Using ANNs to Model Hot Extrusion Manufacturing Process

In metal forming processes, automatic selection of forming tools is heavily depended on the estimation of forming forces. Due to complex relationships between processes parameters like die angle, co-efficient of friction, velocity of dies, and temperature of billet for forming products with sound quality and forming forces related, there is a need to develop approximate models to estimate the forming forces without complex mathematical models or time-consuming simulation techniques. In this paper, an Artificial Neural Networks (ANNs) model has been developed for rapid predication of the forming forces based on process parameters. The results obtained are found to correlate well with the finite element simulation data in case of hot extrusion.

Authors and Affiliations

1. Knowledge Discovery Lab., Department of Production and Quality Engineering, NTNU, N-7491, Norway

   Kesheng Wang & Per Alvestad

2. Cardiff School of Engineering, Cardiff University, Queen’s Buildings, PO Box 925, Cardiff, CF24 0YF, UK

   Yi Wang

3. Knowledge Discovery and Management Lab., CIMS & ROBOT Center, Shanghai University, Shanghai, 200436, China

   Qingfeng Yuan, Minglun Fang & Lingiang Sun

Editors and Affiliations

1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

   Jun Wang

2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

   Xiao-Feng Liao

3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

   Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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