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Automatic 3D machining feature extraction from 3D CSG solid input

โœ Scribed by Der-Baau Perng; Zen Chen; Rong-Kwei Li


Publisher
Elsevier Science
Year
1990
Tongue
English
Weight
786 KB
Volume
22
Category
Article
ISSN
0010-4485

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โœฆ Synopsis


A method for automatically extracting the machining features from 3D CSG solid input is proposed. The method involves converting a part's CSG tree representation into its equivalent DSG tree representation and then identifying the types of machinable features from the DSG tree. A DSG tree is a special case of CSG tree in which all geometric operations are of 'difference' type. The method is proposed for recognizing prismatic part features and its outputs contain feature attributes and possible tooling entrance laces, etc. Computer simulation is provided to illustrate the proposed method.

CSG tree, machining feature extraction, CAD, CAM An important step in the process of combining CAD and CAM systems for manufacturing automation is the integration of the data required for defining the part with the data required for the actual manufacturing of the part on the shop floor.

The information about a part that is provided by conventional CAD modelling systems is geometrical and topological. CAM systems need detailed machinable volumes, machining features, part control information, finish and processing specifications, reference surfaces and so on. Different approaches have been proposed for the integration of CAD and CAM systems ~-6.

A machining feature extraction system is intended to be a system that can provide the capabilities for translating the part definition data created by a CAD system into the machining features needed to drive a process planning system. By the types of machining features recognizable by the system, the known approaches can be classified as โ€ข rotational part feature extraction systems 7-~ โ€ข prismatic part feature extraction systems 12-2~ The general feature extraction techniques used in the first type of system are based on syntactic pattern recognition methods 24. The feature extraction techniques


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