Research Progress on Corrosion Fatigue Behavior and Life Prediction of Magnesium Alloys
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摘要: 镁合金因其优异的比强度和比刚度, 在实现结构轻量化方面具有广阔的应用前景. 然而, 镁合金的腐蚀疲劳问题制约了其在关键承力构件中的可靠应用. 深入理解镁合金的腐蚀疲劳行为, 建立精准的寿命预测模型, 进而提出合理的防护措施, 是推动其工程应用的关键和前提. 为此, 本文系统综述了镁合金腐蚀疲劳行为的宏观演化规律、微观机制以及寿命预测等方面的研究进展: 首先概括了镁合金内在因素、腐蚀介质以及加载条件对其腐蚀疲劳宏观演化规律; 其次, 总结了镁合金的腐蚀疲劳损伤机理, 强调了原位和非原位表征方法以及主流的数值模拟方法的重要作用; 然后, 对镁合金腐蚀疲劳寿命预测模型的研究现状进行了系统梳理; 最后, 简要总结当前研究进展, 并展望了该领域未来的发展方向.Abstract: Magnesium alloys, owing to their high specific strength and stiffness, offer significant potential for lightweight structural applications. However, corrosion fatigue remains a critical challenge that limits their reliable use in safety-critical load-bearing components. A comprehensive understanding of corrosion fatigue behavior, together with the development of robust life prediction models and effective protection strategies, is therefore essential to promote their broader engineering application. In this context, the present paper reviews the research progress on the macroscopic behavior, microscopic mechanisms, and life prediction of corrosion fatigue in magnesium alloys. First, it summarizes the effects of intrinsic factors of magnesium alloys, corrosive media, and loading conditions on their macroscopic evolution characteristics of corrosion fatigue. Second, the underlying damage mechanisms are discussed, with particular emphasis on insights gained from in situ and ex situ characterization techniques, as well as commonly employed numerical simulation approaches. Third, the current state of corrosion-fatigue life prediction models is systematically evaluated. Finally, the main findings are summarized, and key challenges and future research directions are highlighted.
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图 1 合金元素对镁合金阴极和阳极反应的影响及其对耐腐蚀行为的影响(Olugbade et al. 2022)
图 2 Mg-1Y-xCa合金的腐蚀机理示意图: (a-c) Mg-1Y; (d-f) Mg-1Y-0.05Ca (Bian et al. 2016)
图 3 (a) HP-Mg镁、(b) Mg-1Ca镁合金和(c) Mg-2Zn-0.2Ca镁合金分别在空气中和模拟体液中的疲劳S-N曲线(Bian et al. 2016)
图 4 腐蚀疲劳裂纹萌生过程示意图: (a)锻造态镁合金; (b)固溶处理态镁合金(Wang et al. 2019)
图 5 磷酸盐缓冲溶液对AM60镁合金腐蚀疲劳行为的影响(Meng et al. 2019): (a) 模拟体液pH值对镁合金的疲劳S-N曲线的影响; (b) 模拟体液的pH值对镁合金腐蚀速率的影响
图 6 AZ61镁合金在不同温度和不同湿度下的疲劳S-N曲线(Sajuri et al. 2005): (a) 在20 ℃下相对湿度分别为55%和80%的疲劳S-N曲线, (b) 在50 ℃下相对湿度分别为55%和80%的疲劳S-N曲线, 其中, 填充符号旁边的 P 表示由腐蚀坑引起的疲劳失效
图 7 干燥空气和蒸馏水中疲劳裂纹扩展速率与应力强度因子范围的关系(Tokaji et al. 2009): (a) AZ31镁合金; (b) AZ61镁合金
图 8 疲劳裂纹扩展速率与有效应力强度因子范围的关系(Uematsu et al. 2014): (a)未热处理态AZ61镁合金和(b)AZ61-T5镁合金
图 9 ZEK100镁合金在模拟生理环境中预腐蚀后的疲劳行为(Zhao et al. 2015): (a) 浸泡在模拟生理环境中的镁合金腐蚀速率; (b) 镁合金屈服强度、抗拉强度和弹性模量随预腐蚀时间的演化; (c) 镁合金预腐蚀不同时间后的疲劳S-N曲线
图 10 镁合金在模拟体液中的腐蚀疲劳行为(Nachtsheim et al. 2024): (a) WE43镁合金的预腐蚀疲劳和腐蚀疲劳S-N曲线; (b) 预腐蚀后WE43镁合金在空气中疲劳测试时平均应变vs.归一化的循环周次; (c) 未腐蚀和预腐蚀的不同镁合金体系在空气中的疲劳寿命比较; (d) 不同镁合金体系在不同腐蚀环境中经2 h的循环加载后疲劳强度降低比例
图 11 预腐蚀AZ31镁合金棘轮行为(Yuan et al. 2016): (a) 预腐蚀镁合金单轴棘轮试验的应力−应变曲线(σm = 50 MPa, σa = 80 MPa); (b) 镁合金预腐蚀不同时间后的棘轮应变演化曲线(σm = 40 MPa, σa = 80 MPa)
图 12 AZ31镁合金PBS溶液中的棘轮−疲劳交互作用(Chen et al. 2015): (a) 应变/应力控制的腐蚀疲劳测试装置示意图; (b) 镁合金在不同应力幅值下的腐蚀速率; (c) 镁合金在60 ± 180 MPa时的典型应力−应变滞回曲线; (d) 镁合金在σm = 60 MPa时不同应力幅值时的棘轮应变曲线
图 13 AZ31B镁合金在Hank平衡盐溶液中腐蚀行为(Han et al. 2022): (a, b) 在20 MPa的循环载荷作用下, HBSS溶液pH值随时间的变化; (c, d) 在不同加载频率下的腐蚀行为
图 14 加载频率对AZ61镁合金3.5% NaCl溶液中疲劳裂纹扩展行为的影响(Rozali et al. 2011): (a) 低湿度环境和NaCl溶液中da/dN与ΔK的关联关系; (b) 低湿度环境和NaCl溶液中da/dN与ΔKeff的关联关系
图 15 AZ31镁合金在0.05 M NaCl溶液浸泡不同时间后的SVET二维电流分布图(Jayaraj et al. 2024): (a) 1 h; (b) 6 h; (c) 12 h
图 16 基于电化学测试的镁合金腐蚀疲劳原位表征系统及结果(Klein et al. 2017): (a) 腐蚀疲劳原位测试装置照片; (b) 试样在电化学装置中的示意图; (c) 镁合金在0.1 mol·L−1的NaCl溶液中疲劳加载试验时应力幅值σa作为控制变量、塑性应变幅值εa,p和开路电位随循环加载周期N的变化规律; (d) 镁合金在0, 0.01, 0.001 mol·L−1的NaCl溶液中开路电位随循环周次的变化规律
图 17 铸造(a)和热挤压(b, c)镁合金的SKPFM分析结果(Xie et al. 2025): (a-1)-(c-1) 形貌照片; (a-2)-(c-2) 表面电势二维分布图; (a-3)-(c-3) 电势线分布图
图 18 基于声发射技术的镁合金腐蚀疲劳原位表征系统及结果(He et al. 2023): (a, b) 基于声发射技术的腐蚀疲劳损伤原位监测系统; (c) AZ31镁合金焊接接头在3.5wt.% NaCl溶液中的疲劳加载时的声发射累积能量演化
图 19 腐蚀疲劳裂纹随循环周次的三维俯视图(Stannard et al. 2018), 其中浅蓝色为裂纹, 深蓝色为气泡, 红色为含铁夹杂物, 黑色为含镁夹杂物
图 21 Mg-Gd-Y-Nd-Zr系铸造镁合金T4处理前后的应力腐蚀裂纹萌生过程(Wang et al. 2018): (a, b) 第一阶段镁合金优先在第二相附近腐蚀; (c, d) 第二阶段氢吸附在局部腐蚀区域, 然后扩散到镁基体中, 并在第二相或则和低表面能晶面处累积; (e, f) 第三阶段镁合金在应力作用下的脆性开裂
图 22 单个蚀坑的腐蚀示意图(Ding et al. 2024): (a) 表面晶粒的非基面平行于腐蚀表面; (b)表面晶粒的基面平行于腐蚀表面
图 23 镁合金腐蚀疲劳裂纹尖端滑移溶解机理示意图(Behvar & Haghshenas 2023)
图 24 镁合金腐蚀疲劳氢脆机理示意图(Behvar & Haghshenas 2023)
图 25 镁合金腐蚀第一性原理预测模型及模拟结果(黄昱昊 2023): (a) 阴极和阳极溶解时腐蚀电流密度和腐蚀点位演化曲线; (b) 腐蚀电位和腐蚀电流公式; (c) 腐蚀电位模拟结果与实验结果对比; (d) 腐蚀电流密度模拟结果与实验结果对比
图 26 近场动力学腐蚀模型中定义的区域和键示意图(Chen et al. 2021)
图 27 相场模拟中多晶材料在腐蚀环境中和固体(电极ϕ = 1)—液体(电解液 ϕ = 0)扩散界面的描述(Makuch et al. 2024)
图 28 AZ31镁合金在生理环境中浸泡不同天数后力学性能退化和疲劳失效行为(Fu et al. 2014): (a) 弹性模量和抗拉强度随浸泡天数的变化规律; (b) 光滑试样和不同浸泡时间的预腐蚀试样棘轮试验的 S-N 曲线; (c) 基于Miner线性损伤理论的损伤D0随浸泡时间的演化规律和幂律拟合曲线; (d) 预测寿命和实验寿命的比较
图 30 基于总应变能模型的AZ31B镁合金腐蚀疲劳寿命预测(Chen et al. 2015): (a) Ellyin模型的应变能密度; (b) 基于Ellyin模型的预测寿命与试验寿命的关系; (c) 修正的Ellyin模型的应变能密度; (d) 基于修正的Ellyin模型的预测寿命与试验寿命的关系
图 31 金属材料腐蚀疲劳蚀坑及裂纹生长过程示意图(徐会会 等 2023)
图 32 AZ31合金在腐蚀疲劳过程蚀坑深度演化规律(Nan et al. 2008): (a) 腐蚀坑深度随腐蚀疲劳时间演化 (通过测量同一腐蚀坑的深度获得坑数据); (b) 腐蚀坑深度与应力幅值的关联关系
表 1 力—电—化耦合相场腐蚀模型控制方程(Cui et al. 2023)
相场模型 $\dfrac{\partial \phi}{\partial t}=L(\varepsilon ^{\mathrm{p}},\sigma _{\mathrm{h}},\eta )\left( \alpha \nabla ^2\phi -\dfrac{\partial \psi ^{\mathrm{E}}}{\partial \phi} \right) $ 固相离子输运方程 $\dfrac{\partial c_{\mathrm{M}}}{\partial t}-\nabla \cdot D_{\mathrm{M}}\nabla \left[ c_M-h(\phi )(c_{\mathrm{Se}}-c_{\mathrm{Le}})-c_{\mathrm{Le}} \right] -\nabla \cdot \left\{ \dfrac{[1-h(\phi )]D_{\mathrm{M}}c_{\mathrm{M}}}{R_{\mathrm{g}}T}Fn_{\mathrm{M}}\nabla \varphi _{\mathrm{l}} \right\} =\dfrac{R_{\mathrm{M}}}{c_{\mathrm{solid}}}$ 离子输运方程 $\dfrac{\partial c_i}{\partial t}-\nabla \cdot \bigl\{ [1-h(\phi )]D_i\nabla c_i \bigr\} -\nabla \cdot \left\{ \dfrac{[1-h(\phi )]D_ic_i}{R_{\mathrm{g}}T}Fn_i\nabla \varphi _{\mathrm{l}} \right\} =R_i$ 静电式分布方程 $\nabla \cdot (\kappa \nabla \varphi _{\mathrm{l}})=n_MFc_{\mathrm{solid}}\dfrac{\partial \phi}{\partial t}$ 力平衡方程 $\nabla \cdot [h(\phi )\sigma _0]=\mathbf{0}$ 其中 $\dfrac{\partial \psi ^{\mathrm{E}}}{\partial \phi}=-2A\bigl[ c_{\mathrm{M}}-h(\phi )(c_{\mathrm{Se}}-c_{\mathrm{Le}})-c_{\mathrm{Le}} \bigr] (c_{\mathrm{Se}}-c_{\mathrm{Le}})h\prime (\phi ) + wg\prime (\phi )$ $L\left(\varepsilon^{\mathrm{p}},\sigma_{\mathrm{h}},\eta\right) = \begin{cases}k_{\mathrm{m}}\left(\varepsilon^{\mathrm{p}},\sigma_{\mathrm{h}}\right)L_{0}\exp\left(\dfrac{a_{\mathrm{a}}n_{\mathrm{M}}F\eta}{R_{\mathrm{g}}T}\right), & \text{if } 0 \lt t_{i} \leq t_{0} \\k_{\mathrm{m}}\left(\varepsilon^{\mathrm{p}},\sigma_{\mathrm{h}}\right)L_{0}\exp\left(\dfrac{a_{\mathrm{a}}n_{\mathrm{M}}F\eta}{R_{\mathrm{g}}T}\right)\exp\left[-k\left(t_{i}-t_{0}\right)\right], & \text{if } t_{0} \lt t_{i} \leq t_{0}+t_{f}\end{cases}$ $\kappa =h(\phi )\kappa _{\mathrm{s}} + [1-h(\phi )]\dfrac{F^2}{R_{\mathrm{g}}T}\left( c_{\mathrm{M}}c_{\mathrm{solid}}D_{\mathrm{M}}n_{\mathrm{M}}^{2} + \displaystyle\sum_i{c_iD_in_{i}^{2}} \right) $ $\sigma _0=\boldsymbol{C}^{\mathrm{ep}}:(\varepsilon ^{\mathrm{e}} + \varepsilon ^{\mathrm{p}})$ 
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