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Purpose The purpose of this paper is to find a method for key assembly structure identification in complex mechanical assembly. Three-dimensional model reuse plays an increasingly important role in complex product design and innovative design. Assembly model has become important resource of models reuse in enterprises, which contains certain function assembly structures. These assembly structures implicating plenty of design intent and design experience knowledge can be used to support function-structure design, modular design reuse and semantics analysis for complex product. Design/methodology/approach A method for identifying key assembly structures in assembly model is presented from the viewpoint of assembly topology and multi-source attributes. First, assembly model is represented based on complex network. Then, a two-level evaluation model is put forward to evaluate importance of parts assembled, and the key function parts in assembly can be obtained. After that, on the basis of the function parts, a heuristic algorithm upon breadth first searching is given to identify key assembly structures. Findings The method could be used to evaluate key function parts and identify key assembly structures in complex mechanical assembly according to the specific circumstances. Practical implications The method can not only help designers find the key assembly structure in complex mechanical assembly model, facilitate the function-structure designing and semantics analyzing, and thereby improve the efficiency of product knowledge reuse, but also assist in analyzing influence scope of key function part changing and optimization of the assembly process for complex mechanical assembly. Originality/value The paper is the first to propose a method for key assembly structure identification in complex mechanical assembly, where the key function parts can be evaluated through a two-level evaluation model, and the key assembly structures are identified automatically based on complex network.

This paper proposes a novel two-tiered retrieval method for CAD models based on the graph spectral theory and face code to match three dimension (3D) CAD models with undirected graphs. This approach applies a hierarchical feature descriptor, a two-tiered retrieval mechanism to represent and retrieval CAD models. The first tier is rapid rough matching, which is a similarity metric by weighted bipartite graph matching between library models and retrieval models. Second tier is exact matching through comparing face code. Experimental results show the proposed method could improve the efficiency of retrieval performance, and achieve the reuse of CAD model.

Propeller shaft is one of main sources to arouse vehicle noise. To decrease development cost and shorten development cycle, BP Neural Network model of influence of propeller shaft to vehicle noise was established using MATLAB software, combined with historical data. Then, the model was trained, results showed that it was stable and credible and the error of this model was between −4 and 4, which could be acceptable. Last, noise of the developing vehicle in the company was predicted using the model, offering theoretical reference to related people.