A novel quality prediction model based on dual-layer graph supervised embedding with multi-granularity attention enhancement mechanisms

Data-driven soft sensing methods are widely used for product quality prediction in large-scale industrial processes. Traditional approaches face significant challenges, such as limited representation of multivariate couplings, difficulties in modeling nonlinear interactions, and slow adaptation to dynamic changes in complex industrial settings. To address these, we propose a manufacturing quality prediction model integrating Dual-layer Graph Supervised Embedding with Multi-Granularity Attention Enhancement Mechanisms (DGS-MA). The model constructs a dual-layer complementary graph structure: the first layer creates an original parameter relationship graph based on feature similarity to capture local static associations, while the second layer uses a label-aware supervised Node2vec algorithm to generate embedding vectors, reconstructing a global quality-driven topology. This forms a dual-view representation of 'original features - embedding vectors'. A multi-granular graph attention enhancement mechanism is introduced, which employs a dual-pathway attention network to aggregate neighborhood information from both original features and supervised embeddings. A cross-layer attention mechanism adaptively fuses the importance of these two feature types, enabling coordinated optimization of local details and global patterns. Additionally, an explicit supervision constraint is incorporated to enhance prediction accuracy and interpretability, embedding supervision signals in both the graph's embedding space and attention mechanism. The method is validated with a real-world production dataset, showing significant improvements in quality prediction under complex conditions.