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Graph-based feature recognition for injection moulding based on a mid-surface approach

Lockett, Helen L. and Guenov, Marin D. (2005). Graph-based feature recognition for injection moulding based on a mid-surface approach. Computer-Aided Design, 37(2) pp. 251–262.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1016/j.cad.2004.06.010
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Abstract

This paper presents a novel CAD feature recognition approach for thin-walled injection moulded and cast parts in which moulding features are recognised from a mid-surface abstraction of the part geometry. The motivation for the research has been to develop techniques to help designers of moulded parts to incorporate manufacturing considerations into their designs early in the design process. The main contribution of the research has been the development of an attributed mid-surface adjacency graph to represent the mid-surface topology and geometry, and a feature recognition methodology for moulding features. The conclusion of the research is that the mid-surface representation provides a better basis for feature recognition for moulded parts than a B-REP solid model. A demonstrator that is able to identify ribs, buttresses, bosses, holes and wall junctions has been developed using C++, with data exchange to the CAD system implemented using ISO 10303 STEP. The demonstrator uses a commercial algorithm (I-DEAS) to create the mid-surface representation, but the feature recognition approach is generic and could be applied to any mid-surface abstraction. The software has been tested on a range of simple moulded parts and found to give good results.

Item Type: Journal Item
Copyright Holders: 2004 Elsevier
ISSN: 0010-4485
Keywords: Feature Recognition; Mid-surface; STEP; Injection-moulding
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Engineering and Innovation
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 49616
Depositing User: Helen Lockett
Date Deposited: 15 Jun 2017 15:21
Last Modified: 07 Dec 2018 17:12
URI: http://oro.open.ac.uk/id/eprint/49616
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