The identification of key functional parts can improve the retrieval efficiency of assembly model and the reuse level of retrieval

and also provide critical reference information for autonomous design. In order to reduce the subjectivity of the expert system

the rough set theory was introduced into the automatic identification of key subassembly functional parts

and the ranking process of functional parts was driven by the data of the assembly model itself. The characteristics and connection relationships of parts in assembly were analyzed

and assembly model was established based on complex network. The topological layer

part attribute layer data

and part types were extracted as condition attributes and decision attributes. The algorithm based on dynamic hierarchical clustering was used to discretize the decision information table of the subassembly parts. The heuristic reduced algorithm based on attribute importance was adopted to dig knowledge

eliminate redundant condition attributes

and obtain attributes reduction set as well as corresponding attribute weight. Finally

the order of the importance of the subassembly parts with key functions was obtained through comprehensive evaluation. The worm gear reducer model was taken as an example to verify the performance of the proposed method. Experimental results show that the final ranking results of the proposed model were consistent with those of previous research results

and since the model is driven by the assembly model data itself

it is more objective.