Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges

Jiewu Leng, Xiaofeng Zhu, Zhiqiang Huang, Xingyu Li, Pai Zheng, Xueliang Zhou, Dimitris Mourtzis, Baicun Wang, Qinglin Qi, Haidong Shao, Jiafu Wan, Xin Chen, Lihui Wang, Qiang Liu

Research output: Journal article publicationReview articleAcademic researchpeer-review

67 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)349-363
Number of pages15
JournalJournal of Manufacturing Systems
Volume73
DOIs
Publication statusPublished - Apr 2024

Keywords

  • Collaborative intelligence
  • Crowd intelligence
  • Industrial artificial intelligence
  • Industry 5.0
  • Self-learning intelligence

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Hardware and Architecture
  • Industrial and Manufacturing Engineering

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