Volume 38 Issue 6
Dec.  2023
Turn off MathJax
Article Contents
Article Navigation >  Development and Application of Materials  > 2023  >  38(6): 41-51
ZHANG Guitao, HUANG Xiang, HOU Bingyu, WANG Mingzhi. Determination of Material Properties of Al 2024-T3 Aluminum Alloy Using Nano-indentation Experiment and Interval Optimization[J]. Development and Application of Materials, 2023, 38(6): 41-51.
Citation: ZHANG Guitao, HUANG Xiang, HOU Bingyu, WANG Mingzhi. Determination of Material Properties of Al 2024-T3 Aluminum Alloy Using Nano-indentation Experiment and Interval Optimization[J]. Development and Application of Materials, 2023, 38(6): 41-51.
shu

Determination of Material Properties of Al 2024-T3 Aluminum Alloy Using Nano-indentation Experiment and Interval Optimization

  • School of Mechanical and Electrical Engineering, Xidian University, Xi'an 710071, China

  • Received Date: 2023-04-03
    Available Online: 2024-01-10
    Abstract
  • Nanoindentation experiment has been widely concerned in the field of material mechanics testing due to its advantages of simple sample preparation and wide range of use. A plastic parameter identification method of the Al 2024-T3 aluminum alloy material considering the uncertainty of nanoindentation experiment is established. First of all, the nanoindentation experiment is carried out on the Al 2024-T3 alloy, and the load-displacement curve is obtained. Due to the inhomogeneity of the material, the experimental curve is uncertain. Based on the artificial neural network of superparametric optimization, the relationship between the material performance parameters and indentation response loading curve is established. Based on the interval optimization theory, the uncertainty of the indentation test curve is introduced. Taking the loading curvature of the indentation test curve as the uncertainty quantity, the interval optimization model of the material parameter identification based on the double-layer nested genetic algorithm is proposed, and the inverse problem of the parameter identification is solved. The advantage of this method is that it can take into account the uncertainty of experimental measurement, and the recognition result is more reliable. The validity of the established method has been verified in the identification of the Al 2024-T3 alloy plastic parameters. The identification errors of the yield stress and hardening index are-0.87% and 2.76%, respectively. This recognition method is expected to be used in the detection of mechanical properties of small size specimens.

     

    • FullText(HTML)
  • loading
    • References(27)
  • [1]
    吴建军, 王明智. 基于压痕测试的材料力学性能识别理论与方法[M]. 西安:西北工业大学出版社, 2021.
    [2]
    LEE J H, KIM T, LEE H. A study on robust indentation techniques to evaluate elastic-plastic properties of metals[J]. International Journal of Solids and Structures, 2010, 47(5):647-664.
    [3]
    WU S B, GUAN K S. Evaluation of tensile properties of austenitic stainless steel 316L with linear hardening by modified indentation method[J]. Materials Science and Technology, 2014, 30(12):1404-1409.
    [4]
    MOUSSA C, HERNOT X, BARTIER O, et al. Evaluation of the tensile properties of a material through spherical indentation:definition of an average representative strain and a confidence domain[J]. Journal of Materials Science, 2014, 49(2):592-603.
    [5]
    KHAN M K, HAINSWORTH S V, FITZPATRICK M E, et al. A combined experimental and finite element approach for determining mechanical properties of aluminium alloys by nanoindentation[J]. Computational Materials Science, 2010, 49(4):751-760.
    [6]
    SUN G Y, XU F X, LI G Y, et al. Determination of mechanical properties of the weld line by combining micro-indentation with inverse modeling[J]. Computational Materials Science, 2014, 85:347-362.
    [7]
    IRACHETA O, BENNETT C J, SUN W. Characterization of material property variation across an inertia friction welded CrMoV steel component using the inverse analysis of nanoindentation data[J]. International Journal of Mechanical Sciences, 2016, 107:253-263.
    [8]
    HAN G, MARIMUTHU K P, LEE H. Evaluation of thin film material properties using a deep nanoindentation and ANN[J]. Materials & Design, 2022, 221:111000.
    [9]
    XIA J P, WON C, KIM H, et al. Artificial neural networks for predicting plastic anisotropy of sheet metals based on indentation test[J]. Materials, 2022, 15(5):1714.
    [10]
    陈怀宁, 胡凯雄, 吴昌忠. 压痕应变法测量残余应力的不确定度分析[J]. 中国测试, 2010, 36(1):24-27.
    [11]
    闫鹏, 郭伟超, 李淑娟, 等. 压痕轮廓信息在材料本构关系反演测量中的应用[J]. 机械科学与技术, 2019, 38(4):639-645.
    [12]
    DE BONO D M, LONDON T, BAKER M, et al. A robust inverse analysis method to estimate the local tensile properties of heterogeneous materials from nanoindentation data[J]. International Journal of Mechanical Sciences, 2017, 123:162-176.
    [13]
    BONATTI C, MOHR D. Neural network model predicting forming limits for Bi-linear strain paths[J]. International Journal of Plasticity, 2021, 137:102886.
    [14]
    HUANG D M, HE S Q, HE X H, et al. Prediction of wind loads on high-rise building using a BP neural network combined with POD[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2017, 170:1-17.
    [15]
    PARK S, FONSECA J H, MARIMUTHU K P, et al. Determination of material properties of bulk metallic glass using nanoindentation and artificial neural network[J]. Intermetallics, 2022, 144:107492.
    [16]
    JEONG K, LEE H, KWON O M, et al. Prediction of uniaxial tensile flow using finite element-based indentation and optimized artificial neural networks[J]. Materials & Design, 2020, 196:109104.
    [17]
    KIM Y, GU G H, ASGHARI-RAD P, et al. Novel deep learning approach for practical applications of indentation[J]. Materials Today Advances, 2022, 13:100207.
    [18]
    马济民,李成功,邓炬.中国航空材料手册(第4卷)[M]. 北京:清华大学出版社,2001.
    [19]
    SESHAGIRI S, KHALIL H K. Output feedback control of nonlinear systems using RBF neural networks[J]. IEEE Transactions on Neural Networks, 2000, 11(1):69-79.
    [20]
    李卫东, 柳焯, 郭玉红, 等. 基于电力系统运行模式及人工神经网络的潮流并行算法[J]. 电力系统自动化, 1997, 21(5):10-14.
    [21]
    李晓峰, 徐玖平, 王荫清, 等. BP人工神经网络自适应学习算法的建立及其应用[J]. 系统工程理论与实践, 2004, 24(5):1-8.
    [22]
    刘平, 梁逸曾, 张林, 等. 人工神经网络用于化学数据解析的研究(Ⅰ):逼近规律与过拟合[J]. 高等学校化学学报, 1996, 17(6):861-865.
    [23]
    郭井宽. 基于Dropout法优化的BP神经网络地铁列车塞拉门故障检测[J]. 城市轨道交通研究, 2022, 25(12):39-45.
    [24]
    范勇, 裴勇, 杨广栋, 等. 基于改进PSO-BP神经网络的爆破振动速度峰值预测[J]. 振动与冲击, 2022, 41(16):194-203.
    [25]
    ABAQUS. Analysis user's manual,Version 6.9[M]. Paris:ABAQUS, 2009.
    [26]
    刘鸿文. 材料力学-Ⅱ[M]. 5版. 北京:高等教育出版社, 2011.
    [27]
    PHAM T H, KIM J J, KIM S E. Estimating constitutive equation of structural steel using indentation[J]. International Journal of Mechanical Sciences, 2015, 90:151-161.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (57) PDF downloads(14) Cited by()
    Proportional views
    Related

    Copyright © 《Development and Application of Materials》豫ICP备13020589号-2

    Adress: No.169, Binhe South Road, Luolong District, Luoyang City, Henan Province，China  Tel: (0379)67256265;  (0379)67256345  Email: TG023@163.com

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return