Consistent and Minimal Springback Using a Stepped Binder Force Trajectory and Neural Network Control

Jian Cao, Brad Kinsey, Sara A. Solla

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

One of the greatest challenges of manufacturing sheet metal parts is to obtain consistent part dimensions. Springback, the elastic material recovery when the tooling is removed, is the major cause of variations and inconsistencies in the final part geometry. Obtaining a consistent and desirable amount of springback is extremely difficult due to the nonlinear effects and interactions between process and material parameters. In this paper, the exceptional ability of a neural network along with a stepped binder force trajectory to control springback angle and maximum principal strain in a simulated channel forming process is demonstrated. When faced with even large variations in material properties, sheet thickness, and friction condition, our control system produces a robust final part shape.

Original languageEnglish (US)
Pages (from-to)113-118
Number of pages6
JournalJournal of Engineering Materials and Technology, Transactions of the ASME
Volume122
Issue number1
DOIs
StatePublished - Jan 2000

ASJC Scopus subject areas

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

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