Using ANNs to model hot extrusion manufacturing process

Authors:
Kesheng Wang;Per Alvestad;Yi Wang;Qingfeng Yuan;Minglun Fang;Lingiang Sun
Affiliations:
Knowledge Discovery Lab., Department of Production and Quality Engineering, NTNU, Norway;Knowledge Discovery Lab., Department of Production and Quality Engineering, NTNU, Norway;Cardiff School of Engineering, Cardiff University, Cardiff, UK;Knowledge Discovery and Management Lab., CIMS & ROBOT Center, Shanghai University, Shanghai, China;Knowledge Discovery and Management Lab., CIMS & ROBOT Center, Shanghai University, Shanghai, China;Knowledge Discovery and Management Lab., CIMS & ROBOT Center, Shanghai University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

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.