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基于修正J-C和BP神經(jīng)網(wǎng)絡(luò)模型的超細(xì)晶純鈦動態(tài)本構(gòu)行為
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西安建筑科技大學(xué)冶金工程學(xué)院

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中圖分類號:

TG146.23

基金項目:

陜西省自然科學(xué)基金面上項目(2023-JC-YB-312)；陜西省教育廳重點實驗室項目(20JS075)


Dynamic constitutive behavior of ultrafine-grained pure titanium based on modified J-C and BP artificial neural network model
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The General Program of Shaanxi Natural Science Foundation(2023-JC-YB-312); The Key Laboratory Project of Shaanxi Provincial Department of Education(20JS075)

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    摘要:

    為研究超細(xì)晶純鈦在高溫、高應(yīng)變速率加載下的復(fù)雜力學(xué)行為，建立能夠準(zhǔn)確描述其動態(tài)力學(xué)行為的模型，對超細(xì)晶純鈦在溫度為300~450 ℃，應(yīng)變速率為2000~3000 s-1下進(jìn)行了動態(tài)沖擊實驗，獲得真應(yīng)力-真應(yīng)變曲線。結(jié)果表明：在所研究的條件下，真應(yīng)力-真應(yīng)變曲線均表現(xiàn)為明顯的“雙應(yīng)力峰”特征，晶界處的位錯湮滅、重新排列及后續(xù)絕熱剪切帶的形成是兩次應(yīng)力減小的主要因素，流動應(yīng)力均表現(xiàn)出正應(yīng)變速率敏感性和負(fù)溫度敏感性。綜合考慮應(yīng)變硬化、應(yīng)變速率硬化和熱軟化效應(yīng)，提出一種修正J-C本構(gòu)模型和BP人工神經(jīng)網(wǎng)絡(luò)模型，并對兩種模型進(jìn)行了準(zhǔn)確性分析。結(jié)果表明BP人工神經(jīng)網(wǎng)絡(luò)模型能夠更好地預(yù)測超細(xì)晶純鈦的動態(tài)力學(xué)行為，相關(guān)系數(shù)可達(dá)0.97065，平均相對誤差僅為4.63%。

    Abstract:

    To study the intricate mechanical behavior of ultrafine-grained(UFG) pure titanium under high temperature and high strain rate loading, a model that can accurately describe its dynamic mechanical behavior was established. The dynamic impact test of UFG pure titanium was carried out at loading temperatures of 300~450℃ and strain rates of 2000~3000 s-1, the true stress-strain curves were also obtained. The results show that under the studied conditions, the true stress-strain curves show obvious double stress peaks, the annihilation and rearrangement of dislocations at grain boundaries and the subsequent formation of adiabatic shear bands(ASB) are the main factors for the two stress reduction. UFG pure titanium shows positive strain rate sensitivity and negative temperature sensitivity. Considering strain hardening effect, strain rate hardening and thermal softening effect, a modified Johnson-Cook(J-C) constitutive model and a BP artificial neural network(BP-ANN) model are proposed, and the accuracy of the two models is analyzed. The results show that the BP-ANN model can better predict the dynamic mechanical behavior of UFG pure titanium, the correlation coefficient can reach 0.97065, and the average relative error is only 4.63%.

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引用本文

劉曉燕,李帥康,楊西榮.基于修正J-C和BP神經(jīng)網(wǎng)絡(luò)模型的超細(xì)晶純鈦動態(tài)本構(gòu)行為[J].稀有金屬材料與工程,2024,53(2):409~416.[Liu Xiaoyan, Li Shuaikang, Yang Xirong. Dynamic constitutive behavior of ultrafine-grained pure titanium based on modified J-C and BP artificial neural network model[J]. Rare Metal Materials and Engineering,2024,53(2):409~416.]
DOI：10.12442/j. issn.1002-185X.20230015

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  • 收稿日期:2023-01-11
  • 最后修改日期:2023-02-17
  • 錄用日期:2023-03-07
  • 在線發(fā)布日期: 2024-02-28
  • 出版日期: 2024-02-23