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Computer understanding of machining features such as holes and pockets is
essential for bridging the communication gap between Computer Aided Design and
Computer Aided Manufacture. This thesis describes a prototype machining feature
extraction system that is implemented by integrating the VAX-OPS5 rule-based
artificial intelligence environment with the PADL-2 solid modeller. Specification of
original stock and finished part geometry within the solid modeller is followed by
determination of the nominal surface boundary of the corresponding cavity volume
model by means of Boolean subtraction and boundary evaluation. The boundary model
of the cavity volume is managed by using winged-edge and frame-based data
structures. Machining features are extracted using two methods : (1) automatic feature
recognition, and (2) machine learning of features for subsequent recognition. [Continues.
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