XMKR: Explainable manufacturing knowledge recommendation for collaborative design with graph embedding learning

Abstract

Design for manufacturability is crucial to ensure high-quality products. It fully considers manufacturing-related factors in a collaborative design manner. However, since manufacturing knowledge is unstructured and hard to reuse, designers mainly depend on frequent design iterations to meet complex manufacturing constraints. Such insufficient design-manufacturing collaboration is not conducive to design efficiency improvement. To alleviate the problem, this paper proposes a novel graph embedding-based approach of Explainable Manufacturing Knowledge Recommendation (XMKR) for collaborative product design, which could achieve designer-oriented manufacturing knowledge reuse to avoid design errors proactively and minimize excessive iterations. Firstly, a graph embedding model based on the graph neural network (MKGE-GNN) is proposed to learn implicit semantic information of manufacturing knowledge. Secondly, with MKGE-GNN learning results, the potential manufacturing knowledge preferences of designers are identified in the design context graph by Personalized PageRank to achieve context-aware explainable manufacturing knowledge recommendation. Finally, a car body collaborative design case is conducted to validate the efficacy of the proposed approach, which not only provides accurate manufacturing knowledge in designers’ decision-making, but also enhances designers’ cognitions for manufacturability in the dynamic design context.