Title
Towards Flexibility In Future Industrial Manufacturing: A Global Framework For Self-Organization Of Production Cells
Abstract
The future of manufacturing leads to flexible industrial facilities in which production lines or systems are composed by several production cells. Production cells can be reorganized and reconfigured by introducing new devices, equipment, functionalities or even by re-configuring the communication network. In this context, machine-to-machine communication does not only provide a transport layer for monitoring and control, but also provide a high-level distributed service framework and data management system. In this contribution, the authors address the challenge to manage the self-organization of production cells by means of a global framework. This framework bases on the following technologies: RobotML for the scenario description, OPC UA for service orchestration, object memories for distributed data sharing, Frama-C/Para-C for code verification and SDN for network reconfiguration. This framework has been deployed within a use case involving the SYBOT collaborative robot and a reconfigurable Raspberry-Pi based camera to enhance human operator safety. Experiments show that from a high-level description of the scenario, it was possible to automatically orchestrate at the OPC UA level the different reconfigurations of the production cell. (C) 2016 The Authors. Published by Elsevier B.V.
Year
DOI
Venue
2016
10.1016/j.procs.2016.04.264
7TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2016) / THE 6TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2016) / AFFILIATED WORKSHOPS
Keywords
Field
DocType
Reconfigurable Manufacturing, Machine-to-machine Communication, Orchestration Framework
Machine to machine,Data mining,Manufacturing,Telecommunications network,Computer science,Data sharing,Production line,Robot,Orchestration (computing),Data management,Operating system,Distributed computing
Conference
Volume
ISSN
Citations 
83
1877-0509
3
PageRank 
References 
Authors
0.51
1
13