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應(yīng)用人工神經(jīng)網(wǎng)絡(luò)研究化學(xué)元素對(duì)鈦合金相變點(diǎn)的影響
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國家“973”計(jì)劃（2007CB613807）；新世紀(jì)優(yōu)秀人才支持計(jì)劃（NCET-07-0696）；凝固技術(shù)國家重點(diǎn)實(shí)驗(yàn)室開放課題（35-TP-2009）


Study on Effects of Alloying Elements on β Transus Temperature of Titanium Alloys Using Artificial Neural Network
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    摘要:

    鈦合金的化學(xué)元素與相變點(diǎn)之間具有復(fù)雜的非線性關(guān)系，人工神經(jīng)網(wǎng)絡(luò)(artificial neural network, ANN)是解決非線性映射關(guān)系的一種有效可行的方法。本工作以鈦合金相變點(diǎn)與化學(xué)元素的關(guān)系為研究對(duì)象，建立鈦合金相變點(diǎn)的BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型，運(yùn)用訓(xùn)練好的網(wǎng)絡(luò)模型研究典型化學(xué)元素對(duì)相變點(diǎn)的影響規(guī)律，并與傳統(tǒng)經(jīng)驗(yàn)公式進(jìn)行比較。結(jié)果發(fā)現(xiàn)：神經(jīng)網(wǎng)絡(luò)模型的預(yù)測(cè)結(jié)果精度較高，誤差小。各合金元素對(duì)相變點(diǎn)的影響并不是傳統(tǒng)經(jīng)驗(yàn)公式表現(xiàn)出來的單調(diào)線性關(guān)系，而是由于各元素之間的交互作用引起的復(fù)雜非線性關(guān)系

    Abstract:

    Artificial Neural Network (ANN) is a feasible method to reflect the complicated nonlinear relationship between β transus temperature and the alloy composition. In this paper, back propagation neural network (BP neural network) was developed and trained using data from various sources of published literature. The influence of aluminum, molybdenum and zirconium on β transus temperature in titanium alloys was assessed on the base of the trained neural network. It is found that the predicted results are in good agreement with experimental values. The effect of element contents on β transus temperature simulated by ANN model presents nonlinear relationship caused by the interaction among the elements, which is different from the results of the traditional equations.

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孫 宇,曾衛(wèi)東,趙永慶,韓遠(yuǎn)飛,邵一濤,周義剛.應(yīng)用人工神經(jīng)網(wǎng)絡(luò)研究化學(xué)元素對(duì)鈦合金相變點(diǎn)的影響[J].稀有金屬材料與工程,2010,39(6):1031~1036.[Sun Yu, Zeng Weidong, Zhao Yongqing, Han Yuanfei, Shao Yitao, Zhou Yigang. Study on Effects of Alloying Elements on β Transus Temperature of Titanium Alloys Using Artificial Neural Network[J]. Rare Metal Materials and Engineering,2010,39(6):1031~1036.]
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  • 收稿日期:2009-06-01
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