TY - GEN
T1 - Design RFCS with LonWorks technology in internet of things manufacturing
AU - Chen, Hong
AU - Zhu, Yue
AU - Wu, Jiande
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2017/3/2
Y1 - 2017/3/2
N2 - In order to get the real-time visibility into manufacturing productivity, the effective data network control in IoT manufacturing must be set up. In this paper the Real-time Factory Control System (RFCS) is presented. The key technologies including the Power Line Communication, the LonWorks technology and Six Sigma Methodology are utilized to design and evaluate RFCS. The performance of this RFCS is verified with 32% improvement on average production output. In comparison to the previous publications, this RFCS achieves effective and smart data center network control with superiority of the IoT manufacturing efficiency. © 2016 IEEE.
AB - In order to get the real-time visibility into manufacturing productivity, the effective data network control in IoT manufacturing must be set up. In this paper the Real-time Factory Control System (RFCS) is presented. The key technologies including the Power Line Communication, the LonWorks technology and Six Sigma Methodology are utilized to design and evaluate RFCS. The performance of this RFCS is verified with 32% improvement on average production output. In comparison to the previous publications, this RFCS achieves effective and smart data center network control with superiority of the IoT manufacturing efficiency. © 2016 IEEE.
KW - Cloud Computing
KW - IoT manufacturing
KW - Lonworks technology
KW - PLC
KW - Real-time Factory Control System
KW - Six Sigma Methodology
UR - http://www.scopus.com/inward/record.url?scp=85017100020&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85017100020&origin=recordpage
U2 - 10.1109/SCNS.2016.7870559
DO - 10.1109/SCNS.2016.7870559
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509044764
T3 - 2016 3rd Smart Cloud Networks and Systems, SCNS 2016
BT - 2016 3rd Smart Cloud Networks and Systems, SCNS 2016
PB - IEEE
T2 - 3rd Smart Cloud Networks and Systems, SCNS 2016
Y2 - 19 December 2016 through 21 December 2016
ER -