In the field of neutronics analysis, it is imperative to develop computer-aided modeling technology for Monte Carlo codes to address the increasing complexity of reactor core components by converting 3D CAD model(boun...In the field of neutronics analysis, it is imperative to develop computer-aided modeling technology for Monte Carlo codes to address the increasing complexity of reactor core components by converting 3D CAD model(boundary representation, BREP) to MC model(constructive solid geometry, CSG). Separation-based conversion from BREP to CSG is widely used in computer-aided modeling MC codes because of its high efficiency, reliability, and easy implementation. However, the current separation-based BREP-CSG conversion is poor for processing complex CAD models, and it is necessary to divide a complex model into several simple models before applying the separation-based conversion algorithm, which is time-consuming and tedious. To avoid manual segmentation, this study proposed a MeshCNN-based 3D-shape segmentation algorithm to automatically separate a complex model. The proposed 3D-shape segmentation algorithm was combined with separation-based BREP-CSG conversion algorithms to directly convert complex models.The proposed algorithm was integrated into the computeraided modeling software cosVMPT and validated using the Chinese fusion engineering testing reactor model. The results demonstrate that the MeshCNN-based BREP-CSG conversion algorithm has a better performance and higher efficiency, particularly in terms of CPU time, and the conversion result is more intuitive and consistent with the intention of the modeler.展开更多
In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD models.Many previous CAD segmentation methods have achieved impressive performance using singl...In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD models.Many previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point clouds.However,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological information.Hence,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation accuracy.To combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh models.For complex CAD models,model segmentation is crucial for model retrieval and reuse.In partial retrieval,it aims to segment a complex CAD model into several simple components.The first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh models.The second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD models.This combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object semantics.This study uses the Fusion 360 Gallery dataset.Experimental results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.展开更多
基金supported by the National Key R&D Program of China(Nos.2019YFE03110000 and 2017YFE0300501)the Chinese National Natural Science Foundation(No.11775256)。
文摘In the field of neutronics analysis, it is imperative to develop computer-aided modeling technology for Monte Carlo codes to address the increasing complexity of reactor core components by converting 3D CAD model(boundary representation, BREP) to MC model(constructive solid geometry, CSG). Separation-based conversion from BREP to CSG is widely used in computer-aided modeling MC codes because of its high efficiency, reliability, and easy implementation. However, the current separation-based BREP-CSG conversion is poor for processing complex CAD models, and it is necessary to divide a complex model into several simple models before applying the separation-based conversion algorithm, which is time-consuming and tedious. To avoid manual segmentation, this study proposed a MeshCNN-based 3D-shape segmentation algorithm to automatically separate a complex model. The proposed 3D-shape segmentation algorithm was combined with separation-based BREP-CSG conversion algorithms to directly convert complex models.The proposed algorithm was integrated into the computeraided modeling software cosVMPT and validated using the Chinese fusion engineering testing reactor model. The results demonstrate that the MeshCNN-based BREP-CSG conversion algorithm has a better performance and higher efficiency, particularly in terms of CPU time, and the conversion result is more intuitive and consistent with the intention of the modeler.
基金Supported by the National Key Research and Development Program of China(2024YFB3311703)National Natural Science Foundation of China(61932003)Beijing Science and Technology Plan Project(Z221100006322003).
文摘In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD models.Many previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point clouds.However,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological information.Hence,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation accuracy.To combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh models.For complex CAD models,model segmentation is crucial for model retrieval and reuse.In partial retrieval,it aims to segment a complex CAD model into several simple components.The first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh models.The second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD models.This combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object semantics.This study uses the Fusion 360 Gallery dataset.Experimental results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.
