Implementation Of The Artificial Neural Networks To Control The Springback Of Metal Sheets

Journal Article · Thu May 17 00:00:00 EDT 2007 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.2740862· OSTI ID:21061721

Crina, Axinte ^[1]

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1. University of Bacau, Calea Marasesti 157, 600115 Bacau (Romania)

Geometrical inaccuracy of sheet metal parts due to the springback phenomenon is the reason for considerable efforts in tools and process development. Prediction of springback is a key issue to design the tools and control the process parameters in order to obtain close tolerances in the formed parts. The objective of this paper is to use simulation procedure coupled with neural networks method to get the best relation between process parameters and tools geometry in order to minimize the shape deviations of the formed parts related to the target geometry.

• OSTI ID: 21061721
• Journal Information: AIP Conference Proceedings, Vol. 908, Issue 1; Conference: NUMIFORM 2007: 9. international conference on numerical methods in industrial forming processes, Porto (Portugal), 17-21 Jun 2007; Other Information: DOI: 10.1063/1.2740862; (c) 2007 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
ALLOYS
COMPUTERIZED SIMULATION
DESIGN
IMPLEMENTATION
MATERIALS WORKING
METALS
NEURAL NETWORKS
PROCESS CONTROL
SHAPE
SHEETS
TOLERANCE