Based on both wavelet theory and fuzzy theory, a soft computing system (SCS) is proposed for feature extraction of signals. The proposed SCS approach possesses the advantages of soft decision-making on wavelet coe cients for feature extraction, adaptive selectivity of mapping factors to coarse-to-ΓΏn
A feature-based approach to extracting machining features
β Scribed by Jae Yeol Lee; Kwangsoo Kim
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 1001 KB
- Volume
- 30
- Category
- Article
- ISSN
- 0010-4485
No coin nor oath required. For personal study only.
β¦ Synopsis
Feature-based modeling has been considered an indispensable tool for integrating design and manufacturing processes. In this paper, we propose a new approach to extracting machining features from a feature-based design model, based on an integrated geometric modeling system that supports both feature-based modeling and feature recognition. Feature recognition is achieved through an incremental feature converter. The incremental feature converter not only keeps the design model consistent, but also incrementally extracts machining features from design features as a design evolves. By combining the strength of feature-based design and feature recognition, the proposed approach can handle feature interactions and protrusion features effectively so that it deals with a large set of complex design models. Moreoever, the incremental nature makes it possible for the design process to be an ongoing negotiation between the design and the manufacturing planner at the initial design stage.
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