Journal of Intelligent Manufacturing

, Volume 16, Issue 4–5, pp 527–548 | Cite as

Neurovision-based logic control of an experimental manufacturing plant using neural net le-net5 and automation Petri nets

  • B. Karlik
  • M. Uzam
  • M. Cinsdikici
  • A. H. Jones


In this paper, Petri nets and neural networks are used together in the development of an intelligent logic controller for an experimental manufacturing plant to provide the flexibility and intelligence required from this type of dynamic systems. In the experimental setup, among deformed and good parts to be processed, there are four different part types to be recognised and selected. To distinguish the correct part types, a convolutional neural net le-net5 based on-line image recognition system is established. Then, the necessary information to be used within the logic control system is produced by this on-line image recognition system. Using the information about the correct part types and Automation Petri nets, a logic control system is designed. To convert the resulting Automation Petri net model of the controller into the related ladder logic diagram (LLD), the token passing logic (TPL) method is used. Finally, the implementation of the control logic as an LDD for the real time control of the manufacturing system is accomplished by using a commercial programmable logic controller (PLC).


Image recognition neural networks manufacturing plant control Petri nets ladder logic diagrams Programmable logic controller 


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  1. Adeli, H., Hung S., L. 1993Fuzzy neural network learning model for image recognitionIntegrated Computer-Aided Engineering.14355Google Scholar
  2. Belongie, S., Malik, J., Puzicha, , J.,  2002Shape matching and object recognition using shape contextsIEEE Transition on Pattern Analysis and Machine Intelligence.24509522CrossRefGoogle Scholar
  3. Cun, Y.L., Bottou, L., Bengio, Y., Haffner, P. 1998Gradient-based learning applied to document recognitionProceedings of IEEE.8622782324CrossRefGoogle Scholar
  4. Cun, Y.L., Bengio, Y. 1995Convolutional networks for images, speech, and time seriesArbib, M.A. eds. The Handbook of Brain Theory and Neural Networks.MIT PressCambridge, Massachusetts255258Google Scholar
  5. Gonzales R.C., Woods R.E. (1993) Digital Image Processing. Addison-Wesley Publishing Com., IncGoogle Scholar
  6. Guh, R.S., Tannock, J.D.T. 1999A neural network approach to characterize pattern parameters in process control chartsJournal of Intelligent Manufacturing.10449462CrossRefGoogle Scholar
  7. Haouani, M., Lefebvre, D., Zerhouni, N., El Moudni, A. 2000Neural networks implementation for modeling and control design of manufacturing systemsJournal of Intelligent Manufacturing.112940CrossRefGoogle Scholar
  8. Jones, A. H., Uzam, M., Khan, A. H., Karimzadgan D., Kenway, S B. (1996) A general methodology for converting petri nets into ladder logic: the TPLL methodology, in Proceedings of the 5th International Conference on Computer Integrated Manufacturing and Automation Technology – CIMAT’96, France, pp 357–362Google Scholar
  9. Karlık, B., Aydın, S. (1996) Pattern recognition by using ANN for tactile sensor of a robot manipulator, in Proceedings of the First Symposium on Mathematical and Computational Applications, Manisa, Turkey, pp. 86–90Google Scholar
  10. Karlık, B. (1998) An Image Interpreter for Vision-Based Logic Control of Manufacturing Plant, Technical Report, AMME-98-7, University of Salford, UKGoogle Scholar
  11. Karlık, B., Cinsdikici, M., Jones, A. H. (2001) A manufacturing plant control using convolutional neural net le-net5, in Proceedings of NMIA-SC2001-2001 NATO Advanced Study Institute on Neural Networks For Instrumentation, Measurement, and Related Industrial Applications: Study Cases, Crema, Italy. pp. 31–36Google Scholar
  12. Kubota, N., Fukuda, T. 1999Structured Intelligence for self-organizing manufacturing systemsJournal of Intelligent Manufacturing.10121133CrossRefGoogle Scholar
  13. Kusiak, A. (1986) Modelling and Design of Flexible Manufacturing Systems, Elsevier PublishersGoogle Scholar
  14. Lee, S. W., Song, H. H. (1997) A new recurrent neural network architecture for visual pattern recognition, IEEE Transition on Neural Networks, 8(2), 331–340Google Scholar
  15. Lee, S.Y., Fischer, G. 1999Grouping parts based on geometrical shapes and manufacturing attributes using a neural networkJournal of Intelligent Manufacturing.10199209CrossRefGoogle Scholar
  16. Lucas, M.R., Tilbury, D.M. 2003A study of current logic design practices in the automotive manufacturing industryInternational Journal of Human-Computer Studies.59725753CrossRefGoogle Scholar
  17. Magee, M., Seida, S. 1995An industrial model based computer vision systemJournal of Manufacturing Systems.14471471Google Scholar
  18. Malakooti, B., Raman, V. 2000An interactive multi-objective artificial neural network approach for machine setup optimization.Journal of Intelligent Manufacturing.114150CrossRefGoogle Scholar
  19. Murata, T. 1989Petri nets: properties, analysis and Proceedings of IEEE.44541579CrossRefGoogle Scholar
  20. O‘kane, J.F. 2000A knowledge-based system for reactive scheduling decision-making in FMSJournal of Intelligent Manufacturing.11461474CrossRefGoogle Scholar
  21. Peng, S., Zhou, M.C. 2004Ladder diagram and petri net based discrete-event control design methodsIEEE Trans. on Systems, Man and Cybernetics Part C-Applications and Reviews.34523531Google Scholar
  22. Peterson, J.L. 1981Petri net Theory and the Modelling of SystemsPrentice-HallEngle-wood Cliffs, NJGoogle Scholar
  23. Tsujimura, Y., Gen, M. 1999Parts loading scheduling in a flexible forging machine using an advanced genetic algorithmJournal of Intelligent Manufacturing.10149159CrossRefGoogle Scholar
  24. Uzam, M., Jones, A. H. (1996) Design of a discrete event control system for a manufacturing system using token passing ladder logic, in Proceedings of the CESA’96, Lille, France, pp. 513–518Google Scholar
  25. Uzam, M. (1998) Petri-net-based supervisory control of discrete event systems and their ladder logic diagram implementations, PhD Thesis, University of Salford, Salford, UK. Posted at URL: Scholar
  26. Uzam, M., Jones, A.H. 1998Discrete event control system design using automation petri nets and their ladder diagram implementationInternational Journal of Advanced Manufacturing Technology.14716728CrossRefGoogle Scholar
  27. Uzam, M., Jones, A.H., Yücel, İ. 2000Using a petri-net-based approach for the real-time control of an experimental manufacturing systemThe International Journal of Advanced Manufacturing Technology.16498515CrossRefGoogle Scholar
  28. Yu Z., Ghosh, B. K., Xi, N., Tarn, T-.J (1995) Multi-sensor based planning and control for robotic manufacturing systems, in Proceedings of the International Conference on Intelligent Robots and Systems, Pittsburgh, USA, 3, pp. 3222–3227Google Scholar
  29. Zhou, M. C., Venkatesh, K. (1999) Modelling, Simulation and Control of Flexible Manufacturing Systems, A Petri Net Approach, World ScientificGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • B. Karlik
    • 1
  • M. Uzam
    • 2
  • M. Cinsdikici
    • 3
  • A. H. Jones
    • 4
  1. 1.Department of Computer EngineeringHaliç UniversityİstanbulTurkey, Bahrain
  2. 2.Niğde Üniversitesi, Mühendislik-Mimarlik FakültesiElektrik-Elektronik Mühendisliği BölümüNiğdeTurkey
  3. 3.International Computer InstituteEge UniversityİzmirTurkey
  4. 4.School of Computing, Science and EngineeringUniversity of SalfordSalfordUK

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