Determination of the beta-approach curve and beta-transus temperature for titanium alloys using sensitivity analysis of a trained neural network
SCIE
SCOPUS
- Title
- Determination of the beta-approach curve and beta-transus temperature for titanium alloys using sensitivity analysis of a trained neural network
- Authors
- Reddy, NS; Lee, CS; Kim, JH; Semiatin, SL
- Date Issued
- 2006-10-25
- Publisher
- ELSEVIER SCIENCE SA
- Abstract
- A feed-forward neural-network (FFNN) technique with a back-propagation-learning algorithm was used to estimate the beta-approach curve and beta-transus temperature for alphalbeta titanium alloys. The input parameters were the alloy composition (Al, V, Fe, O, and N) and heat-treatment temperature, and the output parameter was the beta-phase volume percentage. The model was trained using selected data from the literature as well as new measurements. The trained model was used to predict the beta-phase volume percentage for the remaining data and to perform a sensitivity analysis to estimate the beta-transus temperature for other titanium alloys. The sensitivity analysis showed that a trained neural network can be used for extrapolated predictions (outside the range of measurements) unlike previous neural-network techniques used primarily for interpolation or approximation. Comparisons between model predictions and experimental data indicated that the NN model thus holds promise for estimating the beta-transus temperature of titanium alloys. (c) 2006 Elsevier B.V. All rights reserved.
- Keywords
- alpha/beta titanium alloys; beta-transus temperature; neural networks; sensitivity analysis; ISOTHERMAL TRANSFORMATION KINETICS; MATERIALS SCIENCE; TI-6AL-4V ALLOY; HOT-WORKING; MICROSTRUCTURE; PREDICTION; DEFORMATION; PHASE; RESISTIVITY; COMPOSITES
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/23795
- DOI
- 10.1016/j.msea.2006.06.104
- ISSN
- 0921-5093
- Article Type
- Article
- Citation
- MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, vol. 434, no. 1-2, page. 218 - 226, 2006-10-25
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