Heterogeneous knowledge graph-driven subassembly identification with ensemble deep learning in Industry 4.0
针对传统子装配体识别仅考虑几何信息的局限，提出一种基于异质知识图谱和集成深度学习的方法，利用MBD模型中的形状与工程信息，结合图神经网络与社区检测算法，有效识别子装配体，并通过汽车悬架案例验证。
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.