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Estimates suggest that more than 75% of engineering design activity comprises reuse of previous design knowledge to address a new design problem. Reusing design knowledge has great potential to improve product quality, shorten lead time, and reduce cost. However, PLM systems, which address the issue of reuse by searching for keywords in filenames, part numbers or context attached to CAD models, do not provide a robust tool to search reusable knowledge. This paper presents a brief overview of a novel approach to search for 3D models. The system is built on a client-server-database architecture. The client takes in the query input from the user along with his search preferences and passes it to the server. The server converts the shape input into feature vectors and a unique skeletal graph representation. Details of the algorithms to perform these steps are presented here. Principal advantages of our graph representation are: (i) it preserves geometry and topology of the query model, (ii) it is considerably smaller than the B-Rep graph, and (iii) it is insensitive to minor perturbations in shape, but sensitive enough to capture the major features of a shape. The combined distance of feature vectors and skeletal graphs in the database provide an indirect measure of shape similarity between models. Critical database issues such as search system efficiency, semantic gap reduction and the subjectivity of the similarity definition are addressed. This paper reports our initial results in designing, implementing and running the shape search system. q

This paper presents an approach for a reconfigurable shape search system for 3D engineering models using a client-serverdatabase architecture. The current paper focuses on the server functionality, while a subsequent paper will focus on the database issues. The server takes the shape query as input from the client and converts it into feature vectors and a new skeletal graph representation which we have developed. The algorithms such as voxelization, skeletonization, and skeletal graph extraction for accomplishing these are described in detail. The principal advantages of the skeletal graph representation are that (i) it preserves the geometry and topology of the query model, (ii) it is considerably smaller than the B-Rep graph, and (iii) it is insensitive to minor perturbations in shape, while sensitive enough to capture the major features of a shape. Our representation is also synergistic with the human cognitive representation of shape. The results indicate that the skeletal graph is considerably smaller than the B-Rep graph even for complicated shapes.

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