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• W4200305835 abstract "The trend of customized production results in the demand for higher level of automation, in which artificial intelligence decision-making dominates. As the core of smart manufacturing systems, intelligent services stand a significant role in the analysis, prediction, and adjustment of production process, which is inseparable from the effective semantic modeling of the procedures and elements involved. However, there is an absence of unified modeling of assembly process, including both geometric and non-geometric information, leading to the incomprehensiveness when providing data support for intelligent services. To fill this gap, a generic node2vec-based parameterized representation of geometric elements and assembly constraints approach is proposed. Firstly, the information structure of assembly process is established, in which the geometric elements and topological relationships of the product are abstracted into a network. Secondly, node2vec is adopted for the graph embedding to generate preset dimension vectors corresponding to the nodes in the geometric network. As the edges in the network, the vectors corresponding to the assembly constraints, which are regarded as the parameterized representations, can be obtained through node vector calculation. Moreover, an assembly workstep execution time prediction method based on historical data is introduced with the parameterized representations of assembly constraints as the carriers of geometric topological information. At last, an industrial case study is illustrated to show the entire process of constraint parameterized representations and workstep execution time prediction, indicating the feasibility and availability of the method proposed." @default.
• W4200305835 created "2021-12-31" @default.
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• W4200305835 date "2022-01-01" @default.
• W4200305835 modified "2023-10-01" @default.
• W4200305835 title "A node2vec-based graph embedding approach for unified assembly process information modeling and workstep execution time prediction" @default.
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• W4200305835 doi "https://doi.org/10.1016/j.cie.2021.107864" @default.
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