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A method for intelligently optimizing hierarchical assembly structure sequences by assembly hybrid G-diagram

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Abstract

To improve the efficiency of complex assemblies in large-scale assembly sequence planning, an intelligent sequence planning method for constructing an assembly hybrid G-diagram model to realize the hierarchy of assembly structures is proposed. The assembly hybrid G-diagram model is constructed according to the assembly relationship semantics, and the assembly relationship semantics can be transformed into the corresponding assembly connection matrix and assembly priority matrix. Subassembly discriminant conditions are given to realize subassembly and isolated parts extraction, and the assembly structure is divided into part-level and subassembly-level. According to the assembly hybrid G-diagram, all feasible assembly sequences of part-level (within subassembly) and subassembly-level (subassembly as a whole) are solved, respectively. The particle swarm algorithm is used to optimize the assembly sequence with the goal of aggregation and redirection. The optimal sequence of part-level and subassembly-level is obtained, respectively. The sequence information is integrated to obtain the complete assembly sequence with the highest assembly efficiency under parallel planning. The feasibility and effectiveness of the assembly sequence optimization method are verified using a V-type dual-cylinder engine as an example. This planning method can greatly reduce the search space and avoid infeasible sequences when solving assembly sequences. In parallel planning, the sequence optimization process can be greatly shortened to ensure the assembly efficiency.

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 51705392).

Funding

This study was funded by National Natural Science Foundation of China (Grant: 51705392).

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Authors and Affiliations

  1. School of mechatronic Engineering, Xi’an Technological University, Xi’an, China

    Xiaoxi Kou, Yan Cao & Hu Qiao

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  1. Xiaoxi Kou
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  2. Yan Cao
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  3. Hu Qiao
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Contributions

Kou Xiaoxi: writing—original draft preparation, data curation, software, validation

Cao Yan: conceptualization, methodology, reviewing and editing

Qiao Hu: methodology, reviewing and editing

All authors read and approved the final manuscript.

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Correspondence to Xiaoxi Kou.

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This article is part of the Topical Collection: New Intelligent Manufacturing Technologies through the Integration of Industry 4.0 and Advanced Manufacturing.

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Kou, X., Cao, Y. & Qiao, H. A method for intelligently optimizing hierarchical assembly structure sequences by assembly hybrid G-diagram. Int J Adv Manuf Technol 122, 27–39 (2022). https://doi.org/10.1007/s00170-021-07951-1

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  • DOI: https://doi.org/10.1007/s00170-021-07951-1

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