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International Journal of AI for
            Materials and Design
                                                                               A unified industrial AI foundation framework


            10.  Su H, Lee J. Machine learning approaches for diagnostics      doi: 10.1016/j.mfglet.2022.09.004
               and prognostics of industrial systems using open source   21.  Buchgeher G, Gabauer D, Martinez-Gil J, Ehrlinger L.
               data from PHM data challenges: A review. Int J Progn Health   Knowledge  graphs in manufacturing and production: A
               Manag. 2024;15(2):1-26.
                                                                  systematic literature review. IEEE Access. 2021;9:55537-55554.
               doi: 10.36001/ijphm.2024.v15i2.3993
                                                                  doi: 10.1109/access.2021.3070395
            11.  Yan J, Meng Y, Lu L, Li L. Industrial big data in an industry
               4.0 environment: Challenges, schemes, and applications for   22.  Zhong L, Wu J, Li Q, Peng H, Wu X. A comprehensive survey
               predictive maintenance. IEEE Access. 2017;5:23484-23491.  on automatic knowledge graph construction. ACM Comput
                                                                  Surv. 2023;56(4):1-62.
               doi: 10.1109/access.2017.2765544
                                                                  doi: 10.1145/3618295
            12.  Wang J, Xu C, Zhang J, Zhong R. Big data analytics for
               intelligent manufacturing systems: A review. J Manuf Syst.   23.  Pan S, Luo L, Wang Y, Chen C, Wang J, Wu X. Unifying large
               2021;62:738-752.                                   language models and knowledge graphs: A roadmap. IEEE
                                                                  Trans Knowl Data Eng. 2024;36(7):3580-3599.
               doi: 10.1016/j.jmsy.2021.03.005
                                                                  doi: 10.1109/tkde.2024.3352100
            13.  Pivoto DGS, de Almeida LFF, da Rosa Righi R,
               Rodrigues JJPC, Lugli AB, Alberti AM. Cyber-physical   24.  Kirk JR, Wray RE, Lindes P, Laird JE. Improving knowledge
               systems  architectures  for  industrial  internet  of  things   extraction from LLMs for task learning through agent
               applications in Industry 4.0: A  literature review.  J  Manuf   analysis.  Proc  AAAI  Conf  Artif  Intell. 2024;38(16):
               Syst. 2021;58:176-192.                             18390-18398.
               doi: 10.1016/j.jmsy.2020.11.017                    doi: 10.1609/aaai.v38i16.29799
            14.  Lee J, Bagheri B, Kao HA. A  cyber-physical systems   25.  Han  X, Cao  S,  Lv X,  et al.  OpenKE:  An Open  Toolkit
               architecture for industry 4.0-based manufacturing systems.   for Knowledge Embedding. In:  Proceedings of the 2018
               Manuf Lett. 2014;3:18-23.                          Conference on Empirical Methods in Natural Language
                                                                  Processing: System Demonstrations. Association for
               doi: 10.1016/j.mfglet.2014.12.001                  Computational Linguistics; 2018.
            15.  Zhang X, Ming X, Liu Z, Yin D, Chen Z, Chang Y.   doi: 10.18653/v1/D18-2024
               A reference framework and overall planning of industrial
               artificial intelligence  (I-AI)  for  new application scenarios.   26.  Zhang H, Khashabi D, Song Y, Roth D. TransOMCS:
               Int J Adv Manuf Technol. 2018;101(9-12):2367-2389.  From Linguistic Graphs to Commonsense Knowledge.
                                                                  In:  Proceedings of the Twenty-Ninth International Joint
               doi: 10.1007/s00170-018-3106-3
                                                                  Conference on Artificial Intelligence (IJCAI-20). 2020.
            16.  Yang T, Yi X, Lu S, Johansson KH, Chai T. Intelligent      doi: 10.24963/ijcai.2020/554
               manufacturing for the process industry driven by industrial
               artificial intelligence. Engineering. 2021;7(9):1224-1230.  27.  Li Y, Zou L. gBuilder: A  Scalable knowledge  graph
                                                                  construction system for unstructured corpus. arXiv. 2023.
               doi: 10.1016/j.eng.2021.04.023
                                                                  doi: 10.48550/arXiv.2208.09705
            17.  Ahmed I, Jeon G, Piccialli F. From artificial intelligence to
               explainable artificial intelligence in industry 4.0: A survey   28.  Brown T, Mann B, Ryder N, et al. Language models are few-
               on what, how, and where.  IEEE Trans Industr Inform.   shot learners. arXiv. 2020.
               2022;18(8):5031-5042.
                                                                  doi: 10.48550/arXiv.2005.14165
               doi: 10.1109/tii.2022.3146552
                                                               29.  Achiam J, Adler S, Agarwal S, et al. GPT-4 technical report.
            18.  Tao F, Zhang H, Liu A, Nee AYC. Digital twin in industry:   arXiv. 2024.
               State-of-the-art.  IEEE  Trans  Industr  Inform. 2019;15(4):      doi: 10.48550/arXiv.2303.08774
               2405-2415.
                                                               30.  Touvron H, Lavril T, Izacard G, et al. LLaMA: Open and
               doi: 10.1109/tii.2018.2873186
                                                                  efficient foundation language models. arXiv. 2023.
            19.  Lee J, Azamfar M, Singh J, Siahpour S. Integration of digital      doi: 10.48550/arXiv.2302.13971
               twin and deep learning in cyber-physical systems: Towards
               smart manufacturing. IET Collab Intell Manuf. 2020;2:34-36.  31.  Touvron H, Martin L, Stone  K,  et al. LLaMA  2: Open
                                                                  foundation and fine-tuned chat models. arXiv. 2023.
               doi: 10.1049/iet-cim.2020.0009
                                                                  doi: 10.48550/arXiv.2307.09288
            20.  Lee J, Gore P, Jia X, Siahpour S, Kundu P, Sun K. Stream-
               of-quality methodology for industrial internet-based   32.  Chowdhery A, Narang S, Devlin J,  et al. PaLM: Scaling
               manufacturing system. Manuf Lett. 2022;34:58-61.   language modeling with pathways. arXiv. 2022.


            Volume 2 Issue 2 (2025)                         66                        doi: 10.36922/IJAMD025080006
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