粉末冶金Fe−2Cu−0.5C钢高温拉伸变形行为

简杰 郭彪 李强 李肖 宋久鹏 张羽 敖进清 黄勇

简杰, 郭彪, 李强, 李肖, 宋久鹏, 张羽, 敖进清, 黄勇. 粉末冶金Fe−2Cu−0.5C钢高温拉伸变形行为[J]. 粉末冶金技术, 2023, 41(6): 577-585. doi: 10.19591/j.cnki.cn11-1974/tf.2021080001
引用本文: 简杰, 郭彪, 李强, 李肖, 宋久鹏, 张羽, 敖进清, 黄勇. 粉末冶金Fe−2Cu−0.5C钢高温拉伸变形行为[J]. 粉末冶金技术, 2023, 41(6): 577-585. doi: 10.19591/j.cnki.cn11-1974/tf.2021080001
JIAN Jie, GUO Biao, LI Qiang, LI Xiao, SONG Jiupeng, ZHANG Yu, AO Jinqing, HUANG Yong. Elevated temperature tensile deformation behavior of powder metallurgy Fe−2Cu−0.5C steels[J]. Powder Metallurgy Technology, 2023, 41(6): 577-585. doi: 10.19591/j.cnki.cn11-1974/tf.2021080001
Citation: JIAN Jie, GUO Biao, LI Qiang, LI Xiao, SONG Jiupeng, ZHANG Yu, AO Jinqing, HUANG Yong. Elevated temperature tensile deformation behavior of powder metallurgy Fe−2Cu−0.5C steels[J]. Powder Metallurgy Technology, 2023, 41(6): 577-585. doi: 10.19591/j.cnki.cn11-1974/tf.2021080001

粉末冶金Fe−2Cu−0.5C钢高温拉伸变形行为

doi: 10.19591/j.cnki.cn11-1974/tf.2021080001
基金项目: 四川省科技厅重点研发计划项目(2022YFG0346)
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    E-mail: biaoguo_mse@126.com

  • 中图分类号: TF124.8

Elevated temperature tensile deformation behavior of powder metallurgy Fe−2Cu−0.5C steels

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  • 摘要: 使用Gleeble-3500型热模拟机对粉末冶金Fe−2Cu−0.5C钢在变形温度850~1000 ℃、应变速率0.1~10.0 s−1下进行高温拉伸测试,定量分析变形温度、应变速率对粉末冶金钢高温拉伸变形行为的影响。通过应力应变曲线计算了该粉末冶金钢高温拉伸过程的断裂功,建立了断裂功与变形温度、应变速率的数学模型。基于Hensel-Spittel模型和BP神经网络模型建立了该粉末冶金钢的本构模型,用于表征其高温拉伸流变行为,并将两种模型的预测结果进行比较。结果表明:所建立的断裂功模型能够描述该粉末冶金钢在不同变形温度和应变速率下的抗断裂能力。Hensel-Spittel本构模型的预测值与实验值的平均绝对相对误差为3.16%,决定系数为0.9743,而BP神经网络模型的预测值与实验值的平均绝对相对误差为0.17%,决定系数为0.9999,说明BP神经网络模型的预测能力更强,能更好地表征粉末冶金Fe−2Cu−0.5C钢的高温拉伸流变行为。
  • 图  2  断裂功模型预测值与实验测算值

    Figure  2.  Fracture work values of the model prediction and the experimental calculation

    图  3  断裂功与变形温度及应变速率之间的关系

    Figure  3.  Relationships of the fracture work, deformation temperature, and strain rate

    图  4  不同变形温度下lnσ−ln$ \dot \varepsilon $关系曲线:(a)850 ℃;(b)900 ℃;(c)950 ℃;(d)1000 ℃

    Figure  4.  Relationship of lnσ−ln$ \dot \varepsilon $ at the different deformation temperatures: (a) 850 ℃; (b) 900 ℃; (c) 950 ℃; (d) 1000 ℃

    图  5  不同应变下m3+m7T与温度关系曲线

    Figure  5.  Relationship between m3+m7T and temperature under the different strains

    图  7  不同应变速率下S与ln(ε+1)关系曲线

    Figure  7.  Relationship between S and ln(ε+1) at the different strain rates

    图  8  不同变形温度下lnσε关系:(a)850 ℃;(b)900 ℃;(c)950 ℃;(d)1000 ℃

    Figure  8.  Relationship of lnσ and ε at the different deformation temperatures: (a) 850 ℃; (b) 900 ℃; (c) 950 ℃; (d) 1000 ℃

    图  9  BP神经网络结构图

    Figure  9.  Structure diagram of the BP neural network

    图  10  BP神经网络收敛过程

    Figure  10.  Convergence process of the neural BP network

    图  11  不同温度下两种模型的应力预测值与实验值:(a)850 ℃;(b)900 ℃;(c)950 ℃;(d)1000 ℃

    Figure  11.  Experimental stress and the predicted stress by Hensel-Spittel and BP modes at the different temperatures: (a) 850 ℃; (b) 900 ℃; (c) 950 ℃; (d) 1000 ℃

    图  12  应力实验值与应力预测值线性相关曲线:(a)Hensel-Spittel本构模型;(b)BP神经网络模型

    Figure  12.  Correlation curves of the experimental and predicted stress: (a) Hensel-Spittel constitution model; (b) BP neural network model

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  • 收稿日期:  2022-02-12
  • 刊出日期:  2023-12-28

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