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Aic: an industrial knowledge graph with Abstraction-Instance-Capability reasoning abilities for personalized customization

Author

Listed:
  • Kai Zhang

    (Harbin Institute of Technology (Weihai))

  • Zhiying Tu

    (Harbin Institute of Technology (Weihai)
    State Key Laboratory of Massive Personalized Customization System and Technology)

  • Dianhui Chu

    (Harbin Institute of Technology (Weihai)
    State Key Laboratory of Massive Personalized Customization System and Technology)

  • Xiaoping Lu

    (State Key Laboratory of Massive Personalized Customization System and Technology
    COSMOPlat Institute of Industrial Intelligence Research (Qingdao) Co., Ltd.)

  • Lucheng Chen

    (State Key Laboratory of Massive Personalized Customization System and Technology
    COSMOPlat Institute of Industrial Intelligence Research (Qingdao) Co., Ltd.)

Abstract

In the era of the internet, people are increasingly interested in highly personalized customization, which has been leading the global industry to transform from “Enterprise centered Mass Manufacturing” to “User centered Mass Customization”. In recent decades, ontology-based semantic models have been used for semantic association description and syntax consistency alignment of product structures. However, the description of industrial terminology and general concepts has difficulty achieving accurate end-to-end matching of personalized user customization requirements and manufacturing capabilities. This requires detailed descriptions of process standards, component/assembly/part specifications, etc. Therefore, we propose the Abstraction-Instance-Capability (AIC) model based on a knowledge graph, which is a multigrained and multiview industrial knowledge model. By modeling industrial knowledge at a deeper level from various perspectives and by considering the expression of the relationships between knowledge, an industrial knowledge graph was constructed to support personalized customization. This graph can be used for personalized customization plan generation and other related operations, such as Design BOM (Bill of Materials) modification and verification. First, a meta-object facility (MOF)-based meta-model is defined for AIC model design. Second, the proposed model is used to define a multiview structure for modeling knowledge from various perspectives. Such a design realizes efficient industrial knowledge retrieval from various aspects, such as manufacturing resource retrieval, and production capacity analysis. Finally, in this paper we validate operability and introduce two case studies to demonstrate that the model has good performance in the recommendation of components (BOM modification) and the verification of personalized customization schemes (BOM verification).

Suggested Citation

  • Kai Zhang & Zhiying Tu & Dianhui Chu & Xiaoping Lu & Lucheng Chen, 2024. "Aic: an industrial knowledge graph with Abstraction-Instance-Capability reasoning abilities for personalized customization," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3419-3440, October.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02216-y
    DOI: 10.1007/s10845-023-02216-y
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    References listed on IDEAS

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    1. Yingxin Ye & Tianliang Hu & Yan Yang & Wendan Zhu & Chengrui Zhang, 2020. "A knowledge based intelligent process planning method for controller of computer numerical control machine tools," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1751-1767, October.
    2. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    3. Eeva Järvenpää & Niko Siltala & Otto Hylli & Minna Lanz, 2019. "The development of an ontology for describing the capabilities of manufacturing resources," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 959-978, February.
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