Joinable: Learning bottom-up assembly of parametric cad joints
Physical products are often complex assemblies combining a multitude of 3D parts modeled
in computer-aided design (CAD) software. CAD designers build up these assemblies by …
in computer-aided design (CAD) software. CAD designers build up these assemblies by …
Complexgen: Cad reconstruction by b-rep chain complex generation
We view the reconstruction of CAD models in the boundary representation (B-Rep) as the
detection of geometric primitives of different orders, ie, vertices, edges and surface patches …
detection of geometric primitives of different orders, ie, vertices, edges and surface patches …
Deep learning methods of cross-modal tasks for conceptual design of product shapes: A review
Conceptual design is the foundational stage of a design process that translates ill-defined
design problems into low-fidelity design concepts and prototypes through design search …
design problems into low-fidelity design concepts and prototypes through design search …
Secad-net: Self-supervised cad reconstruction by learning sketch-extrude operations
Reverse engineering CAD models from raw geometry is a classic but strenuous research
problem. Previous learning-based methods rely heavily on labels due to the supervised …
problem. Previous learning-based methods rely heavily on labels due to the supervised …
Automate: A dataset and learning approach for automatic mating of cad assemblies
Assembly modeling is a core task of computer aided design (CAD), comprising around one
third of the work in a CAD workflow. Optimizing this process therefore represents a huge …
third of the work in a CAD workflow. Optimizing this process therefore represents a huge …
A State‐of‐the‐Art Computer Vision Adopting Non‐Euclidean Deep‐Learning Models
A distance metric known as non‐Euclidean distance deviates from the laws of Euclidean
geometry, which is the geometry that governs most physical spaces. It is utilized when …
geometry, which is the geometry that governs most physical spaces. It is utilized when …
Brepgen: A b-rep generative diffusion model with structured latent geometry
This paper presents BrepGen, a diffusion-based generative approach that directly outputs a
Boundary representation (B-rep) Computer-Aided Design (CAD) model. BrepGen …
Boundary representation (B-rep) Computer-Aided Design (CAD) model. BrepGen …
AAGNet: A graph neural network towards multi-task machining feature recognition
Machining feature recognition (MFR) is an essential step in computer-aided process
planning (CAPP) that infers manufacturing semantics from the geometric entities in CAD …
planning (CAPP) that infers manufacturing semantics from the geometric entities in CAD …
Deep reinforcement learning for heat exchanger shape optimization
H Keramati, F Hamdullahpur, M Barzegari - International Journal of Heat …, 2022 - Elsevier
We present a parametric approach for heat exchanger shape optimization utilizing Deep
Reinforcement Learning (Deep RL) and Boundary Representation (BREP). In this study, we …
Reinforcement Learning (Deep RL) and Boundary Representation (BREP). In this study, we …
FuS-GCN: Efficient B-rep based graph convolutional networks for 3D-CAD model classification and retrieval
Performing 3-dimensional computer-aided design (3D-CAD) model classification, retrieval,
and reuse is of vital importance in industrial manufacturing, as it considerably shortens the …
and reuse is of vital importance in industrial manufacturing, as it considerably shortens the …