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.
Original language | English |
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Pages (from-to) | 85-101 |
Number of pages | 17 |
Journal | International Journal of Computational Materials Science and Surface Engineering |
Volume | 5 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2013 |
Externally published | Yes |
Keywords
- D-optimal designs
- Metamodels
- Neural network
- Springback
ASJC Scopus subject areas
- Modelling and Simulation
- Materials Science(all)
- Engineering(all)
- Computer Science Applications