Adaptive Aluminum Extrusion Die Design Using Case-Based Reasoning and Artificial Neural Networks

Aluminum extrusion die design involves with two critical parts; die features and its parameters. Presently, die design process is performed by adaptation approach. The previous dies together with their parameters are collected and stored in a database under the well-memory organization. Case-Based Reasoning (CBR) has been applied and enhanced the design productivity. However, the CBR method has an excellent ability only that an exact or similar design features are existed. Reality, aluminum die design requires regularly changed according to the profile changes. Therefore, it needs to predict optimum parameters to assist in the process of aluminum profile extrusion. This paper presents the redesign process using adaptive method. In this case, CBR & ANN method are combined and development. The CBR uses for die feature adaptation; whereas the ANN is used for parameter adaptation and prediction to a new profile and die design. The actual production yield is given and the ANN will find the best size of billet length in order to receive the maximum yield.