AccScience Publishing / IJAMD / Online First / DOI: 10.36922/ijamd.3681
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PERSPECTIVE ARTICLE

A unified industrial large knowledge model framework in Industry 4.0 and smart manufacturing

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 2024, 1(2), 41–47; https://doi.org/10.36922/ijamd.3681
Submitted: 16 May 2024 | Accepted: 4 June 2024 | Published: 24 July 2024
© 2024 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

The recent emergence of large language models (LLMs) demonstrates the potential for artificial general intelligence, revealing new opportunities in Industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such specialized domain knowledge is vital for effectively addressing the complex needs of industrial applications. To bridge this gap, this paper proposes a unified industrial large knowledge model (ILKM) framework, emphasizing its potential to revolutionize future industries. In addition, ILKMs and LLMs are compared from eight perspectives. Finally, the “6S Principle” is proposed as the guideline for ILKM development, and several potential opportunities are highlighted for ILKM deployment in Industry 4.0 and smart manufacturing.

Keywords
Industrial large knowledge model
Large language model
Machine learning
Industrial artificial intelligence
Industry 4.0
Smart manufacturing
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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