TY - GEN
T1 - Software-defined Cloud Manufacturing in the Context of Industry 4.0
AU - Yang, Chen
AU - Lan, Shulin
AU - Shen, Weiming
AU - Huang, George Q.
AU - Wang, Lihui
N1 - Funding Information:
Research supported by the Beijing Intitute of Technology Research Fund Program for Young Scholars
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - In the practice of 'Cloud Manufacturing (CMfg)' or 'Industrial Internet', there still exist key problems, including: 1) big data analytics and decision-making in the cloud could not meet the requirements of time-sensitive manufacturing applications, moreover uploading ZettaBytes of future device data to the cloud may cause serious network congestion, 2) the manufacturing system lacks openness and evolvability, thus restricting the rapid optimization and transformation of the system, 3) big data from the shop-floor IoT devices and the internet has not been effectively utilized to guide the optimization and upgrade of the manufacturing system. In view of these key practical problems, we propose an open evolutionary architecture of intelligent CMfg system with collaborative edge and cloud processing capability. Hierarchical gateways near shop-floor things are introduced to enable fast processing for time-sensitive applications. Big data in another dimension from the software defined perspective will be used to decide the efficient operations and highly dynamic upgrade of the system. From the software system view, we also propose a new mode - AI-Mfg-Ops (AI-enabled Cloud Manufacturing Operations) with a supporting framework, which can promote the fast operation and upgrading of CMfg systems with AI enabled monitoring-analysis-planning-execution close loop. This work can improve the universality of CMfg for real-time fast response and operation upgrading.
AB - In the practice of 'Cloud Manufacturing (CMfg)' or 'Industrial Internet', there still exist key problems, including: 1) big data analytics and decision-making in the cloud could not meet the requirements of time-sensitive manufacturing applications, moreover uploading ZettaBytes of future device data to the cloud may cause serious network congestion, 2) the manufacturing system lacks openness and evolvability, thus restricting the rapid optimization and transformation of the system, 3) big data from the shop-floor IoT devices and the internet has not been effectively utilized to guide the optimization and upgrade of the manufacturing system. In view of these key practical problems, we propose an open evolutionary architecture of intelligent CMfg system with collaborative edge and cloud processing capability. Hierarchical gateways near shop-floor things are introduced to enable fast processing for time-sensitive applications. Big data in another dimension from the software defined perspective will be used to decide the efficient operations and highly dynamic upgrade of the system. From the software system view, we also propose a new mode - AI-Mfg-Ops (AI-enabled Cloud Manufacturing Operations) with a supporting framework, which can promote the fast operation and upgrading of CMfg systems with AI enabled monitoring-analysis-planning-execution close loop. This work can improve the universality of CMfg for real-time fast response and operation upgrading.
UR - http://www.scopus.com/inward/record.url?scp=85077810101&partnerID=8YFLogxK
U2 - 10.1109/WRC-SARA.2019.8931920
DO - 10.1109/WRC-SARA.2019.8931920
M3 - Conference article published in proceeding or book
AN - SCOPUS:85077810101
T3 - WRC SARA 2019 - World Robot Conference Symposium on Advanced Robotics and Automation 2019
SP - 184
EP - 190
BT - WRC SARA 2019 - World Robot Conference Symposium on Advanced Robotics and Automation 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2019
Y2 - 21 August 2019
ER -