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

Conference paper


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.


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


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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

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