Recognition and Decomposition of Rib Features in Thin-Shell Plastic Parts for Finite Element Analysis

Lai, Jiing Yih; Wang, Ming-Hsuan; Song, Pei-Pu; Hsu, Chia-Hsiang; Tsai, Yao-Chen

doi:10.14733/cadconfp.2017.132-137

Cited by 1 publication

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“…Additional information on simulation-oriented morphology types can benefit from feature recognition techniques which identifies specific geometric configurations (e.g. identification of fillets (Zhu and Menq 2002;Venkataraman, Sohoni, and Rajadhyaksha 2002), or ribs (Lai et al 2018)). Similarly to (L. Sun et al 2017aSun et al , 2017b, additional segmentation techniques can divide the B-Rep into simpler volumes adapted for a particular FE analysis (Lu, Gadh, and Tautges 2001;Chong, Kumar, and Lee 2004;Boussuge, Léon, et al 2014;Liu and Gadh 1997;L.…”

Section: Morphology Conceptmentioning

confidence: 99%

Capturing simulation intent in an ontology: CAD and CAE integration application

Boussuge

Tierney

Vilmart

et al. 2019

Journal of Engineering Design

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Computational simulation is critical in the modern engineering design process. Currently, the use of simulation is limited by the time-consuming process of converting CAD assemblies into FEA models which are efficient to run and yet sufficiently accurate. In addition to the geometric representation of components, analysts require additional knowledge to describe the complete 3D simulation model. To speed up the generation of CAE models from CAD assemblies, we propose to capture high-level modelling and idealisation decisions, characterising the simulation intent, into a knowledge-based CAE model. In this framework, a simulation intent ontology formalises and structures the analysis parameters, the modelling and idealisation decisions. The ontological concepts and relations required to incorporate two of the key capabilities, cellular modelling and equivalencing, are described. Cellular modelling introduces the concept of cells, which subdivide the 3D space, and to which simulation attributes can be attached. Equivalencing maintains the link between different representations of the cells required for different analyses throughout the analysis lifecycle. Illustrative examples show how the knowledge-based CAE model is used to manage the idealisation decisions and to apply inference rules replicating current modelling practices.

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