Founded in 1971 >
Chinese Sci-tech Core Periodicals >
British Science Abstracts (SA, INSPEC) Indexed Journals >
United States, Cambridge Scientific Abstract: Technology (CSA: T) Indexed Journals >
United States, Ulrich's Periodicals Directory(UPD)Indexed Journals >
United States, Cambridge Scientific Abstract: Natural Science (CSA: NS) Indexed Journals >
Poland ,Index of Copernicus(IC) Indexed Journals >
International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
Add:
No.9 Gaoguannong,Daxue Road,Hangzhou,China
P.C:
310009
E-mail:
meem_contribute@163.com
XV Jia chen, YOU You peng
(College of Mechanical & Elecirical Engineering, Nanjing University of Aeronautics and Astronautics,
Nanjing 210016, China)
Abstract: Aiming at the external reading and feature recognition of data model from sheet metal design software, the data sharing between software, the structure of the IGES file and the feature classification of part were researched. The postprocessing method that traversed the IGES file to get all the cutting parameter surface information based on IGES surface model was proposed. Then, the data structure of doublelinked list structure with the bending surface as the node was established, and according to the IGES file data characteristic, the feature of sheet metal was classified and the necessary bend features and dependence was extracted. At last, the method of feature recognition was tested on the sheet metal parts drawn by the Pro/E software. The results indicate that the method can accurately obtain the characteristic of sheet metal and provide reliable data for sheet metal part bending simulation and process planning and make the product development cycle by 30%. Ultimately, the accuracy and efficiency of processing are achieved.
Key words: IGES file; sheet metal; feature recognition