@inproceedings{47d0a668381148edbcc09ea6e19e1624,
title = "Quality Prediction of Helicopter Structural Assembly Process Based on Bayesian Network",
abstract = "The helicopter, due to its complex structure and intricate functional mechanisms, often encounters quality issues during the assembly process. These issues include non-conforming holes, substandard riveting, and incorrect positioning. Quality prediction is a key support technology for quality control and continuous improvement in the helicopter structural assembly production line. Influenced by deviations and disturbances in assembly process routes, tooling, and measurements, quality issues such as out-of-tolerance or instability in the helicopter structural assembly process frequently occur. Modeling and solving the problem of quality prediction in the helicopter structural assembly process has become an urgent issue. This paper introduces the use of Bayesian networks to model and predict the quality of the helicopter structural assembly process and verifies its effectiveness through actual assembly cases.",
keywords = "Bayesian network, Helicopter structure assembly, Quality prediction",
author = "Kai Guo and Haoming Rong and Zhongchuan Ouyang and Bin Xie and Guijiang Duan",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 7th International Conference on Materials and Intelligent Manufacturing, ICMIM 2025 ; Conference date: 30-06-2025 Through 02-07-2025",
year = "2026",
doi = "10.1007/978-981-95-6075-2\_2",
language = "英语",
isbn = "9789819560745",
series = "Lecture Notes in Mechanical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "13--20",
editor = "Han-Yong Jeon",
booktitle = "Proceedings of the 7th International Conference on Materials and Intelligent Manufacturing - Proceedings of ICMIM 2025",
address = "德国",
}