大模型时代的智能设计与制造综述：研究现状、技术挑战及应用展望

doi:10.3901/JME.260645

机械工程学报 ›› 2026, Vol. 62 ›› Issue (15): 1-23.doi: 10.3901/JME.260645

• 特邀专栏：大模型时代的智能设计与制造 • 上一篇

扫码分享

大模型时代的智能设计与制造综述：研究现状、技术挑战及应用展望

郏维强^1,2, 刘鑫³, 彭翔⁴, 王勋¹, 刘振宇², 刘惠², 朱伟东⁵, 程锦⁵, 谭建荣^2,5

1. 浙江工商大学计算机科学与技术学院杭州 310018;
2. 浙江大学计算机辅助设计与图形系统全国重点实验室杭州 310058;
3. 长沙理工大学工程车辆安全性设计与可靠性技术湖南省重点实验室长沙 410114;
4. 浙江工业大学机械工程学院杭州 310023;
5. 浙江大学流体动力基础件与机电系统全国重点实验室杭州 310058

收稿日期:2025-09-10 修回日期:2026-03-18 发布日期:2026-10-13
作者简介:郏维强，男，1986年出生，博士，研究员。主要研究方向为智能设计与制造、数字孪生、人机交互等。E-mail：wqjia@zjgsu.edu.cn;刘鑫，男，1981年出生，博士，教授。主要研究方向为可靠性设计与优化设计等。E-mail：lxym810205@163.com;彭翔，男，1989年出生，博士，教授。主要研究方向为数字化设计与优化设计等。E-mail：pengxiang@zjut.edu.cn;王勋，男，1967年出生，博士，教授。主要研究方向为智能图形计算、数字孪生等。E-mail：wx@zjgsu.edu.cn;刘振宇(通信作者)，男，1974年出生，博士，教授。主要研究方向为数字化智能化设计、数字孪生等。E-mail：liuzy@zju.edu.cn;刘惠，男，1994年出生，博士，助理研究员。主要研究方向为可靠性设计与装备健康管理。E-mail：liuhui2017@zju.edu.cn;朱伟东，男，1977年出生，博士，教授。主要研究方向为飞机数字化装配、复合材料构件制造工艺等。E-mail：wdzhu@zju.edu.cn;程锦，女，1978年出生，博士，教授。主要研究方向为数智化设计与制造，不确定性分析与优化等。E-mail：jcheng@zju.edu.cn;谭建荣，男，1954年出生，博士，教授，中国工程院院士。主要研究方向为数据建模与虚拟现实，数字设计与智能制造等。E-mail：egi@zju.edu.cn
基金资助:
国家重点研发计划(2024YFB3309503)、浙江省应用基础研究计划“领雁”(2026C02A1243)、浙江大学计算机辅助设计与图形系统全国重点实验室开放课题(A2503)和国家自然科学基金(52505288)资助项目。

A Survey of Intelligent Design and Manufacturing in the Era of Large Models: Research Status, Technical Challenges, and Application Prospects

JIA Weiqiang^1,2, LIU Xin³, PENG Xiang⁴, WANG Xun¹, LIU Zhenyu², LIU Hui², ZHU Weidong⁵, CHENG Jin⁵, TAN Jianrong^2,5

1. School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou 310018;
2. State Key Laboratory of Computer-Aided Design and Computer Graphic, Zhejiang University, Hangzhou 310058;
3. Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114;
4. School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023;
5. State Key Laboratory of Fluid Power Components and Mechatronic Systems, Zhejiang University, Hangzhou 310058

Received:2025-09-10 Revised:2026-03-18 Published:2026-10-13

摘要/Abstract

摘要： 大模型时代，以生成式预训练为基础、后训练对齐和工具增强为支撑的智能技术体系正加速向工业垂直领域渗透，推动智能设计与制造由“局部数字化”迈向“全局认知智能化”。系统综述相关技术在智能设计与制造领域的研究进展，归纳形成多模态工程语义对齐、工程知识增强与规则/机理注入、概念生成式建模与可编辑表示、性能/可靠性一体化推演评估、显式推理优化与多智能体协同执行五个研究方向。在此基础上，从系统级工业应用视角提炼跨域工程语义状态空间不匹配、知识与机理约束可执行性薄弱、创成构型向制造驱动长序列映射困难、极端工况性能/可靠性推演物理一致性不足、长链条协同优化与自治执行失稳五类关键瓶颈。进一步，提出制造约束反向嵌入的方案创成、约束动态注入的知识服务与辅助决策、自然语言驱动的跨阶段操作序列转化、物理先验增强的性能/可靠性预测，以及显式推理引导的协同优化与资源自治重构等潜在应用形态。最后，结合交互式CAD生成模型和航空制造知识垂域大模型案例，揭示大模型时代的智能技术由算法能力向工程系统能力转化的路径。

关键词: 大模型技术体系, 智能设计与制造, 多模态语义对齐, 工程知识增强, 生成式建模, 性能/可靠性评估, 多智能体协同

Abstract: In the era of large models, intelligent technology systems underpinned by generative pretraining and supported by post-training alignment and tool augmentation are rapidly penetrating industrial verticals, driving intelligent design and manufacturing from “local digitalization” toward “global cognitive intelligence.” This study systematically reviews recent progress in large-model-enabled intelligent design and manufacturing, and summarizes five major research directions: multimodal engineering semantic alignment, engineering knowledge augmentation with rule/mechanism injection, conceptual generative modeling with editable representations, integrated performance/reliability inference and evaluation, and explicit reasoning optimization with multi-agent collaborative execution. From the perspective of system-level industrial applications, five key bottlenecks are further identified: mismatch of cross-domain engineering semantic state spaces, insufficient executability of knowledge and mechanism constraints, difficulty in mapping generative configurations to manufacturing-driven long operation sequences, inadequate physical consistency in performance/reliability inference under extreme conditions, and instability in long-chain collaborative optimization and autonomous execution. Accordingly, several potential application forms are discussed, including manufacturing-constraint-embedded design synthesis, constraint-dynamic knowledge services and decision support, natural-language-driven cross-stage operation sequence transformation, physics-prior-enhanced performance/reliability prediction, and explicit-reasoning-guided collaborative optimization and autonomous resource reconfiguration. Finally, case studies on an interactive CAD generation model and an aviation manufacturing domain-specific large model are presented to reveal the pathway through which large-model technologies evolve from algorithmic capabilities to engineering system capabilities.

Key words: large model technology system, intelligent design and manufacturing, multimodal alignment, knowledge augmentation, generative modeling, performance/reliability assessment, multi-agent collaboration

中图分类号:

TP301

郏维强, 刘鑫, 彭翔, 王勋, 刘振宇, 刘惠, 朱伟东, 程锦, 谭建荣. 大模型时代的智能设计与制造综述：研究现状、技术挑战及应用展望[J]. 机械工程学报, 2026, 62(15): 1-23.

JIA Weiqiang, LIU Xin, PENG Xiang, WANG Xun, LIU Zhenyu, LIU Hui, ZHU Weidong, CHENG Jin, TAN Jianrong. A Survey of Intelligent Design and Manufacturing in the Era of Large Models: Research Status, Technical Challenges, and Application Prospects[J]. Journal of Mechanical Engineering, 2026, 62(15): 1-23.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260645

http://www.cjmenet.com.cn/CN/Y2026/V62/I15/1

参考文献

[1] 澎湃新闻. ChatGPT火爆全球，这些互联网巨头坐不住了[EB/OL]. (2023-02-07)[2026-05-17]. https://www.thepaper.cn/newsDetail_forward_21830301. The Paper. ChatGPT goes viral globally，and these Internet giants can no longer sit still[EB/OL]. (2023-02-07)[2026-05-17]. https://www.thepaper.cn/newsDetail_forward_21830301.
[2] KINGMA D P，WELLING M. Auto-encoding variational bayes[EB/OL]. (2013-12-20)[2026-05-17]. https://arxiv. org/abs/1312.6114.
[3] GOODFELLOW I J，POUGET-ABADIE J，MIRZA M，et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems. 2014，1-27.
[4] HO J，JAIN A，ABBEEL P. Denoising diffusion probabilistic models[C]//Advances in Neural Information Processing Systems，2020，33：6840-6851.
[5] MIRZA M，OSINDERO S. Conditional generative adversarial nets[EB/OL]. (2014-11-06)[2026-05-17]. https://arxiv.org/abs/1411.1784.
[6] DEVLIN J，CHANG M W，LEE K，et al. BERT：Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies. 2019：4171-4186.
[7] RADFORD A，NARASIMHAN K，SALIMANS T，et al. Improving language understanding by generative pre-training[R]. OpenAI，2018.
[8] PETERS M E，NEUMANN M，IYYER M，et al. Deep contextualized word representations[EB/OL]. (2018-02-15) [2026-05-17]. https://arxiv.org/abs/1802.05365.
[9] LIU Y，OTT M，GOYAL N，et al. RoBERTa：A robustly optimized BERT pretraining approach[EB/OL]. (2019-07- 26)[2026-05-17]. https://arxiv.org/abs/1907.11692.
[10] TOUVRON H，LAVRIL T，IZACARD G，et al. LLaMA：Open and efficient foundation language models[EB/OL]. (2023-02-27)[2026-05-17]. https://arxiv.org/abs/2302.13971.
[11] LEWIS P，PEREZ E，PIKTUS A，et al. Retrieval- augmented generation for knowledge-intensive NLP tasks[C]//Advances in Neural Information Processing Systems. 2020，33：9459-9474.
[12] GRAVES A，WAYNE G，DANIHELKA I. Neural Turing machines[EB/OL]. (2014-10-20)[2026-05-17]. https:// arxiv.org/abs/1410.5401.
[13] BORGEAUD S，MENSCH A，HOFFMANN J，et al. Improving language models by retrieving from trillions of tokens[C]//International Conference on Machine Learning. PMLR，2022：2206-2240.
[14] RIEGEL R，GRAY A，LUUS F，et al. Logical neural networks[EB/OL]. (2020-06-23)[2026-05-17]. https:// arxiv.org/abs/2006.13155.
[15] YAO L，MAO C，LUO Y. KG-BERT：BERT for knowledge graph completion[EB/OL]. (2019-09-06) [2026-05-17]. https://arxiv.org/abs/1909.03193.
[16] WEI J，WANG X，SCHUURMANS D，et al. Chain-of-thought prompting elicits reasoning in large language models[C]//Advances in Neural Information Processing Systems. 2022，35：24824-24837.
[17] YAO S，YU D，ZHAO J，et al. Tree of thoughts：Deliberate problem solving with large language models[C]//Advances in Neural Information Processing Systems. 2023，36：11809-11822.
[18] BESTA M，BLACH N，KUBICEK A，et al. Graph of thoughts：Solving elaborate problems with large language models[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024，38(16)：17682-17690.
[19] HUANG W，ABBEEL P，PATHAK D，et al. Language models as zero-shot planners：Extracting actionable knowledge for embodied agents[C]//International Conference on Machine Learning. PMLR，2022：9118-9147.
[20] YE H，GUI H，ZHANG A，et al. Beyond isolation：Multi-agent synergy for improving knowledge graph construction[C]//China Conference on Knowledge Graph and Semantic Computing. Singapore：Springer Nature，2024：69-81.
[21] YAO S，ZHAO J，YU D，et al. ReAct：Synergizing reasoning and acting in language models[C]//The Eleventh International Conference on Learning Representations. 2023.
[22] SCHICK T，DWIVEDI-YU J，DESSÌ R，et al. Toolformer：Language models can teach themselves to use tools[C]//Advances in Neural Information Processing Systems. 2023，36：68539-68551.
[23] KARPAS E，ABEND O，BELINKOV Y，et al. MRKL systems：A modular，neuro-symbolic architecture that combines large language models，external knowledge sources and discrete reasoning[EB/OL]. (2022-05-01) [2026-05-17]. https://arxiv.org/abs/2205.00445.
[24] WU Q，BANSAL G，ZHANG J，et al. AutoGen：Enabling next-gen LLM applications via multi-agent conversations[C]//First Conference on Language Modeling. 2024：1-7.
[25] WANG G，XIE Y，JIANG Y，et al. Voyager：An open-ended embodied agent with large language models[EB/OL]. (2023-05-25)[2026-05-17]. https://arxiv.org/abs/2305.16291.
[26] STRATTON J. An introduction to Microsoft Copilot[M]. Berkeley：Apress，2024.
[27] RANE N，CHOUDHARY S，RANE J. Gemini versus ChatGPT：Applications，performance，architecture，capabilities，and implementation[J]. Journal of Applied Artificial Intelligence，2024，5(1)：69-93.
[28] ZHU Q，GUO D，SHAO Z，et al. DeepSeek-Coder-V2：Breaking the barrier of closed-source models in code intelligence[EB/OL]. (2024-06-17)[2026-05-17]. https:// arxiv.org/abs/2406.11931.
[29] LUTZE R，WALDHÖR K. Generating specifications from requirements documents for smart devices using large language models (LLMs)[C]//International Conference on Human-Computer Interaction. Cham：Springer Nature Switzerland，2024：94-108.
[30] ARVIDSSON S, AXELL J. Prompt engineering guidelines for LLMs in requirements engineering[R]. Lund：Lund University，2023.
[31] MA Q，PENG W，YANG C，et al. What should we engineer in prompts? Training humans in requirement-driven LLM use[J]. ACM Transactions on Computer-Human Interaction，2025，32(4)：1-27.
[32] MIRABITO Y，LIU X，GOUCHER-LAMBERT K. Characterizing design rationales using computational linguistics and human evaluations[J]. Journal of Mechanical Design，2025，147(12)：121404.
[33] YUE G，LIU J，ZHANG W. Extracting design rationale in technical literature to provide inspirational design stimuli[J]. Journal of Mechanical Design，2025，147(7)：071401.
[34] DORIS A C，GRANDI D，TOMICH R，et al. DesignQA：A multimodal benchmark for evaluating large language models' understanding of engineering documentation[J]. Journal of Computing and Information Science in Engineering，2025，25(2)：021009.
[35] WU S，KHASAHMADI A H，KATZ M，et al. CADVLM：Bridging language and vision in the generation of parametric CAD sketches[C]//European Conference on Computer Vision. Cham：Springer Nature Switzerland，2024：368-384.
[36] PICARD C，EDWARDS K M，DORIS A C，et al. From concept to manufacturing：Evaluating vision-language models for engineering design[J]. Artificial Intelligence Review，2025，58(9)：288.
[37] KARADENIZ A S，MALLIS D，MEJRI N，et al. PICASSO：A feed-forward framework for parametric inference of CAD sketches via rendering self-supervision[C]//2025 IEEE/CVF Winter Conference on Applications of Computer Vision. IEEE，2025：6475-6484.
[38] 董清钰，梅标，傅云，等. 集成改进半监督分割方法的航空发动机短舱声衬孔检测系统[J]. 光学精密工程，2024，32(23)：3457-3468. DONG Qingyu，MEI Biao，FU Yun，et al. Aero-engine nacelle acoustic liner hole detection system integrating improved semi-supervised segmentation method[J]. Optics and Precision Engineering，2024，32(23)：3457-3468.
[39] MCKINNEY M，GARLAND A，CILLESSEN D，et al. Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring[J]. Journal of Manufacturing Systems，2025，78：271-282.
[40] ZAMIELA C，JIANG Z，STOKES R，et al. Deep multi-modal U-Net fusion methodology of thermal and ultrasonic images for porosity detection in additive manufacturing[J]. Journal of Manufacturing Science and Engineering，2023，145(6)：061009.
[41] SHENG Y，ZHANG G，ZHANG Y，et al. A multimodal data sensing and feature learning-based self-adaptive hybrid approach for machining quality prediction[J]. Advanced Engineering Informatics，2024，59：102324.
[42] XIE J，GUO Y，LIU D，et al. A multimodal fusion method for soldering quality online inspection[J]. Journal of Intelligent Manufacturing，2025，36(5)：3271-3284.
[43] BIANCHINI F，CALAMO M，MARINACCI M，et al. Automating industrial quality control：A multimodal LLM and RAG framework for anomaly detection[C]//IFIP International Conference on Artificial Intelligence Applications and Innovations. Cham：Springer Nature Switzerland，2025：253-266.
[44] FANG Q，XIONG G，WANG F，et al. Large language models as few-shot defect detectors for additive manufacturing[C]//2024 China Automation Congress. IEEE，2024：6900-6905.
[45] ZHAO S, ZHANG G, LIU S, et al. Interpretable verification mechanism for trustworthy industrial large model in intelligent manufacturing[J]. Engineering，2025：1-22.
[46] ZHANG D，LIU Z，JIA W，et al. Dual attention graph convolutional network for relation extraction[J]. IEEE Transactions on Knowledge and Data Engineering，2024，36(2)：530-543.
[47] 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. Review on research status and application prospects of knowledge graph in intelligent manufacturing[J]. Journal of Mechanical Engineering，2021，57(5)：90-113.
[48] HUANG Y，YU S，CHU J，et al. Design knowledge graph-aided conceptual product design approach based on joint entity and relation extraction[J]. Journal of Intelligent & Fuzzy Systems，2023，44(3)：5333-5355.
[49] WANG R，SUN Y，PENG T，et al. A knowledge graph-aided decision guidance method for product conceptual design[J]. Journal of Engineering Design，2025，36(2)：216-255.
[50] HUANG Y，WANG R，LI Y，et al. Knowledge graph-driven methodology for complex product architecture solution generation and simulation verification[J]. Advanced Engineering Informatics，2025，68：103590.
[51] GARCIA C I，DIBATTISTA M A，LETELIER T A，et al. Framework for LLM applications in manufacturing[J]. Manufacturing Letters，2024，41：253-263.
[52] XIA L，LI C，ZHANG C，et al. Leveraging error-assisted fine-tuning large language models for manufacturing excellence[J]. Robotics and Computer-Integrated Manufacturing，2024，88：102728.
[53] KERNAN FREIRE S，WANG C，FOOSHERIAN M，et al. Knowledge sharing in manufacturing using LLM-powered tools：User study and model benchmarking[J]. Frontiers in Artificial Intelligence，2024，7：1293084.
[54] ZHOU B，LI X，LIU T，et al. CausalKGPT：Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing[J]. Advanced Engineering Informatics，2024，59：102333.
[55] ÁLVARO J A H，BARREDA J G. An advanced retrieval-augmented generation system for manufacturing quality control[J]. Advanced Engineering Informatics，2025，64：103007.
[56] SHI D，LIEDL P，BAUERNHANSL T. Interoperable information modelling leveraging asset administration shell and large language model for quality control toward zero defect manufacturing[J]. Journal of Manufacturing Systems，2024，77：678-696.
[57] MA K，GRANDI D，MCCOMB C，et al. Conceptual design generation using large language models[C]//International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASME，2023：1-7.
[58] NOMAGUCHI Y，KUROISHI K，SYAMIMI A，et al. Creating design catalog from patent documents with large language model for design concept generation[J]. Proceedings of the Design Society，2025，5：1051-1060.
[59] LI S，ZHOU X，LIU Y，et al. Agile conceptual design and validation based on multi-source product data and large language models：A review，framework，and outlook[J]. Journal of Engineering Design，2025，36(4)：473-503.
[60] JING L，ZHANG H，DOU Y，et al. Conceptual design decision-making considering multigranularity heterogeneous evaluation semantics with uncertain beliefs[J]. Expert Systems with Applications，2024，244：122963.
[61] CHEN L，ZUO H，CAI Z，et al. Toward controllable generative design：A conceptual design generation approach leveraging the function-behavior-structure ontology and large language models[J]. Journal of Mechanical Design，2024，146(12)：121401.
[62] FANG C，ZHU Y，FANG L，et al. Generative AI-enhanced human-AI collaborative conceptual design：A systematic literature review[J]. Design Studies，2025，97：101300.
[63] 程锦，叶虎强，谭建荣，等. 三维CAD技术研究进展及其发展趋势综述[J]. 机械工程学报，2023，59(23)：158-185. CHENG Jin，YE Huqiang，TAN Jianrong，et al. Review of research progress and development trends of three-dimensional CAD technology[J]. Journal of Mechanical Engineering，2023，59(23)：158-185.
[64] WU R，XIAO C，ZHENG C. DeepCAD：A deep generative network for computer-aided design models[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021：6772-6782.
[65] XU X，JAYARAMAN P K，LAMBOURNE J G，et al. Hierarchical neural coding for controllable CAD model generation[EB/OL]. (2023-07-01)[2026-05-17]. https:// arxiv.org/abs/2307.00149.
[66] QI D，WANG C，XU J，et al. Pointer-CAD：Unifying B-Rep and command sequences via pointer-based edges and faces selection[EB/OL]. (2026-03-05)[2026-05-17]. https://arxiv.org/abs/2603.04337.
[67] ZHANG A，JIA W，ZOU Q，et al. Diffusion-CAD：Controllable diffusion model for generating computer-aided design models[J]. IEEE Transactions on Visualization and Computer Graphics，2025，31(12)：10188-10199.
[68] CHEN H，JOGLEKAR A，RUBINSTEIN Z，et al. Generative manufacturing：A requirements and resource- driven approach to part making[EB/OL]. (2024-09-04) [2026-05-17]. https://arxiv.org/abs/2409.03089.
[69] KUSIAK A. Generative artificial intelligence in smart manufacturing[J]. Journal of Intelligent Manufacturing，2025，36(1)：1-3.
[70] SANCHEZ-GONZALEZ A，GODWIN J，PFAFF T，et al. Learning to simulate complex physics with graph networks[C]//International Conference on Machine Learning. PMLR，2020：8459-8468.
[71] PFAFF T，FORTUNATO M，SANCHEZ-GONZALEZ A，et al. Learning mesh-based simulation with graph networks[J]. //International Conference on Learning Representations. 2021.
[72] HARTMANN S，MURUA O，ARRIZUBIETA J I，et al. Physics-informed neural network approach to speed up Laser-DED modelling[J]. Procedia CIRP，2024，124：352-357.
[73] KARNIADAKIS G E，KEVREKIDIS I G，LU L，et al. Physics-informed machine learning[J]. Nature Reviews Physics，2021，3(6)：422-440.
[74] BRANDSTETTER J，WORRALL D，WELLING M. Message passing neural PDE solvers[EB/OL]. (2022-02- 07)[2026-05-17]. https://arxiv.org/abs/2202.03376.
[75] BASTEK J H，SUN W，KOCHMANN D M. Physics-informed diffusion models[EB/OL]. (2024-03-21) [2026-05-17]. https://arxiv.org/abs/2403.14404.
[76] SHU D，LI Z，FARIMANI A B. A physics-informed diffusion model for high-fidelity flow field reconstruction[J]. Journal of Computational Physics，2023，478：111972.
[77] LIU Y，LIN Q，PAN W，et al. SR-M-GAN：A generative model for high-fidelity stress fields prediction of the composite bolted joints[J]. Advanced Engineering Informatics，2024，61：102537.
[78] LIU Y，YU W，LIN Q，et al. TF-F-GAN：A GAN-based model to predict the assembly physical fields under multi-modal variables fusion on vision transformer[J]. Advanced Engineering Informatics，2024，62：102871.
[79] CHEN Y，LIU C. Causal inference based transfer learning with LLMs：An efficient framework for industrial RUL prediction[EB/OL]. (2025-03-22)[2026-05-17]. https:// arxiv.org/abs/2503.17686.
[80] REN J, LIU X, WANG T, et al. PHM-GPT：A large language model for prognostics and health management[J/OL]. (2025-11-13)Engineering，2025，https://doi.org/10.1016/j.end.2025.11.001.
[81] JADHAV Y，PAK P，FARIMANI A B. LLM-3D print：Large language models to monitor and control 3D printing[J]. Additive Manufacturing，2025，114：105027.
[82] LI M，LOU S，ZHENG H，et al. A cognitive analysis-based key concepts derivation approach for product design[J]. Expert Systems with Applications，2024，236：121289.
[83] LIU M，HU Y. Application potential of stable diffusion in different stages of industrial design[C]//International Conference on Human-Computer Interaction. Cham：Springer Nature Switzerland，2023：590-609.
[84] CHANG C，WANG S，ZHANG J，et al. LLMScenario：Large language model driven scenario generation[J]. IEEE Transactions on Systems，Man，and Cybernetics：Systems，2024，54(11)：6581-6594.
[85] ZHANG J，WANG W，GUO S，et al. Solving general natural-language-description optimization problems with large language models[EB/OL]. (2024-07-10)[2026-05- 17]. https://arxiv.org/abs/2407.07924.
[86] ZHANG S，LIU S，LU N，et al. LLM-driven instance-specific heuristic generation and selection[EB/OL]. (2025-05-31)[2026-05-17]. https:// arxiv.org/abs/2506.00490.
[87] ZHANG R，LIU F，LIN X，et al. Understanding the importance of evolutionary search in automated heuristic design with large language models[C]//International Conference on Parallel Problem Solving from Nature. Cham：Springer Nature Switzerland，2024：185-202.
[88] 王沐晨，朱泓宇，宋学官. 大语言模型赋能优化设计初探[J/OL]. 机械工程学报，1-15[2026-05-17]. https://link. cnki.net/urlid/11.2187.TH.20251119.1411.036. WANG Muchen，ZHU Hongyu，SONG Xueguan. Preliminary study on optimization design empowered by large language models[J/OL]. Journal of Mechanical Engineering，1-15[2026-05-17]. https://link.cnki. net/urlid/11.2187.TH.20251119.1411.036.
[89] ZHANG Z，ZHAO H，ZHAO W，et al. LLM-assisted non-dominated sorting genetic algorithm for solving distributed heterogeneous no-wait permutation flowshop scheduling[J]. Applied Sciences，2025，15(18)：10131.
[90] LI R，WANG L，SANG H，et al. LLM-assisted automatic memetic algorithm for lot-streaming hybrid job shop scheduling with variable sublots[J]. IEEE Transactions on Evolutionary Computation，2025：1-28.
[91] ZHOU Q，LI M，LI W，et al. LLM-assisted advanced planning and scheduling framework for smart manufacturing systems[C]//Proceedings of the 202517th International Conference on Computer Modeling and Simulation. 2025：162-167.
[92] ÇETINKAYA İ O，BÜYÜKTAHTAKIN İ E，SHOJAEE P，et al. Discovering heuristics with large language models (LLMs) for mixed-integer programs：Single-machine scheduling[J]. Computers & Operations Research，2025：107325.
[93] WANG Y，WANG J，CHU Z. Multi-agent large language models as evolutionary optimizers for scheduling optimization[J]. Computers & Industrial Engineering，2025，206：111197.
[94] LIU Y，ZHANG H，LI Z，et al. Optimizing the utilization of large language models via schedule optimization：An exploratory study[C]//Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. 2024：84-95.
[95] XIA Y，SHENOY M，JAZDI N，et al. Towards autonomous system：Flexible modular production system enhanced with large language model agents[C]//2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation. IEEE，2023：1-8.
[96] LIU Z，ZENG R，WANG D，et al. Agents4PLC：Automating closed-loop PLC code generation and verification in industrial control systems using LLM-based agents[EB/OL]. (2024-10-18)[2026-05-17]. https://arxiv.org/abs/2410.14209.
[97] FAN J，ZHENG P. A vision-language-guided robotic action planning approach for ambiguity mitigation in human-robot collaborative manufacturing[J]. Journal of Manufacturing Systems，2024，74：1009-1018.
[98] GKOURNELOS C，KONSTANTINOU C，ANGELAKIS P，et al. Enabling seamless human-robot collaboration in manufacturing using LLMs[C]//European Symposium on Artificial Intelligence in Manufacturing. Cham：Springer Nature Switzerland，2023：81-89.
[99] WANG Z，ZHOU X，MAO J，et al. LLM-MTMP：A large language model-based multi-agent task and motion planning framework for power inspection robots[J]. Journal of Industrial Information Integration，2025：101014.
[100] BARBOSA R，SANTOS R，NOVAIS P. Collaborative problem-solving with LLM：A multi-agent system approach to solve complex tasks using AutoGen[C]// International Conference on Practical Applications of Agents and Multi-Agent Systems. Cham：Springer Nature Switzerland，2024：203-214.
[101] LI L，CHEN Z，KAN Z. Environment-driven and LLM-guided multi-robot task inference and allocation under temporal logic specifications[J]. IEEE Transactions on Automation Science and Engineering，2026，23：2925-2940.
[102] WASEEM M，BHATTA K，LI C，et al. Pretrained LLMs as real-time controllers for robot operated serial production line[EB/OL]. (2025-03-05)[2026-05-17]. https:// arxiv.org/abs/2503.03889.
[103] LING S，WANG Y，FAN C，et al. ELHPlan：Efficient long-horizon task planning for multi-agent collaboration[EB/OL]. (2025-09-30)[2026-05-17]. https:// arxiv.org/abs/2509.24230.

大模型时代的智能设计与制造综述：研究现状、技术挑战及应用展望

A Survey of Intelligent Design and Manufacturing in the Era of Large Models: Research Status, Technical Challenges, and Application Prospects

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	方遒, 宋豪杰, 卢弘, 毛建旭, 王耀南. 集成工人和AGV的多要素柔性作业车间调度方法[J]. 机械工程学报, 2025, 61(18): 330-343.
[2]	谢俊华, 徐志佳. 基于交互结构认知的高效智能装配建模方法[J]. 机械工程学报, 2025, 61(12): 327-335,343.
[3]	黄学文, 王强, 陈桢. 作业车间调度中非改进邻域解的识别理论与方法[J]. 机械工程学报, 2025, 61(10): 427-438.
[4]	赵诗奎, 黄林, 刘子辉, 郑岩. 基于障碍图的移动工件搜索混合算法求解分布式作业车间调度问题[J]. 机械工程学报, 2025, 61(10): 479-494.
[5]	邓文章, 张倩, 吴乐, 杨万然, 杨春柳. 基于ESPN的柔性制造车间多层级表征建模研究[J]. 机械工程学报, 2025, 61(8): 399-412.
[6]	何轩, 潘全科, 高亮. 基于问题特征知识的迭代贪心算法求解多目标零等待流水车间调度问题[J]. 机械工程学报, 2025, 61(4): 344-354.
[7]	张晋, 刘检华, 赵文浩, 庄存波, 张旭. 基于预训练模型与相似度算法的复杂产品装配技术问题风险预警方法[J]. 机械工程学报, 2024, 60(24): 317-329.
[8]	王彦森, 冯立杰, 王金凤, 刘鹏, 赵华东. 面向节能的复杂机械零部件生产与物流集成调度[J]. 机械工程学报, 2024, 60(20): 403-416.
[9]	李亚栋, 陶友瑞, 王崇帅, 段书用, 李长峰, 姬昭鑫. 短距离压电式喷射点胶卫星滴形成机理及抑制策略[J]. 机械工程学报, 2024, 60(19): 347-355.
[10]	周亚勤, 肖蒙, 吕志军, 汪俊亮, 张洁. 基于DQN算法的考虑AGV小车搬运的离散制造车间调度方法[J]. 机械工程学报, 2024, 60(18): 338-348.
[11]	王文波, 张映锋, 顾寄南, 张耿, 完严. 数据驱动的离散制造系统性能退化机理建模与预测控制方法研究[J]. 机械工程学报, 2024, 60(16): 400-411.
[12]	吕海利, 黄志文, 陈建华, 王正国, 吴姝, 韩国震. 基于GEP的装配作业车间调度复合派工法则研究[J]. 机械工程学报, 2023, 59(16): 427-434.
[13]	黄林, 赵诗奎, 黄森. 基于障碍图模型和禁忌搜索混合算法求解作业车间调度问题[J]. 机械工程学报, 2023, 59(16): 435-444,426.
[14]	王彦凯, 王时龙, 杨波, 王四宝. 一种实际多约束环境下的云制造服务组合动态自适应重构方法[J]. 机械工程学报, 2023, 59(14): 339-351.
[15]	华丰, 王亚森, 金骏阳, 袁烨. 一种数据隐私保护下多方无损线性模型学习方法[J]. 机械工程学报, 2023, 59(12): 17-27.