TY - JOUR
T1 - Machine Learning for Industry 4.0 [From the Guest Editors]
AU - Zhou, Mengchu
AU - Qiao, Yan
AU - Liu, Bin
AU - Vogel-Heuser, Birgit
AU - Kim, Heeyoung
N1 - Publisher Copyright:
© 1994-2011 IEEE.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - The Fourth Industrial Revolution, also known as Industry 4.0, marks the technological shift from traditional manufacturing systems to smart cyberphysical systems. It leads to an improvement in overall productivity and a reduction in environmental impact and promotes sustainable economic development. Industry 4.0 has been driven by emerging technologies such as the Internet of Things (IoT), also called the Industrial Internet of Things; digital twins; artificial intelligence; cloud computing; and edge/fog computing [1], [2], [3], [4], [5]. It is a hot topic in both academia and industry. The implementation of IoT connects physical assets to cybernetworks and captures a significant amount of data. These data, often 'big', are then fed to AI-based mission-critical systems to perform production monitoring, quality inspection, fault root cause analysis, quality prediction, and process control. The proper adoption of relevant Industry 4.0 technologies should lead to significant efficiency improvements and cost reductions in various industrial sectors.
AB - The Fourth Industrial Revolution, also known as Industry 4.0, marks the technological shift from traditional manufacturing systems to smart cyberphysical systems. It leads to an improvement in overall productivity and a reduction in environmental impact and promotes sustainable economic development. Industry 4.0 has been driven by emerging technologies such as the Internet of Things (IoT), also called the Industrial Internet of Things; digital twins; artificial intelligence; cloud computing; and edge/fog computing [1], [2], [3], [4], [5]. It is a hot topic in both academia and industry. The implementation of IoT connects physical assets to cybernetworks and captures a significant amount of data. These data, often 'big', are then fed to AI-based mission-critical systems to perform production monitoring, quality inspection, fault root cause analysis, quality prediction, and process control. The proper adoption of relevant Industry 4.0 technologies should lead to significant efficiency improvements and cost reductions in various industrial sectors.
UR - http://www.scopus.com/inward/record.url?scp=85162814081&partnerID=8YFLogxK
U2 - 10.1109/MRA.2023.3266618
DO - 10.1109/MRA.2023.3266618
M3 - Review article
AN - SCOPUS:85162814081
SN - 1070-9932
VL - 30
SP - 8
EP - 9
JO - IEEE Robotics and Automation Magazine
JF - IEEE Robotics and Automation Magazine
IS - 2
ER -