Prediction of flow stress in Ti-6Al-4V alloy with an equiaxed alpha plus beta microstructure by artificial neural networks
SCIE
SCOPUS
- Title
- Prediction of flow stress in Ti-6Al-4V alloy with an equiaxed alpha plus beta microstructure by artificial neural networks
- Authors
- Reddy, NS; Lee, YH; Park, CH; Lee, CS
- Date Issued
- 2008-09-25
- Publisher
- ELSEVIER SCIENCE SA
- Abstract
- Flow stress during hot deformation depends mainly on the strain, strain rate and temperature, and shows a complex and nonlinear relationship with them. A number of semi-empirical models were reported by others to predict the flow stress during hot deformation. This work attempts to develop a back-propagation neural network model to predict the flow stress of Ti-6Al-4V alloy for any given processing conditions. The network was successfully trained across different phase regimes (alpha + beta to beta phase) and various deformation domains. This model can predict the mean flow stress within an average error of similar to 5.6% from the experimental values, using strain, strain rate and temperature as inputs. This model seems to have an edge over existing constitutive model, like hyperbolic sine equation, and has a great potential to be employed in industries. (C) 2008 Elsevier B.V. All rights reserved.
- Keywords
- hot deformation; neural networks; hyperbolic sine function; flow stress; HOT DEFORMATION-BEHAVIOR; HIGH-SPEED STEEL; AUSTENITIC STEELS; STRENGTH; WORKING; ZR-2.5NB-0.5CU; TEMPERATURES; MODEL
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/22545
- DOI
- 10.1016/j.msea.2008.03.030
- ISSN
- 0921-5093
- Article Type
- Article
- Citation
- MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, vol. 492, no. 1-2, page. 276 - 282, 2008-09-25
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