工业工程2025，Vol.28Issue(6)：14-29,16.DOI:10.3969/j.issn.1007-7375.250061
基于改进图注意力网络的复杂制造系统产品质量建模
Product Quality Modeling for Complex Manufacturing Systems Based on an Improved Graph Attention Network
摘要
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.关键词
产品质量建模/数据驱动/图注意力网络(GAT)/层间对比损失过滤机制/过平滑问题/目标注意力解码器Key words
product quality modeling/data-driven/graph attention network(GAT)/inter-layer contrastive loss filtering mechanism/over-smoothing problem/target attention decoder分类
通用工业技术引用本文复制引用
LIANG Jiaxian,LI Chenglong,TONG Shurong..基于改进图注意力网络的复杂制造系统产品质量建模[J].工业工程,2025,28(6):14-29,16.基金项目
教育部人文社会科学研究一般资助项目(23YJC630077) （23YJC630077）
陕西省自然科学基础研究计划资助项目(2025JC-YBQN-965) （2025JC-YBQN-965）
