Comparison between neural network and response surface metamodels based on D-optimal designs

Fayiz Y. Abu Khadra, Jaber E. Abu Qudeiri

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)85-101
Number of pages17
JournalInternational Journal of Computational Materials Science and Surface Engineering
Issue number2
Publication statusPublished - 2013
Externally publishedYes


  • D-optimal designs
  • Metamodels
  • Neural network
  • Springback

ASJC Scopus subject areas

  • Modelling and Simulation
  • General Materials Science
  • General Engineering
  • Computer Science Applications


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