Experimental implementation of neural network springback control for sheet metal forming

Vikram Viswanathan*, Brad Kinsey, Jian Cao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

The forming of sheet metal into a desired and functional shape is a process, which requires an understanding of materials, mechanics, and manufacturing principles. Furthermore, producing consistent sheet metal components is challenging due to the nonlinear interactions of various material and process parameters. One of the major causes for the fabrication of inconsistent sheet metal parts is springbuck, the elastic strain recovery in the material after the tooling is removed. In this paper, springbuck of a steel channel forming process is controlled using an artificial neural network and a stepped binder force trajectory. Punch trajectory, which reflects variations in material properties, thickness and friction condition, was used as the key control parameter in the neural network. Consistent springbuck angles were obtained in experiments using this control scheme.

Original languageEnglish (US)
Pages (from-to)141-147
Number of pages7
JournalJournal of Engineering Materials and Technology, Transactions of the ASME
Volume125
Issue number2
DOIs
StatePublished - Apr 1 2003

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

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