Case-based reasoning for adaptive aluminum extrusion die design together with parameters by neural networks

S. Butdee (1) , Serge Tichkiewitch (2)

1 KMRC - Knowledge Management Research center
2 G-SCOP_CPP - Conception Produit Process

Nowadays Aluminum extrusion die design is a critical task forimproving 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 areuseful onlyretrievingprevious 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 theprimitive die modification. The most satisfactory is to accommodate the optimal parameters of extrusion processes.

Mots clés: Case-based reasoning; Neural networks; Adaptive die design and parameters; Optimal aluminum extrusion

Domaines: Génie mécanique [physics.class-ph]; Génie mécanique [physics.class-ph]

S. Butdee, Serge Tichkiewitch. Case-based reasoning for adaptive aluminum extrusion die design together with parameters by neural networks. Alain Bernard. Global Product Development : Proceedings of the 20th CIRP Design Conference, Ecole Centrale de Nantes, Nantes, France, 19th-21st April 2010, Springer, pp.491-496, 2011. ⟨hal-00573160⟩