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PERSPECTIVE ARTICLE

Rethinking industrial artificial intelligence: A unified foundation framework

Jay Lee1 ,  Hanqi Su1*
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1 Center for Industrial Artificial Intelligence, Department of Mechanical Engineering, A. James Clark School of Engineering, University of Maryland, College Park, Maryland, United States of America
IJAMD, 025080006 https://doi.org/10.36922/IJAMD025080006
Submitted: 21 February 2025 | Revised: 28 March 2025 | Accepted: 2 April 2025 | Published: 15 April 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.

Keywords
Industrial artificial intelligence
Industry 4.0
Machine learning
Deep learning
Large language model
Domain knowledge
Funding
None.
Conflict of interest
Jay Lee is an Editorial Board Member of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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