Research on Traceability Method of Aerospace Product Assembly Quality Based on Knowledge Graph

doi:10.3901/JME.260230

Abstract

Abstract: To address the challenges of integrating multi-source heterogeneous data and inefficient association and tracing of quality issues in the assembly process of aerospace products, this study proposes a knowledge graph-based quality tracing method. By constructing a knowledge graph encompassing critical scenarios including logistics and hoisting, cable network conductivity, and comprehensive testing, we integrate structured and unstructured data to achieve cross-process semantic association and deep reasoning of data. The approach combines YOLOv8 and DeepSORT algorithms for real-time object detection and behavior analysis in logistics/hoisting scenarios, while employing the K-nearest neighbors (KNN) algorithm for anomaly detection in cable network conductivity and comprehensive testing scenarios. A collaborative reasoning mechanism of "anomaly-cause-solution" is established through the knowledge graph, breaking through the data silo limitations of traditional quality tracing methods and providing interpretable support for quality issue localization and root cause analysis in product assembly. Case validation demonstrates that this method effectively resolves data silo issues in aerospace product assembly, enhances the accuracy and efficiency of anomaly localization and quality tracing, and offers technical support for intelligent quality management in complex assembly scenarios.

Key words: knowledge graph, quality traceability, aerospace product final assembly, multi-source data fusion

CLC Number:

TP18

ZHENG Xiaohu, CAO Lijun, LIU Xiaojia, DU Siqi, WU Wenqiang, ZHANG Jie, DING Siyi. Research on Traceability Method of Aerospace Product Assembly Quality Based on Knowledge Graph[J]. Journal of Mechanical Engineering, 2026, 62(5): 88-99.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260230

http://www.cjmenet.com.cn/EN/Y2026/V62/I5/88

References

[1] 黄海金，邱凯，俞劲松，等. 航天产品总装质量追溯技术研究[J]. 机械制造，2013，51(4):86-89. HUANG Haijin，QIU Kai，YU Jinsong，et al. Research on quality tracing technology for aerospace product assembly[J]. Machinery，2013，51(4):86-89.
[2] 冯锦丹，何楠，刘金山，等. 基于知识驱动的航天复杂产品装配工艺重构方法[J]. 计算机集成制造系统，2024，30(12):4314-4327. FENG Jindan，HE Nan，LIU Jinshan，et al. Knowledge-driven assembly process reconfiguration method for aerospace complex products[J]. Computer Integrated Manufacturing System，2024，30(12):4314-4327.
[3] 马肖瑶，黎睿，李自力，等. 面向工业场景带钢表面缺陷检测的LF-YOLO[J]. 计算机工程与应用，2024，60(18):78-87. MA Xiaoyao，LI Rui，LI Zili，et al. LF-YOLO for strip surface defect detection in industrial scenes[J]. Computer Engineering and Applications，2024，60(18):78-87.
[4] 张红民，庄旭，郑敬添，等. 优化YOLO网络的人体异常行为检测方法[J]. 计算机工程与应用，2023，59(7):242-249. ZHANG Hongmin，ZHUANG Xu，ZHENG Jingtian，et al. Optimizing human abnormal behavior detection method of YOLO network[J]. Computer Engineering and Applications，2023，59(7): 242-249.
[5] 张燕宁，杨兆建，丁华，等. 基于XML技术的虚拟装配信息表达及其应用[J]. 机械设计与制造，2014，(9):205-207，210. ZHANG Yanning，YANG Zhaojian，DING Hua，et al. Virtual assembly information expression based on XML technology and its application[J]. Machinery Design & Manufacture，2014，(9):205-207，210.
[6] JIA Yuetian，QIAN Chao，FAN Zhixiang，et al. A knowledge-inherited learning for intelligent metasurface design and assembly[J]. Light:Science & Applications，2023，12(1):82-82.
[7] MIYAGI P，RIASCOS L. Modeling and analysis of fault-tolerant systems for machining operations based on Petri nets[J]. Control Engineering Practice，2005，14(4):397-408.
[8] 顾星海，鲍劲松，吕超凡. 基于知识图谱的装配语义信息建模[J]. 航空制造技术，2021，64(4):74-81. GU Xinghai，BAO Jinsong，LÜ Chaofan. Assembly semantic information modeling based on knowledge graph[J]. Aeronautical Manufacturing Technology，2021，64(4):74-81.
[9] 张栋豪，刘振宇，郏维强，等. 知识图谱在智能制造领域的研究现状及其应用前景综述[J]. 机械工程学报，2021，57(5):90-113. ZHANG Donghao，LIU Zhenyu，JIA Weiqiang，et al. A review on knowledge graph and its application prospects to intelligent manufacturing[J]. Journal of Mechanical Engineering，2021，57(5):90-113.
[10] 从靖晨，项忠霞，李心雨，等. 基于知识图谱的智能产品服务系统交互设计研究[J]. 机械工程学报，2023，59(11):94-105. CONG Jingchen，XIANG Zhongxia，LI Xinyu，et al. A knowledge graph-based interaction design method for smart product-service system development[J]. Journal of Mechanical Engineering，2023，59(11):94-105.
[11] 郭鑫，黄泽川，王杰，等. 人机混合交互的产品设计知识图谱构建及智能推理方法[J]. 机械工程学报，2025，61(3):142-153. GUO Xin，HUANG Zechuan，WANG Jie，et al. Knowledge graph construction and intelligent reasoning methods for human-machine hybrid interaction in product design[J]. Journal of Mechanical Engineering，2025，61(3):142-153.
[12] 聂同攀，曾继炎，程玉杰，等. 面向飞机电源系统故障诊断的知识图谱构建技术及应用[J]. 航空学报，2022，43(8):46-62. NIE Tongpan，ZENG Jiyan，CHENG Yujie，et al. Knowledge graph construction technology and its application in aircraft power system fault diagnosis[J]. Acta Aeronautica et Astronautica Sinica，2022，43(8):625499.
[13] 肖彪，徐宝德，彭仕鑫，等. 基于知识图谱的复杂薄壁零件机械加工工艺知识建模研究[J]. 航空制造技术，2024，67(11):76-86. XIAO Biao，XU Baode，PENG Shixin，et al. Study on machining knowledge modeling of complex thin-walled parts based on knowledge graph[J]. Aeronautical Manufacturing Technology，2024，67(11):76-86.
[14] 马杰，孙望淳，王平辉，等. 基于相关性提示的知识图谱问答[J]. 软件学报，2025，36(9):4056-4071. MA Jie，SUN Wangchun，WANG Pinghui，et al. Relevance prompts based knowledge graph question answering[J]. Journal of Software，2025，36(9):4056-4071.
[15] XU Z，DANG Y. Data-driven causal knowledge graph construction for root cause analysis in quality problem solving[J]. International Journal of Production Research, 2023，61(10):3227-3245.
[16] 张佳琪，凌卫青. 基于知识图谱推理的热轧带钢产品质量缺陷追溯[J]. 计算机集成制造系统，2024，30(3):1105-1114. ZHANG Jiaqi，LING Weiqing. Quality defect tracing of hot rolled strip based on knowledge graph reasoning[J]. Computer Integrated Manufacturing System，2024，30(3):1105-1114.
[17] 杨佳幸，温沛涵，胡亚萍. 核电装备价值链协同质量管控知识图谱构建研究[J]. 机械工程学报，2025，61(13):174-191. YANG Jiaxing，WEN Peihan，HU Yaping. Research on knowledge graph construction of nuclear power equipment quality control with value chain collaboration[J]. Journal of Mechanical Engineering，2025，61(13):174-191.
[18] LIM K Y H，YOSAL T S，CHEN C H，et al. Graph-enabled cognitive digital twins for causal inference in maintenance processes[J]. International Journal of Production Research，2024，62(13):4717-4734.
[19] DONG Q，FAN Q，XING Z，et al. Reinforced fuzzy neural networks based on maximum entropy clustering and conjugate gradient method[J]. Engineering Applications of Artificial Intelligence，2025，142:109909.
[20] CICMILOVIĆ D. Hidden markov model for correlated ornstein-uhlenbeck observations and application to gasoline prices forecasting[J]. Communications in Nonlinear Science & Numerical Simulation，2025，143:108630.
[21] MA G，HE C，HE Z，et al. Probability analysis on tunnels in heterogeneous strata based on borehole data-driven conditional random fields and convolutional neural network[J]. Tunnelling and Underground Space Technology，2025，158:106402.
[22] QIN Y，SHEN G，ZHAO W，et al. A network security entity recognition method based on feature template and CNN-BiLSTM-CRF[J]. Frontiers of Information Technology & Electronic Engineering，2019，20(6):872-885.
[23] ZHOU S，LIU K，LI D，et al. Augmenting general-purpose large-language models with domain-specific multimodal knowledge graph for question-answering in construction project management[J]. Advanced Engineering Informatics，2025，65(PA):103142-103142.
[24] SUN S，LI J，ZHANG K，et al. A novel feature integration method for named entity recognition model in product titles[J]. Computational Intelligence，2024，40(3) :e12654.
[25] WU C，LIU J，HUANG C，et al. Cybersecurity entity recognition model for IoT via hierarchical attention mechanism[J]. International Journal of Modern Physics C, Physics and Computers，2025:2442013.
[26] MENG L，WANG Y，HUANG Y，et al. A named entity recognition model for Chinese electricity violation descriptions based on word-character fusion and multi-head attention mechanisms[J]. Energies，2025，18(2):401-401.
[27] JIN H，ZHANG Y，JIA Y，et al. Condition diagnosis of composite insulator based on knowledge graph[J]. IEEE Transactions on Dielectrics and Electrical Insulation:A Publication of the IEEE Dielectrics and Electrical Insulation Society，2025，32(1):28-35.