Predicting Force in Single Point Incremental Forming by Using Artificial Neural Network

Authors

Faculty of Production Engineering., Birla Institute of Technology, Mesra, off Campus-Patna Bihar, India

Abstract

In this study, an artificial neural network was used to predict the minimum force required to single point incremental forming (SPIF) of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, the feed rate of the tool, spindle speed, wall angle, thickness of metal sheets and type of material were selected as input and the minimum vertical force component was selected as the model output. To train the model, a Multilayer perceptron neural network structure and feed-forward backpropagation algorithm have been employed. After testing many different artificial neural network (ANN)  architectures, an optimal structure of the model i.e. 6-14-1 was obtained. The results, with a correlation relation between experiments to predicted force,-0.215 mean absolute error, show a very good agreement.

Keywords

References

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International Journal of Engineering
Volume 31, Issue 1
TRANSACTIONS A: Basics
January 2018
Pages 88-95

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