Freeform surface models are conventionally used to model sheet metal components, such as automobile body parts. Finite element meshes generated automatically for such models have poor quality around small detailed features. Manual correction of the mesh is extremely tedious. An approach presently receiving attention in industry aims to alleviate this problem by automatically simplifying these features in the surface model such that an acceptable mesh is automatically generated. Simplification involves recognition of the feature and modification of its geometry or complete suppression of the feature. Since features such as holes, notches, etc. are punched after the basic shape has been formed, such a simplification will also help in the modelling of forming dies and molds. The ability to detect features will also allow part comparison and classification in surface models. This paper proposes techniques to directly query the CAD data structure to recognize and suppress two basic features, viz. holes and fillets in freeform surface models. It further demonstrates how these techniques can be extended to suppress compound features that are composed of a combination of basic features. Results of a software implementation for the same are discussed with suitable examples and the improvement in mesh quality is demonstrated.

1.
Shah, J., and Ma¨ntyla¨, M., 1995, Parametric and Feature-Based CAD/CAM, John Wiley and Sons, Inc., NY, Chap. 3.
2.
Kakazu, Y., and Okino, N., 1984, “Pattern Recognition Approaches to GT Code Generation on CSG,” Proceedings of 16th CIRP International Seminar on Manufacturing Systems, Tokyo.
3.
Vergeest, J. S. M., and Horvath, I., 2000, “Fitting Freeform Shape Patterns to Scanned 3D Objects,” Proceedings of ASME2000 DETC, Baltimore.
4.
Sheffer
,
A.
,
2001
, “
Model Simplification for Meshing Using Face Clustering
,”
Comput.-Aided Des.
,
33
, pp.
925
934
.
5.
Joshi
,
S.
, and
Chang
,
T. C.
,
1988
, “
Graph-Based Heuristics for Recognition of Machined Features From 3D Solid Model
,”
Comput.-Aided Des.
,
20
(
2
), pp.
58
86
.
6.
Stanley
,
S. M.
,
Henderson
,
M. R.
, and
Anderson
,
D. C.
, 1983, “Using Syntactic Pattern Recognition to Extract Feature Information From a Solid Geometric Database,” Computers in Mechanical Engineering, 2(2), pp. 61–66.
7.
Vosniakos
,
G. C.
, and
Davies
,
B. J.
, 1993, “A Shape Feature Recognition Framework and its Application to Holes in Prismatic Parts,” International Journal of Advanced Manufacturing Technology, 8(5), pp. 345–351.
8.
Vandenbrande
,
J. H.
, and
Requicha
,
A. A. G.
,
1993
, “
Spatial Reasoning for the Automatic Recognition of Machinable Features in Solid Models
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
15
, pp.
1
17
.
9.
Ferreira
,
J. C. E.
, and
Hinduja
,
S.
,
1990
, “
Convex-Hull-Based Feature Recognition Method for 2.5D Components
,”
Comput.-Aided Des.
,
22
(
1
), pp.
41
49
.
10.
Kim, Y. S., 1994, “Volumetric Features Recognition Using Convex Decomposition,” Advances in Feature Based Manufacturing, J. J. Shah, M. Ma¨ntyla¨, and D. S. Nau, eds., Elsevier, Amsterdam.
11.
Prabhakar
,
S.
, and
Henderson
,
M. R.
,
1992
, “
Automatic Form-Feature Recognition Using Neural-Network-Based Techniques on Boundary Representations of Solid Models
,”
Comput.-Aided Des.
,
24
(
7
), pp.
381
393
.
12.
Nezis
,
K.
, and
Vosniakos
,
G.
,
1997
, “
Recognizing 21/2 D Shape Features Using a Neural Network and Heuristics
,”
Comput.-Aided Des.
,
29
(
7
), pp.
523
539
.
13.
Wu
,
M. C.
,
Chen
,
J. R.
, and
Jen
,
S. R.
, 1994, “Global Shape Information Modelling and Classification of 2D Workpieces,” International Journal of Computer Integrated Manufacturing, 7(5), pp. 261–275.
14.
Greska
,
W.
,
Franke
,
V.
, and
Geiger
,
M.
,
1996
, “
Classification Problems in Manufacturing of Sheet Metal Parts
,”
Comput. Ind.
,
33
, pp.
17
30
.
15.
Berg
,
E. V. D.
,
Bronsvoort
,
W. F.
, and
Vergeest
,
J. S. M.
,
2002
, “
Freeform Feature Modelling: Concepts and Prospects
,”
Comput. Ind.
,
49
, pp.
217
233
.
16.
Rossignac
,
J. R.
, and
Requicha
,
A. A. G.
, 1984, “Constant Radius Blending in Solid Modeling,” Computers in Mechanical Engineering, July, pp. 65–73.
17.
Venkatraman, S., and Sohoni, M., 2001, “Blend Recognition Algorithm and Applications,” Proceedings of the Sixth ACM Symposium on Solid Modeling and Applications, D. C. Anderson and K. Lee, eds., ACM press, Ann Arbor, MI, pp. 99–108.
You do not currently have access to this content.