首页|Heterogeneous knowledge graph-driven subassembly identification with ensemble deep learning in Industry 4.0
Heterogeneous knowledge graph-driven subassembly identification with ensemble deep learning in Industry 4.0
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Taylor & Francis
In the context of Industry 4.0, model-based definition (MBD) has been an effective approach to creating 3D models contained all heterogeneous information needed to define a product, which proposes new challenges for the traditional subassembly identification method that only considers the geometric information of a product in assembly sequence planning. To bridge the gap, we propose a novel heterogeneous knowledge graph-driven subassembly identification method to enhance assembly sequence planning in the model-based systems engineering (MBSE) paradigm. Specifically, a heterogeneous knowledge graph is first constructed based on the shape information and engineering details of an MBD model. Next, an ensemble deep learning approach that combines graph neural networks with the community detection algorithm is proposed to effectively detect the subassembly from the MBD model. Finally, the feasibility and effectiveness of the proposed method are demonstrated through an example of car suspension subassembly identification, providing insight into the industrial implementation.
Knowledge graphensemble deep learningmodel-based definitionmodel-based system engineeringsubassembly identification
NETL
NSTL
Taylor & Francis
