Abstract: In measuring a dimension of an object to be measured W made of a single material, a plurality of transmission images of the object to be measured W are obtained by using an X-ray CT apparatus, and then respective projection images are generated. The projection images are registered with CAD data used in designing the object to be measured W. The dimension of the object to be measured W is calculated by using a relationship between the registered CAD data and projection images. In such a manner, high-precision dimension measurement is achieved by using several tens of projection images and design information without performing CT reconstruction.

Abstract: An isosurface mesh M is generated by extracting voxels having a certain CT value from volume data obtained by X-ray CT. A gradient vector g of a CT value is calculated at each vertex p of the isosurface mesh M. A plurality of sample points S are generated in positive and negative directions of the calculated gradient vector g. Gradient norms N of CT values at the respective generated sample points S are calculated. The vertex p of the isosurface mesh is moved and corrected to a sample point Sm having the maximum norm Nm calculated.

Abstract: Projection images of reduced resolution are generated by reducing resolution of filtered projection images and/or reducing the number of filtered projection images. Volume data of reduced resolution is generated by performing CT reconstruction using the projection images of reduced resolution. Each voxel of the volume data of reduced resolution is provisionally divided. The provisionally divided voxels are compared in voxel value before and after provisional division. If a difference in voxel value before and after the provisional division is greater than a threshold, the provisional division is determined to be valid, and division is further continued. If the difference in voxel value before and after the provisional division is less than or equal to the threshold, the provisional division is determined to be invalid and the voxel ends being divided.

Abstract: A material shape simulation apparatus for simulating deformation of a three-dimensional woven fiber material is provided and includes: an orientation vector field generation unit generating a model shape orientation vector field on three-dimensional meshes of a model shape of a three-dimensional woven fiber material obtained by stacking sheets of two-dimensional woven fabric made of X-yarn and Y-yarn and binding them with Z-yarn; a parameterization unit that searches for a gradient vector for calculating a material shape orientation vector field, being an orientation vector field of a material shape before deformation of the model shape, from the model shape orientation vector field; and an orientation vector updating unit that updates the model shape orientation vector field by applying a condition of preserving a volume between the model shape orientation vector field and the material shape orientation vector field and a condition that neither the X-yarn nor the Y-yarn expands or contracts.

Abstract: An isosurface mesh M is generated by extracting voxels having a certain CT value from volume data obtained by X-ray CT. A gradient vector g of a CT value is calculated at each vertex p of the isosurface mesh M. A plurality of sample points S are generated in positive and negative directions of the calculated gradient vector g. Gradient norms N of CT values at the respective generated sample points S are calculated. The vertex p of the isosurface mesh is moved and corrected to a sample point Sm having the maximum norm Nm calculated.

Abstract: A design support apparatus supporting designing of a product which uses a fiber material includes a processor that creates a predicted shape model by predicting a shape of the product before a deformation processing. The processor: creates a 3-dimensional shape model of the product; creates curved shape models by separating the 3-dimensional shape model into two or more fiber layers; sets a correspondence relationship between the curved shape models; creates an orientation vector field in the curved shape models; and predicts the shape of the product before the deformation processing by developing the curved shape models on a flat surface based on the correspondence relationship between the curved shape models and the orientation vector field in the curved shape models, and creates the predicted shape model based on the predicted shape.

Abstract: In measuring a dimension of an object to be measured W made of a single material, a plurality of transmission images of the object to be measured W are obtained by using an X-ray CT apparatus, and then respective projection images are generated. The projection images are registered with CAD data used in designing the object to be measured W. The dimension of the object to be measured W is calculated by using a relationship between the registered CAD data and projection images. In such a manner, high-precision dimension measurement is achieved by using several tens of projection images and design information without performing CT reconstruction.

Abstract: Projection images of reduced resolution are generated by reducing resolution of filtered projection images and/or reducing the number of filtered projection images. Volume data of reduced resolution is generated by performing CT reconstruction using the projection images of reduced resolution. Each voxel of the volume data of reduced resolution is provisionally divided. The provisionally divided voxels are compared in voxel value before and after provisional division. If a difference in voxel value before and after the provisional division is greater than a threshold, the provisional division is determined to be valid, and division is further continued. If the difference in voxel value before and after the provisional division is less than or equal to the threshold, the provisional division is determined to be invalid and the voxel ends being divided.

Abstract: A material shape simulation apparatus for accurately simulating deformation of a three-dimensional woven fiber material is provided.

Abstract: An image analysis apparatus, which analyzes orientations of fiber bundles of X-yarns and Y-yarns from a three-dimensional image of a woven fabric made of fiber bundles of the X-yarns, the Y-yarns, and Z-yarns, includes: a binarization unit that binarizes the three-dimensional image; an overlapping area extraction unit that extracts an overlapping area from the binarized image; a reference direction determination unit that averages an overlapping direction of each voxel included in the overlapping area and determines the averaged direction as a reference direction; a Z-yarn removal unit that removes the Z-yarns from the binarized image by applying a directional distance method on a reference plane perpendicular to the reference direction; and a fiber bundle orientation estimation unit that applies the directional distance method on the reference plane and estimates the orientations of the fiber bundles of the X-yarns and the Y-yarns based on a calculated directional distance.

Abstract: Proposed are an image analyzing apparatus and program in which the orientation of fiber bundles can be easily analyzed from a three-dimensional image of CMC.

Abstract: A design support apparatus supporting designing of a product which uses a fiber material includes a processor that creates a predicted shape model by predicting a shape of the product before a deformation processing. The processor: creates a 3-dimensional shape model of the product; creates curved shape models by separating the 3-dimensional shape model into two or more fiber layers; sets a correspondence relationship between the curved shape models; creates an orientation vector field in the curved shape models; and predicts the shape of the product before the deformation processing by developing the curved shape models on a flat surface based on the correspondence relationship between the curved shape models and the orientation vector field in the curved shape models, and creates the predicted shape model based on the predicted shape.

Abstract: An image analysis apparatus, which analyzes orientations of fiber bundles of X-yarns and Y-yarns from a three-dimensional image of a woven fabric made of fiber bundles of the X-yarns, the Y-yarns, and Z-yarns, includes: a binarization unit that binarizes the three-dimensional image; an overlapping area extraction unit that extracts an overlapping area from the binarized image; a reference direction determination unit that averages an overlapping direction of each voxel included in the overlapping area and determines the averaged direction as a reference direction; a Z-yarn removal unit that removes the Z-yarns from the binarized image by applying a directional distance method on a reference plane perpendicular to the reference direction; and a fiber bundle orientation estimation unit that applies the directional distance method on the reference plane and estimates the orientations of the fiber bundles of the X-yarns and the Y-yarns based on a calculated directional distance.

Abstract: Proposed are an image analyzing apparatus and program in which the orientation of fiber bundles can be easily analyzed from a three-dimensional image of CMC.

Abstract: An unknown surface shape of a physical object can be extracted with good precision. Image data of a projective image that has been acquired by radiation projection to an object is acquired. Next, a predetermined mesh structure is used to acquire cross-sectional images of the subject from image data of the projective images by reconstruction using tomography. Lattice points constituting the mesh structure are then moved in conformity with the surface shape of the object, based on the cross-sectional image that has been acquired by reconstruction. Reconstruction is carried out again using the mesh whose lattice point positions have been corrected. Movement on the reconstruction of the lattice points is then repeated as many times as required.

Abstract: An unknown surface shape of a physical object can be extracted with good precision. Image data of a projective image that has been acquired by radiation projection to an object is acquired. Next, a predetermined mesh structure is used to acquire cross-sectional images of the subject from image data of the projective images by reconstruction using tomography. Lattice points constituting the mesh structure are then moved in conformity with the surface shape of the object, based on the cross-sectional image that has been acquired by reconstruction. Reconstruction is carried out again using the mesh whose lattice point positions have been corrected. Movement on the reconstruction of the lattice points is then repeated as many times as required.