Abstract: The disclosure notably relates to a computer-implemented method for designing, with a CAD system, a 3D modeled object representing a mechanical part. The method includes obtaining a B-rep representing the mechanical part. The B-Rep has faces and edges. The method further includes obtaining a first set of faces and a feature type. The feature type is either a depression feature type or a protrusion feature type. The method further comprises, automatically by the CAD system, recognizing a second set of faces within the first set of faces. The second set of faces represents a feature of the provided feature type. This constitutes an improved method for designing a mechanical part.

Abstract: An embodiment of the invention involves increasing the penalty stiffness within a finite element simulation increment, which is more accurate because it avoids following a solution path with significant non-physical penetrations. An embodiment of the present invention begins by determining a first value of a parameter used by a finite element simulation of a load increment. Next, a first solution of the finite element simulation is determined by performing Newton iterations using the first value of the parameter until a first convergence check is satisfied. Then, a second value the parameter is determined wherein the second value of the parameter is unequal to the first value of the parameter. Finally, a second solution of the finite element simulation is determined by continuing the Newton iterations using the second value of the parameter until a second convergence check is satisfied, the first convergence check being different than the second convergence check.

Abstract: The disclosure notably relates to a computer-implemented method of machine-learning. The method includes obtaining a dataset including 3D modeled objects which each represent a respective mechanical part. The dataset has one or more sub-datasets. Each sub-dataset forms at least a part of the dataset. The method further includes, for each respective sub-dataset, determining a base template and learning a neural network configured for inference of deformations of the base template each into a respective 3D modeled object. The base template is a 3D modeled object which represents a centroid of the 3D modeled objects of the sub-dataset. The learning includes a training based on the sub-dataset. This constitutes an improved method of machine-learning with a dataset including 3D modeled objects which each represent a respective mechanical part.

Abstract: The disclosure notably relates to a computer-implemented method for forming a dataset configured for learning a neural network. The neural network is configured for inference, from a discrete geometrical representation of a 3D shape, of an editable feature tree representing the 3D shape. The editable feature tree comprises a tree arrangement of geometrical operations applied to leaf geometrical shapes. The method includes obtaining respective data pieces, and inserting a part of the data pieces in the dataset each as a respective training sample. The respective 3D shape of each of one or more first data pieces inserted in the dataset is identical to the respective 3D shape of respective one or more second data pieces not inserted in the dataset. The method forms an improved solution for digitization.

Abstract: The invention notably relates to a computer-implemented method for designing a 3D assembly of modeled objects. The method comprises rendering on a second computer a 3D assembly of modeled objects by merging a second 3D modeled object with at least one raster image of a first 3D modeled object, the at least one raster image having being streamed from a first computer to the second computer; sending from the second computer to the first computer first data related to the second 3D modeled object for contact computation between the first and second 3D modeled objects; and computing on the first computer a contact between the first and second 3D modeled objects.

Abstract: A computer-implemented method queries a database that comprises modeled objects. Each modeled object represents a physical attribute of a respective real object. The database comprises for each modeled object, a respective simplicial complex. The method comprises providing a query that includes a signature criterion, and returning, as results of the query, respective modeled objects of the database. The respective modeled object is returned based on an extent to which the respective modeled object has a respective simplicial complex that respects the signature criterion. Such method system improves the field of searching modeled objects in a database.

Abstract: A computer-implemented method of drawing a polyline in a three-dimensional scene: a) draws a segment (S1) of said polyline in said three-dimensional scene, said segment having a starting point (P1) and an endpoint (P2); b) displays, in the three-dimensional scene, a graphical tool (PST) representing a set of three orthogonal planes (PLA, PLB, PLC), one of said planes being orthogonal to the segment; c) selects one of said planes; and d) draws another segment of the polyline (S2), having a starting point coinciding with the endpoint of the segment drawn in step a) and lying in the plane (PLA) selected in step c). Steps a), c) and d) are carried out based on input commands provided by a user. A computer program product, non-volatile computer-readable data-storage medium and Computer Aided Design or three-dimensional illustration authoring system carries out such a method.

Abstract: Described is a computer-implemented method for partitioning a 3D scene into a plurality of zones, each zone representing an area or a volume of the 3D scene and being processed by a computing resource. The method comprises obtaining a 3D scene comprising one or more objects, each object generating a computing resource cost, computing a first map that represents a density of computing costs of the provided 3D scene, defining a second map that represents constraints on the shapes of zones that will be obtained as a result of a partitioning of the 3D scene, discretizing the obtained 3D scene into cells by computing a space quantization of the 3D scene free of dynamic objects, computing, for each cell, a computing cost from the first map of the 3D scene, aggregating the cells into one or more zones in accordance with the second map.

Abstract: A computer implemented method for designing a 3D modeled object that represents a mechanical part. The method comprises selecting a group of faces of the B-Rep, defining a trajectory for each respective face of the group of faces, computing, for each face of the group of faces, a respective swept volume, the swept volume corresponding to the volume swept by the respective face with respect to the trajectory, assigning a material removal label or a material adding label to each swept volume, according to the position of the swept volume at the respective face with respect to the interior of the 3D modeled object, and updating the B-Rep with a material removal volume and then a material adding volume. The method improves the design of a 3D modeled object.

Abstract: The disclosure notably relates to a computer-implemented method for forming a dataset configured for learning a neural network. The neural network is configured for inference, from a freehand drawing representing a 3D shape, of a solid CAD feature representing the 3D shape. The method includes generating one or more solid CAD feature includes each representing a respective 3D shape. The method also includes, for each solid CAD feature, determining one or more respective freehand drawings each representing the respective 3D shape, and inserting in the dataset, one or more training samples. Each training sample includes the solid CAD feature and a respective freehand drawing. The method forms an improved solution for inference, from a freehand drawing representing a 3D shape, of a 3D modeled object representing the 3D shape.

Abstract: The disclosure notably relates to a computer-implemented method of machine-learning. The method includes obtaining a dataset including 3D modeled objects which each represent a respective mechanical part and further includes providing a set of neural networks. Each neural network has respective weights. Each neural network is configured for inference of 3D modeled objects. The method further includes modifying respective weights of the neural networks by minimizing a loss. For each 3D modeled object, the loss selects a term among a plurality of terms. Each term penalizes a disparity between the 3D modeled object and a respective 3D modeled object inferred by a respective neural network of the set. The selected term is a term among the plurality of terms for which the disparity is the least penalized. This constitutes an improved method of machine-learning with a dataset including 3D modeled objects which each represent a respective mechanical part.

Abstract: The disclosure notably relates to a computer-implemented method for designing a three-dimensional (3D) model. The method includes obtaining a first 3D model, the first 3D model being defined by: (i) one delegated data object comprising input parameters specific to a type of the delegated data object and (ii) an output topology, and being associated with a sequence of geometric design operations.

Abstract: The disclosure notably relates to a computer-implemented method for extracting a feature tree from a mesh. The method includes providing a mesh, computing a geometric and adjacency graph of the provided mesh, wherein each node of the graph represents one region of the mesh and comprises a primitive type and parameters of the region, each connection between two nodes is an intersection between the respective surfaces of the regions represented by the two connected nodes. The method also includes instantiating for each node of the graph, a surface based on the identified primitive type and parameters of the region.

Abstract: The disclosure notably relates to a computer-implemented method for designing, with a CAD system, a 3D modeled object representing a mechanical part. The method includes providing a B-rep representing the mechanical part. The B-rep has faces, edges and vertices. The method includes providing a sharp edge. The method further includes, automatically by the CAD system, identifying a set of edges. The set of edges includes the provided sharp edge. Each first edge of the set of edges is directed similarly to at least one second edge of the set of edges, the first edge and the second edge sharing a face. The method further includes, automatically by the CAD system, selecting sharp edges within the set of edges. Such a method constitutes an improved method for designing a mechanical part.

Abstract: The disclosure notably relates to a three-dimensional (3D) model. The data structure includes one delegated data object. The one delegate data object includes input parameters specific to a type of the delegated data object, and at least one operator specific to the type of the delegated data object for generating an output topology. The data structure also includes an output topology generated by the operator.

Abstract: A method, performed by a computer system, for designing a multi-physics system including the steps of displaying a block diagram representation of the multi-physics system, including blocks that each correspond to a respective sub-system of the multi-physics system, and, between the blocks, links that correspond to multi-physics connections between the respective sub-systems, and upon a zoom command, sent by a user, displaying a preview of a block diagram representation of at least one respective sub-system, the displaying of the preview being controlled by the detection, by the computer system, of the zoom command. Such a method improves the design of a 3D modeled object.

Abstract: The disclosure notably relates to computer-implemented method for learning a neural network configured for inference, from a freehand drawing representing a 3D shape, of a solid CAD feature representing the 3D shape. The method includes providing a dataset including freehand drawings each representing a respective 3D shape, and learning the neural network based on the dataset. The method forms an improved solution for inference, from a freehand drawing representing a 3D shape, of a 3D modeled object representing the 3D shape.

Abstract: The disclosure notably relates to a computer-implemented method for generating a structured three-dimensional (3D) model from a mesh. The method includes obtaining a mesh that comprises faces, each face of the mesh including a normal and principal curvature values; computing a distribution of the principal curvatures values over the whole mesh by counting the number of occurrences of discretized curvature values; identifying in the computed distribution one or more dominant ranges of principal curvature values; for each identified dominant range, computing one or more regions of the mesh that includes faces belonging to the identified dominant range; for each computed region, detecting a primitive type by using the curvatures values of all faces of the region and identifying parameters of the detected primitive by using the mesh surface of the region.

Abstract: The disclosure notably relates to a computer-implemented method for learning a neural network configured for inference, from a discrete geometrical representation of a 3D shape, of an editable feature tree representing the 3D shape. The editable feature tree includes a tree arrangement of geometrical operations applied to leaf geometrical shapes. The method includes obtaining a dataset including discrete geometrical representations each of a respective 3D shape, and obtaining a candidate set of leaf geometrical shapes. The method also includes learning the neural network based on the dataset and on the candidate set. The candidate set includes at least one continuous subset of leaf geometrical shapes. The method forms an improved solution for digitization.