Paper Titles

Chinese Sentiment Classifier Machine Learning Based on Optimized Information Gain Feature Selection
p.511

Optimization of Signal Intersection with the Combination of VISSIM and SYNCHRO
p.517

The Research on Correction Method of Capacitance Signal Drift for Drop Analysis System
p.521

Use of the Principal Component Analysis (PCA) to Reduce Data Complexity in Qualitative Research: An Electro-Electronics Case Study
p.526

A Neutral Framework for Feature Definition and a Generic Algorithm for Feature Recognition
p.530

The Defect Diagnosis of Sheet Drawing on Self-Associate with Memory of Boltzmann Network
p.540

A High Speed and Ultra Long-Haul Radio-Over-Fiber System Employing Dual Photoelectric Arms Coherent Modulation and Optical Duo-Binary Coding
p.544

Finger Vein Identification Based on 2DPCA
p.548

Nondestructive Detection of Soluble Solids Content in Navel Orange Based on GEP Algorithm
p.552

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 988A Neutral Framework for Feature Definition and a...

A Neutral Framework for Feature Definition and a Generic Algorithm for Feature Recognition

Article Preview

Abstract:

Traditional feature recognition approaches predefine features which can be recognized in systems. Feature definitions and recognitions are characterized by the domains which the approaches are applied to. Therefore, these approaches are not generic. In order to fulfill various feature recognition requirements of different users in different domains, this paper proposes an application independent framework for form feature definition and a generic algorithm for feature recognition based on the framework. Users in different domains can define their own features following the rules of the feature definition framework according to their tasks and user-defined features can be recognized by the uniform recognition algorithm. Hence, the system is flexible and can recognize a wide range of form features in different domains. The testing results show that this approach is effective in defining and recognizing isolated features as well as interacting features.

You might also be interested in these eBooks

Material, Mechanical and Manufacturing Engineering II

Info:

Periodical:

Advanced Materials Research (Volume 988)

Pages:

530-539

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.988.530

Citation:

Cite this paper

Online since:

July 2014

Authors:

De Biao Zeng*, Shi Ming Wan, Chun Ling Zeng, Guo Lei Zheng, Dong Ming Li

Keywords:

Computer-Aided Inspection Planning, Computer-Aided Process Planning, Feature Definition, Feature Recognition, User-Defined Features

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] J.J. Shah, Conceptual development of form features and feature modelers, Research in Engineering Design. Vol. 2, 2(1991)93-108.

DOI: 10.1007/bf01579254

[2] S. Joshia and T.C. Chang, Graph-based heuristics for recognition of machined features from a 3D solid model, Comput Aided Design. Vol. 20(1988)58-66.

DOI: 10.1016/0010-4485(88)90050-4

[3] P.K. Venuvinod and S.Y. Wong, A graph-based expert system approach to geometric feature recognition, J Intell Manuf. Vol. 6, 3(1995)155-162.

DOI: 10.1007/bf00171444

[4] S. Gao and J.J. Shah, Automatic recognition of interacting machining features based on minimal condition subgraph, Comput Aided Design. Vol. 30, 9(1998)727-739.

DOI: 10.1016/s0010-4485(98)00033-5

[5] F. Parient and Y.S. Kim, Incremental and localized update of convex decomposition used for form feature recognition, Comput Aided Design. Vol. 28, 8(1996)589-602.

DOI: 10.1016/0010-4485(95)00074-7

[6] H. Sakurai, Volume decomposition and feature recognition: Part 1-polyhedral objects, Comput Aided Design. Vol. 27, 11(1995)833-843.

DOI: 10.1016/0010-4485(95)00007-0

[7] H. Sakurai and P. Dave, Volume decomposition and feature recognition, part II: Curved objects, Comput Aided Design. Vol. 28, 6-7(1996)519-537.

DOI: 10.1016/0010-4485(95)00067-4

[8] Y. Woo, Fast cell-based decomposition and applications to solid modeling, Comput Aided Design. Vol. 35, 11(2003)969-977.

DOI: 10.1016/s0010-4485(02)00144-6

[9] J. Han, W.C. Regli and S. Brooks, Hint-based reasoning for feature recognition: Status report, Comput Aided Design. Vol. 30, 13(1998)1003-1007.

DOI: 10.1016/s0010-4485(98)00061-x

[10] W.C. Regli, Geometric algorithms for recognition of features from solid models, University of Maryland, (1995).

[11] J.H. Vandenbrande and A.A.G. Requicha, Spatial reasoning for the automatic recognition of machinable features in solid models, IEEE T Pattern Anal. Vol. 15, 12(1993)1-17.

DOI: 10.1109/34.250845

[12] K. Rahmani and B. Arezoo, A hybrid hint-based and graph-based framework for recognition of interacting milling features, Comput Ind. Vol. 58, 4(2007)304-312.

DOI: 10.1016/j.compind.2006.07.001

[13] V. Rameshbabu and M.S. Shunmugam, Hybrid feature recognition method for setup planning from STEP AP-203, Robot Cim-Int Manuf. Vol. 25(2009)393-408.

DOI: 10.1016/j.rcim.2007.09.014

[14] V.B. Sunil, R. Agarwal and S.S. Pande, An approach to recognize interacting features from B-Rep CAD models of prismatic machined parts using a hybrid (graph and rule based) technique, Comput Ind. Vol. 61, 7(2010)686-701.

DOI: 10.1016/j.compind.2010.03.011

[15] D.M. Gaines and C.C. Hayes, Custom-Cut: A customizable feature recognizer, Comput Aided Design. Vol. 31(1999)85-100.

DOI: 10.1016/s0010-4485(98)00082-7

[16] R. Pullat, Manufacturing feature recognition by 3D solid model slicing and contour based geometric reasoning, Cincinnati, University of Cincinnati, (2010).

[17] F. Yu, B. Du and W. Ren, et al, Slicing recognition of aircraft integral panel generalized pocket, Chinese Journal of Aeronautics. Vol. 21, 6(2008)585-592.

DOI: 10.1016/s1000-9361(08)60178-8

[18] B.T. Sheen and C.F. You, Machining feature recognition and tool-path generation for 3-axis CNC milling, Comput Aided Design. Vol. 38, 6(2006)553-562.

DOI: 10.1016/j.cad.2005.05.003

[19] R. Geelink, O.W. Salomons and F. Van Slooten, et al, Unified feature definition for feature based design and feature based manufacturing, in: Computers in Engineering 1995, ASME, Busnaina, (1995).

DOI: 10.1115/cie1995-0785

[20] P. Gibson, H.S. Ismail and M.A. Sabin, Optimisation approaches in feature recognition, International Journal of Machine Tools and Manufacture. Vol. 39, 5(1999)805-821.

DOI: 10.1016/s0890-6955(98)00068-6

[21] P. Gibson, H.S. Ismail and M.A. Sabin, et al, Interactive programmable feature recognisor, CIRP Annals - Manufacturing Technology. Vol. 46, 1(1997)407-410.

DOI: 10.1016/s0007-8506(07)60853-1

[22] M.S. Medichalam, J.J. Shah and R. D'Souza, N-Rep: A neutral feature representation to support feature mapping and data exchange across applications, in: ASME 2004 Design Technical Conferences and Computer and Information in Engineering Conference, ASME, Salt Lake City, Utah, USA, (2004).

DOI: 10.1115/detc2004-57712

[23] S. Li and J.J. Shah, Recognition of User-Defined turning features for Mill/Turn parts, Journal of Computing and Information Science in Engineering. Vol. 7(2007)225-235.

DOI: 10.1115/1.2767256

[24] E. Brousseau, S. Dimov and R. Setchi, Knowledge acquisition techniques for feature recognition in CAD models, J Intell Manuf. Vol. 19, 1(2008)21.

DOI: 10.1007/s10845-007-0043-7

[25] S.S. Dimov, E.B. Brousseau and R. Setchi, A hybrid method for feature recognition in computer-aided design models, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. Vol. 221, 1(2007)79-96.

DOI: 10.1243/09544054jem437

[26] M.G. Marchetta and R.Q. Forradellas, An artificial intelligence planning approach to manufacturing feature recognition, Comput Aided Design. Vol. 42, 3(2010)248-256.

DOI: 10.1016/j.cad.2009.11.007

[27] Information on http: /edge. cs. drexel. edu/repository.