Comparison between neural network and response surface metamodels based on D-optimal designs
by Fayiz Y. Abu Khadra; Jaber E. Abu Qudeiri
International Journal of Computational Materials Science and Surface Engineering (IJCMSSE), Vol. 5, No. 2, 2013

Abstract: In this paper, two metamodelling techniques namely, the neural network and the response surface methodology are used and compared to approximate a multidimensional function to predict the springback amount of metallic sheets in the bending process. The training data required to train the two metamodelling techniques were generated using a verified non-linear finite element algorithm developed in this research. The algorithm is based on the updated Lagrangian formulation, which takes into consideration geometrical, material non-linearity, and contact. A neural network algorithm based on the back propagation algorithm has been developed. This research utilises computer generated D-optimal designs to select training examples for both metamodelling techniques so that a comparison between the two techniques can be considered as fair. Results from this research showed that the neural network metamodels outperform the response surface metamodels.

Online publication date: Sat, 21-Jun-2014

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