Feature Extraction and Classification for Rotational Parts Taking 3d Data Files as Input
نویسنده
چکیده
Feature extraction and classification is considered as the bridge between Computer-Aided Design (CAD) and Computer-Aided Process Planning (CAPP). This paper proposes a method that can extract and classify turning (including symmetric and non-symmetric) and non-turning features that are concave, convex, or complex for rotational parts taking a 3D data file as input. In addition, feature interactions are also taken into consideration in this methodology. The proposed feature extraction and classification method consists of three basic procedures. The first procedure extracts concave, convex and complex features from a 3D CAD data file. The second procedure classifies the extracted features. The third procedure merges and decomposes extracted features. Two sample application descriptions are presented for demonstration purposes. The system has been implemented in C on a PC-based system.
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