Estimation of Hot Torsion Stress Strain Curves in Iron Alloys Using a Neural Network Analysis.
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- Narayan V.
- Department of Materials Science and Metallurgy, University of Cambridge
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- Abad R.
- Centro de Estudios e Investigaciones Tecnicas de Guipuzcoa
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- Lopez B.
- Centro de Estudios e Investigaciones Tecnicas de Guipuzcoa
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- Bhadeshia H. K. D. H.
- Mathematical and Physical Sciences Group, Darwin College
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- Mackay D. J. C.
- Mathematical and Physical Sciences Group, Darwin College
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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.
収録刊行物
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- ISIJ International
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ISIJ International 39 (10), 999-1005, 1999
一般社団法人 日本鉄鋼協会
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詳細情報 詳細情報について
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- CRID
- 1390282681429643008
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- NII論文ID
- 10002555700
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- NII書誌ID
- AA10680712
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- COI
- 1:CAS:528:DyaK1MXns1GrsbY%3D
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- ISSN
- 13475460
- 09151559
- http://id.crossref.org/issn/09151559
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可