Product Quality Modeling for Complex Manufacturing Systems Based on an Improved Graph Attention Network
LIANG Jiaxian
LI Chenglong
TONG Shurong
Abstract：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 insufficient process engineering knowledge,this paper proposes a modelling method based on an improved graph attention network called Inter-layer Contrastive Loss Filtering Graph Attention Network(ICLF-GAT).Initially,a data-driven deep learning framework is adopted to avoid reliance on prior physical knowledge.Subsequently,a directed graph model of the manufacturing system is constructed based on Graph Attention Networks(GAT)to effectively capture complex structural features and nonlinear dependencies between processes.On this basis,a novel inter-layer contrastive loss filtering mechanism is introduced,which dynamically evaluates and filters the quality of node features to significantly mitigate the over-smoothing problem in deep GATs and enhance the modeling capability for long-range error propagation.Finally,a target attention decoder is designed to further improve the modeling accuracy of the systematic complex dependencies.Simulation experiments and a practical industrial case demonstrate that ICLF-GAT significantly reduces the Root Mean Squared Error(RMSE)compared to existing benchmark methods,with particularly advantages in long-range error propagation.
Keywords：product quality modelingdata-drivengraph attention network(GAT)inter-layer contrastive loss filtering mechanismover-smoothing problemtarget attention decoder
Publication Date：2025-12-31
Online Publishing Date：2026-01-16(First online date of this platform, not the publication date of the document)
Pages：16( 14-29 )