Automatic designation of feature faces to recognize interacting and compound volumetric features for prismatic parts
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ORIGINAL ARTICLE
Automatic designation of feature faces to recognize interacting and compound volumetric features for prismatic parts Pramod S. Kataraki1 · Mohd Salman Abu Mansor1 Received: 21 January 2019 / Accepted: 14 May 2019 © Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract The important aspect of computer-aided process planning (CAPP) is to recognize surfaces and features of parts to aid downstream manufacturing of prismatic parts. Ample work is done on recognition of surface and its non-complex shape features by various methods, but there is shortfall in auto-recognition of interacting and compound features. The non-recognition of interacting and compound features limits the user from knowing individual feature type and material removal volume (MRV) of feature leading to lack of feature information provision to subsequent generative process planning. Therefore, this paper presents (i) an enriched classification of regular form features and (ii) a novel algorithm to automatically recognize interacting and compound volumetric features of prismatic part and to auto-generate material removal volume for the recognized volumetric features. All the faces of a feature are designated based on their geometrical shape, and combination of these designations expresses the type of feature present in a part. Keywords Feature recognition · Algorithm · Interacting features · Compound features
1 Introduction The integration of computer-aided design (CAD) and computer-aided manufacturing (CAM) has increased the production efficiency in current manufacturing industries, and integrating generative CAPP [1, 2] to CAD/CAM will enhance the productivity. Automatic feature recognition (AFR) is necessary to achieve generative process planning. AFR approaches provide abilities to recognize high-level geometrical entities and features [3], and approaches such as graph-based, rule-based, hint-based, volume decomposition approaches are the most applied in research works performed so far to recognize features from CAD models [4]. The other methods applied by researchers to recognize features are edge boundary classification, slicing method, intelligent feature recognition methodology, and ontology.
* Mohd Salman Abu Mansor [email protected] Pramod S. Kataraki [email protected] 1
School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia
Graph-based method was applied to generate attributed adjacency graph (AAG), and the generated AAG was decomposed into concave subgraphs whose properties of nodes were used to recognize interacting features such as pocket, open pocket, slot, step, blind hole and through hole. The method is adoptable only if, for every part feature, the AAG has a corresponding subgraph, and the pocket feature is recognized only if it contains a planar base face. In some cases, the generated cavity feature volume is more than required feature volume. The edges with constant attributes we
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