TY - JOUR
T1 - ResourceNet
T2 - A collaboration network among decentralised manufacturing resources for autonomous exception-handling in smart manufacturing
AU - Qian, Cheng
AU - Sun, Wen
AU - Zhang, Yingfeng
N1 - Publisher Copyright:
© 2020 Institution of Engineering and Technology. All rights reserved.
PY - 2020/9
Y1 - 2020/9
N2 - The fast-changing market and increasing demand for customised products have imposed manufacturers to improve the flexibility and robustness of their manufacturing execution systems. The ability to recover from exceptional events is fundamental to autonomous manufacturing systems in the era of smart manufacturing. Currently, the various types of manufacturing exceptions remain unclear. This research investigated the typical exceptions and proposed a general framework that supports the autonomous exception-handling and resource discovery in dynamic environments. A multi-layer peer-to-peer network is used to model the resources, services, and events in decentralised manufacturing systems. The exception-handling mechanism is designed that incorporates rule-based reactions, matching of features, recording of behaviour patterns etc. The feasibility of the proposed methods is also discussed, which shows many exceptions that were ignored in previous scheduling models can be timely identified and easily handled. This research explores the complex relations among manufacturing resources and provides an intelligent overall framework for self-organising manufacturing with self-diagnose capabilities.
AB - The fast-changing market and increasing demand for customised products have imposed manufacturers to improve the flexibility and robustness of their manufacturing execution systems. The ability to recover from exceptional events is fundamental to autonomous manufacturing systems in the era of smart manufacturing. Currently, the various types of manufacturing exceptions remain unclear. This research investigated the typical exceptions and proposed a general framework that supports the autonomous exception-handling and resource discovery in dynamic environments. A multi-layer peer-to-peer network is used to model the resources, services, and events in decentralised manufacturing systems. The exception-handling mechanism is designed that incorporates rule-based reactions, matching of features, recording of behaviour patterns etc. The feasibility of the proposed methods is also discussed, which shows many exceptions that were ignored in previous scheduling models can be timely identified and easily handled. This research explores the complex relations among manufacturing resources and provides an intelligent overall framework for self-organising manufacturing with self-diagnose capabilities.
UR - http://www.scopus.com/inward/record.url?scp=85103803538&partnerID=8YFLogxK
U2 - 10.1049/IET-CIM.2019.0066
DO - 10.1049/IET-CIM.2019.0066
M3 - 文章
AN - SCOPUS:85103803538
SN - 2516-8398
VL - 2
SP - 109
EP - 114
JO - IET Collaborative Intelligent Manufacturing
JF - IET Collaborative Intelligent Manufacturing
IS - 3
ER -