A Comprehensive Approach to Off-line Advanced Error Troubleshooting in Intelligent Manufacturing Systems

Authors

  • Lehel Szabolcs Csokmai Mechatronics Department, University of Oradea Romania, 410087 Oradea, Universitatii St., 1
  • Radu Cătălin Å¢arcă Mechatronics Department, University of Oradea Romania, 410087 Oradea, Universitatii St., 1
  • Constantin Bungău Mechatronics Department, University of Oradea Romania, 410087 Oradea, Universitatii St., 1
  • Géza Husi Department of Electrical Engineering and Mechatronics University of Debrecen Hungary, Debrecen, Dembinszky Ut.

Abstract

The errors recovery in the production systems will be always an open issue. Therefore, the FMSs have to be endowed with tools and techniques allowing an automatic recovery of errors. The objective of this work consists in proposing an off-line version of the software framework for error troubleshooting in a flexible manufacturing system [1]. The main difference between the on-line and off-line version is that the error database is stored on the mobile device and the frame marker device is connected directly to the FMS components without the need of the PC.). Our framework system is designed to solve the failures in the functioning of the FMS and to generate self-training from previous experience.

Author Biography

Lehel Szabolcs Csokmai, Mechatronics Department, University of Oradea Romania, 410087 Oradea, Universitatii St., 1

Department of Mathematics and Computer Science

References

Csokmai,L.; Moldovan, O.; Tarca, I.; Tarca, R. (2013); Software Framework for Advanced rror Troubleshooting In Flexible Manufacturing System, Applied Mechanics and Materials, 97-400: 21-24.

Borchelt, R. D., Thorson, J. (1997), Toward reusable hierarchical cell control software, International ournal of Production Research, 35(2):577-594.

Kao, J.F., (1995), Optimal recovery strategies for manufacturing systems, European Journal f Operational Research, 80(2):252-263. http://dx.doi.org/10.1016/0377-2217(94)00169-D

Wu, H.J., (1999), Methodology of generating recovery procedures in a robotic cell, Proceedings EEE International Conference on Robotics and Automation, 1:799-804.

Toguyeni, A.K.A.; Craye, E.; Gentina, J.C. (1996); A framework to design a distributed iagnosis in FMS, IEEE International Conference on Systems Man and Cybernetics, 4:2774- 779.

Bruccoleri, M.; Pasekb, Z.J.; Koren, Y. (2006); Operation management in reconfigurable anufacturing systems: Reconfiguration for error handling, Int. J. Production Economics, 00:87-100. http://dx.doi.org/10.1016/j.ijpe.2004.10.009

Felea I, Dzitac S., Vesselenyi T., Dzitac I. (2014), Decision Support Model for Production Disturbance stimation, International Journal of Information Technology and Decision Making, 3(3): 623-647. http://dx.doi.org/10.1142/S0219622014500576

Qiang Ruan, Wensheng Xu, Gaoxiang Wang (2011); RFID and ZigBee Based Manufacturing onitoring System, 2011 International Conference on Electric Information and Control ngineering (ICEICE), 1672-1675.

Leito P. (2010); A holonic disturbance management architecture for flexible manufacturing ystems, International Journal of Production Research, 49(5): 1269-1284. http://dx.doi.org/10.1080/00207543.2010.518735

Leito, P., Restivo, F. (2006); ADACOR: A holonic architecture for agile and adaptive manufacturing ontrol, Computers in Industry, 57 (2): 121-130. http://dx.doi.org/10.1016/j.compind.2005.05.005

Bruccoleri, M., Renna, P., Perrone, G. (2005), Reconfiguration: a key to handle exceptions nd performance deteriorations in manufacturing operations, International Journal of roduction Research, 43(19):4125-4145.

Bruccoleri, M. (2007), Reconfigurable control of robotized manufacturing cells, Robotics and omputer-Integrated Manufacturing, 23:94-106.

Zhang X.L., Yan K., Ye J.; Li J. (2012), A Remote Manufacturing Monitoring System Based n the Internet of Things, Proceedings of 2012 2nd International Conference on Computer cience and Network Technology (ICCSNT 2012), 221-224.

Dev Anand M., Selvaraj, T. Kumanan, S. (2012), Detection and Fault Tolerance Methods or Industrial Robot Manipulators Based on Hybrid Intelligent Approach, Advances in roduction Engineering and Management, 7(4):225-236.

Kumanan, S., Selvaraj, T.. Dev Anand, M., Janarthanan, J. (2008), Fault diagnosis system or a robot manipulator through neuro-fuzzy approach, International Journal of Modelling, dentification and Control, 3(2):181-192.

Published

2014-11-17

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.