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航空发动机阻燃钛合金力学性能预测及成分优化

李雅迪 弭光宝 李培杰 曹京霞 黄旭

李雅迪, 弭光宝, 李培杰, 曹京霞, 黄旭. 航空发动机阻燃钛合金力学性能预测及成分优化[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2020.10.12.001
引用本文: 李雅迪, 弭光宝, 李培杰, 曹京霞, 黄旭. 航空发动机阻燃钛合金力学性能预测及成分优化[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2020.10.12.001
LI Ya-di, MI Guang-bao, LI Pei-jie, CAO Jing-xia, HUANG Xu. Predicting the mechanical properties and composition optimization of a burn-resistant titanium alloy for aero-engines[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2020.10.12.001
Citation: LI Ya-di, MI Guang-bao, LI Pei-jie, CAO Jing-xia, HUANG Xu. Predicting the mechanical properties and composition optimization of a burn-resistant titanium alloy for aero-engines[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2020.10.12.001

航空发动机阻燃钛合金力学性能预测及成分优化

doi: 10.13374/j.issn2095-9389.2020.10.12.001
基金项目: 国家自然科学基金资助项目(51471155,U2141222);国家科技重大专项资助项目(J2019-Ⅷ-0003-0165)
详细信息
    通讯作者:

    E-mail: miguangbao@163.com

  • 中图分类号: TG146.2

Predicting the mechanical properties and composition optimization of a burn-resistant titanium alloy for aero-engines

More Information
  • 摘要: 采用支持向量机算法,在实验数据的基础上,建立航空发动机阻燃钛合金的合金化元素与力学性能关系模型,分析合金化元素对力学性能的影响规律。模型的输入参数为V、Al、Si和C元素,输出参数为室温拉伸性能(抗拉强度、屈服强度、延伸率和断面收缩率)。结果表明:各个力学性能支持向量机模型的线性相关系数均在0.975以上,具有较高的预测能力;各个力学性能测试样本实验值与模型预测值的绝对百分误差均在5%以内,具有良好的泛化能力,能够有效地反映出阻燃钛合金的合金化元素与力学性能之间的定量关系,进而实现对该合金的成分优化。对于Ti−35V−15Cr阻燃钛合金,可以通过加入质量分数为0~0.1%的Si元素和质量分数为0.05%~0.125%的C元素,并减少质量分数为2%~5%的V元素,来提高力学性能;对于Ti−25V−15Cr阻燃钛合金,可以通过加入质量分数为1.5%~1.8%的Al元素和质量分数为0.15%~0.2%的C元素,来提高力学性能。

     

  • 图  1  力学性能实验值与模型预测值的线性相关性分析.(a)抗拉强度; (b)屈服强度; (c)伸长率; (d)断面收缩率

    Figure  1.  Linear correlation analysis between the experimental and predicted values using SVM: (a) tensile strength; (b) yield strength; (c) elongation; (d) reduction of area

    图  2  V元素含量对Ti−V−Cr系阻燃钛合金力学性能的影响.(a)强度; (b)塑性

    Figure  2.  Influence of the V element content on the mechanical properties of the Ti−V−Cr burn-resistant titanium alloy: (a) strength;(b) ductility

    图  3  Al元素含量对Ti−V−Cr系阻燃钛合金力学性能的影响。(a)强度;(b)塑性

    Figure  3.  Influence of the Al element content on the properties of the Ti−V−Cr burn-resistant titanium alloy: (a) strength; (b) ductility

    图  4  Si元素含量对Ti−V−Cr系阻燃钛合金力学性能的影响。(a)强度;(b)塑性

    Figure  4.  Influence of the Si element content on the properties of the Ti−V−Cr burn-resistant titanium alloy: (a) strength; (b) ductility

    图  5  C元素含量对Ti−V−Cr系阻燃钛合金力学性能的影响。(a)强度;(b)塑性

    Figure  5.  Influence of the C element content on the properties of the Ti−V−Cr burn-resistant titanium alloy: (a) strength; (b) ductility

    表  1  Ti−V−Cr系阻燃钛合金实验值与支持向量机模型预测值的误差比较

    Table  1.   Error comparison of the mechanical properties of the experimental data with the predicted values using SVM

    SampleMass fraction/%ComparisonMechanical properties
    VAlSiCTensile strength/MPaYield strength/MPaElongation/%Reduction of area/%
    135.00000Experimental1042102810.015.0
    Predicted1042.101028.1010.1015.10
    Absolute error/%0.010.011.000.67
    235.0000.250Experimental1060103215.119.5
    Predicted1059.901031.9015.0019.40
    Absolute error/%0.010.010.660.52
    335.0000.500Experimental111110807.311.0
    Predicted1110.901079.907.4011.10
    Absolute error/%0.010.011.370.91
    435.00000.08Experimental1071100518.433.0
    Predicted1060.60997.3218.3032.90
    Absolute error%0.970.760.550.30
    535.0000.500.08Experimental1065100519.033.5
    Predicted1065.101005.1018.9033.40
    Absolute error/%0.010.010.520.30
    635.00000.15Experimental103495221.038.1
    Predicted1034.10952.1021.1038.20
    Absolute error/%0.010.010.470.26
    7*25.502.6000.27Experimental1070105016.022.5
    Predicted1069.901049.9014.6623.11
    Absolute error/%0.010.018.392.70
    8*20.0000.200Experimental96093324.546.0
    Predicted960.10933.1024.4045.90
    Absolute error/%0.010.010.410.22
    9*30.0000.200Experimental102597320.038.5
    Predicted1024.90973.1019.9032.84
    Absolute error/%0.010.010.5014.70
    1035.0000.300.10Experimental102596417.233.0
    Predicted1024.90963.9017.3033.10
    Absolute error/%0.010.010.580.30
    1135.2000.170.07Experimental102696316.529.4
    Predicted1026.10970.1616.6029.50
    Absolute error/%0.010.740.610.34
    12**25.2000.210Experimental96994218.530.4
    Predicted986.21936.3419.1831.78
    Absolute error/%1.780.603.684.54
    Note: **are test sets and the rest are training sets;* is the data from references [12,16].
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  • [1] Liang X Y, Mi G B, Li P J, et al. Theoretical study on ignition of titanium alloy under high temperature friction condition. Acta Phys Sin, 2020, 69(21): 343

    梁贤烨, 弭光宝, 李培杰, 等. 钛合金高温摩擦着火理论研究. 物理学报, 2020, 69(21):343
    [2] Mi G B, Huang X, Cao J X, et al. Microstructure characteristics of burning products of Ti−V−Cr fireproof titanium alloy by frictional ignition. Acta Phys Sin, 2016, 65(5): 056103 doi: 10.7498/aps.65.056103

    弭光宝, 黄旭, 曹京霞, 等. 摩擦点火Ti−V−Cr阻燃钛合金燃烧产物的组织特征. 物理学报, 2016, 65(5):056103 doi: 10.7498/aps.65.056103
    [3] Zhao Y Q, Zhu K Y, Qu H L, et al. Microstructures of a burn resistant highly stabilized β-titanium alloy. Mater Sci Eng:A, 2000, 282(1-2): 153 doi: 10.1016/S0921-5093(99)00761-3
    [4] Ouyang P X, Mi G B, Cao J X, et al. Microstructure characteristics after combustion and fireproof mechanism of TiAl-based alloys. Mater Today Commun, 2018, 16: 364 doi: 10.1016/j.mtcomm.2018.07.012
    [5] Xiong J S, Huang J F, Xie G L, et al. Effect of electroplating Cr coating on combustion characteristics of TC4 titanium alloy. Chin J Eng, 2020, 42(8): 1007

    熊家帅, 黄进峰, 解国良, 等. 电镀Cr涂层对TC4钛合金燃烧性能的影响. 工程科学学报, 2020, 42(8):1007
    [6] Liang X Y, Mi G B Li P J, et al. Theoretical calculation of characteristics on titanium fire in aero-engine. J Aeronaut Mater, 2021, 41(6): 59

    梁贤烨, 弭光宝, 李培杰, 等. 航空发动机钛火特性理论计算研究. 航空材料学报, 2021, 41(6):59
    [7] Mi G B, Yao K, Min X H. Effect of temperature on wear behavior in a Ti-V-Cr base fireproof titanium alloy. Int J Precis Eng Manuf, 2017, 18: 1553 doi: 10.1007/s12541-017-0184-3
    [8] Hood R, Johnson C M, Soo S L, et al. High-speed ball nose end milling of burn-resistant titanium (BuRTi) alloy. Int J Comput Integr Manuf, 2014, 27(2): 139 doi: 10.1080/0951192X.2013.801563
    [9] Li Y G, Blenkinsop P A, Loretto M H, et al. Effect of carbon and oxygen on microstructure and mechanical properties of Ti−25V−15Cr−2Al (wt%) alloys. Acta Mater, 1999, 47(10): 2889 doi: 10.1016/S1359-6454(99)00173-1
    [10] Li Y G, Blenkinsop P A, Loretto M H, et al. Effect of aluminium on deformation structure of highly stabilised β-Ti−V−Cr alloys. Mater Sci Technol, 1999, 15(2): 151 doi: 10.1179/026708399101505680
    [11] Sun F S, Lavernia E J. Creep behavior of nonburning Ti−35V−15Cr−xC alloys. J Mater Eng Perform, 2005, 14(6): 784 doi: 10.1361/105994905X75619
    [12] Xin S W, Zhao Y Q, Zeng W D, et al. Effect of V on the thermal stability and creep of Ti−V−Cr burn-resistant titanium alloy. Rare Met Mater Eng, 2007, 36(11): 2031 doi: 10.3321/j.issn:1002-185x.2007.11.036

    辛社伟, 赵永庆, 曾卫东, 等. V元素对Ti−V−Cr系阻燃钛合金热强性的影响. 稀有金属材料与工程, 2007, 36(11):2031 doi: 10.3321/j.issn:1002-185x.2007.11.036
    [13] Mi G B, Huang X, Cao J X, et al. Ignition resistance performance and its theoretical analysis of Ti−V−Cr type fireproof titanium alloys. Acta Metall Sin, 2014, 50(5): 575

    弭光宝, 黄旭, 曹京霞, 等. Ti−V−Cr系阻燃钛合金的抗点燃性能及其理论分析. 金属学报, 2014, 50(5):575
    [14] Cao J X, Huang X, Mi G B, et al. Research progress on application technique of Ti−V−Cr burn resistant titanium alloys. J Aeronaut Mater, 2014, 34(4): 92 doi: 10.11868/j.issn.1005-5053.2014.4.009

    曹京霞, 黄旭, 弭光宝, 等. Ti−V−Cr系阻燃钛合金应用研究进展. 航空材料学报, 2014, 34(4):92 doi: 10.11868/j.issn.1005-5053.2014.4.009
    [15] Lai Y J, Zhang P X, Xin S W, et al. Research progress on engineered technology of burn-resistant titanium alloys in China. Rare Met Mater Eng, 2015, 44(8): 2067

    赖运金, 张平祥, 辛社伟, 等. 国内阻燃钛合金工程化技术研究进展. 稀有金属材料与工程, 2015, 44(8):2067
    [16] Sun H Y, Zhao J, Liu Y A, et al. Effect of C addition on microstructure and mechanical properties of Ti−V−Cr burn resistant titanium alloys. Chin J Mater Res, 2019, 33(7): 537 doi: 10.11901/1005.3093.2019.090

    孙欢迎, 赵军, 刘翊安, 等. C含量对Ti−V−Cr系阻燃钛合金微观组织和力学性能的影响. 材料研究学报, 2019, 33(7):537 doi: 10.11901/1005.3093.2019.090
    [17] Zhou T, Song Z, Sundmacher K. Big data creates new opportunities for materials research: A review on methods and applications of machine learning for materials design. Engineering, 2019, 5(6): 1017 doi: 10.1016/j.eng.2019.02.011
    [18] Wang H, Xiang X D, Zhang L T. Data+AI: The core of materials genomic engineering. Sci Technol Rev, 2018, 36(14): 15

    汪洪, 项晓东, 张澜庭. 数据+人工智能是材料基因工程的核心. 科技导报, 2018, 36(14):15
    [19] Wu W, Sun Q. Applying machine learning to accelerate new materials development. Sci Sin Phys Mech Astron, 2018, 48(10): 58

    吴炜, 孙强. 应用机器学习加速新材料的研发. 中国科学:物理学 力学 天文学, 2018, 48(10):58
    [20] Malinov S, Sha W, McKeown J J. Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network. Comput Mater Sci, 2001, 21(3): 375 doi: 10.1016/S0927-0256(01)00160-4
    [21] Noori Banu P S, Devaki Rani S. Knowledge-based artificial neural network model to predict the properties of alpha+ beta titanium alloys. J Mech Sci Technol, 2016, 30(8): 3625 doi: 10.1007/s12206-016-0723-3
    [22] Sun L N. Heat treatment process optimization of directional solidification titanium alloys based on neural network. Ordnance Mater Sci Eng, 2017, 40(4): 30

    孙丽娜. 定向凝固钛合金热处理工艺的神经网络优化. 兵器材料科学与工程, 2017, 40(4):30
    [23] Noori Banu P S, Devaki Rani S. Artificial neural network based optimization of prerequisite properties for the design of biocompatible titanium alloys. Comput Mater Sci, 2018, 149: 259 doi: 10.1016/j.commatsci.2018.03.039
    [24] Xu J J, Wang F. Tensile strength forecasting model foundation and checking of Ti−Al−V series Ti alloys. Hot Work Technol, 2018, 47(10): 72

    许佳佳, 王飞. Ti−Al−V系钛合金抗拉强度预测模型的建立及验证. 热加工工艺, 2018, 47(10):72
    [25] Zhang X M, Xi Y Q, Li M, et al. Prediction of superplastic deformation behavior of WSTi3515S burn-resistant titanium alloy based on BP artificial neural network. Special Cast Nonferrous Alloys, 2019, 39(6): 668

    张学敏, 惠玉强, 李咪, 等. 基于BP神经网络的WSTi3515S阻燃钛合金超塑性变形行为预测. 特种铸造及有色合金, 2019, 39(6):668
    [26] Zhou X H, Lou M Q, Zhang X M, et al. Prediction of effect of thermal exposure on tensile properties of TC4 titanium alloy based on neural network. Hot Work Technol, 2019, 48(14): 128

    周晓虎, 楼美琪, 张学敏, 等. 基于神经元网络的热暴露对TC4钛合金拉伸性能影响预测. 热加工工艺, 2019, 48(14):128
    [27] Anand P, Rastogi R, Chandra S. A class of new support vector regression models. Appl Soft Comput, 2020, 94: 106446 doi: 10.1016/j.asoc.2020.106446
    [28] Sun H Y, Zhao J, Liu Y A, et al. Microstructure and mechanical properties of a new type burn resistant titanium alloy with lower cost. Rare Met Mater Eng, 2019, 48(6): 1892

    孙欢迎, 赵军, 刘翊安, 等. 一种新型低成本阻燃钛合金的微观组织与力学性能. 稀有金属材料与工程, 2019, 48(6):1892
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  • 收稿日期:  2020-10-12
  • 网络出版日期:  2021-04-16

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