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Industrial Agents for the Fast Deployment of Evolvable Assembly Systems
Linköping University, Department of Management and Engineering, Manufacturing Engineering. Linköping University, Faculty of Science & Engineering. (Manufacturing Engineering)ORCID iD: 0000-0002-0248-8180
Uninova - CTS, Departamento de Engenharia Electrotécnica, Faculdade de Ciências e Tecnologia, Portugal.
KTH, Royal Institute of Technology, Stockholm, Sweden.
Festo AG & Co. KG, Esslingen, Germany.
2015 (English)In: Industrial Agents: Emerging Applications of Software Agents in Industry / [ed] Paulo Leitao and Stamatis Karnouskos, Amsterdam, Netherlands: Elsevier, 2015, 1st ed., 301-321 p.Chapter in book (Refereed)
Abstract [en]

The current manufacturing scenario is characterized by high market unpredictability. Agility is therefore a central challenge for modern companies that need to understand and be proactive towards their product offer in respect to “what is offered, when it is offered, where, how and by whom” (Brown & Bessant 2003).

The “what” and the “when” are particularly relevant to the research in emerging paradigms as they account for variety, customization and volume; and timing, speed and seasonality (Brown & Bessant 2003).

In this scenario, several design approaches and models have been proposed in the last decade to enable re-configurability and subsequently enhance the companies’ ability to adjust their offer in nature and time.

From a paradigmatic point of view research has concentrated on the organizational structure of the shop-floor and the associated controls aspects. Concepts like Reconfigurable Manufacturing Systems (RMS) (Koren & Shpitalni 2010) and Fractal Factories (FF) (Montreuil 1999) support the physical construction of production systems by regulating their layout and making a few assumptions on their logical organization. On the other hand, concepts like Bionic Manufacturing Systems (BMS)(Ueda 1992), Holonic Manufacturing Systems (HMS)(Van Brussel et al. 1998), Evolvable Assembly Systems (Ribeiro et al. 2010) essentially provide the theoretical guidelines for the logical/computational organization of the system (see (Tharumarajah 1996) for a comparison between BMS, HMS and FF and (Setchi & Lagos 2004) for the rationale supporting the shift from Dedicated Lines to Flexible Manufacturing System and finally RMS).

While these paradigms provide the conceptual framework and the main design guidelines their actual interpretation and implementation has led to a wider set of architectures (Monostori, Váncza & Kumara 2006; Leitão 2009; Parunak 2000; Pěchouček & Mařík 2008).

These architectures align the high-level principles with the technological offer and limitations while seeking to address the re-configurability requirements of (Mehrabi, Ulsoy & Koren 2000; Rösiö & Säfsten 2013):

  • module mobility – modules are easy and quick to move and install;
  • “diagnosability” – it is quick to identify the sources of quality and reliability problems;
  • “integrability” – modules are easy to integrate into the rest of the system.
  • “convertibility” – it is easy and quick to switch between existing products and it is easy to adapt the system to future products;
  • scalability – it is easy to enlarge and downsize the production system;
  • “automatibility” – a dynamic level of automation is enabled;
  • modularity – all system elements are designed to be modular;
  • customization – the capability and flexibility of the production system is designed according to the products to be produced in the system.

Instant deployment, as addressed in the present chapter directly addresses mobility, “integrability”, “convertibility”, scalability and customization. Mechatronic modularity is a prerequisite and is enforced by the proposed architecture and the considered modular design. “Diagnosability” was not specifically tackled.

In this context, the chapter analyses the agent-based architecture related with the Instantly Deployable Evolvable Assembly System (IDEAS) project that is inspired by the Evolvable Assembly System (EAS) paradigm (Ribeiro et al. 2010) as a mechanism to enable fast deployment of mechatronic modules. EAS advocates the use of process-oriented modules and envisions the production system as a collection of processes and the associated interacting agents.

The architecture and the related test cases are used to draw the main lessons learned in respect to technological and conceptual implications.

In this context, the remainder of this text is organized as follows: section 1.1 discusses the main deployment challenges, section 1.2 details the reference architecture and associated concepts, section 1.3 presents the principal implementation decisions, section 1.4 features the main lessons learned, sections 1.5 discusses the benefits of the proposed approach and finally section 1.6 reflects on the main conclusions.

Place, publisher, year, edition, pages
Amsterdam, Netherlands: Elsevier, 2015, 1st ed.. 301-321 p.
Keyword [en]
Multiagent-based Industrial System, Plug and Produce, Intelligent Automation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems Control Engineering Embedded Systems Other Engineering and Technologies not elsewhere specified Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:liu:diva-121688DOI: 10.1016/B978-0-12-800341-1.00017-6ISBN: 978-0-12-800341-1OAI: oai:DiVA.org:liu-121688DiVA: diva2:858175
Available from: 2015-10-01 Created: 2015-10-01 Last updated: 2015-10-07Bibliographically approved

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