Next Article in Journal
Improved Accuracy of the Asymmetric Second-Order Vegetation Isoline Equation over the RED–NIR Reflectance Space
Next Article in Special Issue
A Context-Aware S-Health Service System for Drivers
Previous Article in Journal
Temperature Mapping of 3D Printed Polymer Plates: Experimental and Numerical Study
Open AccessArticle

Holistic Context-Sensitivity for Run-Time Optimization of Flexible Manufacturing Systems

1
Institut für angewandte Systemtechnik Bremen GmbH, 28359 Bremen, Germany
2
DEE-FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Francisco Javier Falcone Lanas
Sensors 2017, 17(3), 455; https://doi.org/10.3390/s17030455
Received: 28 December 2016 / Revised: 13 February 2017 / Accepted: 21 February 2017 / Published: 24 February 2017
(This article belongs to the Special Issue Context Aware Environments and Applications)
Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to diverse parameters, e.g., efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach based on data streaming from high amount of sensors and other data sources. Cyber-physical systems play an important role as sources of information to achieve context sensitivity. Cyber-physical systems can be seen as complex intelligent sensors providing data needed to identify the current context under which the manufacturing system is operating. In this paper, it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of cyber-physical systems integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution encompasses run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes. View Full-Text
Keywords: context sensitivity; cyber-physical systems; sensors for context extraction; flexible manufacturing system; process optimization; self-learning systems; SOA context sensitivity; cyber-physical systems; sensors for context extraction; flexible manufacturing system; process optimization; self-learning systems; SOA
Show Figures

Figure 1

MDPI and ACS Style

Scholze, S.; Barata, J.; Stokic, D. Holistic Context-Sensitivity for Run-Time Optimization of Flexible Manufacturing Systems. Sensors 2017, 17, 455.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop