Abstract: A computer-implemented method includes receiving a digital representation of an image and generating CAD sketches from it. The number of surfaces in a CAD model depends upon the number entities at the sketch level. The method keeps the number of created sketch entities and constraints to a minimum. The method includes a scalable approach for a range of images. Each contour is represented by a sequence of points following a path corresponding to a boundary in the image. The method includes classifying each point in a particular one of the contours as a curve region or a corner region contour point, thereby segmenting the contour into plurality of curve regions separated by corner regions. The method includes optimally fitting a curve to each one of the curve regions to create the best possible representation of the curve region. Additionally, the refine algorithm automatically improves the fit wherever needed.

Abstract: A method in a computer aided drafting application for replicating a component mating in a modeled assembly includes examining constraints and geometry surrounding a selected component of the component mating in a first surface of the assembly. A first descriptor with a plurality of numerical characteristics of the constraints and geometry is captured. The first descriptor is set as a first seed descriptor. A potential first target geometry in the region of the first face is examined and a first target descriptor is computed according to the first target geometry. If first seed descriptor matches the first target descriptor, an instance of a first target component is created according to the first target descriptor.

Abstract: A method for selecting a plurality of edges or faces of a displayed modeled object in a computer-aided design (CAD) system extracts a plurality of features, each feature including a measurable numeric property of one or more of edges or faces of the modeled object. The features are scaled, and a selection of a seed edge or a seed face is received. A suggested edge or face is chosen based upon the seed edge or seed face, and a graphical indication of the suggested edge or face is displayed on the modeled object.

Abstract: A method in a computer aided drafting application for replicating a component mating in a modeled assembly includes examining constraints and geometry surrounding a selected component of the component mating in a first surface of the assembly. A first descriptor with a plurality of numerical characteristics of the constraints and geometry is captured. The first descriptor is set as a first seed descriptor. A potential first target geometry in the region of the first face is examined and a first target descriptor is computed according to the first target geometry. If first seed descriptor matches the first target descriptor, an instance of a first target component is created according to the first target descriptor.

Abstract: Methods and systems identify frequently-used CAD components and apply machine learning techniques to predict mateable entities and corresponding mate types for those components to automatically add components to a CAD model. An example method includes accessing information regarding CAD model parts and related mate information stored in a computer database, and dividing parts into a plurality of clusters having parts with similar global shape signatures. In response to a new part being added, contextual signatures of entities of the new part are input into a mateability predictor neural network to determine a mateable entity of the new part. Input into a mate-type predictor neural network is (i) a contextual signature of the mateable entity and (ii) a contextual signature of an entity of another part of the CAD model to determine a mate type between the entities. A mate between the new part and the other part is automatically added based on the determined mate type.

Abstract: A method preserves shapes in a solid model when distributing material during topological optimization. A 3D geometric model of a part having a boundary shape is received. The geometric model is pre-processed to produce a variable-void distance field and to produce a frozen distance field representing the boundary shape. The geometric model is apportioned into a plurality of voxels, and a density value is adjusted for each voxel according to an optimization process. An iso-surface mesh is extracted from the voxel data, and an iso-surface distance field is generated from the extracted iso-surface mesh. A distance field intersection is derived between the iso-surface distance field and the variable-void distance field. A distance field union is performed between the distance field intersection and the frozen distance field, and a result iso-surface mesh is produced from the distance field union.

Abstract: A method preserves shapes in a solid model when distributing material during topological optimization. A 3D geometric model of a part having a boundary shape is received. The geometric model is pre-processed to produce a variable-void mesh and to produce a frozen mesh representing the boundary shape. The geometric model is apportioned into a plurality of voxels, and a density value is adjusted for each voxel according to an optimization process. An iso-surface mesh is extracted from the voxel data, and a mesh Boolean intersection is derived between the extracted iso-surface mesh and the variable-void mesh. A mesh Boolean union between the mesh Boolean intersection and the frozen mesh.

Abstract: A method preserves shapes in a solid model when distributing material during topological optimization. A 3D geometric model of a part having a boundary shape is received. The geometric model is pre-processed to produce a variable-void mesh and to produce a frozen mesh representing the boundary shape. The geometric model is apportioned into a plurality of voxels, and a density value is adjusted for each voxel according to an optimization process. An iso-surface mesh is extracted from the voxel data, and a mesh Boolean intersection is derived between the extracted iso-surface mesh and the variable-void mesh. A mesh Boolean union between the mesh Boolean intersection and the frozen mesh.

Abstract: A method preserves shapes in a solid model when distributing material during topological optimization. A 3D geometric model of a part having a boundary shape is received. The geometric model is pre-processed to produce a variable-void mesh and to produce a frozen mesh representing the boundary shape. The geometric model is apportioned into a plurality of voxels, and a density value is adjusted for each voxel according to an optimization process. An iso-surface mesh is extracted from the voxel data, and a mesh Boolean intersection is derived between the extracted iso-surface mesh and the variable-void mesh. A mesh Boolean union between the mesh Boolean intersection and the frozen mesh.

Abstract: A method preserves shapes in a solid model when distributing material during topological optimization. A 3D geometric model of a part having a boundary shape is received. The geometric model is pre-processed to produce a variable-void distance field and to produce a frozen distance field representing the boundary shape. The geometric model is apportioned into a plurality of voxels, and a density value is adjusted for each voxel according to an optimization process. An iso-surface mesh is extracted from the voxel data, and an iso-surface distance field is generated from the extracted iso-surface mesh. A distance field intersection is derived between the iso-surface distance field and the variable-void distance field. A distance field union is performed between the distance field intersection and the frozen distance field, and a result iso-surface mesh is produced from the distance field union.

Abstract: A computer-based method includes enabling a user to create or select a geometric entity in a design in a computer-aided design program, predicting a location and orientation in the design for a copy of the geometric entity, and displaying, as a suggestion to the user, a visual representation of the copy of the geometric entity in the predicted location and orientation in the design.

Abstract: A method for selecting a plurality of edges or faces of a displayed modeled object in a computer-aided design (CAD) system extracts a plurality of features, each feature including a measurable numeric property of one or more of edges or faces of the modeled object. The features are scaled, and a selection of a seed edge or a seed face is received. A suggested edge or face is chosen based upon the seed edge or seed face, and a graphical indication of the suggested edge or face is displayed on the modeled object.

Abstract: Methods and systems identify frequently-used CAD components and apply machine learning techniques to predict mateable entities and corresponding mate types for those components to automatically add components to a CAD model. An example method includes accessing information regarding CAD model parts and related mate information stored in a computer database, and dividing parts into a plurality of clusters having parts with similar global shape signatures. In response to a new part being added, contextual signatures of entities of the new part are input into a mateability predictor neural network to determine a mateable entity of the new part. Input into a mate-type predictor neural network is (i) a contextual signature of the mateable entity and (ii) a contextual signature of an entity of another part of the CAD model to determine a mate type between the entities. A mate between the new part and the other part is automatically added based on the determined mate type.