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Modeling Constitutive Relationship of Ti-555211 Alloy by Artificial Neural Network during High-Temperature Deformation
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Modeling Constitutive Relationship of Ti-555211 Alloy by Artificial Neural Network during High-Temperature Deformation
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Project of Introducing Talents of Discipline to Universities (“111” Project No B08040); National Natural Science Foundation of China (51371143)

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    摘要:

    利用Gleeble-3800熱模擬實(shí)驗(yàn)機(jī),在應(yīng)變速率0.001~1 s-1以及變形溫度750~950 ℃范圍內(nèi)對(duì)Ti-555211合金進(jìn)行等溫恒應(yīng)變速率壓縮實(shí)驗(yàn)?;谌斯ど窠?jīng)網(wǎng)絡(luò)的方法建立了Ti-555211合金熱變形本構(gòu)模型。模型的可靠性用平均相對(duì)誤差和相關(guān)系數(shù)來(lái)確定。結(jié)果表明,所建立的本構(gòu)模型與實(shí)驗(yàn)值的平均相對(duì)誤差為1.60%,相關(guān)系數(shù)為0.99938,表明該模型能很好地預(yù)測(cè)該合金的本構(gòu)關(guān)系。用神經(jīng)網(wǎng)絡(luò)來(lái)確定本構(gòu)關(guān)系比傳統(tǒng)的數(shù)學(xué)方程更加具有優(yōu)勢(shì)。熱模擬實(shí)驗(yàn)結(jié)果表明,隨著變形溫度的升高和應(yīng)變速率的減小,該材料的峰值應(yīng)力有所減小,不連續(xù)屈服現(xiàn)象隨著變形溫度升高和應(yīng)變速率的增大變得更加明顯。流變曲線在不同的變形參數(shù)條件下表現(xiàn)形式也不同。

    Abstract:

    Using experimental data gained from hot compression tests in the temperature range of 750~950 °C and strain rate range of 0.001~1 s-1, the constitutive relationship of Ti-555211 titanium alloy was investigated based on the back propagation artificial neural network constitutive model (ANN model). The capability of the model was measured by the average absolute relative error (AARE), and correlation coefficient (R). The simulated values were compared with experimental values. The results show that the R and AARE for the ANN model are 0.99938 and 1.60%, respectively, indicating that the hot deformation behavior of Ti-555211 titanium alloy can be predicted by the ANN model efficiently and accurately. Furthermore, the back propagation artificial neural network model is a more efficient quantitative way to predict the deformation behavior of the Ti-555211 titanium alloy compared to the mathematical equation. The results show that the peak stress of the alloy decreases with increasing of temperature and decreasing of strain rate, and the phenomenon of discontinuous yielding is more obvious with the increase of deformation temperature and strain rate. The flow curve characteristics under different deformation parameters show obvious differences.

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安 震,李金山,馮 勇,劉向宏,杜予晅,馬凡蛟,王 哲. Modeling Constitutive Relationship of Ti-555211 Alloy by Artificial Neural Network during High-Temperature Deformation[J].稀有金屬材料與工程,2015,44(1):62~66.[An Zhen, Li Jinshan, Feng Yong, Liu Xianghong, Du Yuxuan, Ma Fanjiao, Wang Zhe. Modeling Constitutive Relationship of Ti-555211 Alloy by Artificial Neural Network during High-Temperature Deformation[J]. Rare Metal Materials and Engineering,2015,44(1):62~66.]
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  • 收稿日期:2014-04-01
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  • 在線發(fā)布日期: 2015-05-22
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