Product Quality Modeling for Complex Manufacturing Systems Based on an Improved Graph Attention Network

To tackle the key challenges in product quality modeling for complex manufacturing systems—such as nonlinear dependencies across multiple processes, intricate structural relationships, long-range error propagation, and the absence of engineering knowledge on the production process—this paper proposes an improved graph attention network modeling method called ICLF-GAT (Inter-layer Contrastive Loss Filtering Graph Attention Network). The method systematically addresses these issues: it adopts a data-driven deep learning framework that eliminates dependence on prior physical knowledge; constructs a directed graph model of the manufacturing system based on Graph Attention Networks (GAT) to effectively capture complex structural features and nonlinear dependencies between processes; introduces an inter-layer contrastive loss filtering mechanism that dynamically evaluates and filters the quality of node features to significantly mitigate the over-smoothing problem in deep GATs and enhance long-range error propagation modeling; and designs a target attention decoder to further improve the accuracy in modeling complex system-wide dependencies. The results in the simulation experiments and a real-world industrial case show that ICLF-GAT significantly reduces Root Mean Squared Error (RMSE) compared to existing benchmark methods, particularly excelling in long-range error propagation tasks.