Semantic-based subassembly identification considering non-geometric structure attributes and assembly process factors

Autor: Min Zhang, Gangfeng Wang, Dongping Zhao, Xiaolin Shi, Xitian Tian
Rok vydání: 2020
Předmět:
Zdroj: The International Journal of Advanced Manufacturing Technology. 110:439-455
ISSN: 1433-3015
0268-3768
DOI: 10.1007/s00170-020-05881-y
Popis: The generation of sequence planning becomes a difficult task as the number of part increases. As a consequence, dividing the complex product into multiple subassemblies which contain relatively few parts will decrease sequence planning difficulty. In the process of product assembly, semantic knowledge is an important basis for subassembly identification. Therefore, a semantic knowledge-driven subassembly identification framework is proposed. Generating information and knowledge during product design stage can be effectively utilized to become a variety of input constraints in the process of subassembly identification, including non-geometric structure constraints and assembly process constraints. Firstly, an assembly semantic model framework is constructed by mapping among spatial objects, assembly process and assembly relations, which are defined with Web Ontology Language (OWL) assertions. Next, the datum parts can be determined according to assembly directed graph. The influence of non-geometric structure attributes and assembly process factors on the assemblability was quantitatively expressed in semantics, and the characterization values and comprehensive weight value were deduced through Semantics Web Rule Language (SWRL) rules to construct weighted assembly directed graph. Based on this, simplifying weighted assembly directed graph through node merging and assembling is utilized to identify subassembly. Finally, the effectiveness of the framework is verified by transmission subassembly identification. The main contribution is presenting an ontology-based approach for subassembly identification, which can provide a feasible solution for the issue that mathematics-based subassembly identification approaches have great difficulty in explicitly representing assembly experience and knowledge.
Databáze: OpenAIRE
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