以整體及局部外形資訊進行三維方形工件分類

  

Classification of 3D Prismatic Parts Based on Global and Local Shape Information
NSC88-2212-E-009-036  87/08/01---88/07/31    mcwu@cc.nctu.edu.tw 
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(Keywords: Simplified Skeleton, Group Technology, Neural network)

This research presents an automatic classification scheme for punched parts. The classification is performed based on part’s global shape descriptor modeled by their simplified skeletons. In the first stage, the simplified skeleton of a punched part is represented by a sequentially coded tree structure. Based on this tree representation, punched parts with similar tree structure can be clustered using a tree matching method. In the second stage, the parts within a cluster can be further classified by a back-propagation neural network classifier. The two-stage classification scheme can be used in a punched part

retrieval system aiming to facilitate the design and manufacturing tasks, which would greatly reduce the time-to-market and material inventory.

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