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BIOSOARM: a bio-inspired self-organising architecture for manufacturing cyber-physical shopfloors
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.ORCID iD: 0000-0002-6607-9634
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
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2016 (English)In: Journal of Intelligent Manufacturing, ISSN 0956-5515, E-ISSN 1572-8145, 1-24 p.Article in journal (Refereed) Published
Abstract [en]

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.

Place, publisher, year, edition, pages
Springer, 2016. 1-24 p.
Keyword [en]
Bio-inspired production systems, Cyber-physical production systems, Self-organisation, Architecture, Dynamics, Heuristic methods, Manufacture, Optimization, Virtual reality, Design and implementations, Design of production systems, Dynamic environments, Intrinsic characteristics, Optimization problems, Production system, Reference architecture, Self organisation, Memory architecture
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:kth:diva-194859DOI: 10.1007/s10845-016-1258-2ScopusID: 2-s2.0-84986268581OAI: oai:DiVA.org:kth-194859DiVA: diva2:1054873
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QC 20161209

Available from: 2016-12-09 Created: 2016-11-01 Last updated: 2016-12-09Bibliographically approved

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Dias-Ferreira, JoaoAkillioglu, HakanNeves, PedroOnori, Mauro
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