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Knowledge graph network-driven process reasoning for laser metal additive manufacturing based on relation mining

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Abstract

Additive Manufacturing (AM) technology offers remarkable flexibility in fabricating products with intricate geometries, presenting unprecedented advantages in material efficiency and speed. The process planning of AM plays a pivotal role in ensuring overall quality and time-efficiency of printed products. This drives engineers and researchers to explore various approaches to achieve optimal AM process solutions. However, numerous challenges persist, particularly in logical relationship reasoning and information representation for complex manufacturing tasks and design requirements. In this study, a novel AM process reasoning method based on relation mining is proposed, leveraging knowledge graph representation and graph neural networks (GNN). An AM knowledge graph is constructed comprising essential process information, followed by implementing RED-GNN to accomplish graph reasoning tasks for parameter recommendation. We then focus on the process planning scenario of lattice structures, a common geometry used for designing products with weight-relief requirements and high sensitivity to process parameters. A series of lattice structure parts are designed and tested using our proposed method, demonstrating strong performance and unveiling new potentials and opportunities in advancing knowledge-based engineering and intelligent manufacturing.

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Data availability

The sample data used for constructing AM knowledge graph as well as generating test results of proposed method is publicly available and can be found in Github repository: https://github.com/changrixiong/AMKG-data-demo.git.

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Funding

This research is supported by National Natural Science Foundation of China, No. 52305551.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China

    Changri Xiong, Zhuangyu Li, Gang Zhao & Wenlei Xiao

  2. Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Jinhua Xiao

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  1. Changri Xiong
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  4. Gang Zhao
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Contributions

Conceptualization: Jinhua Xiao, Changri Xiong; Methodology: Changri Xiong, Jinhua Xiao; Formal analysis and investigation: Changri Xiong, Zhuangyu Li; Writing—original draft preparation: Changri Xiong, Jinhua Xiao; Writing—review and editing: Jinhua Xiao, Wenlei Xiao; Resources: Changri Xiong, Jinhua Xiao, Zhuangyu Li; Supervision: Wenlei Xiao, Gang Zhao.

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Correspondence to Wenlei Xiao.

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We declare that we have no financial or personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be constructed as influencing the position presented in, or the review of, the manuscript entitled.

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Xiong, C., Xiao, J., Li, Z. et al. Knowledge graph network-driven process reasoning for laser metal additive manufacturing based on relation mining. Appl Intell 54, 11472–11483 (2024). https://doi.org/10.1007/s10489-024-05757-8

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