AI Twin Control Method System for Aeronautical Intelligent Manufacturing Driven by Industrial Large Models

doi:10.3901/JME.260228

Abstract

Abstract: To address key challenges in aviation intelligent manufacturing, such as low data-knowledge collaboration efficiency, difficulties in tracing assembly deviation sources, lagging process parameter optimization, and insufficient virtual-real interactive verification, this study proposes an AI twin control methodology framework with industrial large models as the cognitive engine, and constructs a digital twin closed-loop control framework covering the entire "perception-diagnosis-decision-verification" process. Through networked associative modeling of knowledge graphs, semantic fusion and dynamic reasoning of multi-source heterogeneous data are realized, an industrial large model corpus for aviation manufacturing is established, and a professional knowledge base with autonomous evolution capabilities is formed. An industrial large model algorithm library for multi-scenario intelligent decision closed-loops is developed: Bayesian causal inference is used to analyze multi-level coupled causes of assembly deviations; incremental ensemble learning is integrated to achieve dynamic evolution prediction of multi-source coupled deviations; and transfer reinforcement learning is applied to break through the bottleneck of cross-scenario parameter optimization. Finally, a virtual-real bidirectional driven verification closed-loop is built using digital twin technology. Verification results based on the fuselage panel assembly of a certain type of civil aircraft show that the proposed method significantly improves the automatic assembly accuracy of stringers, with the one-time assembly and adjustment success rate of stringers increased by 24% compared with traditional methods. It also enables real-time inspection of drilling and riveting quality, achieving an accuracy rate of 98% in identifying continuous drilling and riveting defects. By constructing and evolving a domain-specific knowledge base, this study deeply drives the full-process closed-loop from deviation causal tracing to twin verification, and realizes a paradigm shift in manufacturing decision-making from experience-driven to model cognition-driven.

Key words: industrial large models, digital twin, deviation accumulation and transmission, Bayesian causal inference, process parameter optimization

CLC Number:

TH166

XU Hongwei, LIU Lilan, ZHANG Jie, QIN Wei, XING Hongwen, WANG Wei, LIU Siren, Lü Youlong. AI Twin Control Method System for Aeronautical Intelligent Manufacturing Driven by Industrial Large Models[J]. Journal of Mechanical Engineering, 2026, 62(5): 61-73.

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

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260228

http://www.cjmenet.com.cn/EN/Y2026/V62/I5/61

References

[1] 张永亮，张辉，李智，等. 飞机柔性装配技术研究现状与发展趋势[J]. 制造技术与机床，2025(6):73-84. ZHANG Yongliang，ZHANG Hui，LI Zhi，et al. Research status and development trend of aircraft flexible assembly technology[J]. Manufacturing Technology & Machine Tool，2025(6):73-84.
[2] ZHU J Y，QIN W，HU J H，et al. Influential process nodes identification strategy for aircraft assembly system based on complex network and improved PageRank[J]. Adv Eng Inform，2023，58:1-8.
[3] 常笑，贾晓亮. 知识驱动的飞机大修数字孪生车间主动管控方法[J]. 计算机集成制造系统，2021，27(2):390-402. CHANG Xiao，JIA Xiaoliang. Knowledge-driven active management and control method for digital twin workshop of aircraft overhaul[J]. Computer Integrated Manufacturing Systems，2021，27(2):390-402.
[4] 陶飞，张辰源，刘蔚然，等. 数字工程及十个领域应用展望[J]. 机械工程学报，2023，59(13):193-215. TAO Fei，ZHANG Chenyuan，LIU Weiran，et al. Digital engineering and its application prospects in ten fields[J]. Journal of Mechanical Engineering，2023，59(13):193-215.
[5] 宋学官，来孝楠，何西旺，等. 重大装备形性一体化数字孪生关键技术[J]. 机械工程学报，2022，58(10):298-325. SONG Xueguan，LAI Xiaonan，HE Xiwang，et al. Key technologies of shape-performance integrated digital twin for major equipment[J]. Journal of Mechanical Engineering，2022，58(10):298-325.
[6] 施佳宏，刘晓军，刘庭煜，等. 面向生产线仿真的数字孪生逻辑模型构建方法[J]. 计算机集成制造系统，2022，28(2):442-54. SHI Jiahong，LIU Xiaojun，LIU Tingyu，et al. Construction method of digital twin logic model for production line simulation[J]. Computer Integrated Manufacturing Systems，2022，28(2):442-54.
[7] 张洁，高亮，秦威，等. 大数据驱动的智能车间运行分析与决策方法体系[J]. 计算机集成制造系统，2016，22(5):1220-1228. ZHANG Jie，GAO Liang，QIN Wei，et al. Big data-driven operation analysis and decision-making method system for smart workshop[J]. Computer Integrated Manufacturing Systems，2016，22(5):1220-1228.
[8] SUN Y N，QIN W，ZHUANG Z L，et al. An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference[J]. J Intell Manuf，2021，32(7):2007-2021.
[9] HU J H，ZHU J Y，SUN Y N，et al. Network-based two-stage robust scheduling strategy for the aircraft assembly system with uncertain duration[J]. Comput Ind Eng，2024，188:1-15.
[10] 陶飞，刘蔚然，刘检华，等. 数字孪生及其应用探索[J]. 计算机集成制造系统，2018，24(1):1-18. TAO Fei，LIU Weiran，LIU Jianhua，et al. Digital twin and its application exploration[J]. Computer Integrated Manufacturing Systems，2018，24(1):1-18.
[11] 陶飞，张萌，程江峰，等. 数字孪生车间——一种未来车间运行新模式[J]. 计算机集成制造系统，2017，23(1):1-9. TAO Fei，ZHANG Meng，CHENG Jiangfeng，et al. Digital twin workshop—A new operation mode for future workshops[J]. Computer Integrated Manufacturing Systems，2017，23(1):1-9.
[12] 陶飞，刘蔚然，张萌，等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统，2019，25(01):1-18. TAO Fei，LIU Weiran，ZHANG Meng，et al. Five-dimensional model of digital twin and its application in ten fields[J]. Computer Integrated Manufacturing Systems，2019，25(1):1-18.
[13] LI W，CHEN L L，CHEN X H，et al. A data-and- knowledge-driven framework for automated generation of asphalt pavement maintenance strategies[J]. Int J Pavement Eng，2025，26(1):1-14.
[14] EGHBALI P，SATIR O B，BECCE F，et al. Causal associations between scapular morphology and shoulder condition estimated with Bayesian statistics[J]. Comput Meth Prog Bio，2025，263:1-11.
[15] XU C，HAYASHI N，INUIGUCHI M. Enhancing reinforcement learning controllers with GAN-generated data and transfer learning[J]. Sice J Control Meas，2025，18(1):1-15.
[16] CHEN J P，HUANG S H，WANG X W，et al. Perception-decision-execution coordination mechanism driven dynamic autonomous collaboration method for human-like collaborative robot based on multimodal large language model[J]. Robot Cim-Int Manuf，2026，98:1-15.
[17] 郭飞燕，刘检华，邹方，等. 数字孪生驱动的装配工艺设计现状及关键实现技术研究[J]. 机械工程学报，2019，55(17):110-32. GUO Feiyan，LIU Jianhua，ZOU Fang，et al. Research on status and key implementation technologies of digital twin-driven assembly process design[J]. Journal of Mechanical Engineering，2019，55(17):110-32.
[18] 黄彬彬，张映锋，黄博，等. 数字孪生驱动的复杂产品智能运维服务体系与核心技术[J]. 机械工程学报，2022，58(12):250-60. HUANG Binbin，ZHANG Yingfeng，HUANG Bo，et al. Digital twin-driven intelligent operation and maintenance service system and core technologies for complex products[J]. Journal of Mechanical Engineering，2022，58(12):250-260.
[19] 任磊，贾子翟，赖李媛君，等. 数据驱动的工业智能:现状与展望[J]. 计算机集成制造系统，2022，28(7):1913-1939. REN Lei，JIA Zizhai，LAI Liyuanjun，et al. Data-driven industrial intelligence:Status and prospects[J]. Computer Integrated Manufacturing Systems，2022，28(7):1913-39.
[20] 陶飞，张贺，戚庆林，等. 数字孪生模型构建理论及应用[J]. 计算机集成制造系统，2021，27(1):1-15. TAO Fei，ZHANG He，QI Qinglin，et al. Theory and application of digital twin model construction[J]. Computer Integrated Manufacturing Systems，2021，27(1):1-15.
[21] 闫纪红，姬思阳. 大数据驱动的车间数字孪生模型构建方法[J]. 机械工程学报，2023，59(12):62-77. YAN Jihong，JI Siyang. Big data-driven construction method of workshop digital twin model[J]. Journal of Mechanical Engineering，2023，59(12):62-77.
[22] 王昊琪，李旭鹏，李浩，等. 人机环境动静融合的数字孪生车间快速建模方法[J]. 航空制造技术，2024，67(11):14-22. WANG Haoqi，LI Xupeng，LI Hao，et al. Rapid modeling method of digital twin workshop with dynamic and static integration of human-machine-environment[J]. Aeronautical Manufacturing Technology，2024，67(11):14-22.
[23] JI X F，GONG F M，WANG N L，et al. Pixel-level semantic parsing in complex industrial scenarios using large vision-language models[J]. Inform Fusion，2025，116:1-14.
[24] JOSE S，NGUYEN K T P，MEDJAHER K，et al. Advancing multimodal diagnostics:Integrating industrial textual data and domain knowledge with large language models[J]. Expert Syst Appl，2024，255:1-13.
[25] LIAN Z，ZHOU Z J，HU C H，et al. Interpretable large-scale belief rule base for complex industrial systems modeling with expert knowledge and limited data[J]. Adv Eng Inform，2024，62:1-15.
[26] 李浩，陶飞，王昊琪，等. 基于数字孪生的复杂产品设计制造一体化开发框架与关键技术[J]. 计算机集成制造系统，2019，25(6):1320-36. LI Hao，TAO Fei，WANG Haoqi，et al. Digital twin-based integrated development framework and key technologies for complex product design and manufacturing[J]. Computer Integrated Manufacturing Systems，2019，25(6):1320-36.
[27] 庄存波，刘检华，熊辉，等. 产品数字孪生体的内涵、体系结构及其发展趋势[J]. 计算机集成制造系统，2017，23(4):753-68. ZHUANG Cunbo，LIU Jianhua，XIONG Hui，et al. Connotation，architecture and development trend of product digital twin[J]. Computer Integrated Manufacturing Systems，2017，23(4):753-68.
[28] XU Q H，GAO P J，WANG J L，et al. AKGNN-PC:An assembly knowledge graph neural network model with predictive value calibration module for refrigeration compressor performance prediction with assembly error propagation and data imbalance scenarios[J]. Adv Eng Inform，2024，60:1-11.
[29] ZHOU B，LI X Y，LIU T Y，et al. CausalKGPT:Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing[J]. Adv Eng Inform，2024，59:1-16.
[30] NODA A，ISOZAKI T. Practically effective adjustment variable selection in causal inference[J]. J Phys- Complexity，2025，6(1):1-13.
[31] XU H W，QIN W，SUN Y N，et al. Attention mechanism-based deep learning for heat load prediction in blast furnace ironmaking process[J]. J Intell Manuf，2024，35(3):1207-20.
[32] XU H W，ZHANG Q H，SUN Y N，et al. A fast ramp-up framework for wafer yield improvement in semiconductor manufacturing systems[J]. J Manuf Syst，2024，76:222-33.
[33] ZHANG Q，XU C，LI J，et al. LLM-TSFD:An industrial time series human-in-the-loop fault diagnosis method based on a large language model[J]. Expert Syst Appl，2025，264:1-23.