Title: Prediction of springback effect by the hybridisation of ANN with PSO in wipe bending process of sheet metal

Authors: T. Sathish

Addresses: Research and Development, Vesta Research Institute, Pudukkottai, Tamil Nadu, 622001, India

Abstract: In sheet metal forming springback is a phenomenon that occurs slightly due to residual stresses in the material, while bending the sheet metal. Hence it should avoid improving the metal quality by the prediction of springback angle. By predicting the springback angle, can reduce the angle by changing those parameters. Therefore, a suitable prediction method is required to predict the springback angle. One of the best prediction methods is the artificial neural network (ANN) to predict the springback angle in sheet metal. So this paper aims to improve the prediction efficiency of ANN by integrating particle swarm optimisation (PSO) algorithm. The PSO algorithm is used to train the ANN, so it can predict the springback angle efficiently. The proposed technique is compared with the experimental results and the conventional prediction techniques such as conventional ANN and Genetic algorithm based ANN.

Keywords: sheet metal forming; spring back effect; artificial neural network; ANN; particle swarm optimisation; PSO.

DOI: 10.1504/PIE.2018.095881

Progress in Industrial Ecology, An International Journal, 2018 Vol.12 No.1/2, pp.112 - 119

Received: 11 Oct 2017
Accepted: 02 May 2018
Published online: 25 Oct 2018 *

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