Page 52 - IJAMD-1-2

International Journal of AI for
Materials and Design
A unified ILKM in smart manufacturing

Funding doi: 10.1016/j.mfglet.2014.12.001

None. 7. Sisinni E, Saifullah A, Han S, Jennehag U, Gidlund M.
Industrial internet of things: Challenges, opportunities, and
Conflict of interest directions. IEEE Trans Ind Inform. 2018;14(11):4724-4734.

Jay Lee is an Editorial Board Member of this journal, but doi: 10.1109/tii.2018.2852491
was not in any way involved in the editorial and peer-review 8. Zhao WX, Zhou K, Li J, et al. A Survey of Large Language
process conducted for this paper, directly or indirectly. Models. arXiv.org.
Separately, other authors declared that they have no known doi: 10.48550/arXiv.2303.18223
competing financial interests or personal relationships that
could have influenced the work reported in this paper. 9. Chang Y, Wang X, Wang J, et al. A survey on evaluation
of large language models. ACM Trans Intell Syst Technol.
Author contributions 2024;15(3):1-45.
Conceptualization: All authors doi: 10.1145/3641289
Writing – original draft: Hanqi Su 10. Raptis TP, Passarella A, Conti M. Data management in
Writing – review & editing: All authors industry 4.0: State of the art and open challenges. IEEE
Access. 2019;7:97052-97093.
Ethics approval and consent to participate doi: 10.1109/access.2019.2929296
Not applicable. 11. Shafiq SI, Szczerbicki E, Sanin C. Proposition of the
methodology for data acquisition, analysis and visualization
Consent for publication in support of industry 4.0. Procedia Comput Sci.
Not applicable. 2019;159:1976-1985.
doi: 10.1016/j.procs.2019.09.370
Availability of data
12. Jan Z, Ahamed F, Mayer W, et al. Artificial intelligence for
Not applicable. industry 4.0: Systematic review of applications, challenges,
and opportunities. Expert Syst Appl. 2023;216(216):119456.
References
doi: 10.1016/j.eswa.2022.119456
1. Lasi H, Fettke P, Kemper HG, Feld T, Hoffmann M. Industry
4.0. Bus Inf Syst Eng. 2014;6(4):239-242. 13. Goertzel B. Artificial general intelligence: Concept, state
of the art, and future prospects. J Artif General Intell.
doi: 10.1007/s12599-014-0334-4 2014;5(1):1-48.
2. Yan J, Meng Y, Lu L, Li L. Industrial big data in an industry doi: 10.2478/jagi-2014-0001
4.0 environment: Challenges, schemes, and applications for
predictive maintenance. IEEE Access. 2017;5:23484-23491. 14. Yandrapalli V. Revolutionizing supply chains using
power of generative AI. Int J Res Publication Rev.
doi:10.1109/access.2017.2765544 2023;4(12):1556-1562.
3. Lee J, Davari H, Singh J, Pandhare V. Industrial artificial doi: 10.55248/gengpi.4.1223.123417
intelligence for industry 4.0-based manufacturing systems.
Manuf Lett. 2018;18:20-23. 15. Lahat D, Adali T, Jutten C. Multimodal data fusion: An
overview of methods, challenges and prospects. Proc IEEE.
doi: 10.1016/j.mfglet.2018.09.002
2015;103(9):1449-1477.
4. Sharp M, Ak R, Hedberg T. A survey of the advancing use and doi: 10.1109/jproc.2015.2460697
development of machine learning in smart manufacturing.
J Manuf Syst. 2018;48:170-179. 16. Zhang S, Dong L, Li X, et al. Instruction Tuning for Large
Language Models: A Survey. arXiv.org.
doi: 10.1016/j.jmsy.2018.02.004
doi: 10.48550/arXiv.2308.10792
5. Wang J, Ma Y, Zhang L, Gao RX, Wu D. Deep learning for
smart manufacturing: Methods and applications. J Manuf 17. Gao Y, Xiong Y, Gao X, et al. Retrieval-Augmented
Syst. 2018;48:144-156. Generation for Large Language Models: A Survey. arXiv.org.
doi: 10.1016/j.jmsy.2018.01.003 doi: 10.48550/arXiv.2312.10997
6. Lee J, Bagheri B, Kao HA. A cyber-physical systems 18. Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D.
architecture for industry 4.0-based manufacturing systems. Deep Reinforcement Learning from Human Preferences.
Manuf Lett. 2015;3:18-23. California: Neural Information Processing Systems; 2017.

Volume 1 Issue 2 (2024) 46 doi: 10.36922/ijamd.3681