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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2009.1
- 수록면
- 427 - 437 (11page)
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초록· 키워드
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The microstructural evolution of titanium alloy under isothermal and non-isothermal hot forging conditions was
predicted using artificial neural networks (ANN) and finite element (FE) simulation. In the present work,
the change in phase volume fraction, grain size, and the volume fraction of dynamic globularization were
modelled considering hot working conditions. Initially, an ANN model was developed for steady-state phase
volume fraction. The input parameters were the alloy chemical composition (Al, V, Fe, O, and N) and the holding
temperature, and the output parameter was the alpha/beta phase volume fraction at steady state. The non-steady
state phase volume fraction under non-isothermal conditions was subsequently modelled on the basis of 4
input parameters such as initial specimen temperature, die (or environment) temperature, steady-state phase
volume fraction at die (or environment) temperature, and elapsed time during forging. Resulting ANN models
were coupled with the FE simulation (DEFORM-3D) in order to predict the variation of phase volume fraction
during isothermal and non-isothermal forging. In addition, a grain size variation and a globularization model
were developed for hot forging. To validate the predicted results from the models, Ti-6Al-4V alloy was hotworked
at various conditions and then the resulting microstructures were compared with simulated data.
Comparisons between model predictions and experimental data indicated that the ANN model holds promise
for microstructure evolution in two phase Ti-6Al-4V alloy.
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