Classification and Application of Large, Small, and Micro AI Models in Advanced Manufacturing

doi:10.3901/JME.260292

Abstract

Abstract: As a key indicator for measuring a country’s scientific and technological development level, advanced manufacturing is closely linked to national economic growth and national defense security, and exerts a profound impact on a country’s standing in global competition. With the widespread application of large language models such as DeepSeek, artificial intelligence technology is driving profound transformations across various industries at an unprecedented pace. Given the absence of explicit model classification criteria in the field of advanced manufacturing, a parameter-scale-based classification method for artificial intelligence models in the manufacturing industry (i.e., large, small, and micro models) from the perspective of model parameters is proposed, drawing on the classification methodologies in the computer field. This effort is intended to deepen the understanding of such technologies. Based on this framework, then a systematic literature review and investigation into the research and application of large is conducted, small, and micro AI models across the four core manufacturing processes: engineering materials, design optimization, processing and assembly, and operation and maintenance, while elaborating on the latest development trends. Subsequently, the technical bottlenecks in the empowerment of advanced manufacturing by artificial intelligence are discussed. Finally, this study summarizes and prospects the main development directions for the integration of artificial intelligence technology and advanced manufacturing in the future.

Key words: artificial intelligence, advanced manufacturing, large models, small models, micro models, model classification

CLC Number:

TH11
TP391

SONG Xueguan, ZHU Hongyu, WANG Muchen, PANG Yong, ZHANG Shuai, YANG Liangliang, HE Xiwang, KAN Ziyun. Classification and Application of Large, Small, and Micro AI Models in Advanced Manufacturing[J]. Journal of Mechanical Engineering, 2026, 62(15): 24-51.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260292

http://www.cjmenet.com.cn/EN/Y2026/V62/I15/24

References

[1] 郭东明. 高性能制造[J].机械工程学报，2022，58(21)：225-242.GUO Dongming. High-performance manufacturing[J]. Journal of Mechanical Engineering，2022，58(21)：225-242.
[2] 魏琪嘉. 当前制造业亟待解决和破解五大问题[J]. 宏观经济管理，2022(3)：24-31.WEI Qijia. Five urgent problems to be solved in the current manufacturing industry[J]. Macroeconomic Management，2022(3)：24-31.
[3] 王国彪，宋建丽. 美国先进制造国家战略回顾与未来技术[J]. 机械工程学报，2023，59(19)：187-201.WANG Guobiao，SONG Jianli. Review of US national strategy for advanced manufacturing and future technologies[J]. Journal of Mechanical Engineering，2023，59(19)：187-201.
[4] 连续15年规模全球第一！2024年中国制造“成绩单”请收下[EB/OL]. [2025-06-18]. https://www.gov.cn/ lianbo/ bumen/202501/content_7000364.htm.Top for 15 consecutive years globally! Here's the "report card" for made-in-China in 2024w [EB/OL]. [2025-06-18]. https://www.gov.cn/lianbo/bumen/202501/content_7000364.htm.
[5] 张辉. 我国产业现代化发展的结构性问题与应对策略[J]. 人民论坛·学术前沿，2023(5)：6-14.ZHANG Hui. Structural problems and countermeasures of China's industrial modernization development[J]. People's Tribune · Academic Front，2023(5)：6-14.
[6] 郭鹏，罗腾飞，李婧雯. 地缘政治风险对高端制造业出口的影响研究——来自全球17国的证据[J]. 中国商论，2024，33(23)：144-148.GUO Peng，LUO Tengfei，LI Jingwen. Research on the impact of geopolitical risks on the export of high-end manufacturing industry——Evidence from 17 countries worldwide[J]. China Journal of Commerce，2024，33(23)：144-148.
[7] 李继堂. 人工智能对科学发现范式的创新[N]. 社会科学报，2024-12-12(005).LI Jitang. Innovation of Artificial Intelligence on the Paradigm of Scientific Discovery[N]. Social Sciences News，2024-12-12(005).
[8] 周凤林，王晓芜，胡炳元. 物理大视野·改变昨天的世界[M]. 上海：上海科技教育出版社，2012.ZHOU Fenglin，WANG Xiaowu，HU Bingyuan. Great vision of physics：Changing the world of yesterday[M]. Shanghai：Shanghai Science and Technology Education Press，2012.
[9] Galileo Galilei[EB/OL]. [2025-06-18]. https://en. wikipedia.org/wiki/Galileo_Galilei.
[10] Daniel Bernoulli[EB/OL]. [2025-06-18]. https://en. wikipedia.org/w/index.php?title=Daniel_Bernoulli.
[11] HENDRICKSON B. Computational science：Emerging opportunities and challenges[J]. Journal of Physics：Conference Series，2009，180(1)：012013.
[12] 梁娜，曾燕. 推进数据密集科学发现提升科技创新能力：新模式、新方法、新挑战——《第四范式:数据密集型科学发现》译著出版[J]. 中国科学院院刊，2013，28(1)：115-121.LIANG Na，ZENG Yan. Promoting data-intensive scientific discovery to enhance scientific and technological innovation capability：New modes，new methods，new challenges——Publication of the Chinese translation of the fourth paradigm：Data-intensive scientific discovery[J]. Bulletin of the Chinese Academy of Sciences，2013，28(1)：115-121.
[13] 舒文韬，李睿潇，孙天祥，等. 大型语言模型：原理、实现与发展[J]. 计算机研究与发展，2024，61(2)：351-361.SHU Wentao，LI Ruixiao，SUN Tianxiang，et al. Large language models：Principles，implementation and Development[J]. Journal of Computer Research and Development，2024，61(2)：351-361.
[14] HONG S，ZHOU Z，ZIO E，et al. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method[J]. Digital Signal Processing，2014，27：159-166.
[15] NIU G，YANG B S. Intelligent condition monitoring and prognostics system based on data-fusion strategy[J]. Expert Systems with Applications，2010，37(12)：8831-8840.
[16] CARINO J A，ZURITA D，DELGADO M，et al. Remaining useful life estimation of ball bearings by means of monotonic score calibration[C]//2015 IEEE International Conference on Industrial Technology (ICIT). IEEE，2015：1752-1758.
[17] PANG B，TANG G，ZHOU C，et al. Rotor fault diagnosis based on characteristic frequency band energy entropy and support vector machine[J]. Entropy，2018，20(12)：932.
[18] YUAN S F，CHU F L. Support vector machines-based fault diagnosis for turbo-pump rotor[J]. Mechanical Systems and Signal Processing，2006，20(4)：939-952.
[19] JEGADEESHWARAN R，SUGUMARAN V. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines[J]. Mechanical Systems and Signal Processing，2015，52：436-446.
[20] WANG S，XU Q，WEI N. Airfoil aerodynamic optimization design using ensemble learning surrogate model[J]. Journal of Aerospace Engineering，2024，37(6)：04024093.
[21] KUDELA J，MATOUSEK R. Recent advances and applications of surrogate models for finite element method computations：A review[J]. Soft Computing，2022，26(24)：13709-13733.
[22] TAO J，SUN G. Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization[J]. Aerospace Science and Technology，2019，92：722-737.
[23] SOOD A K，CHATURVEDI V，DATTA S，et al. Optimization of process parameters in fused deposition modeling using weighted principal component analysis[J]. Journal of Advanced Manufacturing Systems，2011，10(2)：241-259.
[24] NGUYEN T D，NGUYEN T T，BUI C N，et al. Optimizing and predicting additive manufacturing parameters using a variational autoencoder combined with data stratification[J]. Progress in Additive Manufacturing，2026，11(1)：447-483.
[25] LUI C F，MAGED A，XIE M. A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing[J]. Journal of Intelligent Manufacturing，2024，35(7)：3543-3558.
[26] SABERIRONAGHI A，REN J，EL-GINDY M. Defect detection methods for industrial products using deep learning techniques：A review[J]. Algorithms，2023，16(2)：95.
[27] RODRIGUEZ P C，MARTI-PUIG P，CAIAFA C F，et al. Exploratory analysis of SCADA data from wind turbines using the K-means clustering algorithm for predictive maintenance purposes[J]. Machines，2023，11(2)：270.
[28] OLIOSI E，CALZAVARA G，FERRARI G. On sensor data clustering for machine status monitoring and its application to predictive maintenance[J]. IEEE Sensors Journal，2023，23(9)：9620-9639.
[29] PARK I B，HUH J，KIM J，et al. A reinforcement learning approach to robust scheduling of semiconductor manufacturing facilities[J]. IEEE Transactions on Automation Science and Engineering，2020，17(3)：1420-1431.
[30] ZHOU L，ZHANG L，HORN B K P. Deep reinforcement learning-based dynamic scheduling in smart manufacturing[J]. Procedia CIRP，2020，93：383-388.
[31] LU R，LI Y C，LI Y，et al. Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management[J]. Applied Energy，2020，276：115473.
[32] VERMA V K，R I，VERMA S，et al. Energy consumption optimization in smart factories using iot and reinforcement learning[C]//2024 IEEE 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS)，2024：1-8.
[33] OLIFF H，LIU Y，KUMAR M，et al. Reinforcement learning for facilitating human-robot-interaction in manufacturing[J]. Journal of Manufacturing Systems，2020，56：326-340.
[34] GUO K，CHENG A，LI Y，et al. Cooperative markov decision process model for human-machine co-adaptation in robot-assisted rehabilitation[J]. Knowledge-Based Systems，2024，291：111572.
[35] LIM J，LIM J，BASKARAN V M，et al. A deep context learning based PCB defect detection model with anomalous trend alarming system[J]. Results in Engineering，2023，17：100968.
[36] LI S，ZHENG P，ZHENG L. An AR-assisted deep learning-based approach for automatic inspection of aviation connectors[J]. IEEE Transactions on Industrial Informatics，2021，17(3)：1721-1731.
[37] NASSER A，AL-KHAZRAJI H. A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance[J]. International Journal of Electrical and Computer Engineering (IJECE)，2022，12(1)：721.
[38] WANG L，ZHU Z，ZHAO X. Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method[J]. Reliability Engineering & System Safety，2024，245：110012.
[39] LI R，ZHANG Y，CHEN H. Learning the aerodynamic design of supercritical airfoils through deep reinforcement learning[J]. AIAA Journal，2021，59(10)：3988-4001.
[40] YILMAZ E，GERMAN B. Conditional generative adversarial network framework for airfoil inverse design[C]//AIAA Aviation 2020 Forum，2020：3185.
[41] REGENWETTER L，NOBARI A H，AHMED F. Deep generative models in engineering design：A review[J]. Journal of Mechanical Design，2022，144：071704.
[42] MERCHANT A，BATZNER S，SCHOENHOLZ S S，et al. Scaling deep learning for materials discovery[J]. Nature，2023，624(7990)：80-85.
[43] ZENI C，PINSLER R，ZÜGNER D，et al. A generative model for inorganic materials design[J]. Nature，2025，639(8055)：624-632.
[44] CHEN W，SUN R，LEE D，et al. Generative inverse design of metamaterials with functional responses by interpretable learning[J]. Advanced Intelligent Systems，2024：2400611.
[45] LITTY A. Generative AI for tailored functionalities in nanocomposite materials[EB/OL]. [2025-06-15]. https:// easychair.org/publications/preprint/sDm2.
[46] SONG Y，SIRIWARDANE E M D，ZHAO Y，et al. Computational discovery of new 2D materials using deep learning generative models[J]. ACS Applied Materials & Interfaces，2021，13(45)：53303-53313.
[47] YU J，WENG Y，YU J，et al. Generative AI for performance-based design of engineered cementitious composite[J]. Composites Part B：Engineering，2023，266：110993.
[48] BADINI S，REGONDI S，PUGLIESE R. Enhancing mechanical and bioinspired materials through generative AI approaches[J]. Next Materials，2025，6：100275.
[49] ZENG G S，HU C，ZOU S，et al. BP neural network model for predicting the mechanical performance of a foamed wood-fiber reinforced thermoplastic starch composite[J]. Polymer Composites，2019，40(10)：3923-3928.
[50] YADOLLAHI M M，BENLI A，DEMIRBOGA R. Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites[J]. Neural Computing and Applications，2017，28：1453-1461.
[51] AL-HAIK M，HUSSAINI M，ROGAN C. Artificial intelligence techniques in simulation of viscoplasticity of polymeric composites[J]. Polymer Composites，2009，30(11)：1701-1708.
[52] EL KADI H. Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using artificial neural networks—A review[J]. Composite Structures，2006，73(1)：1-23.
[53] IDRISI A H，MOURAD A H I. Wear performance analysis of Aluminum matrix composites using Artificial neural network[C]//2019 Advances in Science and Engineering Technology International Conferences (ASET). IEEE，2019：1-5.
[54] ARIF S，ALAM M T，ANSARI A H，et al. Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique[J]. Materials Research Express，2018，5(5)：056506.
[55] AKBARI M K，SHIRVANIMOGHADDAM K，HAI Z，et al. Nano TiB2 and TiO2 reinforced composites：A comparative investigation on strengthening mechanisms and predicting mechanical properties via neural network modeling[J]. Ceramics International，2017，43(18)：16799-16810.
[56] ALTINKOK N，KOKER R. Modelling of the prediction of tensile and Density properties in particle reinforced metal matrix composites by using neural networks[J]. Materials & Design，2006，27(8)：625-631.
[57] ALTINKOK N. Use of artificial neural network for prediction of mechanical properties of α-Al2O3 particulate-reinforced Al–Si10Mg alloy composites prepared by using stir casting process[J]. Journal of Composite Materials，2006，40(9)：779-796.
[58] DENG L，FENG B，ZHANG Y. An optimization method for multi-objective and multi-factor designing of a ceramic slurry：Combining orthogonal experimental design with artificial neural networks[J]. Ceramics International，2018，44(13)：15918-15923.
[59] XU Y，ZHU Y，XIAO G，et al. Application of artificial neural networks to predict corrosion behavior of Ni–SiC composite coatings deposited by ultrasonic electrodeposition[J]. Ceramics International，2014，40(4)：5425-5430.
[60] RAO H，MUKHERJEE A. Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites[J]. Computational Materials Science，1996，5(4)：307-322.
[61] SZYMANSKI N J，RENDY B，FEI Y，et al. An autonomous laboratory for the accelerated synthesis of novel materials[J]. Nature，2023，624(7990)：86-91.
[62] TOM G，SCHMID S P，BAIRD S G，et al. Self-driving laboratories for chemistry and materials science[J]. Chemical Reviews，2024，124(16)：9633-9732.
[63] SOLDATOV M A，BUTOVA V V，PASHKOV D，et al. Self-driving laboratories for development of new functional materials and optimizing known reactions[J]. Nanomaterials，2021，11(3)：619.
[64] 上海硅酸盐所发布材料科学与工程大模型MatMind [EB/OL]. [2025-05-06]. https://sic.cas.cn/xwzx/tpxw/202504/t20250407_7589586.html.Shanghai Institute of Ceramics Launches the Large-Scale Model for Materials Science and Engineering—MatMind [EB/OL]. [2025-05-06]. https://sic. cas.cn/xwzx/tpxw/ 202504/t20250407_7589586.html.
[65] CHEN W，AHMED F. PaDGAN：Learning to generate high-quality novel designs[J]. Journal of Mechanical Design，2020，143：031703.
[66] KHAN M S，SINHA S，UDDIN S T，et al. Text2CAD：Generating sequential CAD designs from beginner-to-expert level text prompts[C]//Advances in Neural Information Processing Systems：Vol. 37. Curran Associates，Inc.，2024：7552-7579.
[67] CHEN C，WEI J，CHEN T，et al. Cadcrafter：Generating computer-aided design models from unconstrained images[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway：IEEE，2025：11073-11082.
[68] Leo AI：AI-powered engineering design copilot for mechanical engineers[EB/OL].[2025-05-06]. https://www.getleo.ai/.
[69] LIU Y，XU D，YU X，et al. HoLa：B-rep generation using a holistic latent representation[J]. ACM Transactions on Graphics (Proceedings of SIGGRAPH)，2025，44(4)，1-25.
[70] LIU X R，ZANG Y H，WANG L Q，et al. LLMs driven fusion AI-AD system for mechanical design：From understanding to generation[J]. Advanced Engineering Informatics，2025，68：103745.
[71] 何西旺，杨亮亮，冉仁杰，等. 基于多评价标准的代理模型综合比较研究[J]. 机械工程学报，2022，58(16)：403-419.HE Xiwang，YANG Liangliang，RAN Renjie，et al. A comprehensive comparative study of surrogate models based on multiple evaluation criteria[J]. Journal of Mechanical Engineering，2022，58(16)：403-419.
[72] SONG X，SUN G，LI G，et al. Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models[J]. Structural and Multidisciplinary Optimization，2013，47(2)：221-231.
[73] SONG X，LV L，SUN W，et al. A radial basis function-based multi-fidelity surrogate model：Exploring correlation between high-fidelity and low-fidelity models[J]. Structural and Multidisciplinary Optimization，2019，60(3)：965-981.
[74] WANG X，LI M，LIU Y，et al. Surrogate based multidisciplinary design optimization of lithium-ion battery thermal management system in electric vehicles[J]. Structural and Multidisciplinary Optimization，2017，56(6)：1555-1570.
[75] LIU F，GONG Z，MA X，et al. Based on the combination of fluid–solid interaction mechanism model and surrogate model for peristaltic pump performance analysis and multi-objective optimization design[J]. Advanced Engineering Informatics，2024，62：102675.
[76] RAISSI M，PERDIKARIS P，KARNIADAKIS G E. Physics-informed neural networks：A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational physics，2019，378：686-707.
[77] HU H，QI L，CHAO X. Physics-informed Neural Networks (PINN) for computational solid mechanics：Numerical frameworks and applications[J]. Thin-Walled Structures，2024，205：112495.
[78] WANG Y，SUN J，BAI J，et al. Kolmogorov– arnold-informed neural network：A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov–Arnold networks[J]. Computer Methods in Applied Mechanics and Engineering，2025，433：117518.
[79] HAGHIGHAT E，RAISSI M，MOURE A，et al. A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics[J]. Computer Methods in Applied Mechanics and Engineering，2021，379：113741.
[80] BAI J，RABCZUK T，GUPTA A，et al. A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics[J]. Computational Mechanics，2023，71(3)：543-562.
[81] BAI J，JEONG H，BATUWATTA-GAMAGE C P，et al. An introduction to programming physics-informed neural network-based computational solid mechanics[J]. International Journal of Computational Methods，2023，20(10)：2350013.
[82] SHARMA R，GUO Y B，RAISSI M，et al. Physics-informed machine learning of argon gas-driven melt pool dynamics[J]. Journal of Manufacturing Science and Engineering，2024，146：081008.
[83] SUN L，GAO H，PAN S，et al. Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data[J]. Computer Methods in Applied Mechanics and Engineering，2020，361：112732.
[84] CAI S，WANG Z，WANG S，et al. Physics-informed neural networks for heat transfer problems[J]. Journal of Heat Transfer，2021，143：060801.
[85] JIN X，CAI S，LI H，et al. NSFnets (Navier-Stokes flow nets)：Physics-informed neural networks for the incompressible Navier-Stokes equations[J]. Journal of Computational Physics，2021，426：109951.
[86] ZHOU W，MIWA S，OKAMOTO K. Advancing fluid dynamics simulations：A comprehensive approach to optimizing physics-informed neural networks[J]. Physics of Fluids，2024，36(1)：013615.
[87] HE H，PATHAK J. An unsupervised learning approach to solving heat equations on chip based on auto encoder and image gradient[EB/OL]. (2020-07-19)[2025- 05-15]. https://arxiv.org/abs/2007.09684.
[88] MAI J，LI Y，LONG L，et al. Two-dimensional temperature field inversion of turbine blade based on physics-informed neural networks[J]. Physics of Fluids，2024，36(3).
[89] DU H，XU Q，BU Y，et al. Rapid prediction of structural thermal loads and temperature field based on physics and data co-driven approach under partial labeled data[J]. International Communications in Heat And Mass Transfer，2024，159：108007.
[90] RAISSI M，YAZDANI A，KARNIADAKIS G E. Hidden fluid mechanics：Learning velocity and pressure fields from flow visualizations[J]. Science，2020，367(6481)：1026-1030.
[91] WANG S，YU X，PERDIKARIS P. When and why PINNs fail to train：A neural tangent kernel perspective[J]. J. Comput. Phys.，2020，449：110768.
[92] FANG Z. A high-efficient hybrid physics-informed neural networks based on convolutional neural network[J]. IEEE Transactions on Neural Networks and Learning Systems，2022，33(10)：5514-5526.
[93] LU L，JIN P，PANG G，et al. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators[J]. Nature Machine Intelligence，2021，3(3)：218-229.
[94] LI Z，KOVACHKI N，AZIZZADENESHELI K，et al. Fourier neural operator for parametric partial differential equations[EB/OL]. (2020-10-17)[2026-01-28]. https:// arxiv.org/abs/2010.08895.
[95] PFAFF T，FORTUNATO M，SANCHEZ- GONZALEZ A，et al. Learning mesh-based simulation with graph networks[C]//International Conference on Learning Representations. 2020.
[96] RIBEIRO M D，REHMAN A，AHMED S，et al. DeepCFD：Efficient steady-state laminar flow approximation with deep convolutional neural networks[EB/OL]. (2020-04-18)[2026-01-28]. https:// arxiv.org/abs/2004.08826.
[97] SOUSA P，RODRIGUES C V，AFONSO A. Enhancing CFD solver with machine learning techniques[J]. Computer Methods in Applied Mechanics and Engineering，2024，429：117133.
[98] YONEKURA K，MARUOKA K，TYOU K，et al. Super-resolving 2D stress tensor field conserving equilibrium constraints using physics-informed U-Net[J]. Finite Elements in Analysis and Design，2023，213：103852.
[99] OOMMEN V，ROBERTSON A E，DIAZ D，et al. Equilibrium conserving neural operators for super- resolution learning[EB/OL]. (2025-04-25) [2026-01-28]. https://arxiv.org/abs/2504.13422.
[100] PANG Z，LIU K，XIAO H，et al. A deep-learning super-resolution reconstruction model of turbulent reacting flow[J]. Computers& Fluids，2024，275：106249.
[101] SOFOS F，DRIKAKIS D. A review of deep learning for super-resolution in fluid flows[J]. Physics of Fluids，2025，37(4)：041303.
[102] ROWBOTTOM J，MAIERHOFER G，DEVENEY T，et al. G-Adaptive mesh refinement–leveraging graph neural networks and differentiable finite element solvers[EB/OL]. (2024-07-05)[2026-01-28]. https://arxiv.org/abs/2407.04516.
[103] DONG Z，LU Z，YANG Y. Fine-tuning a large language model for automating computational fluid dynamics simulations[J]. Theoretical and Applied Mechanics Letters，2025，15(3)：100594.
[104] PANDEY S，XU R，WANG W，et al. OpenFOAMGPT：a RAG-Augmented LLM agent for OpenFOAM-based computational fluid dynamics[EB/OL]. (2025-01-16) [2025-05-15]. https://arxiv.org/abs/2501.06327.
[105] FENG J，XU R，CHU X. OpenFOAMGPT 2.0：End-to-end，trustworthy automation for computational fluid dynamics[EB/OL]. (2025-04-30)[2026-01-28]. https://arxiv.org/abs/2504.19338.
[106] 国家自然科学基金委员会工程与材料科学部. 机械工程学科发展战略报告(2021～2035)[M]. 北京：科学出版社，2021.Department of Engineering and Materials Science，National Natural Science Foundation of China. Strategic Report on the Development of Mechanical Engineering Discipline (2021-2035)[M]. Beijing：Science Press，2021.
[107] 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 digital twin for shape and property integration of major equipment[J]. Journal of Mechanical Engineering，2022，58(10)：298-325.
[108] LAI X，WANG S，GUO Z，et al. Designing a shape–performance integrated digital twin based on multiple models and dynamic data：A boom crane example[J]. Journal of Mechanical Design，2021，143(7)：071703.
[109] WANG S，LAI X，HE X，et al. Building a trustworthy product-level shape-performance integrated digital twin with multifidelity surrogate model[J]. Journal of Mechanical Design，2022，144(3)：031703.
[110] LAI X，HE X，WANG S，et al. Building a lightweight digital twin of a crane boom for structural safety monitoring based on a multifidelity surrogate model[J]. Journal of Mechanical Design，2022，144(6)：064502.
[111] HE X，LAI X，YANG L，et al. M-LFM：A multi-level fusion modeling method for shape-performance integrated digital twin of complex structure[J]. Frontiers of Mechanical Engineering，2022，17(4)：52.
[112] LAI X，HE X，PANG Y，et al. A scalable digital twin framework based on a novel adaptive ensemble surrogate model[J]. Journal of Mechanical Design，2023，145(2)：021701.
[113] KAPTEYN M G，PRETORIUS J V R，WILLCOX K E. A probabilistic graphical model foundation for enabling predictive digital twins at scale[J]. Nature Computational Science，2021，1(5)：337-347.
[114] SHARMA N，CHAWLA V K，RAM N. Comparison of machine learning algorithms for the automatic programming of computer numerical control machine[J]. International Journal of Data and Network Science，2020，4(1)：1-14.
[115] DITTRICH M A，UHLICH F，DENKENA B. Self-optimizing tool path generation for 5-axis machining processes[J]. CIRP Journal of Manufacturing Science and Technology，2019，24：49-54.
[116] GHOSH T，MARTINSEN K. Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms[J]. Engineering Science and Technology，An International Journal，2020，23(3)：650-663.
[117] FENG Y fei，MA H Y，SHEN L Y，et al. Real-time tool-path planning using deep learning for subtractive manufacturing[J]. IEEE Transactions on Industrial Informatics，2023，20(4)：5979-5988.
[118] VAISHNAV S，AGARWAL A，DESAI K A. Machine learning-based instantaneous cutting force model for end milling operation[J]. Journal of Intelligent Manufacturing，2020，31(6)：1353-1366.
[119] ZACHARIA K，KRISHNAKUMAR P. Chatter prediction in high speed machining of titanium alloy (Ti-6Al-4V) using machine learning techniques[J]. Materials Today：Proceedings，2020，24：350-358.
[120] GRAPPIOLO C，PRUIM R，FAETH M，et al. ViTroVo：in vitro assembly search for in vivo adaptive operator guidance[J]. The International Journal of Advanced Manufacturing Technology，2021，117(11)：3873-3893.
[121] SUSZYŃSKI M，PETA K. Assembly sequence planning using artificial neural networks for mechanical parts based on selected criteria[J]. Applied Sciences，2021，11(21)：10414.
[122] 朱子璐，刘永奎，张霖，等. 基于深度强化学习的机器人轴孔装配策略仿真研究[J]. 系统仿真学报，2024，36(6)：1414-1424.ZHU Zilu，LIU Yongkui，ZHANG Lin，et al. Simulation study on robot shaft-hole assembly strategy based on deep reinforcement learning[J]. Journal of System Simulation，2024，36(6)：1414-1424.