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基于BP神經(jīng)網(wǎng)絡(luò)Ti600合金本構(gòu)關(guān)系模型的建立
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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（西北工業(yè)大學(xué)）開放課題（35-TP-2009）


Modeling of Constitutive Relationship of Ti600 Alloy Using BP Artificial Neural Network
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    摘要:

    運(yùn)用Gleeble-1500熱模擬機(jī)對Ti600合金的圓柱試樣進(jìn)行等溫壓縮變形試驗(yàn)，以試驗(yàn)所得數(shù)據(jù)（變形溫度800~1100 ℃，應(yīng)變速率0.01~10 s-1）為基礎(chǔ)，基于BP神經(jīng)網(wǎng)絡(luò)方法建立了該合金的高溫本構(gòu)關(guān)系模型。結(jié)果表明：BP神經(jīng)網(wǎng)絡(luò)本構(gòu)關(guān)系模型具有很高的預(yù)測精度，可以很好地描述Ti600合金在高溫變形時各熱力學(xué)參數(shù)之間高度非線性的復(fù)雜關(guān)系，為本構(gòu)關(guān)系模型的建立提供了一種更加準(zhǔn)確有效的方法。

    Abstract:

    Isothermal compression deformation tests were conducted for Ti600 alloy column samples by Gleeble-1500 thermal simulator. According to the obtained experimental data (deformation temperatures of 800-1100 oC and strain rates of 0.01-10 s-1), the high temperature constitutive relationship model for the alloy was built based on the BP neural network. Results show that the constitutive relationship model of BP neural network is of high prediction accuracy, which can describe the complicated nonlinear relationship of thermodynamical parameters well. Therefore it provides a more convenient and more effective way to establish the model of constitutive relationship for titanium alloys.

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孫 宇,曾衛(wèi)東,趙永慶,戚運(yùn)蓮,韓遠(yuǎn)飛,邵一濤,馬 雄.基于BP神經(jīng)網(wǎng)絡(luò)Ti600合金本構(gòu)關(guān)系模型的建立[J].稀有金屬材料與工程,2011,40(2):220~224.[Sun Yu, Zeng Weidong, Zhao Yongqing, Qi Yunlian, Han Yuanfei, Shao Yitao, Ma Xiong. Modeling of Constitutive Relationship of Ti600 Alloy Using BP Artificial Neural Network[J]. Rare Metal Materials and Engineering,2011,40(2):220~224.]
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  • 收稿日期:2010-03-01
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