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應(yīng)用人工神經(jīng)網(wǎng)絡(luò)建立TC11鈦合金化學(xué)元素與力學(xué)性能關(guān)系模型
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國(guó)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(guó)家重點(diǎn)實(shí)驗(yàn)室開(kāi)放課題 (35-TP-2009)


Modeling of Chemical Elements and Mechanical Property for TC11 Titanium Alloy Based on the Artificial Neural Network
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    摘要:

    在TC11鈦合金大量實(shí)驗(yàn)數(shù)據(jù)的基礎(chǔ)上，應(yīng)用人工神經(jīng)網(wǎng)絡(luò)建立TC11鈦合金的化學(xué)元素與力學(xué)性能關(guān)系模型。模型的輸入?yún)?shù)包括Al、Mo、Zr、Si、Fe、C、O、N和H共9種化學(xué)元素；輸出為常規(guī)力學(xué)性能指標(biāo) (抗拉強(qiáng)度、屈服強(qiáng)度、延伸率和斷面收縮率)。運(yùn)用未知數(shù)據(jù)樣本對(duì)已建立神經(jīng)網(wǎng)絡(luò)模型的預(yù)測(cè)能力進(jìn)行檢驗(yàn)，并以Al、Mo、Zr和C元素為研究對(duì)象，利用該模型分析TC11鈦合金化學(xué)元素對(duì)力學(xué)性能的影響規(guī)律。結(jié)果表明：網(wǎng)絡(luò)的預(yù)測(cè)值與實(shí)驗(yàn)值的相對(duì)誤差均在10%以內(nèi)，說(shuō)明所建立的神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型具有較精確的預(yù)測(cè)能力，而且能夠清楚地反映出該合金化學(xué)元素與力學(xué)性能之間的非線性關(guān)系

    Abstract:

    Based on a large amount of experimental data, the relationship model of chemical elements and mechanical property for TC11 titanium alloy has been developed using artificial neural network. The input parameters of this model were 9 kinds of elements, including Al, Mo, Zr, Si, Fe, C, O, N and H. The mechanical properties were used as output parameters, including ultimate tensile strength, yield strength, elongation and reduction of area. The prediction capability of the established model was tested by the unseen data sample. Additionally, the effect of chemical elements (Al、Mo、Zr and C) on the mechanical property was studied using the present model. It is found that the relative errors between predicted and experimental values all within 10%, indicating that the neural network model possesses excellent prediction capability. With the help of the trained ANN model, the nonlinear relationship of chemical elements and mechanical property can also be clearly presented.

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孫 宇,曾衛(wèi)東,趙永慶,韓遠(yuǎn)飛,馬 雄.應(yīng)用人工神經(jīng)網(wǎng)絡(luò)建立TC11鈦合金化學(xué)元素與力學(xué)性能關(guān)系模型[J].稀有金屬材料與工程,2012,41(4):594~598.[Sun Yu, Zeng Weidong, Zhao Yongqing, Han Yuanfei, Ma Xiong. Modeling of Chemical Elements and Mechanical Property for TC11 Titanium Alloy Based on the Artificial Neural Network[J]. Rare Metal Materials and Engineering,2012,41(4):594~598.]
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  • 收稿日期:2011-04-08
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