公开(公告)号：US11321605B2

公开(公告)日：2022-05-03

申请号：US15810840

申请日：2017-11-13

Applicant: Dassault Systemes SolidWorks Corporation

Inventor： Ameya Divekar ,  Makarand Apte ,  Shrikant Savant

IPC: G06F30/00 ,  G06N3/04 ,  G06N3/08 ,  G06T19/00 ,  G06F30/17 ,  G06F30/27 ,  G06N3/02 ,  G06F111/04 ,  G06F111/20

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.

公开(公告)号：US20190147317A1

公开(公告)日：2019-05-16

申请号：US15810840

申请日：2017-11-13

Applicant: Dassault Systemes SolidWorks Corporation

Inventor： Ameya Divekar ,  Makarand Apte ,  Shrikant Savant

IPC: G06N3/04 ,  G06N3/08 ,  G06F17/50

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.