TY - JOUR
T1 - Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges
AU - Leng, Jiewu
AU - Zhu, Xiaofeng
AU - Huang, Zhiqiang
AU - Li, Xingyu
AU - Zheng, Pai
AU - Zhou, Xueliang
AU - Mourtzis, Dimitris
AU - Wang, Baicun
AU - Qi, Qinglin
AU - Shao, Haidong
AU - Wan, Jiafu
AU - Chen, Xin
AU - Wang, Lihui
AU - Liu, Qiang
N1 - Publisher Copyright:
© 2024 The Society of Manufacturing Engineers
PY - 2024/4
Y1 - 2024/4
N2 - 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.
AB - 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.
KW - Collaborative intelligence
KW - Crowd intelligence
KW - Industrial artificial intelligence
KW - Industry 5.0
KW - Self-learning intelligence
UR - http://www.scopus.com/inward/record.url?scp=85186608494&partnerID=8YFLogxK
U2 - 10.1016/j.jmsy.2024.02.010
DO - 10.1016/j.jmsy.2024.02.010
M3 - Review article
AN - SCOPUS:85186608494
SN - 0278-6125
VL - 73
SP - 349
EP - 363
JO - Journal of Manufacturing Systems
JF - Journal of Manufacturing Systems
ER -