Abstract
Cold roll-forming is a highly efficient and economical method for the production of sheet metal products, compared to rival processes. It has a major difficulty, however, because it frequently experiences quality problems such as longitudinal curvature. The tool design method is largely subjective and often involves a trial and error procedure where rolls are tested in the workshop. This paper develops a technique using an artificial neural network to assist in the design of roll-forming tools. (c) 2006 Elsevier B.V. All rights reserved.
Original language | English |
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Pages (from-to) | 319-322 |
Number of pages | 4 |
Journal | Journal of Materials Processing Technology |
Volume | 177 |
DOIs | |
Publication status | Published - 3 Jul 2006 |
Keywords
- radial basis functions
- artificial neural networks
- integral shapes
- cold roll-forming
- longitudinal curvature
- springback