Explicit function-based design modelling methodology with features

Cheng, Zhengrong; Ma, Yongsheng

doi:10.1080/09544828.2017.1291920

Cited by 19 publications

(3 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For some use cases, high-quality top-3 predictions could be sufficient to give enough context to the design agent to support the designer. For example, function data could support the designer during the conceptual design stage in assessing the feasibility of a design [24], searching for functionally similar parts [20], or by enabling automated functional modeling [36,37,86]. In the detail design stages, it could aid in verifying the satisfaction of higher-level design requirements [26].…”

Section: Discussionmentioning

confidence: 99%

Classifying Component Function in Product Assemblies with Graph Neural Networks

Ferrero¹,

Hassani²,

Grandi³

et al. 2021

Preprint

View full text Add to dashboard Cite

Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design, where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Functionbased design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learning from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F 1 -score of 0.832 for tier 1 (broad), 0.756 for tier 2, and 0.783 for tier 3 (specific) functions. Given the imbalance of data features, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems and Design-for-X consideration in function-based design.

show abstract

Section: Discussionmentioning

confidence: 99%

Classifying Component Function in Product Assemblies with Graph Neural Networks

Ferrero¹,

Hassani²,

Grandi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on the process, feature-based modeling technology can be generally defined as three main steps: the setting of reference, justification of dimension and constraint, and preservation of nongeometric information. erefore, it can be expressed as follows [35]:…”

Section: Multiphysics Modeling For Simulationmentioning

confidence: 99%

Multiphysics Modeling of Gas Turbine Based on CADSS Technology

2020

Shock and Vibration

View full text Add to dashboard Cite

Product modeling has been applied in product engineering with success for geometric representation. With the application of multidisciplinary analysis, application-driven models need specific knowledge and time-consuming adjustment work based on the geometric model. This paper proposes a novel modeling technology named computer-aided design-supporting-simulation (CADSS) to generate multiphysics domain models to support multidisciplinary design optimization processes. Multiphysics model representation was analyzed to verify gaps among different domain models’ parameters. Therefore, multiphysics domain model architecture was integrated by optimization model, design model, and simulation model in consideration of domain model’s parameters. Besides, CADSS uses requirement space, domain knowledge, and software technology to describe the multidisciplinary model’s parameters and its transition. Depending on the domain requirements, the CADSS system extracts the required knowledge by decomposing product functions and then embeds the domain knowledge into functional features using software technology. This research aims to effectively complete the design cycle and improve the design quality by providing a consistent and concurrent modeling environment to generate an adaptable model for multiphysics simulation. This system is demonstrated by modeling turbine blade design with multiphysics simulations including computational fluid dynamics (CFD), conjugate heat transfer (CHT), and finite element analysis (FEA). Moreover, the blade multiphysics simulation model is validated by the optimization design of the film hole. The results show that the high-fidelity multiphysics simulation model generated through CADSS can be adapted to subsequent simulations.

show abstract

“…In fact, in the perspective of CAD modeling, the modeling strategy above could only ensure the accuracy of geometric shape, but not necessarily meet the requirements of subsequent simulation and optimization, because their modeling strategy did not take the design/analysis intent [18][19][20][21], reusability [22,23] and model quality [24] into account. Although this does not affect the first surface construction, it will lead to cumbersome modification iteration and optimization [24][25][26]. In addition, the addendum surfaces mentioned above are mainly aimed at workpiece with closed contour, which is not suitable for hatch hollow parts with semi-opening contour.…”

Section: Introductionmentioning

confidence: 99%

Smoothing Parametric Design of Addendum Surfaces for Sheet Metal Forming

Tao

et al. 2020

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

The manual design of addendum surfaces on common CAD platforms is very tedious which requires many trialscorrections, which will certainly affect the construction efficiency and quality of addendum surfaces, and then affect the formability and quality of the workpiece in the process of sheet forming. In this paper, an automatic procedure based on parametric design method is proposed for the rapid construction of the addendum surfaces. The kernel of the parametric method is constructing boundary curves based on the shape of surfaces of workpiece and designing guide curves based on Hermite curve interpolation. By some simple parameters, the shape of the addendum surfaces could be controlled and adjusted easily. In addition, a minimum energy optimization method is employed to further optimize the constructed addendum surface. A finite element analysis for the sheet forming process is performed to evaluate the forming quality of constructed addendum surfaces. The instance illustrates that the addendum surface constructed by the proposed method could ensure both the overall smoothing of surfaces and the final forming quality, and it has a good effect on springback after forming. This research proposes a smoothing parametric design method for addendum surfaces construction which could construct and optimize addendum surfaces rapidly.

show abstract

Explicit function-based design modelling methodology with features

Cited by 19 publications

References 55 publications

Classifying Component Function in Product Assemblies with Graph Neural Networks

Classifying Component Function in Product Assemblies with Graph Neural Networks

Multiphysics Modeling of Gas Turbine Based on CADSS Technology

Smoothing Parametric Design of Addendum Surfaces for Sheet Metal Forming

Contact Info

Product

Resources

About