Advertisement

Case-Based Reasoning for Adaptive Aluminum Extrusion Die Design Together with Parameters by Neural Networks

Conference paper

Abstract

Nowadays Aluminum extrusion die design is a critical task for improving productivity which involves with quality, time and cost. Case-Based Reasoning (CBR) method has been successfully applied to support the die design process in order to design a new die by tackling previous problems together with their solutions to match with a new similar problem. Such solutions are selected and modified to solve the present problem. However, the applications of the CBR are useful only retrieving previous features whereas the critical parameters are missing. In additions, the experience learning to such parameters are limited. This chapter proposes Artificial Neural Network (ANN) to associate the CBR in order to learning previous parameters and predict to the new die design according to the primitive die modification. The most satisfactory is to accommodate the optimal parameters of extrusion processes.

Keywords

Adaptive die design and parameters Optimal aluminum extrusion Case-based reasoning Neural networks 

References

  1. 1.
    Laue, K., Stenger, H. (1981) Extrusion Processes, Machinery, Tooling, 3rd ed. American Society for Metals, Metals Park, OH.Google Scholar
  2. 2.
    Negnevitsky, M. (2002) Artificial Intelligence: A Guide to Intelligent Systems. Person Addison Wesley, EnglandGoogle Scholar
  3. 3.
    Singh, N. (1996) Systems Approach to Computer-Integrated Design and Manufacturing. Wiley, New York, NY.Google Scholar
  4. 4.
    Shah, J.J., Mentyla, M. (1995) Parametric and Feature-Based CAD/CAM, Concept, Techniques and Application. Wiley, New York, NY.Google Scholar
  5. 5.
    Thompson, W.B., et al. (1999) Feature-based reverse engineering of mechanical parts. IEEE Transactions on Robotics and Automation, 15:57–66.CrossRefGoogle Scholar
  6. 6.
    Gayretli, A., Abdalla, H.S. (1999) A feature based prototypes system for the evaluation and optimization of manufacturing processes. Computer & Industrial Engineering, 37:481–484.CrossRefGoogle Scholar
  7. 7.
    Aamodt, A., Plaza, E. (1994) Case-based reasoning: Foundational issues methodological variations, and system approaches. AI Communications, 7:39–59.Google Scholar
  8. 8.
    Heylighen, A., Neuckermans, H. (2001) A case base of case-based design tools for architecture. Computer-Aided Design, 33:1111–1122.CrossRefGoogle Scholar
  9. 9.
    Avramenko, Y., Kraslawski, A. (2006) Similarity concept for case-based design in process engineering. Computer & Chemical Engineering, 30:548–557.CrossRefGoogle Scholar
  10. 10.
    Vong, C.M., Leung, T.P., Wong, P.K. (2002) Case-based reasoning and adaptation in hydraulic production machine design. Engineering Application of Artificial Intelligence, 15:567–585.CrossRefGoogle Scholar
  11. 11.
    Tseng, M.M., Jiao, A. (1997) Case-based evolutionary design for mass customization. Computers and Industrial Engineering, 33(1–2):319–324.CrossRefGoogle Scholar
  12. 12.
    Praszkiewicz, I.K. (2008) Application of artificial neural network for determination of standard time in machining. Journal of Intelligent Manufacturing, 19(2):233–240.CrossRefGoogle Scholar
  13. 13.
    Scholz-Reiter, B., Hamann, T., Zschintzsch, M. (2007) Case-based reasoning for production control with neural networks. CIRP Journal of Manufacturing Systems, 36(1):71–79.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Production Engineering, Faculty of EngineeringKing Mongkut’s University of Technology North BangkokBangkokThailand
  2. 2.G-SCOP LaboratoryGrenoble Institute of TechnologyGrenobleFrance
  3. 3.EMIRAcle, European Manufacturing and Innovation Research Association, a cluster leading excellenceBrusselsBelgium

Personalised recommendations