Abstract
Aiming at the problem that the assembly body model is difficult to classify and retrieve (large information redundancy and poor data consistency), an assembly body retrieval method oriented to key structures was presented. In this paper, a decision formal context is transformed from the 3D structure model. The 3D assembly structure model of parts is defined by the adjacency graph of function surface and qualitative geometric constraint graph. The assembly structure is coded by the linear symbol representation of compounds in chemical database. An importance or cohesion as the weight to a decision-making objective on the context is defined by a rough set method. A weighted concept lattice is introduced on it. An important formal concept means a key structure, since the concept represents the relations between parts’ function surfaces. It can greatly improve the query efficiency.
Highlights
Introduction and Problem StatementCurrently, engineering drawing retrieval is mainly text-based, which makes full use of spatial relationships and properties of components between these geometric elements
The S3 system mainly relies on matching the contour of the graphics, while the spatial relationships are ignored
Muller and Rigoll [2] put forward a new stochastic model retrieval method, using a pseudo
Summary
Engineering drawing retrieval is mainly text-based, which makes full use of spatial relationships and properties of components between these geometric elements. The main purpose to propose the decision formal context is for decision analysis for relational data Along this idea, Shao [16] and Qu et al [15] studied the rules extraction problem of decision formal context using concept lattice. The knowledge extraction can be accelerated, the storage space can be saved, and the time to get results can be shortened This is the underlying idea that motivates researchers to propose a weighted concept lattice [10]. This construct inherits a structure of general concept lattices while facilitates extraction of interesting knowledge.
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