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Iterative Channel Theoretic Analysis for the Identification of Relationships of Interest in Complex Environments

Building up a Matching Framework for Smart Manufacturing
: Bildstein, Andreas
: Feng, Jungkang; Crowe, Malcom; Riekert, Wolf-Fritz

Fulltext urn:nbn:de:0011-n-5866344 (8.8 MByte PDF)
MD5 Fingerprint: 5ca1fa77e3d6fbe519ef93f448d2da06
Created on: 5.5.2020

Paisley, 2018, 270 pp.
Paisley, Univ., Diss., 2018
Dissertation, Electronic Publication
Fraunhofer IPA ()
Smart Manufacturing; framework; Informationsverarbeitung; Autonomisierung

Finding relationships between two or more different sets of things is a common challenge in research and daily life. The mathematically rigorous theory of Information Flow, also known as Channel Theory put forward by Barwise and Seligman in 1997, deals with the identification and formulation of informational relationships among components of a distributed system whereby the behaviour of such a system can be well understood. This thesis presents a novel approach to the application of Channel Theory in complex situations and its implementation. The core of this approach is an iterative Channel theoretic analysis for the identification of relationships of interest. This analysis is based on an understanding of how complex situations can be handled and on using ontologies and description logic to build the main components of an IF framework. The latter is also proved to be an effective means of constructing information channels for tackling complex situations. The proposed approach and associated methodology can be used in the application area of manufacturing where the current movement of smart manufacturing seeks for means that are able to support a certain degree of self-organisation and autonomy of future production systems. This research work contributes towards this goal by showing how a matching framework based on the presented novel approach for the application of Channel Theory can be built both theoretically and practically to support production systems in the complex task of equipment selection for a specific production step.
A comprehensive literature review about the state of the art in the application of Channel Theory showed that Channel Theory had not been applied in complex environments like the manufacturing domain until this project started. Furthermore, an attempt to apply Channel Theory in this application field according to the state of the art approaches for the application of Information Flow showed that these approaches are not able to address the high complexity in this application area adequately. Based on this observation and an investigation of the essence of complex environments, a methodology for the application of Channel Theory was developed, which iteratively constructs information channels on several layers of abstraction to tame the complexity in the matching process of finding suitable production equipment for a specific production step. After successfully constructing this theoretical matching framework in the context of the equipment selection process, a generalisation of this methodology was elaborated to show that this way of applying Channel Theory has universal characteristics. That universality enables the methodology to be used in any complex application scenario where relationships between two sets of things have to be identified as long as the application scenario can be modelled hierarchically. Finally, the proposed methodology and its components were transformed into a prototypical design and implementation of an IF matching framework demonstrating that the used Semantic Web technologies and service-oriented architecture are perfectly suitable to build up such an IF matching framework.