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.

[1]  Jami J. Shah,et al.  Parametric and Feature-Based CAD/CAM: Concepts, Techniques, and Applications , 1995 .

[2]  Andrzej Kraslawski,et al.  Similarity concept for case-based design in process engineering , 2006, Comput. Chem. Eng..

[3]  Nanua Singh.  Systems Approach to Computer-Integrated Design and Manufacturing , 1995 .

[4]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[5]  Ann Heylighen,et al.  A case base of Case-Based Design tools for architecture , 2001, Comput. Aided Des..

[6]  Serge Tichkiewitch,et al.  Collaborative Aluminum Profile Design to Adaptable Die Process Planning Using Neural Networks , 2010 .

[7]  Izabela Kutschenreiter-Praszkiewicz,et al.  Application of artificial neural network for determination of standard time in machining , 2008, J. Intell. Manuf..

[8]  Hassan Abdalla,et al.  A feature-based prototype system for the evaluation and optimisation of manufacturing processes , 1999 .

[9]  Pak Kin Wong,et al.  Case-based Reasoning and Adaptation in Hydraulic Production Machine Design , 2002 .

[10]  S. Butdee,et al.  A process planning system with feature based neural network search strategy for aluminum extrusion die manufacturing , 2009, ArXiv.

[11]  Thomas C. Henderson,et al.  Feature-based reverse engineering of mechanical parts , 1999, IEEE Trans. Robotics Autom..

[12]  Mitchell M. Tseng,et al.  Case-based evolutionary design for mass customization , 1997 .