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

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Abstract

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.

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Acknowledgments

This work was partially supported by the Natural Science Basic Research Project of Shaanxi Province, China (Grant Nos. 2019JM-073 and 2019JM-435) and the China Postdoctoral Science Foundation (Grant No. 2018M633439). The authors would also like to thank the editors and anonymous referees for their insightful comments and suggestions.

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Authors and Affiliations

  1. Institute of Intelligent Manufacturing, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Xiaolin Shi, Xitian Tian & Min Zhang

  2. Key Laboratory of Road Construction Technology and Equipment of MOE, School of Construction Machinery, Chang’an University, Xi’an, 710064, China

    Gangfeng Wang

  3. School of Aircraft Engineering, Xi’an Aeronautical University, Xi’an, 710077, China

    Dongping Zhao

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  1. Xiaolin Shi
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  2. Xitian Tian
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  3. Gangfeng Wang
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  4. Dongping Zhao
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  5. Min Zhang
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Correspondence to Gangfeng Wang.

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Shi, X., Tian, X., Wang, G. et al. Semantic-based subassembly identification considering non-geometric structure attributes and assembly process factors. Int J Adv Manuf Technol 110, 439–455 (2020). https://doi.org/10.1007/s00170-020-05881-y

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  • DOI: https://doi.org/10.1007/s00170-020-05881-y

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