公开(公告)号：US20240370612A1

公开(公告)日：2024-11-07

申请号：US18611942

申请日：2024-03-21

Applicant: DASSAULT SYSTEMES

Inventor： Léopold MAILLARD ,  Julien BOUCHER

IPC: G06F30/27

Abstract: A computer-implemented method of machine-learning for CAD model retrieval based on a mating score. The method includes obtaining a dataset of pairs of Boundary Representations (B-Reps) representing mechanical parts, each pair being labeled with mating compatibility data, the mating compatibility data representing an extent of mating compatibility between the mechanical parts represented by the pair. The method also includes training a neural network based on the dataset, the neural network being configured for taking as input a pair of B-reps representing mechanical parts, and outputting a mating score of a pair of single embeddings, each single embedding corresponding to a B-Rep of the pair, the mating score representing a score of mating compatibility between the mechanical parts represented by the pair.

公开(公告)号：US20240078353A1

公开(公告)日：2024-03-07

申请号：US18460054

申请日：2023-09-01

Applicant: DASSAULT SYSTEMES

Inventor： Julien BOUCHER ,  Mariem MEZGHANNI ,  Arthur NDOKO

IPC: G06F30/13 ,  G06F30/20 ,  G06T7/564

CPC classification number: G06F30/13 ,  G06F30/20 ,  G06T7/564 ,  G06T2207/10028

Abstract: A computer-implemented method for generating a 3D model representing a factory. The method includes obtaining a point cloud from a scan of the factory and fitting the point cloud with linear CAD extrusions. Such a method is an improved solution for generating a 3D model representing a factory.

公开(公告)号：US20230014934A1

公开(公告)日：2023-01-19

申请号：US17866193

申请日：2022-07-15

Applicant: DASSAULT SYSTEMES

Inventor： Mariem MEZGHANNI ,  Julien BOUCHER ,  Paul VILLEDIEU

IPC: G06T17/10 ,  G06T17/20 ,  G06F30/27 ,  G06F30/12 ,  G06F30/17

Abstract: The disclosure relates to a computer-implemented method comprising inputting a representation of a 3D modeled object to an abstraction neural network which outputs a first set of a first number of first primitives fitting the 3D modeled object; and determining, from the first set, one or more second sets each of a respective second number of respective second primitives. The second number is lower than the first number. The determining includes initializing a third set of third primitives as the first set and performing one or more iterations, each comprising merging one or more subsets of third primitives together each into one respective single fourth primitive, to thereby obtain a fourth set of fourth primitives. Each iteration further comprises setting the third set of a next iteration as the fourth set of a current iteration and setting the one or more second sets as one or more obtained fourth sets.

公开(公告)号：US20250117528A1

公开(公告)日：2025-04-10

申请号：US18908244

申请日：2024-10-07

Applicant: DASSAULT SYSTEMES

Inventor： Pierre CLAROU ,  Mariem MEZGHANNI ,  Julien BOUCHER

IPC: G06F30/12 ,  G06N3/0464 ,  G06N3/08

Abstract: A machine-learning method including obtaining a training dataset of B-rep graphs. Each B-rep graph represents a respective B-rep. Each B-rep graph includes graph nodes each representing an edge, a face or a co-edge of the respective B-rep and being associated with one or more geometrical and/or topological features. Each B-rep graph further comprises graph edges each between a respective first graph node representing a respective co-edge and a respective second graph node representing a face, an edge, an adjacent co-edge, or a mating co-edge associated with the respective co-edge. The method further includes learning, based on the training dataset, a local Deep CAD neural network. The local Deep CAD neural network takes as input a B-rep graph and to output, for each graph node of the input B-rep graph, a local topological signature of the B-rep element represented by the graph node.

公开(公告)号：US20240061980A1

公开(公告)日：2024-02-22

申请号：US18451551

申请日：2023-08-17

Applicant: DASSAULT SYSTEMES

Inventor： Mariem MEZGHANNI ,  Julien BOUCHER ,  Rémy SABATHIER

IPC: G06F30/27

CPC classification number: G06F30/27

Abstract: A computer-implemented method of machine-learning including obtaining a training dataset of B-rep graphs. Each B-rep graph represents a respective B-rep. Each B-rep graph comprises graph nodes each representing an edge, a face or a co-edge of the respective B-rep and being associated with one or more geometrical and/or topological features. Each B-rep graph includes graph edges each between a respective first graph node representing a respective co-edge and a respective second graph node representing a face, an edge, an adjacent co-edge, or a mating co-edge associated with the respective co-edge. The method further includes learning, based on the training dataset, a Deep CAD neural network. The Deep CAD neural network is configured to take as input a B-rep graph and to output a topological signature of the B-rep represented by the input B-rep graph.