Similarity analysis of the assembly model is widely concerned in the field of product information reuse. At present, some methods utilize the graph theory to excavate structural information, but they are usually complicated. There are also some methods considering the vector collection to get better computing efficiency, but they ignore the connection between parts. Based on the advantages and disadvantages of these methods, this paper proposes an assembly model information quantization method, which integrates structural information into vectorization descriptor and establishes a corresponding index structure and similarity measurement method. First, a connection graph is used to represent the structure features of the assembly, and interconnected part models are divided into several structural units. Then, each structural unit is quantified by the structure-shape distribution function. On this basis, the point set-based assembly descriptor is constructed. Finally, an inverted index and filtering mechanism is established based on the Bag of Word (BOW) algorithm and hypersphere soft allocation strategy. By solving the optimal matching between the query assembly model and the library model, similarity analysis of the assembly model is ultimately realized.
ZHANG Jie
,
JI Baoning
,
YANG Ning
,
TANG Wenbin
. A method for discrete quantization and similarity analysis of assembly model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021
, 42(10)
: 524992
-524992
.
DOI: 10.7527/S1000-6893.2021.24992
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