Shape distribution-based approach to comparing 3D CAD assembly models

Kim, Hyungki; Cha, Moohyun; Mun, Duhwan

doi:10.1007/s12206-017-1103-3

Cited by 13 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, geometric features such as point cloud [24,25], feature curves [26,27], and voxelisation [28] have been applied in parts retrieval and classification. Wei et al…”

Section: Geometric Feature Selectionmentioning

confidence: 99%

Prediction of Mechanical Properties of Three-Dimensional Printed Lattice Structures Through Machine Learning

Tang

Liu

et al. 2021

Journal of Computing and Information Science in Engineering

View full text Add to dashboard Cite

Lattice structures (LS) manufactured by 3D printing are widely applied in many areas, such as aerospace and tissue engineering, due to their lightweight and adjustable mechanical properties. It is necessary to reduce costs by predicting the mechanical properties of LS at the design stage since 3D printing is exorbitant at present. However, predicting mechanical properties quickly and accurately poses a challenge. To address this problem, this study proposes a novel method that is applied to different LS and materials to predict their mechanical properties through machine learning. First, this study voxelized 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine learning model. The sample set includes 57 samples collected from previous studies. Support vector regression (SVR) was used in this study to predict the mechanical properties. The results indicate that the proposed method can predict the mechanical properties of LS effectively and is suitable for different LS and materials. The significance of this work is that it provides a method with great potential to promote the design process of lattice structures by predicting their mechanical properties quickly and effectively.

show abstract

“…In general, geometric features such as point cloud [24,25], feature curves [26,27], and voxelisation [28] have been applied in parts retrieval and classification. Wei et al…”

Section: Geometric Feature Selectionmentioning

confidence: 99%

Prediction of Mechanical Properties of Three-Dimensional Printed Lattice Structures Through Machine Learning

Tang

Liu

et al. 2021

Journal of Computing and Information Science in Engineering

View full text Add to dashboard Cite

show abstract

“…In general, geometric features such as point cloud [23,24], feature curves [25,26], and voxelisation [27] have been applied in parts retrieval and classification. Wei et al voxelised and calculated the entropy of 3D models to represent and retrieve different machine parts [28], they all be proved as the promising methods to represent the geometric features of 3d models.…”

Section: Geometric Feature Selectionmentioning

confidence: 99%

Predicting Mechanical Properties of 3D Printed Lattice Structures

Tang

et al. 2021

Volume 2: 41st Computers and Information in Engineering Conference (CIE)

View full text Add to dashboard Cite

Lattice structures (LS) manufactured by 3D printing are widely applied in many areas, such as aerospace and tissue engineering, due to their lightweight and adjustable mechanical properties. It is necessary to reduce costs by predicting the mechanical properties of LS at the design stage since 3D printing is exorbitant at present. However, predicting mechanical properties quickly and accurately poses a challenge. To address this problem, this study proposes a novel method that is applied to different LS and materials to predict their mechanical properties through machine learning. First, this study voxelised 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine learning model. The sample set includes 57 samples collected from previous studies. Support vector regression was used in this study to predict the mechanical properties. The results indicate that the proposed method can predict the mechanical properties of LS effectively and is suitable for different LS and materials. The significance of this work is that it provides a method with great potential to promote the design process of lattice structures by predicting their mechanical properties quickly and effectively.

show abstract

“…Chen et al [5] proposed a new assembly retrieval approach for flexible input queries. Kim et al [6] extracted shape features of partial models and the assembly relationships between partial models to realize the assembly model retrieval. Lupinetti et al [39] proposed an assembly descriptor for an assembly CAD model, called Enriched Assembly Model(EAM).…”

Section: Assembly Model Matchingmentioning

confidence: 99%

“…The extracted features are compressed shape representation of 3D&CAD models, and the results mainly reveal the shape similarities. Some other works attempt to solve the matching problem between assembly models [5], [6]. Both shape features and assembly information are used to perform global matching between assembly models.…”

Section: Introductionmentioning

confidence: 99%

Partial Matching of Large Scale Process Plant Models Using Random Walk on Graphs

Mao

Dai

et al. 2020

IEEE Access

View full text Add to dashboard Cite

3D process plant models(PPMs) in the process industry normally consists of thousands of components. And, there are many similar local structures in the PPM. Due to the complex process flow, the topology relationship among components is very complicated. Therefore, designing a new PPM is quite time consuming. In order to shorten the design cycle, content based model retrieval for PPMs is an imperative requirement. In this paper, we propose a partial matching framework for PPMs based on graph matching aiming at improving design efficiency and realizing design reuse. The random walk algorithm is employed to distinguish similar local structures. Specifically, each PPM is represented by an undirected labeled graph. The local topological feature of each component is extracted based on the random walk algorithm. For partial matching, a subgraph isomorphism algorithm is introduced. The matching process is accelerated by using the local topological feature to generate an optimized initial state and alleviate the computation of feasible rules. Experimental results show the feasibility and effectiveness of our matching framework.

show abstract

Shape distribution-based approach to comparing 3D CAD assembly models

Cited by 13 publications

References 28 publications

Prediction of Mechanical Properties of Three-Dimensional Printed Lattice Structures Through Machine Learning

Prediction of Mechanical Properties of Three-Dimensional Printed Lattice Structures Through Machine Learning

Predicting Mechanical Properties of 3D Printed Lattice Structures

Partial Matching of Large Scale Process Plant Models Using Random Walk on Graphs

Contact Info

Product

Resources

About