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Ti43.76Zr12.50Cu37.49-xNi6.25Cox非晶钎料真空钎焊TC4钛合金/316L不锈钢

韩文倩, 董红刚, 马月婷, 李鹏, 吴宝生, 张亮亮

韩文倩, 董红刚, 马月婷, 李鹏, 吴宝生, 张亮亮. Ti43.76Zr12.50Cu37.49-xNi6.25Cox非晶钎料真空钎焊TC4钛合金/316L不锈钢[J]. 焊接学报, 2024, 45(1): 47-57. DOI: 10.12073/j.hjxb.20221123001
引用本文: 韩文倩, 董红刚, 马月婷, 李鹏, 吴宝生, 张亮亮. Ti43.76Zr12.50Cu37.49-xNi6.25Cox非晶钎料真空钎焊TC4钛合金/316L不锈钢[J]. 焊接学报, 2024, 45(1): 47-57. DOI: 10.12073/j.hjxb.20221123001
HAN Wenqian, DONG Honggang, MA Yueting, LI Peng, WU Baosheng, ZHANG Liangliang. Vacuum brazing TC4 titanium alloy / 316L stainless steel with Ti43.76Zr12.50Cu37.49-xNi6.25Cox amorphous filler metals[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(1): 47-57. DOI: 10.12073/j.hjxb.20221123001
Citation: HAN Wenqian, DONG Honggang, MA Yueting, LI Peng, WU Baosheng, ZHANG Liangliang. Vacuum brazing TC4 titanium alloy / 316L stainless steel with Ti43.76Zr12.50Cu37.49-xNi6.25Cox amorphous filler metals[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(1): 47-57. DOI: 10.12073/j.hjxb.20221123001

Ti43.76Zr12.50Cu37.49-xNi6.25Cox非晶钎料真空钎焊TC4钛合金/316L不锈钢

基金项目: 国家自然科学基金资助项目(52275314,52075074)
详细信息
    作者简介:

    韩文倩,硕士研究生;主要从事钛合金/不锈钢异种金属钎焊工艺研究; Email: duthanwenqian@163.com

    通讯作者:

    董红刚,博士,教授,博士研究生导师;Email: donghg@dlut.edu.cn

  • 中图分类号: TG 454

Vacuum brazing TC4 titanium alloy / 316L stainless steel with Ti43.76Zr12.50Cu37.49-xNi6.25Cox amorphous filler metals

  • 摘要:

    根据双团簇模型设计并制备了Ti-Zr-Cu-Ni-Co系非晶钎料,用于真空钎焊TC4钛合金和316L不锈钢,分析了钎料中Co元素含量对钎焊接头界面微观组织形貌、力学性能及断裂行为的影响规律.结果表明,钎焊接头可划分为TC4/扩散区(I区)/钎缝中心区(II区)/界面区(III区)/316L,界面典型微观组织结构为TC4/β-Ti + Ti2Cu/(Ti, Zr)2(Cu, Ni) + Ti2Cu + Ti2(Cu, Ni) + TiFe/(Fe, Cr)2Ti + α-(Fe, Cr) + τ + γ-(Fe, Ni) + σ/316L,随着Co元素含量的增加,接头剪切强度先升高后降低再升高,当Co元素含量为1.56%时达到最大310 MPa,不添加Co元素时,接头断裂于钎缝中心区(II区);当Co元素含量为1.56% ~ 6.24%时,接头断裂于靠近316L母材的界面区(III区)附近,断裂模式为典型的解理断裂.

    Abstract:

    Ti-Zr-Cu-Ni-Co amorphous filler metals were designed and prepared for vacuum brazing of TC4 titanium alloy to 316L stainless steel according to the dual-cluster model. The effect of Co content in filler metals on the microstructure, mechanical properties and fracture behavior of brazed joints was investigated. The results showed that the cross section of brazed joint could be divided into TC4/diffusion zone I/brazing seam center zone II/interface zone III/316L. The typical interfacial microstructure of the brazed joints was TC4/β-Ti + Ti2Cu/(Ti, Zr)2(Cu, Ni) + Ti2Cu + Ti2(Cu, Ni) + TiFe/(Fe, Cr)2Ti + α-(Fe, Cr) + τ + γ -(Fe, Ni) + σ/316L. The shear strength of brazed joints first increased, then decreased and then increased with the increase of Co content. The maximum shear strength of 310 MPa was obtained at 1.56% Co. When Co element was not added, brazed joints fractured in the center of the brazing seam (zone II). And when the Co content was 1.56 ~ 6.24%, brazed joints fractured near the interface zone (zone III) of 316L base metal. The fracture mode was typical cleavage fracture.

  • 自冲铆(self-piercing riveting, SPR)是一种针对薄板材料的新型机械变形连接技术.连接工艺过程中,作为连接元件的半空心铆钉在高速运行的冲头推动作用下刺入板材,铆钉脚端部产生径向扩张;同时,在底模(通常为弧形凸模或平模)的引导下,被连接材料发生大塑性变形,最终与铆钉形成紧密的机械内锁结构.自冲铆连接具有工艺过程简单、高效、材料适应性广等特点,近年来在航空航天、汽车和家电等轻量化制造领域,成为具有代表性的一种“绿色”机械变形连接技术[1-4].然而,自冲铆连接面对强度较高、塑性相对较差的材料,如7075铝合金板材、铸铝等,连接时形成的机械内锁区容易出现裂纹缺陷,导致结构体的力学性能和抗腐蚀性能下降[5].

    针对这一问题,国内外学者提出了不同的解决方案.一方面,通过材料处理的方法,提升基板的延展性,从而改善自冲铆接头的成形质量.Ying等人[6]研究了准静态加载条件下AA7075-T6铝合金板材热自冲铆接的连接质量,通过将铆接温度提高到400 ℃,可以大大提高T-SPR对AA7075-T6铝合金的连接质量.Durandet[7]和Easton[8]等人提出激光-自冲铆接复合工艺,在自冲铆接之前通过激光对镁合金预加热以改善其延展性.Jäckel等人[9]利用底模直径为10 mm、底模深度为1.5 mm的球形模具对6016和7075铝合金进行局部加热并进行自冲铆接,有效地避免了裂纹的产生.另一方面,通过优化底(凹)模结构,降低材料形变过程中局部应力集中.Ma等人[10]探究不同工艺参数对异种板材自冲铆接性能的影响规律,为获得较好的接头强度和避免接头底部开裂可选取较软和较长的铆钉,且模具与铆钉体积比大于1.0的组合.Drossel等人[11]通过优化模具形状且降低模具深度可避免铆接裂纹的产生,但模具深度降低会带来内锁长度过低,并且接头底部也会有裂纹产生.Neuser等人[12]通过减小底模深度,解决了6014铝合金和铸铝在自冲铆接过程中的裂纹问题.Mori等人[13]通过有限元模拟优化了模具的形状,成功将超高强度钢板与铝合金板接合在一起.Li等人[14]研究了模具轮廓对接头质量的影响,具有深角和尖角的模具会使某些材料产生严重的裂纹.

    文中提出了一种基于球形底模的自冲铆连接工艺;同时,针对延展性相对较低的7075铝合金,应用响应面法建立自冲铆接头失效载荷和能量吸收值的多元回归模型,以探究自冲铆成形过程中各影响因素及其交互作用对力学性能的影响规律.

    图1为所选用的自冲铆接试验设备,铆接对象为高强度7075-T6铝合金,铆钉管腿内径为3.5 mm,外径为5.3 mm,硬度均为H4(330 HV)的半空心铆钉.预试验分析后发现使用传统底膜均有裂纹产生(图2),选用底模深度为1 mm,直径为10 mm的球形模具没有裂纹产生,其实物图、截面尺寸和成形接头底部如图3所示. 采用不同底模和相同铆接参数成形的接头强度数据对比如图4所示. 7075-T6铝合金板材的力学性能如表1所示.被连接板材的厚度为1.5 mm + 2 mm,试件尺寸均为110 mm × 20 mm,采用单搭铆接,搭接区域为20 mm × 20 mm,试件几何形状和尺寸如图5所示.

    图  1  自冲铆接设备
    Figure  1.  Self-piercing riveting equipment
    图  2  传统底模的实物图、截面尺寸和成形接头底部(mm)
    Figure  2.  Dies, sectional dimensions, and the bottom of the joints formed with conventional dies. (a) pip die; (b) flat die
    图  3  球形底模实物图、截面尺寸和成形接头底部(mm)
    Figure  3.  Dies, sectional dimensions, and the bottom of the joint formed with ball-shaped die
    图  4  不同模具成形的接头强度数据对比
    Figure  4.  Comparison of joint strength data formed with different dies
    表  1  7075-T6铝合金力学性能参数
    Table  1.  Mechanical property parameters of 7075-T6 aluminium alloy
    材料弹性模量E/GPa屈服强度ReL/MPa抗拉强度Rm/MPa断后伸长率A(%)
    7075-T67132543013
    下载: 导出CSV 
    | 显示表格
    图  5  自冲铆接接头搭接示意图 (mm)
    Figure  5.  Schematic diagram of self-piercing riveting joint lap

    BBD法是通过试验设计并借助试验得到一定的试验数据,采用多元回归方程建立响应值与输入变量之间的函数关系式,通过分析变量与响应值之间的变化趋势,从而进行工艺调控与参数预测. 文中分别以自冲铆接头的失效载荷、能量吸收值为响应值,以铆钉长度、冲头速度和冲头行程为影响因素,利用Box-Behnken试验设计中的三因素三水平试验设计方法,试验因素水平设计如表2所示,利用Design Expert10.0.1软件得到表3中的BBD试验组合.为减小误差,每组试件制备四个,取其中任意一个进行实际剖切,测量内锁长度、残余底厚等机械内锁结构尺寸,其余三个在MTSlandmark100材料试验机上进行拉伸速率为5 mm/min的拉伸-剪切试验(图6),以探究各工艺参数对接头静力学性能的影响.考虑到试件两端未在同一水平面上,因此在两端夹持与试件等厚的垫片来消除扭矩.

    表  2  三因素三水平设计表
    Table  2.  Design table of three factors and levels
    影响因素因子水平
    −101
    铆钉长度L/mm55.56
    冲头速度V/(mm·s−1)204060
    冲头行程D/mm646566
    下载: 导出CSV 
    | 显示表格
    表  3  自冲铆接头拉伸试验结果
    Table  3.  Tensile test results of self-piercing riveting joints
    组数铆钉长度L/mm冲头速度V/(mm·s−1)冲头行程D/mm失效载荷F/N能量吸收值W/J
    1540664667.6144.673
    25.540655286.16011.845
    3640645259.39419.826
    4520654395.8524.320
    55.540655386.50511.941
    6640665232.43416.000
    7560654196.8943.955
    85.520644600.11611.527
    95.520665381.42810.946
    105.540655218.26410.174
    115.560665390.00714.960
    125.560644895.33314.012
    13660655064.89517.496
    14540644376.3585.617
    15620655353.07520.230
    下载: 导出CSV 
    | 显示表格
    图  6  静力学试验拉伸设备
    Figure  6.  Static test tensile equipment

    通过对试验数据采用多元二次回归方程拟合,剔除显著性较低项,优化后得到接头的失效载荷和能量吸收值的响应面回归模型如式(1)、式(2)所示,并通过方差分析对模型显著性进行检验,$P$值反映模型中各因素的显著性,当$P < 0.01$时,认为该因素高度显著;当$0.01 < P < 0.05$时,则该因素显著,并根据各因素$P$值进行模型优化.

    失效载荷模型:

    $$ \begin{split} {y_1} = &- 122\;626 + 27\;077.652\;69{x_1} + 279.406\;62{x_2} +\\& 1\;210.958\;75{x_3} - 2.230\;55{x_1}{x_2} - 159.110\;00{x_1}{x_3} - \\& 3.582\;97{x_2}{x_3} - 1\;438.910\;15x_1^2 - 0.442\;39x_2^2 \end{split} $$ (1)

    能量吸收值模型:

    $$\begin{split} {y_2} =& - 639.685\;84 + 139.968\;68{x_1} + 0.346\;99{x_2} + \\& 7.375\;13{x_3} - 0.059\;225{x_1}{x_2} - 1.441\;00{x_1}{x_3} - \\& 2.744\;36x_1^2 \end{split} $$ (2)

    式中:${y_1}$、${y_2}$为响应值,分别表示失效载荷和能量吸收值,${x_1}$、${x_2}$、${x_3}$为输入变量,分别表示铆钉长度、冲头速度和冲头行程.其中失效载荷是评价构件抵抗外力破坏能力最直接的手段,也是评价自冲铆接头质量的重要衡量指标.由表4单因素方差分析可知,失效载荷和能量吸收值均受铆钉长度的影响最为显著,冲头行程次之,冲头速度影响最弱.各组铆接接头对应的载荷—位移曲线和截面如图7图8所示.

    表  4  方差分析表
    Table  4.  Analysis of variance table
    响应值项目平方和自由度均方误差F值P值显著性
    失效载荷模型2.254 × 10682.817 × 1056.830.0154显著
    $ {x}_{1} $1.339 × 10611.339 × 10632.490.0013
    $ {x}_{2} $4.202 × 10314.202 × 1030.100.7603
    $ {x}_{3} $2.966 × 10512.966 × 1057.190.0364
    $ {x}_{1}{x}_{2} $1.990 × 10311.990 × 1030.050.8334
    $ {x}_{1}{x}_{3} $2.532 × 10412.532 × 1040.610.4630
    $ {x}_{2}{x}_{3} $2.054 × 10412.054 × 1040.500.5067
    $ {x}_{1}^{2} $4.806 × 10514.806 × 10511.660.0142
    $ {x}_{2}^{2} $1.163 × 10511.163 × 1052.820.1440
    失拟项2.330 × 10545.825 × 1048.130.1125不显著
    能量吸收值模型387.05664.5122.090.0001显著
    $ {x}_{1} $377.951377.95129.42<0.0001
    $ {x}_{2} $1.4411.440.490.5017
    $ {x}_{3} $2.4212.420.830.3890
    $ {x}_{1}{x}_{2} $1.4011.400.480.5078
    $ {x}_{1}{x}_{3} $2.0812.080.710.4236
    $ {x}_{1}^{2} $1.7611.760.600.4602
    失拟项21.3963.563.610.2327不显著
    下载: 导出CSV 
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    图  7  各组接头载荷—位移曲线
    Figure  7.  Load-displacement curve of each group joints
    图  8  接头内锁区钉脚张开度 (mm)
    Figure  8.  Opening degree of rivet in the locking area of joint

    为验证所建立响应面模型的准确性,对回归模型进行试验验证,在所设定的铆接工艺参数范围内随机选取两组参数进行试验设计,并将设计的铆接工艺参数分别代入失效载荷和能量吸收值回归模型计算公式中.表5为输入参数下试验结果的均值、模型预测值以及误差率.第Ⅰ组的失效载荷和能量吸收值试验均值与模型预测值误差分别为2.45%、9.51%;第Ⅱ组的失效载荷和能量吸收值试验均值与模型预测值误差分别为4.64%、7.45%,误差均在合理范围内,表明所建立的回归模型具有较高的可靠性,可据此进行基于球形底模的铝合金自冲铆接工艺调控及参数预测.

    表  5  回归模型验证
    Table  5.  Regression model verification
    组号铆钉长度
    L/mm
    冲头速度
    V/(mm·s−1)
    冲头行程
    D/mm
    失效载荷试验均值
    Fx/N
    失效载荷预测值
    Fy/N
    误差
    ef (%)
    能量吸收值试验均值
    Wx/J
    能量吸收值预测值
    Wy/J
    误差
    ew (%)
    5.530655108.7275233.7672.4513.24811.9889.51
    5.550644830.0335054.1484.6414.00812.9647.45
    下载: 导出CSV 
    | 显示表格

    为探究各单因素对失效载荷的影响规律,可把其余两个因素固定在中间值.如图9(a)所示,铆钉长度的增加,接头的失效载荷会先增大后减小,当铆钉长度达到5.7 mm左右,失效载荷达到最大值.冲头速度对失效载荷的影响呈现先上升后下降的变化规律,变化幅度不大,在40 mm/s左右时,失效载荷达到最大值.不同冲头行程下的失效载荷变化趋势呈线性增加.交互作用方差分析中Px1x3(0.4630)<Px2x3(0.5067)<Px1x2(0.8334),即铆钉长度与冲头行程的交互作用对失效载荷的影响最显著,冲头速度与冲头行程的交互作用影响次之,而铆钉长度和冲头速度的交互作用影响最弱. 图9(b)、(c)对铆钉长度和冲头行程的交互作用分析,可以看出左侧等高线比右侧稠密,且上半部分等高线比下半部分稠密.表明铆钉长度和冲头行程对失效载荷的影响都比较明显,当冲头行程处于设定范围内的最大值,铆钉长度位于5.7 mm左右时,失效载荷可达到峰值.这是因为较长的铆钉搭配合适的冲头行程使铆钉下行越充分,更容易促进铆钉与板材之间的塑性流动,致使较长的铆钉可以在上下板之间充分扩张,形成更加显著的机械内锁结构.

    图  9  铆接参数对失效载荷的影响
    Figure  9.  Influence of riveting parameters on failure load. (a) the influence of single factor on failure load; (b) response surface plot; (c) contour plot

    能量吸收值大小是评价构件的缓冲吸震能力,能量吸收值愈大,构件抵抗弹塑性变形的能力愈大. 铆接参数对能量吸收值的影响见图10.由图10(a)可以看出,铆钉长度对能量吸收值的影响最为显著,且随着铆钉长度的不断增大,能量吸收值呈现线性增加.冲头速度的变化对能量吸收值的影响较小,其变化趋势近似为一条直线.随着冲头行程的增加能量吸收值逐渐减小,但总体变化幅度不大.方差分析结果Px1x3(0.4236)<Px1x2(0.5078),冲头速度为40 mm/s时,铆钉长度与冲头行程的交互作用对能量吸收值的影响规律如图10(b)、(c)所示,可以看到能量吸收值会随着铆钉长度的增大而显著增加,且能量吸收值的最高点出现在铆钉长度为最大值,冲头行程为最小值处.结合等高线图可知,冲头行程的改变对能量吸收值的影响并不显著,但是,随着铆钉长度的增大,能量吸收值呈线性增加,这是由于铆钉长度的增大增加了接头的内锁长度,接头抵抗变形的能力也随之增大.因此为获得较高的能量吸收值,应优先考虑合适的铆钉长度.

    图  10  铆接参数对能量吸收值的影响
    Figure  10.  Influence of riveting parameters on energy absorption value. (a) the influence of single factor on energy absorption value; (b) response surface plot; (c) contour plot

    基于响应面模型,运用NSGA-II遗传算法对目标函数进行迭代优化.设置交叉概率80%,变异概率20%,种群规模为50,迭代次数为200.结合试验结果可知,由于铆钉尺寸已标准化,选取5.5 mm长的铆钉时自冲铆接头有较大的内锁长度和接头强度,故铆钉长度选取5.5 mm.为获得较优的失效载荷和能量吸收值,建立多目标优化的目标函数和约束条件为

    $$ \begin{gathered} g(x) = min{\text{ }}[{y_1},{y_2}] \\ {\text{ }}s.t.\left\{ {\begin{array}{*{20}{c}} {{x_1} = 5.5} \\ {20 \leqslant {x_2} \leqslant 60} \\ {64 \leqslant {x_3} \leqslant 66} \end{array}} \right. \\ \end{gathered} $$ (3)

    式中:${y_1}$、${y_2}$分别为失效载荷和能量吸收值,${x_1}$、${x_2}$、${x_3}$分别对应为铆钉长度、冲头速度和冲头行程.

    经运算后得到了目标函数的Pareto最优解解集,Pareto最优解解集是指在可行决策空间内,一组各目标都较优的参数解的集合[15].图11为Pareto前沿图,每个点对应了一个Pareto最优解.最优参数解的选取应使接头有较高的失效载荷和能量吸收值.因此选择图11中的M点作为最优工艺参数:铆钉长度5.5 mm,冲头速度36.6 m/s,冲头行程66 mm.对应的失效载荷为5438.820 N,能量吸收值为11.662 J, 并对最优工艺参数进行试验验证.失效载荷和能量吸收值的试验值分别为5191.482 N、12.376 J.试验值与模型预测值误差分别为4.76%、5.77%.通过工艺参数优化,不仅抑制自冲铆接头机械内锁区裂纹的产生,而且在保证连接质量的情况下获得较高的失效载荷和能量吸收值.

    图  11  Pareto前沿图
    Figure  11.  Pareto frontier map

    (1) 对于低延展性铝合金薄板材料,采用球形底模在保证接头强度下可以抑制接头机械内锁区裂纹的产生.

    (2) 采用BBD响应面法,建立基于球形底模的铝合金自冲铆接工艺参数多元非线性回归模型.实际应用中可以选用铆钉长度、冲头速度以及冲头行程实现自冲铆接工艺参数优化和接头质量调控.

    (3) 铆钉长度对失效载荷和能量吸收值的影响最为显著,冲头行程影响次之,冲头速度影响最弱;多因素交互影响中,铆钉长度和冲头行程的交互作用对失效载荷和能量吸收值的影响最大,应优先进行铆钉长度和冲头行程的调控.

  • 图  1   母材的微观组织和XRD图谱

    Figure  1.   Microstructure and XRD patterns of base metals. (a) TC4 microstructure; (b) 316L microstructure; (c) TC4 XRD; (d) 316L XRD

    图  2   Ti-Zr-Cu-Ni-Co系非晶钎料

    Figure  2.   Ti-Zr-Cu-Ni-Co Ingots amorphous filler metals. (a) ingots; (b) width; (c) thickness

    图  3   钎焊装配和剪切夹具示意图

    Figure  3.   Schematic diagram of brazing assembly and shear test fixture. (a) brazing assembly; (b) shear test fixture

    图  4   钎焊工艺加热曲线

    Figure  4.   Heating curve for brazing process

    图  5   Ti-Zr-Cu-Ni-Co系非晶钎料表征

    Figure  5.   Ti-Zr-Cu-Ni-Co amorphous filler metals. (a) XRD patterns; (b) DTA curves

    图  6   Ti43.76Zr12.50Cu35.93Ni6.25Co1.56钎料钎焊接头的微观组织

    Figure  6.   Microstructure of brazed joint with Ti43.76Zr12.50Cu35.93Ni6.25Co1.56 filler metal. (a) low-magnification image of the joint and (b) magnified image of zone b in a

    图  7   采用Ti43.76Zr12.50Cu35.93Ni6.25Co1.56非晶钎料钎焊接头的元素分布

    Figure  7.   Elemental distribution of brazed joint with Ti43.76Zr12.50Cu35.93Ni6.25Co1.56 amorphous filler metal

    图  8   采用Ti43.76Zr12.50Cu35.93Ni6.25Co1.56非晶钎料钎焊接头316L母材侧的元素分布

    Figure  8.   Elemental distribution at 316L base metal side of brazed joint with Ti43.76Zr12.50Cu35.93Ni6.25Co1.56 amorphous filler metal

    图  9   采用Ti43.76Zr12.50Cu35.93Ni6.25Co1.56非晶钎料钎焊接头元素线扫描分布

    Figure  9.   Elemental line scanning distribution of brazed joint with Ti43.76Zr12.50Cu35.93Ni6.25Co1.56 amorphous filler metal. (a) integral joint; (b) magnified image of zone b in 9(a)

    图  10   950 ℃/10 min条件下不同Co含量非晶钎料钎焊接头界面微观组织

    Figure  10.   Effect of Co content in filler metals on interfacial microstructure of brazed joint at 950 ℃/10 min. (a) 0%; (b) 1.56%; (c) 3.12%; (d) 4.68%; (e) 6.24%

    图  11   不同Co元素含量钎焊接头III区Ti-Fe化合物层的厚度

    Figure  11.   Thickness of Ti-Fe reaction layer in zone III of brazed joints with different Co content

    图  12   950 ℃/10 min条件下不同Co元素含量非晶钎料钎焊接头的剪切强度

    Figure  12.   Shear strength of brazed joints with different Co content filler metals at 950 ℃/10 min

    图  13   950 ℃/10 min条件下不同Co元素含量非晶钎料钎焊接头断裂路径

    Figure  13.   Fracture path of brazed joints with different Co content filler metals at 950 ℃/10 min. (a) 0%; (b) 1.56%; (c) 3.12%; (d) 4.68%; (e) 6.24%

    图  14   使用不同钎料钎焊接头断口形貌

    Figure  14.   Fracture morphology of brazed joints. (a) Ti43.76Zr12.50Cu35.93Ni6.25Co1.56; (b) Ti43.76Zr12.50Cu34.37Ni6.25Co3.12

    图  15   使用不同钎料钎焊接头断口XRD图谱

    Figure  15.   XRD patterns of fracture surfaces. (a) Ti43.76Zr12.50Cu35.93Ni6.25Co1.56; (b) Ti43.76Zr12.50Cu34.37Ni6.25Co3.12

    表  1   母材化学成分(质量分数,%)

    Table  1   Chemical compositions of base metals

    母材AlVCrNiMoMnFeTi
    TC45.93.6余量
    316L16.510.22.01.3余量
    下载: 导出CSV

    表  2   钎料的固相线温度(Tm)和液相线温度(Tl)

    Table  2   Solidus temperature and liquidus temperature of filler metals

    钎料 固相线
    温度
    Tm/℃
    液相线
    温度
    Tl/℃
    熔化温度
    区间
    Tl ~ Tm/℃
    Ti43.76Zr12.50Cu37.49Ni6.25Co084786215
    Ti43.76Zr12.50Cu35.93Ni6.25Co1.5684385512
    Ti43.76Zr12.50Cu34.37Ni6.25Co3.1284987223
    Ti43.76Zr12.50Cu32.81Ni6.25Co4.6885086414
    Ti43.76Zr12.50Cu31.25Ni6.25Co6.2485487319
    下载: 导出CSV

    表  3   图6中标记位置的EPMA点元素分析结果(原子分数,%)

    Table  3   EPMA analysis results of the marked locations in Fig. 6

    TiCuZrNiCoAlVFeCr可能相
    A85.60.80.30.111.31.60.2α-Ti
    B75.84.60.61.40.37.25.83.90.6β-Ti
    C66.123.12.52.30.21.91.12.10.8Ti2Cu
    D72.67.02.81.40.34.52.76.22.5β-Ti
    E40.825.015.84.40.45.31.35.41.7(Ti, Zr)2(Cu, Ni)
    F63.027.93.72.60.20.80.41.30.2Ti2Cu
    G50.822.02.23.90.92.51.014.32.5Ti2(Cu, Ni) + TiFe
    H32.00.90.94.00.40.50.352.48.5(Fe, Cr)2Ti
    I3.90.63.40.30.10.859.131.8α-(Fe, Cr) + τ
    J1.00.39.30.50.170.818.0γ-(Fe, Ni) + σ
    下载: 导出CSV

    表  4   图14中标记位置的EDS点元素分析结果(原子分数,%)

    Table  4   EDS analysis results of the marked locations in Fig. 14

    TiCuZrNiCoAlVFeCr可能相
    A48.425.45.74.81.32.70.79.81.3Ti2Cu
    B53.825.211.02.80.23.20.42.90.6(Ti, Zr)2(Cu, Ni)
    C48.124.713.93.80.43.40.73.91.2(Ti, Zr)2(Cu, Ni)
    D30.80.72.13.40.61.10.450.510.5TiFe2
    E48.521.73.23.81.33.70.714.92.4Ti2(Cu, Ni) + TiFe
    F48.323.63.55.21.22.313.92.0Ti2(Cu, Ni) + TiFe
    G58.112.110.23.40.86.51.95.61.6(Ti, Zr)2(Cu, Ni)
    H49.118.92.07.12.62.60.515.31.9Ti2(Cu, Ni) + TiFe
    I49.919.12.75.72.63.50.614.21.7Ti-Cu-Fe
    J11.50.40.72.90.30.70.657.026.0FeCr
    K38.25.61.35.41.51.60.336.99.3TiFe
    L47.919.43.06.02.42.90.316.02.2Ti-Cu-Fe
    下载: 导出CSV
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  • 收稿日期:  2022-11-22
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