evaluated by an integrated value of evaluation values based on data values of voxels included in the virtual cylinder.
  • the extraction step includes fitting a virtual pipe with a virtual cylinder, and then, as a shape model, a virtual pipe having a spline curve defined by a plurality of control points as a central axis.
  • the coordinates of the control points are changed randomly as parameters in the candidate area, the degree of fitting between the virtual pipe and the candidate area is evaluated, and the evaluation using the virtual pipe improves by a predetermined percentage or more than the evaluation using the virtual cylinder
  • fitting information using a virtual pipe may be employed, and if not, fitting information using a virtual cylinder may be employed to extract filler orientation information.
  • the extraction step uses a virtual pipe having a spline curve defined by a plurality of control points as a shape model as a shape model and uses control pipes in the candidate region. Coordinates may be randomly changed as parameters, and the degree of fitting between the virtual pipe and the candidate area may be evaluated by an integrated value of evaluation values based on data values of voxels included in the virtual pipe.
  • the extraction step sequentially changes the coordinates one by one for a plurality of control points of the spline curve one by one, and each time the one control point is changed, the spline is changed. All control points may be rearranged at equal intervals on the determined spline curve before the curve is determined to evaluate the degree of fitting and the next control point is changed.
  • the extraction step sets evaluation points at intervals equal to or smaller than the voxel size in the shape model, and sets evaluation values based on the voxel data values to the evaluation points.
  • the degree of fitting may be evaluated by the integrated value of the evaluation values given to the evaluation points.
  • the extraction step may arrange the evaluation points concentrically on a plane perpendicular to the central axis of the shape model.
  • the evaluation value given to the evaluation point may be a value obtained by linear interpolation using the voxel data value.
  • the extraction step may use a value obtained by subtracting the threshold value from the evaluation value as a new evaluation value.
  • the extraction step may use a value obtained by multiplying the value by a predetermined positive number as the new evaluation value. Good.
  • the extraction step may calculate the integrated value by weighting so that the evaluation value at a position closer to the central axis of the shape model becomes larger.
  • the three-dimensional image processing apparatus for fibrous filler in the composite material of the present invention is a three-dimensional image processing apparatus for extracting filler orientation information from a three-dimensional image of a composite material containing a fibrous filler in a base material.
  • the filler orientation information can be extracted quantitatively.
  • FIG.3 The figure which shows the three-dimensional image by X-ray CT of the composite material shown in FIG.3 (b).
  • A) is a figure which shows the three-dimensional image which shows the example which threshold-processed the three-dimensional image of the fibrous filler in a composite material
  • (b) is a figure which shows the black-and-white reversal image of the image of (a).
  • the flowchart of the other modification of the image processing method are the top views of the voxel explaining the interpolation process in the modification.
  • the flowchart of the other modification of the image processing method The figure which shows the three-dimensional image which shows the example of the candidate area
  • (A) is a perspective view of a virtual cylinder used in the image processing method
  • (b) is a perspective view expressing the virtual cylinder in an XYZ coordinate space
  • (c) is a perspective view expressing the virtual cylinder in a polar coordinate space.
  • the flowchart of the extraction process of the image processing method Sectional drawing of the voxel and shape model explaining the integration
  • (A) (b) (c) is a figure which shows the candidate area
  • (A) is the perspective view which shows a mode that the position of the end point of a virtual cylinder is changed regarding the other modification of the extraction process of the image processing method
  • (b) is the perspective view of the virtual cylinder after changing the end point
  • (C) is a top view explaining the evaluation point set to the virtual cylinder of (b).
  • the figure which shows a three-dimensional image in case the fibrous filler bent in the composite material is contained.
  • the perspective view which shows typically a mode that the virtual cylinder model was fitted with respect to the bent fibrous filler.
  • (A) is a perspective view showing a virtual pipe by a control point and a spline curve
  • (b) is a perspective view of the spline curve when one of the control points is changed.
  • (A) is the perspective view which has arrange
  • (b) is the top view which has arrange
  • (A) (b) (c) is a conceptual diagram which shows the example of a mutually different procedure which fluctuates each control point of the virtual pipe, respectively.
  • (A)-(h) is a conceptual diagram explaining the procedure of performing fitting by changing each control point using the virtual pipe.
  • the block block diagram of the three-dimensional image processing apparatus of the fibrous filler in the composite material which concerns on one Embodiment.
  • the frequency distribution figure which shows the frequency ratio of the fiber length of the fibrous filler extracted by the image processing method and the image processing apparatus.
  • FIG. 1 shows a three-dimensional image processing method (hereinafter referred to as an image processing method) of a fibrous filler in a composite material according to an embodiment.
  • this image processing method includes a region selection step (# 1) and an extraction step (# 2), and these steps are executed to include a composite material including a fibrous filler in the base material.
  • the orientation information of the filler in the composite material is extracted from the three-dimensional image.
  • the orientation information is, for example, information on where the filler is arranged, in what shape and orientation.
  • the region selection step (# 1) a three-dimensional image of the composite material is given, and a candidate region estimated to include a pixel (stereoscopic pixel) representing a filler is selected from the three-dimensional image by referring to a predetermined threshold value. To do.
  • the shape model is fitted to the candidate region selected in the region selection step by using the Monte Carlo method that randomly changes the parameters defining the shape and arrangement of the determined filler shape model. Extract filler orientation information.
  • the filler orientation information is extracted from the shape and arrangement of the shape model of the fitting result.
  • the composite material to be treated is, for example, a glass fiber reinforced resin in which a glass fiber for reinforcement is contained as a fibrous filler in a base material made of a liquid crystal polymer. In the case of this composite material, this image processing method extracts the orientation information of the glass fiber in the liquid crystal polymer resin.
  • FIG. 2 shows a modification of the above-described image processing method.
  • This image processing method includes a data input step (# 0) as a previous step of the region selection step (# 1) in the embodiment of FIG. 1 described above, and the extraction step (# 2) is a model setting step (S1). And an extraction main process (S2) and an iterative processing process (S3, S4).
  • a three-dimensional image of the fibrous filler in the composite material is input as voxel data.
  • the voxel data is acquired by X-ray CT of the composite material, and each voxel of the voxel data has a value based on the X-ray intensity value as a data value.
  • the composite material C is a part of a glass fiber reinforced resin molded product 1 that is injection-molded into a long box shape. In such a resin molded product 1, when resin injection is performed in the longitudinal direction indicated by the arrow a, the glass fibers are generally oriented along the direction of the arrow a.
  • FIG. 4 shows a three-dimensional image G1 by X-ray CT of the composite material C shown in FIG.
  • the three-dimensional image G1 allows the composite material C to transmit X-rays from various directions, collects profiles formed according to the distribution of the amount of X-ray absorption, and obtains a number of tomographic images of the composite material C from these profiles. Is reconstructed as a black and white grayscale image and displayed three-dimensionally.
  • the three-dimensional image G1 including the three-dimensional image information of the fibrous filler is obtained by the difference in the amount of X-ray absorption between the base material (for example, liquid crystal polymer) and the fibrous filler (for example, glass fiber).
  • the voxel data value represents the abundance of the filler.
  • the data value of the voxel can be arbitrarily set depending on, for example, whether to use gray or black and white to display the fibrous filler, and depending on the purpose of using the data value.
  • the term voxel originally means a general three-dimensional pixel that constitutes a three-dimensional image space, but in this image processing method, it means a three-dimensional pixel that is a target of image processing selected with a certain threshold, For example, the expression may be used in a limited manner such as deleting voxels from the candidate area.
  • a candidate region including a region estimated to be a region including a pixel representing a filler in the three-dimensional image of the composite material C is selected.
  • the data value of the voxel is discriminated by a threshold for selecting the candidate region, and for example, a set of voxels having a data value larger than the threshold is set as the candidate region.
  • the data value is an X-ray intensity value
  • the threshold value is set based on the distribution of X-ray intensity values.
  • the three-dimensional images G2 and G3 in FIGS. 5A and 5B show examples in which candidate regions are selected by such threshold processing.
  • the model setting step (S1) of the extraction step (# 2) can reproduce the filler shape with parameters for changing the shape and the arrangement of the candidate region set by the region selection step (# 1).
  • the shape of the shape model is set according to the shape of the filler contained in the composite material.
  • the shape model is a cylinder, an ellipse, a prism, a curved tube in which a plurality of cylinders are continuous along a curve, or the like. All have the orientation in which the longitudinal direction is defined.
  • the shape model shape parameters determine the shape and size of the shape model. For example, in the case of a cylinder, the radius and length, in the case of a long ellipse, the three-axis length of a three-dimensional ellipse, and in the case of a prism, the cross section The shape and size, the length of the prism, etc. are set to change within a predetermined range. In the case of a curved tube (pipe), the radius, curve length, curve bending (for example, approximating the broken line, the length of each line segment, the direction and size of the mutual folding angle, etc.), etc., vary within the specified range.
  • the shape parameters may be set so that the shape model placement parameters determine the position and orientation of the shape model whose shape is determined with respect to the candidate area in the space in which the three-dimensional space coordinate axis is set.
  • the position coordinates at both ends can be used as parameters for the arrangement.
  • the arrangement parameters may be any parameters that can uniquely determine the arrangement of the shape model, and the center position coordinates and azimuth angle of the shape model may be used.
  • the shape model parameters set in the model setting step (S1) are changed to fit the shape model to the candidate region, and the shape and arrangement information of the fitted shape model is used to determine the filler orientation. Information.
  • a process of fitting by changing the parameters of the shape model at random is performed based on the Monte Carlo method.
  • the parameters of the shape model are optimized so that a parameter with a higher evaluation is adopted for a voxel group that has a high probability of the presence of fillers by using random numbers to vary the parameter within a certain range. .
  • the degree of fitting between a candidate area and a shape model is evaluated based on an overall evaluation value obtained by a predetermined method.
  • the shape model at that time is regarded as a filler, and the extraction processing of one filler converges.
  • the process of extracting the orientation information of one filler from the candidate region by the extraction main step (S2) is repeated, and all fillers are included from the entire candidate region including a plurality of fillers.
  • the process for extracting the orientation information is performed.
  • the processing step (S3) for the iteration after one filler is extracted, the region involved in the final fitting of one shape model is selected from the candidate regions in order to efficiently perform the next extraction that follows. Remove. After the area is removed, if there is a remaining candidate area (Yes in S4), the process from the model setting step (S1) is repeated (No in S4), and the image processing ends.
  • the filler orientation information is quantitatively determined. Can be extracted.
  • this image processing method can extract the orientation information of a filler quantitatively nondestructively, when the three-dimensional image of the fibrous filler in a composite material is obtained nondestructively by X-ray CT etc.
  • FIG. 6 shows another modification of the image processing method described above.
  • the data input step (# 0) includes an interpolation step (# 0a) for linearly interpolating voxel data values between the voxels. It is.
  • the linear interpolation can be performed using triple linear interpolation, for example.
  • the three-dimensional image data includes a voxel Bx having a data value and a voxel B0 not having a data value that should be originally included due to the influence of measurement conditions.
  • the interpolation step (# 0a) constitutes new voxel data having a new voxel bx whose data value is a value obtained by linearly interpolating data values between the voxels Bx. be able to.
  • the voxel Bx is divided to generate a small voxel bx, and a value obtained by linear interpolation between the voxels Bx is used as the data value of the voxel bx.
  • This voxel divided interpolation can improve the fitting accuracy when the size of the voxel is the same as or larger than the size of the filler.
  • the above-described region selection step (# 1) includes a division step (# 1a) for dividing a large candidate region into smaller candidate regions.
  • a group (group) composed of voxels adjacent to each other is generated from the voxels belonging to the candidate area, and each group is set as a new candidate area.
  • Voxels include inter-surface adjacency, inter-edge adjacency, and inter-vertex adjacency as situations where two voxels are adjacent to each other.
  • a set of voxels adjacent to each other and having a predetermined number or more is recognized as a group, and the group is set as a new individual candidate area. Even if the set of voxels adjacent to each other does not include a predetermined number or more of voxels, and isolated voxels that are not adjacent to each other are deleted from the candidate area.
  • the candidate area originally selected as one set of voxels having a data value larger than the threshold is subdivided into a plurality of candidate areas through this division step (# 1a). Further, island-like voxels that are not recognized as constituting a filler and are dispersed to a predetermined number or less are excluded. The predetermined number is set based on the shape of the filler and other prior knowledge, or knowledge obtained by executing the extraction main process (S2) experimentally.
  • a three-dimensional image G4 in FIG. 10 shows an example of a candidate region set through the division step (# 1a). In the process of these division processes, not only the inter-surface adjacency but also the inter-adjacent adjacency and further the inter-vertex adjacent voxels may be included in the group.
  • this image processing method includes such a division step (# 1a), the search range by the Monte Carlo method can be narrowed, so that image processing can be performed efficiently and the calculation load of the extraction main step (S2) can be reduced. Can do.
  • this method sets candidate regions based on adjacent voxels, it is possible to avoid the adverse effect that fillers that cross between groups are divided and determined as two fillers when grouping by coordinates. .
  • the dividing step (# 1a) can be applied to the data that has undergone the above-described interpolation step (# 0a).
  • FIGS. 11A, 11B, and 11C show still another modification of the above-described image processing method.
  • a virtual cylinder M is used as a shape model that reproduces the shape of the filler.
  • the shape of the virtual cylinder M is determined by the radius R and the length L, and the coordinates P1 (x1, y1, z1) and P2 (x2, y2, z2) of the end points P1, P2 on the central axis are Since the spatial arrangement in the XYZ rectangular coordinate space is determined, it has eight parameters. Since the distance between the end points P1 and P2 gives the length L, the number of parameters can be seven.
  • the radius R can be fixed to obtain six parameters.
  • the arrangement of the virtual cylinder M is not limited to the display using the coordinates of the end points P1 and P2, but the coordinates of the center point Pc are used, or the inclination of the virtual cylinder is displayed using the polar coordinates (r, ⁇ , ⁇ ). Can do.
  • the parameters of the virtual cylinder include the three-dimensional coordinates of the end points, the three-dimensional coordinates of the center, the direction (azimuth, altitude), the length, the radius, and the like. And the rest is derived from other independent variables.
  • the relationship between the independent variable and the dependent variable is complementary, and which one is treated as an independent variable can be flexibly changed within the software. For example, when the position of the end point is changed, the center position, the length, and the direction are derived from the position of the end point. When the length is varied, the position of the end point is derived from the center position and the length.
  • a virtual cylinder is set as a shape model that reproduces the shape of the filler in the model setting step (S1) of the extraction step (# 2), and the virtual cylinder is extracted in the extraction main step (S2).
  • the filler is extracted using a cylinder.
  • parameters of the shape (radius, length) and arrangement (coordinates at both ends) of the virtual cylinder M are randomly changed in the candidate area, and the virtual cylinder and candidate having the changed shape and arrangement are changed.
  • the filler is extracted by evaluating the degree of fitting with the region. As shown in FIG.
  • the degree of fitting is evaluated based on the integrated value of the evaluation values with the data value of the voxel Bx (shown by a black circle) included in the virtual cylinder M among the voxels Bx as an evaluation value. Is done.
  • the evaluation is comprehensively performed using, for example, the integrated value, the number N of voxels Bx included in the virtual cylinder M, a value obtained by dividing the integrated value by the number N, and the like. For example, when the normalized value is constant and the number N tends to increase, it is determined that the fitting has not yet converged and the virtual cylinder M can be further expanded.
  • the shape and arrangement of the virtual cylinder M with the maximum integrated value are extracted as filler orientation information.
  • the three-dimensional image G5 of FIGS. 14A, 14B, and 14C for example, is a comprehensive evaluation value, and a three-dimensional image of a candidate region viewed from different viewpoints and one virtual cylinder M fitted in the region. Show.
  • the filler extraction process using the virtual cylinder M will be described in more detail.
  • the filler three-dimensional image for example, the above-described three-dimensional image G4 includes a group of a plurality of candidate regions, and each group is considered to include a plurality of filler images. Therefore, in the model setting step (S1), a plurality of virtual cylinders M are randomly generated for one group of them, the degree of fitness is evaluated for each virtual cylinder M, and the highest evaluation virtual cylinder M is selected. Then, the parameter is adopted as an initial parameter. Next, in the extraction main process (S2), starting from the initial parameters, evaluation is performed by moving the parameters randomly within a predetermined variation range, and the parameters are updated when the evaluation is improved.
  • the Monte Carlo method is used as the optimization basic algorithm, and the parameter is varied within a certain range by using a random number, and a parameter with higher evaluation of the fitness is adopted. This procedure is repeated for the selected virtual cylinder, and if the evaluation does not improve, the parameters are determined as converged, thereby extracting one filler.
  • the voxels relating to the extracted filler that is, the voxels belonging to the virtual cylinder M are deleted from the candidate areas, and the remaining candidate areas are again initialized within the same group.
  • the subsequent procedure is repeated from the procedure of adopting the parameter. Whether or not the voxel belongs to the virtual cylinder M may be determined based on whether or not a point preset in the voxel such as the center or one vertex of the voxel is included in the virtual cylinder M. If fillers cannot be extracted within one group, fillers are extracted for the other group. When the filler extraction is completed for all groups, the extraction step (# 2) ends.
  • the island-shaped voxel is deleted at the same time, and the subsequent processing Reduce computational load. Further, when the number of voxels to be processed in the group becomes equal to or less than the predetermined number, it is determined that the filler cannot be extracted in the group, that is, the extraction is completed in the group.
  • FIG. 15 shows still another modification of the above-described image processing method.
  • the extraction main step (S2) after the model setting step (S1) includes an evaluation point setting step (S21).
  • the evaluation point setting step (S21) a large number of evaluation points (sampling points) are set in the virtual cylinder M at intervals equal to or smaller than the size of the voxel, and an evaluation value based on the data value of the voxel is given to each evaluation point.
  • the evaluation points are, for example, the representative points of small voxels obtained by subdividing the voxel Bx in the virtual cylinder M shown in FIG. 13 as shown in FIG. 8 and the representative points of the original large voxel. And it is sufficient.
  • the evaluation value of each evaluation point is a data value of an original large voxel including voxels generated by subdivision. Such evaluation points and evaluation values are generated only within the virtual cylinder M. Accordingly, there is an advantage that the calculation can be completed locally as compared with the case where the data density is increased in the entire voxel data (for example, FIG. 8).
  • the evaluation point is a point fixed in the voxel data space composed of the position of the voxel and the data value, and the virtual cylinder M changes its position in the voxel data space and expands / contracts as its parameters change. It is evaluated according to the acquired evaluation points.
  • the extraction main step (S2) instead of the integrated value of the voxel data values in the extraction main step (S2) of FIG. 12 described above, the integrated value of the evaluation value given to each evaluation point is used, and the degree of fitting To evaluate.
  • the volume of the voxels contained in the virtual cylinder M can be taken into the integrated value with higher accuracy, and the degree of fitting can be evaluated with higher accuracy. Can do.
  • FIG. 16 shows still another modification of the above-described image processing method.
  • the extraction step (# 2) of the image processing method includes an evaluation value interpolation step (S22) after the above-described evaluation point setting step (S21). That is, in the evaluation value interpolation step (S22), a value obtained by linear interpolation using the data value of the original voxel is used as the evaluation value of the evaluation point as the evaluation value to be given to the evaluation point.
  • the evaluation point is a point interpolated between the representative point of the voxel and the representative point of the voxel, and the evaluation value of the evaluation point by the interpolation is given by linear interpolation using the data value of the voxel.
  • Triple linear interpolation can be used as a linear interpolation method.
  • the evaluation value of the evaluation point may be determined by triple linear interpolation of data values (evaluation values) of eight voxels surrounding the evaluation point.
  • FIG. 17 shows still another modification of the above-described image processing method.
  • the extraction step (# 2) of the image processing method further includes an evaluation value subtraction step (S23), a penalty step (S24), and a weighting step (S25) after the evaluation value interpolation step (S22).
  • the evaluation value subtraction step (S23) a value obtained by subtracting a predetermined threshold value from the evaluation value is newly set as the evaluation value.
  • the penalty step (S24) when the evaluation value is negative, a value obtained by multiplying the value by a predetermined positive number is set as a new evaluation value. For example, when the evaluation value takes a negative value, a value obtained by multiplying the evaluation value by 10 is set as a new evaluation value.
  • the weighting step (S25) is a step of weighting so that the evaluation value at a position closer to the central axis of the virtual cylinder becomes larger, for example, weighting so as to be proportional to the reciprocal of the distance from the central axis. By such weighting, the processing can be accelerated so that the central axis of the virtual cylinder quickly approaches a portion with a high evaluation value.
  • FIG. 18, FIG. 19, and FIG. 20 show still another modification of the image processing method.
  • the evaluation point instead of using the evaluation point fixed in the voxel data space in the above-described method, the evaluation point whose position changes in accordance with the change in the position, posture, and shape of the shape model (virtual cylinder M) Is used.
  • FIG. 18A a case is considered in which the end point P1 of the virtual cylinder M having the length L0 is moved to the position Px due to the change of the parameter. Fitting evaluation is performed on an inclined virtual cylinder M having a length L and having end points P1 and P2 shown in FIG. The end point P1 has moved from the original position P0 to the position Px.
  • the evaluation point b is set such that a disk B perpendicular to the central axis of the virtual cylinder M (shape model) is set at a constant interval ⁇ 1 between the end points P1 and P2.
  • a concentric arrangement is set on B.
  • Evaluation points b on the disk B are arranged at a constant interval ⁇ 2 in the radius R direction and at a constant interval ⁇ 3 in the circumferential direction.
  • FIG. 19 shows a state in which the virtual cylinder M is finely divided in each of height, radius, and circumferential direction, and evaluation points b are set at the respective division points.
  • a gray portion around the virtual cylinder M in the image G6 represents a voxel having a data value equal to or greater than a threshold value and forming a candidate area.
  • the extraction process (# 2) of this image processing method will be described with reference to FIG.
  • the model setting step (S1) the positions of the end points P1, P2 of the virtual cylinder M and the initial value of the radius R are set.
  • the extraction main step (S2) the steps (S101 to S110) are repeated to extract fillers on a single straight line.
  • the model setting process (S1) and the extraction main process (S2) are collectively referred to as a single straight line process (# 21).
  • the end point P1 is set to the variable Pi (S101), and the position of the end point P1 is changed (S102).
  • Discs B are arranged at equal intervals between the end point P2 and the changed end point P1 (S103), and evaluation points b (sampling points) are arranged concentrically on each disc B (S104).
  • the disk B is also arranged at the end points P1 and P2. Since the evaluation point b is set for the virtual cylinder M, the evaluation point b is not fixed with respect to the voxel data space and is arbitrarily arranged. An evaluation value obtained by triple linear interpolation based on the voxel data value is assigned to each evaluation point b (S105). The evaluation values of all evaluation points b in the virtual cylinder M are integrated, and the degree of fitting is evaluated by the integrated value (S106).
  • step (S102) is repeated. If the process is converged (Yes in S107), the end point P2 is set to the variable Pi via the step (S108) (S109), and the steps (S102 to S107) are performed for the end point P2 as in the case of the end point P1. Done.
  • the process from step (S101) is forcibly repeated at least once to determine convergence.
  • the evaluation points b arranged concentrically on the above-described disk B By using the evaluation points b arranged concentrically on the above-described disk B, the information included in the shape model, that is, the virtual cylinder M can be effectively used without being missed in the outer boundary portion of the virtual cylinder M.
  • the methods of the evaluation value subtraction step (S23), penalty step (S24), and weighting step (S25) in FIG. 17 described above can be used.
  • FIG. 21 to 28 show still another modification of the image processing method.
  • This image processing method uses a shape model that can cope with a curved filler.
  • a curved filler F may be generated in the three-dimensional image of the fibrous filler in the composite material. This is because the filler is more easily bent as a result of increasing the length of the fibrous filler to improve the strength of the composite material.
  • an image G8 in FIG. 22 when such a curved filler is fitted with the virtual cylinder M, a result of being divided into a plurality of short virtual cylinders M is obtained. Therefore, a virtual pipe (tube) having a constant radius having a spline curve defined by a plurality of control points as a central axis is used as a shape model that can deal with a curved filler.
  • the extraction process (# 2) of this image processing method includes a single curve process (# 22) in which the model setting process (S1) and the extraction main process (S2) are combined, and subsequent processes. (S3, S4).
  • a straight spline curve is set.
  • a radius R is added to the spline curve to set a virtual pipe MP having a constant radius R with the spline curve as the central axis.
  • a spline curve is a curve in which control points are connected by an arbitrary polynomial. Therefore, the curve is always a curve or a straight line passing through the control points.
  • FIG. 24B shows a state in which all control points Ci are rearranged at equal intervals on the spline curve Sp by the step (S200) after the control point C1 is moved.
  • the disks B are arranged vertically and at equal intervals (S203).
  • the evaluation points b are arranged concentrically (S204).
  • the setting of the arrangement interval of the disks B and the arrangement of the evaluation points on the disks B may be performed using the same values as ⁇ 1, ⁇ 2, and ⁇ 3 shown in FIGS. Since the evaluation point b is set for the virtual pipe MP, it is not fixed with respect to the voxel data space and is arbitrarily arranged.
  • Each evaluation point b is given an evaluation value obtained by triple linear interpolation based on the voxel data value (S205).
  • the evaluation values of all evaluation points b in the virtual pipe MP are integrated, and the degree of fitting is evaluated based on the integrated value (S206).
  • step (S201) When convergence for each of all the control points C1 to Cn is confirmed (Yes in S108), the process from step (S201) is forcibly repeated at least once to determine convergence. If it is determined that the improvement in evaluation with respect to the fluctuations of the end points P1 and P2 has converged (Yes in S210), the extraction main process (S2) ends. Thereby, a single curve process (# 22) is complete
  • An image G9 in FIG. 26 shows a state in which the evaluation point b is set for the virtual pipe MP.
  • An image G10 in FIG. 27 shows a state during fitting using the virtual pipe MP.
  • the number n of the control points Ci may be variable.
  • n 2, that is, a virtual cylinder M, and n may be increased at the timing of the step (S200 to S208) for arranging the control points Ci.
  • the position may be changed from the control point C1, and the position may be changed from the control point Cn in the reverse order when the whole is repeated. Further, as shown in FIG.
  • the order of variation of the position of the control point Ci can be arbitrarily selected, for example, in order to converge at an early stage by first varying the portion where the variation amount is large.
  • this image processing method includes the single straight line process (# 21) shown in FIG. 20, the single curve process (# 22) shown in FIG. 23, and a new determination process thereafter. (# 23). That is, in this image processing method, fitting to a single filler by the virtual cylinder M is performed (# 21), and a single virtual pipe MP having the endpoints P1 and P2 of the determined virtual cylinder M as end points is used. Fitting to the filler is performed (# 22). Thereafter, in the determination step (# 23), the evaluation result by the single straight line step (# 21) is compared with the evaluation result by the single curve step (# 22) (S30).
  • FIGS. 30 (a) to 30 (h) A specific example of the above process will be described with reference to FIGS. 30 (a) to 30 (h).
  • a curved filler that is fitted with four virtual cylinders M when fitting with a virtual cylinder M that is a linear shape model is assumed.
  • the short virtual cylinder M is extracted by the single linear process (# 21) with respect to the entire length of the curved filler.
  • a straight spline curve Sp is set for the extracted virtual cylinder M by the single curve step (# 22), and the position of the control point C1 located at the end point P1 Is fluctuated.
  • FIG. 30 (a) a curved filler that is fitted with four virtual cylinders M when fitting with a virtual cylinder M that is a linear shape model is assumed.
  • the short virtual cylinder M is extracted by the single linear process (# 21) with respect to the entire length of the curved filler.
  • a straight spline curve Sp is set for the extracted virtual cylinder M by the single
  • FIGS. 30 (e) to 30 (g) position variation and evaluation are performed in order of the control points C2, C3,..., Cn, and the position of the control point Cn is at the other end of the filler.
  • the fluctuation of the position of the control point Cn converges.
  • the position variation and the evaluation are performed again sequentially from the control point C1 to the control point Cn, and the convergence of the extraction for one filler is confirmed based on the degree of change in the evaluation.
  • FIG. 30H the virtual pipe MP for one curved filler is determined.
  • FIG. 31 shows a three-dimensional image processing apparatus (hereinafter referred to as image processing apparatus 2) for fibrous filler in a composite material according to an embodiment.
  • the image processing apparatus 2 includes an area selection unit 11, a model setting unit 12, an extraction main unit 13, a data input unit 14, an operation unit 15, a display unit 16, and a control unit 10 that controls these, and a fiber as a base material.
  • the orientation information of the filler is extracted from the three-dimensional image of the composite material containing the filler.
  • the three-dimensional image is input from the data input unit 14.
  • the control unit 10 is configured by a computer, and the region selection unit 11, the model setting unit 12, and the extraction unit main 13 are configured by programs that run on the computer.
  • the data input unit 14, the operation unit 15, and the display unit 16 include devices such as a USB port, a DVD player, a hard disk, a keyboard, a mouse, a pointer, and a flat panel display that are provided in a normal computer.
  • the region selection unit 11 selects and sets candidate regions including a region estimated to be a region representing a filler in a three-dimensional image of a composite material.
  • the model setting unit 12 sets a shape model that reproduces the filler shape with parameters that change the shape and the arrangement of the candidate region set by the region selection unit 11.
  • the extraction main unit 13 fits the shape model to the candidate region based on the Monte Carlo method by randomly changing the parameters of the shape model set by the model setting unit 12.
  • the extraction main unit 13 extracts the shape and arrangement of the fitted shape model as filler orientation information.
  • the control unit 10 repeats the operations of the model setting unit 12 and the extraction main unit 13 until extraction of the filler in the three-dimensional image is completed.
  • the region selection unit 11, the model setting unit 12, the extraction main unit 13, and the data input unit 14 are the region selection step (# 1), the model setting step (S1), the extraction main step (S2) in FIG.
  • Each process of the data input process (# 0) is executed.
  • the image processing apparatus 2 is not limited to the processing described with reference to the flowcharts of FIGS. 1 and 2 and can perform the processing described with reference to other flowcharts.
  • FIG. 32 shows a three-dimensional image G11 of the fibrous filler extracted from the composite material C shown in FIG. 3B by using the present image processing method and image processing apparatus.
  • the composite material C (test material) has the outer length 17.8 ⁇ width 1.83 ⁇ height 0.4 mm, opening length 16.5 mm, bottom surface thickness 0 shown in FIG.
  • the central part of a box-shaped thin-walled injection-molded product 1 having dimensions of 12 mm and a side surface thickness of 0.163 mm is cut out.
  • the composite material C is a super heat-resistant type I liquid crystal polymer to which glass fibers having an average fiber length of 88 ⁇ m are added as a filler, and molding was performed using a pre-plastic injection molding machine having a maximum clamping force of 196 kN.
  • the 3D image by X-ray CT was reconstructed from the tomographic image data of the molded product imaged using microfocus X-ray CT.
  • the reconstructed image is shown as the three-dimensional image G1 in FIG. 4 described above, and the L-shaped molded product shape and the presence of the internal glass fiber can be visually confirmed.
  • the size of the three-dimensional image data is 300 ⁇ 348 ⁇ 200 voxels, and the actual size of one side of one voxel is about 3 ⁇ m.
  • extraction was performed using a virtual cylinder as a shape model.
  • the diameter of the glass fiber is known as 10 ⁇ m.
  • FIG. 33 is an example showing the fiber length distribution of the filler extracted using the present image processing method and image processing apparatus, and the measured fiber length distribution of the filler is shown together as a comparative example.
  • the measured values of the comparative examples are obtained by baking only the glass fiber by baking the resin portion of the test material and measuring the length.
  • the fiber length distributions of both the example and the comparative example show good agreement. Further, for example, in the bottle of 60 to 80 ⁇ m that gives the maximum frequency, it is 22.2% in the example and 27.0% in the comparative example, and the difference is within 4.8%.
  • the orientation information of the filler could be extracted non-destructively without destroying the test material.
  • the filler orientation and orientation degree distribution in the composite material C (test material) are the individual filler orientations. It can be directly calculated quantitatively from the information.
  • the present invention is not limited to the above configuration and can be variously modified.
  • it can be set as the structure which mutually combined the structure of each embodiment and modification which were mentioned above, and can be set as the structure which replaced the order of each process suitably.
  • the evaluation point setting step (S21) may be provided after the region selection step (# 1) and before the model setting step (S1).
  • the evaluation points (sampling points) may be set to, for example, each division point obtained by subdividing the voxels, and can be set as evaluation points fixed in the three-dimensional image space without depending on the shape model.
  • the evaluation value subtraction step (S23), penalty step (S24), and weighting step (S25) can also be applied to the case where no evaluation score is used, that is, the case where the voxel data value is used as the evaluation value. it can.
  • the three-dimensional image data is not limited to data obtained by X-ray CT, and data obtained by an arbitrary three-dimensional image acquisition unit can be used.
  • MRI image data can be used, and in the case of a transparent resin, optical CT image data can be used.
  • those data formats are not limited to voxels, and arbitrary-shaped solid pixels can be used.

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Abstract

L'invention concerne un procédé et un dispositif pour le traitement d'image tridimensionnelle d'une charge fibreuse dans un matériau composite, permettant d'extraire quantitativement, à partir d'une image tridimensionnelle d'un matériau composite comprenant une charge fibreuse dans un matériau de base, des informations d'orientation des fibres individuelles dans le matériau composite. Ce matériau comprend une étape d'entrée de données (#0), une étape de sélection de zone (#1), une étape de définition de modèle (S1) et une étape principale d'extraction (S2). Dans l'étape d'entrée de données (#0), l'entrée d'une image tridimensionnelle de la charge fibreuse dans un matériau composite est reçue comme données de voxels. Dans l'étape de sélection de zone (#1), une zone candidate qui comprend la charge est sélectionnée sur la base d'une valeur de données de voxels. Dans l'étape de définition de modèle (S1), un modèle de forme est défini, lequel est configuré pour reproduire la forme de la fibre, ledit modèle de forme ayant des paramètres dans lesquels la forme et le positionnement sont changés. Dans l'étape principale d'extraction (S2), un modèle de forme, lequel est ajusté à la zone candidate par les paramètres de modèle de forme changés sur la base de la méthode de Monte Carlo, est extrait quantitativement comme informations d'orientation de la charge.
PCT/JP2013/006806 2012-11-21 2013-11-20 Procédé et dispositif pour le traitement d'image tridimensionnelle de charge fibreuse dans un matériau composite Ceased WO2014080622A1 (fr)

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