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Title: Neural network modelling and simulation of hot upsetting
Authors: Raj, K Hans
Sharma, Rahul S
Srivastava, S K
Patvardhan, C
Issue Date: Jun-1999
Publisher: NISCAIR-CSIR, India
Abstract: Modern metal forming often caters to rapidly changing product specifications determined by the continuously increasing productivity, flexibility and quality demands. Automatic selection of press tools and accessories heavily relies on the forging force estimation. There is a complex relationship between process parameters like die velocity, temperature of the billet, coefficient of friction at the interface of die and workpiece, tool geometry and forging forces. Models are needed to enable fast computation of the forging forces based on these factors. However, it is not easy to develop mathematical formulations for this purpose. Finite element methods (FEM) now offer reliable means of modelling metal forming processes. The main limitation of these methods is the requirement of lot of man-hours of code development time, and CPU time for simulation and computer resources. This is because a small change in one parameter requires a fresh simulation run to predict the forging force and combinatorial explosion takes over. Computational paradigms like the artificial neural networks (ANNs) offter an approach to this problem. In the present work, the applicability and relative effectiveness of the artificial neural networks based models for rapid estimation of the forging force by invoking the function approximation capabilities of these models have been investigated. Neural network models are developed that can predict forging force given the initial temperatures of billet and die, friction co-efficient at die-billet interface and die velocity. The results obtained by these models correlate well with the finite element modelling results. This work has implications in the real time monitoring and control of the forming process and design of dies, computation of optimal parameters like punch velocity, billet and die temperatures.
Page(s): 111-118
ISSN: 0975-1017 (Online); 0971-4588 (Print)
Appears in Collections:IJEMS Vol.06(3) [June 1999]

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