Estimation of Hot Torsion Stress Strain Curves in Iron Alloys Using a Neural Network Analysis.

• Narayan V.

  Department of Materials Science and Metallurgy, University of Cambridge

• Abad R.

  Centro de Estudios e Investigaciones Tecnicas de Guipuzcoa

• Lopez B.

  Centro de Estudios e Investigaciones Tecnicas de Guipuzcoa

• Bhadeshia H. K. D. H.

  Mathematical and Physical Sciences Group, Darwin College

• Mackay D. J. C.

  Mathematical and Physical Sciences Group, Darwin College

Abstract

The hot torsion stress-strain curves of steels have been modelled using a neural network, within a Bayesian framework. The analysis is based on an extensive database consisting of detailed chemical composition, temperature and strain rate from new hot torsion experiments. Non-linear functions are obtained, describing the variation of stress-strain curves with temperature and chemical composition. Predictions are associated with error bars, whose magnitude depends on their position in the input space. From the population of possible models, a "committee of models" is found to give the most reliable estimate. The results from the neural network model where found to be consistent with known models, and reasonable estimates are obtained beyond the scope of the experimental data.

Journal

• ISIJ International

  ISIJ International 39 (10), 999-1005, 1999

  The Iron and Steel Institute of Japan

Keywords

• hot-deformation
• torsion test
• austenite
• recrystallisation
• hot-rolling
• steel
• neural network

Details

• CRID: 1390282681429643008
• NII Article ID: 10002555700
• NII Book ID: AA10680712
• DOI: 10.2355/isijinternational.39.999
• COI: 1:CAS:528:DyaK1MXns1GrsbY%3D
• ISSN: 13475460
• ISSN: 09151559
• ISSN: http://id.crossref.org/issn/09151559
• Web Site: http://www.jstage.jst.go.jp/article/isijinternational1989/39/10/39_10_999/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed