Research on Intelligent Design Method for Gear Transmission Based on Large Language Models and Multi-agent Collaboration

doi:10.3901/JME.260490

Abstract

Abstract: Gear transmission is a fundamental component in industries such as aerospace, wind power, and new energy. Its design process requires multidisciplinary coupled analyses and multi-constraint optimization involving materials, friction, geometry, and other factors. Artificial intelligence (AI), particularly large language models (LLMs), is driving a new wave of technological innovation and has been broadly applied in domains such as biomedicine and advanced materials. However, due to the stringent demands for specification adherence and robustness, mature LLM applications in gear transmission design remain undeveloped. This study introduces an intelligent gear transmission design methodology leveraging LLMs and collaborative multi-agent systems, employing a hierarchical multi-agent system (HMAS) architecture integrated with state-oriented graphs and ReAct reasoning methods. This approach enables comprehensive process management and parameter coordination in complex gear transmission design workflows, significantly enhancing output reliability and interpretability. The proposed methodology autonomously executes the entire design workflow—including requirements analysis, configuration recommendation, task reasoning and planning, component calculation and verification, system integration, multi-objective optimization, report generation, and CAD modeling—completing the overall scheme design, which involves over 800 parameters related to component materials, processes, structural design, and performance evaluation, in less than 10 minutes. Evaluation using various complex gear transmission cases in aerospace, wind power, and new energy vehicles demonstrates that the proposed method outperforms traditional manual design in terms of both performance and robustness. Additionally, it effectively resolves prevalent issues observed in mainstream LLMs, such as inaccurate parameter calculations and insufficient innovation and justification in design. Overall, the capability of this approach exceeds that of leading LLMs by 23.91% to 53.60%, establishing a new paradigm for AI-driven industrial design.

Key words: gear transmission, large language models, multi-agent systems, artificial intelligence

CLC Number:

TH132

LI Yang, LU Zehua, LIU Guiyuan, ZHU Caichao, LIU Huaiju. Research on Intelligent Design Method for Gear Transmission Based on Large Language Models and Multi-agent Collaboration[J]. Journal of Mechanical Engineering, 2026, 62(15): 111-125.

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

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260490

http://www.cjmenet.com.cn/EN/Y2026/V62/I15/111

References

[1] 刘怀举，张博宇，朱才朝，等. 齿轮接触疲劳理论研究进展[J]. 机械工程学报，2022，58(3)：95-120. LIU Huaiju，ZHANG Boyu，ZHU Caichao，et al. State of art of gear contact fatigue theories[J]. Journal of Mechanical Engineering，2022，58(3)：95-120.
[2] CHEN Taimin，ZHU Caichao，CHEN Jinxiao，et al. A review on gear scuffing studies：Theories，experiments and design[J]. Tribology International，2024，196：109741.
[3] LI Yang，LIU Huaiju，CHEN Yiming，et al. Probabilistic gear fatigue life prediction based on physics-informed transformer[J]. Expert Systems with Applications，2024，249：123882.
[4] LU Zehua，CHEN Yiming，LIU Huaiju，et al. A high-power-density design method for polymer gear systems via an adaptive non-dominated sorting genetic algorithm III and surrogate sub-models[J]. Materials & Design，2024，240：112875.
[5] ETESAM Y，CHEONG H，ATAEI M，et al. Deep generative model for mechanical system configuration design[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2025，39(16)：16496-16504.
[6] CAN E，BOZCA M. Optimising the Geometric parameters of a gear in a tractor transmission under constraints using kisssoft[J]. Acta Mechanica et Automatica，2023，17：145-159.
[7] LI Cheng，LI Jianzhong，MU Song，et al. Simulation analysis of the tooth modification about wind power gearbox based on Romax software[J]. IOP Conf Ser：Mater Sci Eng，2020，788：012005.
[8] JAYAKIRAN R，PANDU R. Development of knowledge based parametric CAD modeling system for spur gear：An approach[J]. Alexandria Engineering Journal，2018，57：3139-3149.
[9] LIU Wanyi，CHEN Long，TANG Zhenzhou. large language model aided multi-objective evolutionary algorithm：A low-cost adaptive approach[J]. ArXiv Preprint，2024，arXiv:2410.02301.
[10] FRENKEL ME，EMARA H. ChatGPT-3.5 and -4.0 and mechanical engineering：Examining performance on the FE mechanical engineering and undergraduate exams[J]. Computer Applications in Engineering Education，2024，32：e22781.
[11] LIU Beiming，CUI Zhizhou，HU Siteng，et al. LLM evaluation based on aerospace manufacturing expertise：Automated generation and multi-model question answering[J]. ArXiv Preprint，2025，arXiv:2501.17183.
[12] YAMADA Y，BAO Y，LAMPINEN AK，et al. Evaluating spatial understanding of large language models[J]. ArXiv Preprint，2023，arXiv：2310.14540.
[13] ZHANG Ran，LI Hongwei，QIAN Xinyuan，et al. On large language models safety，security，and privacy：A survey[J]. Journal of Electronic Science and Technology，2025，23：100301.
[14] FERRAG MA，TIHANYI N，DEBBAH M. From LLM Reasoning to autonomous AI agents：A comprehensive review[J]. ArXiv Preprint，2025，arXiv：2504.19678.
[15] PANDEY S，XU R，WANG W，et al. OpenFOAMGPT：A RAG-augmented LLM agent for OpenFOAM-based computational fluid dynamics[J]. Physics of Fluids，2025，37：035120.
[16] 陈庆，柳雨生，段练达，等. 大语言模型融合知识图谱的风电运维问答系统研究[J]. 综合智慧能源，2024，46(9)：61-68. CHEN Qing，LIU Yusheng，DUAN Lianda，et al. Research on a wind power operation and maintenance Q&A system based on large language models and knowledge graphs[J]. Integrated Intelligent Energy，2024，46(9)：61-68.
[17] TAO Laifa，LIU Haifei，NING Guoao，et al. LLM-based framework for bearing fault diagnosis[J]. Mechanical Systems and Signal Processing，2025，224：112127.
[18] ZHANG Hua，ZHANG Meihang，YAN Wei，et al. Energy consumption prediction method for variable conditions machining based on hybrid fine-tuning large model[J]. Computer Integrated Manufacturing Systems，202531(12)：4441.
[19] LIANG Guannan，TONG Qianqian. LLM-Powered AI agent systems and their applications in industry[J]. ArXiv Preprint，2025，arXiv：2505.16120.
[20] YAO Shunyu，ZHAO J，YU Dian，et al. ReAct：Synergizing reasoning and acting in language models[J]. ArXiv Preprint，2023，arXiv：2210.03629.
[21] KANG Y，KIM J. ChatMOF：An artificial intelligence system for predicting and generating metal-organic frameworks using large language models[J]. Nature communications，2024，15(1)：4705.
[22] ZHANG Huan，SONG Yu，HOU Ziyu，et al. HoneyComb：A flexible LLM-based agent system for materials science[J]. ArXiv Preprint，2024，arXiv：2409.00135.
[23] JADHAV Y，FARIMANI AB. Large language model agent as a mechanical designer[J]. ArXiv Preprint，2024，arXiv：2404.17525.
[24] FAN Haolin，HUANG Junlin，XU Jilong，et al. AutoMEX：Streamlining material extrusion with AI agents powered by large language models and knowledge graphs[J]. Materials & Design，2025，251：113644.
[25] YAVARTANOO M，HONG S，NESHATAVAR R，et al. Text2CAD：Text to 3D CAD generation via technical drawings[J]. ArXiv Preprint，2024，arXiv：2411.06206.
[27] WANG Z，LO FP-W，CHEN Q，et al. An LLM-enabled multi-agent autonomous mechatronics design framework[J]. ArXiv Preprint，2025，arXiv：2504.14681.
[28] TAKERNGSAKSIRI W，PASUKSMIT J，THONGT- ANUNAM P，et al. Human-in-the-loop software development agents[J]. ArXiv Preprint，2024，arXiv：2411.12924.
[29] LI Feiyang，FANG Peng，SHI Zhan，et al. CoT-RAG：Integrating chain of thought and retrieval-augmented generation to enhance reasoning in large language models[J]. ArXiv Preprint，2025，arXiv：2504.13534.
[30] 刘怀举，张洪春，魏沛堂，等. 高性能齿轮传动数据库软件设计与开发[J]. 计算机集成制造系统，2023，29(8)：2513-2523. LIU Huaiju，ZHANG Hongchun，WEI Peitang，et al. Design and development of high performance gear transmission database software[J]. Computer Integrated Manufacturing Systems，2023，29(8)：2513-2523.
[31] WU Qingyun，BANSAL G，ZHANG Jieyu，et al. AutoGen：Enabling next-gen LLM applications via multi-agent conversations[J]. ArXiv Preprint，2023，arXiv：2308.08155.
[32] PELLURU K. LangChain & LangGraph in production：Architectures for multi- agent LLM systems[J]. Journal of Data and Digital Innovation，2025，2(3)：1-9.
[33] YANG An，LI Anfeng，YANG Baosong，et al. Qwen3 technical report[J]. ArXiv Preprint，2025，arXiv：2505.09388.
[34] ALHANAHNAH M，HASAN R，XU L，et al. An empirical evaluation of pre-trained large language models for repairing declarative formal specifications[J]. Empirical Software Engineering，2025，30(5)：1-38.
[35] ISO 6336-2019. Calculation of load capacity of spur and helical gears[S]. International Organization for Standardization，2019.
[36] ISO 10300-2023. Calculation of load capacity of bevel gears[S]. International Organization for Standardization，2023.
[37] ISO 281-2007. Rolling bearings-Dynamic load ratings and rating life[S]. International Organization for Standardization，2007.
[38] ISO 16281-2008. Rolling bearings-Methods for calculating the modified reference rating life for universally loaded bearings[S]. International Organization for Standardization，2008.
[39] ISO 76-2006. Rolling bearings-Static load ratings[S]. International Organization for Standardization，2006.
[40] DIN 743-2012. Calculation of load capacity of shafts and axles[S]. German Institute for Standardization，2012.
[41] ISO 4156-2005. Straight cylindrical involute splines-Metric module，side fit[S]. International Organization for Standardization，2005.
[42] GB/T 17855-2017. 花键承载能力计算方法[S]. 中国国家标准化管理委员会，2017. GB/T 17855-2017. Calculation of load capacity of spline[S]. State Administration of the People’s Republic of China，2017.
[43] DIN 6885-2021. Drive type fastenings without taper action，parallel keys，keyways-Deep pattern[S]. German Institute for Standardization，2021.
[44] 成大先. 机械设计手册：第6版[M]. 北京：化学工业出版社，2017. CHENG Daxian. Mechanical design handbook (in Chinese). 6th ed[M]. Beijing：Chemical Industry Press; 2017.
[45] BEDAKA AK，LIN C-Y. CAD-based robot path planning and simulation using OPEN CASCADE[J]. Procedia Computer Science，2018，133：779-785.
[46] GB/T 10609.1-2008. 技术制图标题栏[S]. 中国国家标准化管理委员会，2008. GB/T 10609.1-2008. Technical drawings-Title blocks[S]. State Administration of the People’s Republic of China，2008.
[47] REIN D，HOU BL，STICKLAND AC，et al. GPQA：A graduate-level google-proof q&a benchmark[C]//First Conference on Language Modeling，2024.