Saturday , June 23 2018

Reconfigurable Knowledge-based Control Solutions for Responsive Manufacturing Systems

Alessandro BRUSAFERI

Institute of Industrial Technologies and Automation, National Research Council, via Bassini 15, Milan 20133, Italy

Andrea BALLARINO

Institute of Industrial Technologies and Automation, National Research Council, via Bassini 15, Milan 20133, Italy

Emanuele CARPANZANO
Institute of Industrial Technologies and Automation, National Research Council, via Bassini 15, Milan 20133, Italy

Abstract: Nowadays, a new generation of responsive factories is needed to face continuous changes in product demand and variety, and to manage complex and variant production processes. To such an aim, innovative self-adaptive automation solutions are required, capable to adapt their control strategies in real-time to cope with planned as well as unforeseen product and process variations. In such a context, the present paper describes an automation solution based on a modular distributed approach for agile factories integration and reconfiguration, integrating a knowledge based cooperation policy providing self-adaptation to endogenous as well as exogenous events. The proposed approach is discussed through its application to a plant for customized shoes manufacturing.

Keywords: Distributed Control, Reconfigurable Manufacturing Systems, IEC 61499, Multi-agent, Semantic Web.

>>Full text

CITE THIS PAPER AS:

Alessandro BRUSAFERRI, Andrea BALLARINO, Emanuele CARPANZANO, Reconfigurable Knowledge-based Control Solutions for Responsive Manufacturing Systems, Studies in Informatics and Control, ISSN 1220-1766, vol. 20 (1), pp. 31-42, 2011.

1. Introduction

To face new consumer centered manufacturing paradigms, like mass customization and personalization, factories must be capable to adapt themselves in real time to continuously changing market demand. Thus, the whole production cycle for small or even single batches has to be executed in very short times, i.e. a few days or even hours. In order to properly approach such complex and strict requirements adaptive knowledge based production systems have to be developed. In particular, the conception and development of a new generation of automation solutions, that integrate all factory levels from machines controls up to shop-floor supervision and production planning in a unique real time framework, is mandatory. Future factory automation systems have to be modular, open, agile and knowledge based in order to promptly self-adapt themselves to changing exogenous conditions, like consumers expectations, market dynamics, design innovation, new materials and components integration. To such an aim, a new generation of intelligent, highly-interoperable and self-reconfigurable control systems is a fundamental enabling technology.

To tackle such a challenge, agile manufacturing paradigms – particularly flexible manufacturing systems (FMS) – have been adopted, often proving to be expensive and difficult to manage due to overall complexity. Furthermore, the integration of flexibility capability is not feasible for any kind of application. Therefore, to overcome such barriers and to provide cost effective flexible solutions, Reconfigurable Manufacturing Systems (RMS) have been introduced, characterized by strongly modular architectures and easy reconfiguration capabilities. Therefore, modularity, integrability, diagnosability, customization and convertibility are identified as key features of a RMS [1]. Among these, system modularity can surely be considered the most important property, as outlined in [2] where implications and relationships between the architecture of a logic control system, its modularity and the overall system reconfigurability, are discussed. The problem of agile systems reconfiguration has been faced mainly from the mechanical point of view with the development of easily pluggable mechatronic solutions.

Nevertheless, proper solutions addressing a fully modular and reconfigurable control system have still to be identified. As a matter of fact, present automation approaches and architectures – adopted in current industrial practice – are still based on rigid, loosely-coupled solutions, difficult to manage and to adapt, while current methods and tools for control system programming do not effectively support control system reconfigurability [3].

Thus, the integration of a new device within the overall production system, or the replacement of a faulty device, very often requires a critical stop of the system, to perform physical connections and allocations of the new device, as well as partial/total reprogramming of some parts of the control system, and modifications in production plans, which need time consuming testing and commissioning operations to be executed afterwards [4].

The present paper proposes a self-adaptive control solution in order to support the RMS agility. Particularly, section II briefly analyzes the current state of the art related to the development of self-adaptive control solutions for RMS and summarizes the main features to be guaranteed. Section III introduces the IEC 61499 standard exploited for the control solution design. In section IV the considered industrial application case is presented. In section V the proposed control solution strategy, architecture and implementation are illustrated, highlighting the exploited paradigms and tools. Section VI describes how the control solution has been verified by means of simulation based methods. Finally, section VII addresses conclusions and next developments. References:

  1. KOREN, Y., U. HEISEL, F. JOVANE, T. MORIWAKI, G. PRITSCHOW, G. ULSOY, H. V. BRUSEEL, Reconfigurable Manufacturing Systems, in Ann. CIRP 1999, vol. 48(2), pp. 527-540.
  2. ALMEIDA, E., J. LUNTZ, D. TILBURY, Event – Condition – Action Systems for Reconfigurable Logic Control, IEE Transaction on Automation Science and Engineering, Vol. 4(2), April 2007, pp. 167-181.
  3. BRUSAFERRI, A., A. BALLARINO, E. CARPANZANO, Enabling Agile Manufacturing through Reconfigurable Control Solutions, Proc. at 14th IEEE International Conference on Enabling Technologies and Factory Automation (ETFA09), September 2009, Mallorca, Spain.
  4. CARPANZANO, E., F. JOVANE, Advanced Automation Solutions for Future Adaptive Factories, Annals of the CIRP, 2007, pp. 435-438.
  5. PECHOUCEK, M., V. MARIK, Industrial Deployment of Multi-agent Technologies: Review and Selected Case Studies, Autonomous Agents and Multi-Agent Systems Journal, Publisher: Springer Netherlands, Published online: 14 May 2008, pp. 397-431.
  6. SIRENA Project www.sirena-itea.org.
  7. RIMACS Project – Radically Innovative Mechatronics and Advanced Control Systems, www.rimacs.org.
  8. SOCRADES Project www.socrades.eu.
  9. LASTRA, J., I. DELAMER, Web Services in Factory Automation: Fundamental Insights and Research Roadmap, IEEE Transactions on Industrial Informatics, Vol. 2(1), February 2006, pp. 1-11.
  10. O3NEIDA Network of Networks to Advance Distributed Industrial Automation, www.oooneida.org
  11. LEPUSCHITZ, W., M. VALLEE, M. MERDAN, P. VRBA, J. RESCH, Integration of a Heterogeneous Low Level Control in a Multi-Agent System for the Manufacturing Domain, Proc. at 14th IEEE International Conference on Enabling Technologies and Factory Automation (ETFA09), September 2009, Mallorca, Spain.
  12. HERRERA, V., A. BEPPERLING, A. LOBOV, H. SMIT, A. W. COLOMBO, J. L. LASTRA, Integration of Multi-Agent Systems and Service-Oriented Architecture for Industrial Automation, Proc. at IEEE International Conference on Industrial Informatics (INDIN2008), Daejeon, Korea, July 13-16.
  13. LEITAO, P., F. RESTIVO, Implementation of a Holonic Control System in a Flexible Manufacturing System, IEEE Transactions on Systems on, Man, and Cybernetics – Part C: Applications and Reviews, Vol. 38, No. 5, September 2008, pp. 699-709.
  14. International Electro – technical Commission, (IEC), International Standard IEC61499, Function Blocks, part 1-4, IEC Jan. 2005 Edition 1.0., http://www.iec.ch/
  15. MEDJOUDJ, M., ESA_PetriNet : a Tool for Extracting Scenarios in Computer Controlled Systems, Studies in Informatics and Control, Vol. 17, No. 1/2008, Published by the National Institute for Research and Development in Informatics, pp. 71-84.
  16. ISaGRAF Workbench-www.isagraf.com.
  17. Java Native Interface -http://java.sun. com/j2se/1.4.2/docs/guide/jni/.
  18. OWL Web Ontology Language – http://www.w3.org/TR/owl-guide/.
  19. OPREA, M., Ontology Mapping in Open Multi-Agent Systems, Studies in Informatics and Control, Vol. 16, No. 2/2007, Published by the National Institute for Research and Development in Informatics.
  20. Jena Semantic Web framework -http://jena.sourceforge.net/
  21. CARPANZANO, E. BALLARINO, A., A Structured Approach to the Design and Simulation-based Testing of Factory Automation Systems, IEEE ISIE’2002 International Symposium on Industrial, Electronics, L’Aquila, July 8-11, 2002.

https://doi.org/10.24846/v20i1y201103