Instead of extracting mid-surfaces from computer-aided design model, an automatic dimensional reduction approach is proposed, which simplifies the finite element analysis model into the mixed-dimensional model. The input finite element analysis model is first decomposed into a set of locally prominent cross-sections. Each prominent cross-section is digitalized as a d-dimensional point and clustered in the embedded space. By the clustered points, both long-slender and thin-wall regions are detected and recognized with the help of aspect ratio. Further the identified features are reduced into skeletons and mid-surfaces, respectively, and the elements of lower dimensionality are generated simultaneously. This dimensional reduction procedure is general and feasible. In this case, multi-resolution mesh models could be created without being transformed back into computer-aided design software, which is essential to the multi-disciplinary simulation. Finally, the simplification degree tests show that the nodes and elements are largely decreased; furthermore, the proposed approach is more effective than the traditional manual method on time consuming.
Holzer SM. Concepts of CAD-integrated finite element analysis. In: Proceedings of the 1999 European Conference on Computational Mechanics (ECCM’99), München, Germany, 31 August–3 September 1999. München, Germany: Technische Universität München.
2.
Nolan D, Tierney C, Armstrong C, et al. Automatic dimensional reduction and meshing of stiffened thin-wall structures. Eng Comput 2013: 1–13, published online.
3.
RobinsonTTArmstrongCGFaireyR. Automated mixed dimensional modelling from 2D and 3D CAD models. Finite Elem Anal Des2011; 47(2): 151–165.
4.
StanleyF. Dimensional reduction and design optimization of gas turbine engine casings for tip clearance studies, Southampton, UK: University of Southampton, 2010.
5.
SheenD-PSonT-gMyungD-K. Transformation of a thin-walled solid model into a surface model via solid deflation. Comput Aided Des2010; 42(8): 720–730.
6.
ChongCSSenthil KumarALeeKH. Automatic solid decomposition and reduction for non-manifold geometric model generation. Comput Aided Des2004; 36(13): 1357–1369.
7.
RezayatM. Midsurface abstraction from 3D solid models: General theory and applications. Comput Aided Des1996; 28(11): 905–915.
8.
GaoSZhaoWLinH. Feature suppression based CAD mesh model simplification. Comput Aided Des2010; 42(12): 1178–1188.
9.
Hanmin L, Yong-Youn N and Seong-Whan P. Graph-based midsurface extraction for finite element analysis. In: Proceedings of the 11th international conference on computer supported cooperative work in design (CSCWD 2007), Melbourne, Australia, 26–28 April 2007, pp.1055–1058. Melbourne, Australia: Swinburne Press.
10.
Makem J, Armstrong C and Robinson T. Automatic decomposition and efficient semi-structured meshing of complex solids. In: Proceedings of the 20th international meshing roundtable, Paris, France, 23–26 October 2012, pp.199–215. Berlin Heidelberg: Springer-Verlag.
11.
WangJGuDYuZ. A framework for 3D model reconstruction in reverse engineering. Comput Ind Eng2012; 63(4): 1189–1200.
12.
XiaoDLinHXianC. CAD mesh model segmentation by clustering. Comput Graphics2011; 35(3): 685–691.
13.
WangJYuZ. Surface feature based mesh segmentation. Comput Graphics2011; 35(3): 661–667.
14.
Xie Y, Liu J, Liu H, et al. Features and interoperability of computer aided engineering systems. In Semantic Modeling and Interoperability in Product and Process Engineering (ed. Ma Y-S), 2013, pp.143–191. London: Springer-Verlag.
15.
HamriOLeonJ-CGianniniF. Software environment for CAD/CAE integration. Adv Eng Softw2010; 41(10–11): 1211–1222.
16.
Lee K-Y and Chuah Y-D. An automated approach to generate mixed-dimensional finite element analysis model. In: Proceedings of the 2nd international conference on computer engineering and technology (ICCET 2010), Chengdu, China, 16–18 April 2010, pp.121–125, NJ, USA: IEEE.
17.
Suresh K. Automating the CAD/CAE dimensional reduction process. In: Proceedings of the 8th ACM symposium on solid modeling and applications, Seattle, Washington, USA, 16–20 June 2003, pp.76–85, NY, USA: ACM.
18.
DonaghyRJArmstrongCGPriceMA. Dimensional reduction of surface models for analysis. Eng Comput2000; 16(1): 24–35.
19.
Robinson T, Fairey R, Armstrong C, et al. Automated mixed dimensional modelling with the medial object. In: Proceedings of the 17th international meshing roundtable, Pittsburgh, PA, USA, 12–15 October 2008, pp.281–298.
20.
QuadrosW. An approach for extracting non-manifold mid-surfaces of thin-wall solids using chordal axis transform. Eng Comput2008; 24(3): 305–319.
21.
Telea A and Vilanova A. A robust level-set algorithm for centerline extraction. In: Proceedings of the 5th joint Eurographics–IEEE TCVG symposium on visualization, Grenoble, France, 26–28 May 2003, pp.185–194. Switzerland: Eurographics Association Aire-la-Ville.
22.
LeeSH. A CAD–CAE integration approach using feature-based multi-resolution and multi-abstraction modelling techniques. Comput Aided Des2005; 37(9): 941–955.
23.
MounirHNizarAAbdelmajidB. CAD model simplification using a removing details and merging faces technique for a FEM simulation. J Mech Sci Technol2012; 26(11): 3539–3548.
24.
CuillièreJ-CBournivalSFrançoisV. A mesh-geometry-based solution to mixed-dimensional coupling. Comput Aided Des2010; 42(6): 509–522.
25.
SellamaniSMuthuganapathyRKalyanaramanY. PCS: Prominent cross-sections for mesh models. Comput Aided Des Appl2010; 7: 601–620.
26.
GoyalMMurugappanSPiyaC. Towards locally and globally shape-aware reverse 3D modeling. Comput Aided Des2012; 44(6): 537–553.
27.
KanungoTMountDMNetanyahuNS. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell2002; 24(7): 881–892.
28.
RoweisSTSaulLK. Nonlinear dimensionality reduction by locally linear embedding. Science2000; 290(5500): 2323–2326.
29.
FreyBJDueckD. Clustering by passing messages between data points. Science2007; 315(5814): 972–976.
30.
KaoJ-H. Process planning for additive/subtractive solid freeform fabrication using medial axis transform, Stanford, CA: Stanford University, 1999.
31.
Roweis ST and Saul LK. LLE algorithm pseudocode. New York: Computer Science Department, New York University, http://cs.nyu.edu/∼roweis/lle/algorithm.html (2001, accessed 4 July 2012).
