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

スポンサーリンク

概要

• 論文の詳細を見る

• 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.

• 社団法人 日本鉄鋼協会の論文

著者

• Mackay D.

  Mathematical And Physical Sciences Group Darwin College

• Bhadeshia H

  Univ. Cambridge

• Abad R

  Univ. Navarra Basque Country Esp

• 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.

  Mathematical and Physical Sciences Group, Darwin College

• Narayan V.

  Department Of Materials Science And Metallurgy University Of Cambridge

関連論文

• ベイズ推定とニューラルネットワークの組み合わせによるニッケル基超合金のクリープ破断寿命の予測
• ベイジアンニューラルネットワークによるNi基超合金のクリープ破断寿命の予測
• Acceleration of Low-temperature Bainite
• Development of Hard Bainite
• Estimation of Hot Torsion Stress Strain Curves in Iron Alloys Using a Neural Network Analysis
• Estimation of the γ and γ' Lattice Parameters in Nickel-base Superalloys Using Neural Network Analysis

スポンサーリンク