Page 71 - IJAMD-2-2

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

7. Conclusion Availability of data

This paper proposes a unified industrial AI foundation The dataset used in the case study is available for download
framework, structured into three core modules – the from https://data.phmsociety.org/phm2023-conference-
knowledge module, data module, and model module – data-challenge/.
which collectively support and enhance the industrial
AI methodology platform. The role of industrial AI is References
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discussed. Overall, our unified industrial AI foundation doi: 10.1016/j.eswa.2022.119456
framework provides a systematic approach to developing, 2. Peres RS, Jia X, Lee J, Sun K, Colombo AW, Barata J.
validating, and deploying industrial AI solutions for future Industrial artificial intelligence in industry 4.0 - systematic
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Acknowledgments
doi: 10.1016/j.mfglet.2018.09.002
None.
5. Sisinni E, Saifullah A, Han S, Jennehag U, Gidlund M.
Funding Industrial internet of things: Challenges, opportunities,
and directions. IEEE Trans Industr Inform. 2018;14(11):
None. 4724-4734.
doi: 10.1109/tii.2018.2852491
Conflict of interest
6. Khalil RA, Saeed N, Masood M, Fard YM, Alouini MS,
Jay Lee is an Editorial Board Member of this journal but was Al-Naffouri TY. Deep learning in the industrial internet of
not in any way involved in the editorial and peer-review Things: Potentials, challenges, and emerging applications.
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Separately, other authors declared that they have no known doi: 10.1109/jiot.2021.3051414
competing financial interests or personal relationships that
could have influenced the work reported in this paper. 7. Lee J, Su H. A unified industrial large knowledge model
framework in Industry 4.0 and smart manufacturing. Int J
Author contributions AI Mater Des. 2024;1(2):41-47.
Conceptualization: All authors doi: 10.36922/ijamd.3681
Writing–original draft: Hanqi Su 8. Chang Y, Wang X, Wang J, et al. A survey on evaluation
Writing–review & editing: All authors of large language models. ACM Trans Intell Syst Technol.
2024;15(3):1-45.
Ethics approval and consent to participate doi: 10.1145/3641289

Not applicable. 9. Zio E. Prognostics and Health Management (PHM): Where
are we and where do we (need to) go in theory and practice.
Consent for publication Reliab Eng Syst Saf. 2021;218:108119.

Not applicable. doi: 10.1016/j.ress.2021.108119

Volume 2 Issue 2 (2025) 65 doi: 10.36922/IJAMD025080006