Practical Model for Predicting Beta Transus Temperature of Titanium Alloys
(주)코리아스칼라
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- 2016.04.02
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- 2014.07
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서지정보
ㆍ발행기관 : 한국재료학회
ㆍ수록지정보 : 한국재료학회지 / 24권 / 7호
ㆍ저자명 : Reddy, N.S., Choi, Hyun Ji, Young, Hur Bo
영어 초록
The β-transus temperature in titanium alloys plays an important role in the design of thermo-mechanical treatments. It primarily depends on the chemical composition of the alloy and the relationship between them is non-linear and complex. Considering these relationships is difficult using mathematical equations. A feed-forward neural-network model with a back-propagation algorithm was developed to simulate the relationship between the β-transus temperature of titanium alloys, and the alloying elements. The input parameters to the model consisted of the nine alloying elements (i.e., Al, Cr, Fe, Mo, Sn, Si, V, Zr, and O), whereas the model output is the β-transus temperature. The model developed was then used to predict the β-transus temperature for different elemental combinations. Sensitivity analysis was performed on a trained neural-network model to study the effect of alloying elements on the β-transus temperature, keeping other elements constant. Very good performance of the model was achieved with previously unseen experimental data. Some explanation of the predicted results from the metallurgical point of view is given. The graphical-user-interface developed for the model should be very useful to researchers and in industry for designing the thermo-mechanical treatment of titanium alloys.
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