Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challengesShow others and affiliations
2024 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 73, p. 349-363Article, review/survey (Refereed) Published
Abstract [en]
With the continuous development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for additional functionalities, new features, and tendencies in the industrial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in full swing. However, there exist many unreasonable designs, configurations, and implementations of Industrial Artificial Intelligence (IndAI) in practice before achieving either Industry 4.0 or Industry 5.0 vision, and a significant gap between the individualized requirement and actual implementation result still exists. To provide insights for designing appropriate models and algorithms in the upgrading process of the industry, this perspective article classifies IndAI by rating the intelligence levels and presents four principles of implementing IndAI. Three significant opportunities of IndAI, namely, collaborative intelligence, self-learning intelligence, and crowd intelligence, towards Industry 5.0 vision are identified to promote the transition from a technology-driven initiative in Industry 4.0 to the coexistence and interplay of Industry 4.0 and a value-oriented proposition in Industry 5.0. Then, pathways for implementing IndAI towards Industry 5.0 together with key empowering techniques are discussed. Social barriers, technology challenges, and future research directions of IndAI are concluded, respectively. We believe that our effort can lay a foundation for unlocking the power of IndAI in futuristic Industry 5.0 research and engineering practice.
Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 73, p. 349-363
Keywords [en]
Collaborative intelligence, Crowd intelligence, Industrial artificial intelligence, Industry 5.0, Self-learning intelligence
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-344372DOI: 10.1016/j.jmsy.2024.02.010ISI: 001225738400001Scopus ID: 2-s2.0-85186608494OAI: oai:DiVA.org:kth-344372DiVA, id: diva2:1844376
Note
QC 20240604
2024-03-132024-03-132024-06-04Bibliographically approved