Issue
Mechanics & Industry
Volume 27, 2026
Artificial Intelligence in Mechanical Manufacturing: From Machine Learning to Generative Pre-trained Transformer
Article Number 11
Number of page(s) 15
DOI https://doi.org/10.1051/meca/2026005
Published online 17 March 2026
  1. Y.H. Son, G.Y. Kim, H.C. Kim et al., Past, present, and future research of digital twin for smart manufacturing, J. Comput. Des. Eng. 9, 1–23 (2022) [Google Scholar]
  2. S. Kumar, T. Gopi, N. Harikeerthana, M.K. Gupta et al., Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control, J. Intell. Manufac. 34, 21–55 (2023) [Google Scholar]
  3. L. Chen, Y. Zhao, X. Chen et al., Repair of spline shaft by laser-cladding coarse TiC reinforced Ni-based coating: process, microstructure and properties, Ceram. Int. 47, 30113–30128 (2021) [Google Scholar]
  4. A. van Oudheusden, J. Bolaños Arriola, J. Faludi et al., 3D printing for repair: an approach for enhancing repair, Sustainability 15, 5168–5201 (2023) [Google Scholar]
  5. S.F. Iftekar, A. Aabid, A. Amir et al., Advancements and limitations in 3D printing materials and technologies: a critical review, Polymers 15, 2519–2542 (2023) [Google Scholar]
  6. A. Kantaros, P. Zacharia, C. Drosos et al., Smart infrastructure and additive manufacturing: synergies, advantages, and limitations, Appl. Sci. 15, 3719–3754 (2025) [Google Scholar]
  7. M.H. Ali, G. Issayev, E. Shehab et al., A critical review of 3D printing and digital manufacturing in construction engineering, Rapid Prototyp. J. 28, 1312–1324 (2022) [Google Scholar]
  8. W. Qin, Q. Hu, Z. Zhuang et al., IPPE-PCR: a novel 6D pose estimation method based on point cloud repair for texture-less and occluded industrial parts, J. Intell. Manufac. 34, 2797–2807 (2023) [Google Scholar]
  9. H. Wang, Innovative application of intelligent mechanical manufacturing based on self-supervised learning and graph neural network fusion optimization, Informatica 49, 1–14 (2025) [Google Scholar]
  10. M.A. Masalha, K.K. VanKoevering, O.S. Latif et al., Simulation of cerebrospinal fluid leak repair using a 3-dimensional printed model, Am. J. Rhinol. Allergy 35, 802–808 (2021) [Google Scholar]
  11. M. Milazzo, F. Libonati, The synergistic role of additive manufacturing and artificial intelligence for the design of new advanced intelligent systems, Adv. Intell. Syst. 4, 1–7 (2022) [CrossRef] [Google Scholar]
  12. D. Mazzaccaro, F. Sturla, A. Rosato et al., Planning the use of endografts in the endovascular repair of complex abdominal and thoraco-abdominal aortic lesions leveraging 3D printing, Expert Rev. Med. Devices 21, 1121–1130 (2024) [Google Scholar]
  13. J. Charton, S. Baek, Y. Kim, Mesh repairing using topology graphs, J. Comput. Des. Eng. 8, 251–267 (2021) [Google Scholar]
  14. P. Wang, M. Xu, S. Xin et al., Robustly watertight manifold surface repair, J. Comput. Aided Des. Comput. Graph. 36, 1047–1056 (2024) [Google Scholar]
  15. X. Wang, N. Lei, Z. Luo, An automatic surface-based mesh repairing algorithm, J. Comput. Aided Des. Comput. Graph. 34, 1391–1401 (2022) [Google Scholar]
  16. S. Sellán, A. Jacobson, Stochastic Poisson surface reconstruction, ACM Trans. Graph. 41, 1–12 (2022) [Google Scholar]
  17. A. Farshian, M. Götz, G. Cavallaro et al., Deep-learning-based 3D surface reconstruction—a survey, Proc. IEEE 111, 1464–1501 (2023) [Google Scholar]
  18. H. Tian, C. Zhu, Y. Shi et al., Superudf: Self-supervised udf estimation for surface reconstruction, IEEE Trans. Vis. Comput. Graph. 30, 5965–5975 (2023) [Google Scholar]
  19. Q. Zou, F. Liu, 3D reconstruction of optical building images based on improved 3D-R2N2 algorithm, Teh. Vjesn. 30, 1594–1602 (2023) [Google Scholar]
  20. Y. Xi, H. Zhang, B. Li, Wear particles image enhancement using long short-term memory 3D recurrent reconstruction neural network (LSTM 3D-R2N2), Proc. Inst. Mech. Eng. C: J. Mech. Eng. Sci. 238, 10864–10872 (2024) [Google Scholar]
  21. C. Jian, Y. Lu, M. Lin et al., A novel graph neural networks approach for 3D product model retrieval, Int. J. Comput. Integr. Manuf. 36, 381–392 (2023) [Google Scholar]
  22. Y. Zhang, J. Chen, L. Chen et al., Automated structural repair based on continuous carbon fiber reinforced plastic 3D printing and online model reconstruction, Polym. Compos. 45, 13627–13638 (2024) [Google Scholar]
  23. L. Li, F. He, R. Fan et al., 3D reconstruction based on hierarchical reinforcement learning with transferability, Integr. Comput. Aided Eng. 30, 327–339 (2023) [Google Scholar]
  24. B. Felbrich, T. Schork, A. Menges, Autonomous robotic additive manufacturing through distributed model‐free deep reinforcement learning in computational design environments, Constr. Robotics 6, 15–37 (2022) [Google Scholar]
  25. A. Zhu, T. Dai, G. Xu et al., Deep reinforcement learning for real-time assembly planning in robot-based prefabricated construction, IEEE Trans. Autom. Sci. Eng. 20, 1515–1526 (2023) [Google Scholar]
  26. A. del Real Torres, D.S. Andreiana, A. Ojeda Roldan et al., A review of deep reinforcement learning approaches for smart manufacturing in industry 4.0 and 5.0 framework, Appl. Sci. 12, 12377–12407 (2022) [Google Scholar]
  27. S.R. Pokhrel, Learning from data streams for automation and orchestration of 6G industrial IoT: toward a semantic communication framework, Neural Comput. Appl. 34, 15197–15206 (2022) [Google Scholar]
  28. H. Shi, M. Yang, I. L. D. Makanda, W. Guo et al., Collective intelligence-driven 3D printing factory for social manufacturing: implementing a testbed for industrial application, Int. J. Comput. Integr. Manufac. 38, 362–385 (2025) [Google Scholar]
  29. W. Peng, W. Wang, Y. Wang et al., Key technologies and trends of active robotic 3-D measurement in intelligent manufacturing, IEEE/ASME Transac. Mechatronics 29, 4778–4799 (2024) [Google Scholar]
  30. X. Zhao, Z. Wang, The factory supply chain management optimization model based on digital twins and reinforcement learning, Scalable Comput. Pract. Exp. 26, 241–249 (2025) [Google Scholar]
  31. Y. Li, C. Yu, Flexible job shop scheduling with job precedence constraints: a deep reinforcement learning approach, J. Manufac. Mater. Process. 9, 216–241 (2025) [Google Scholar]
  32. Z. Guo, Y. Zhang, S. Liu et al., Exploring self-organization and self-adaption for smart manufacturing complex networks, Front. Eng. Manag. 10(2), 206–222 (2023) [Google Scholar]
  33. C.N. Idika, U.U. James, O.M. Ijiga et al., A. digital twin-enabled vulnerability assessment with zero trust policy enforcement in smart manufacturing cyber-physical system, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 9, 1–25 (2023) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.