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The system contains several modules: hierarchical design-for-manufacture rule system, manufacturing feature library, manufacturing feature-based design module, designer advisory module, manufacturing feature recognition module, and design analysis module.
The designers select features from manufacturing feature libraries.
Through combination, decomposition, and (tool approach direction) TAD-led operations, alternative interpretations of manufacturing feature model for the part are then generated, and the manufacturing feature tree is updated and extended with AND/OR operators to store these interpretations.
Finally, a single interpretation with the lowest machining cost will be selected in the manufacturing feature tree.
This paper presents an intelligent manufacturing feature extraction method employing artificial neural network (ANN) techniques, design by features and heuristic algorithms.
Current machine tools for micro manufacturing feature unfavorable ratios of installation space to work space as well as moving masses compared to the work piece mass.
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Inside the library, manufacturing features are organised hierarchically.
Constant radius geometric features are a common type of manufacturing features of ancient ceramics.
The manufacturing features are decomposed into a series of general manufacturing features by using a mixed graph-based and rule-based algorithm.
The features framework makes a mapping from manufacturing features of hole into its machining operations.
This paper presents a methodology for recognizing manufacturing features from a design feature model.
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CEO of Professional Science Editing for Scientists @ prosciediting.com