Experimental Method of Mechanical Melting Point in A6N01-T5 Aluminum Alloy

  • Lichun Meng
  • Xiaohong Sun
  • Yongming Cheng
  • Gongxiang Zhao
  • Jijin XuEmail author
Conference paper
Part of the Transactions on Intelligent Welding Manufacturing book series (TRINWM)


A novel experimental method was proposed to measure the mechanical melting point of A6N01-T5 aluminum alloy. Before test, characteristic parameters of thermal cycle were set according to the welding thermal cycle curves with different peak temperatures. The dimensions of sample were normalized. Based on thermal–mechanical simulation test, the dynamic stress data were acquisitioned and saved. The relationship between the residual stress and peak temperature was established. Based on the measured and predicted evolution of residual stress, the mechanical melting point of A6N01-T5 aluminum alloy was confirmed and validated.


Mechanical melting point Thermal–mechanical simulation test Welding thermal cycle A6N01-T5 aluminum alloy 


  1. 1.
    Xu JJ, Gilles P, Duan YG (2012) Simulation and validation of welding residual stresses based on non-linear mixed hardening model. Strain 48(5):406–414CrossRefGoogle Scholar
  2. 2.
    Kong FR, Ma JJ, Kovacevic R (2011) Numerical and experimental study of thermally induced residual stress in the hybrid laser-GMA welding process. J Mater Process Tech 211(6):1102–1111CrossRefGoogle Scholar
  3. 3.
    Jiang WC, Yucai Zhang, Woo WC (2012) Using heat sink technology to decrease residual stress in 316L stainless steel welding joint: finite element simulation. Int J Press Ves Pip 92:56–62CrossRefGoogle Scholar
  4. 4.
    De A, DebRoy T (2011) A perspective on residual stresses in welding. Sci Technol Weld Joining 16(3):204–208CrossRefGoogle Scholar
  5. 5.
    Smith MC, Smith AC (2009) NeT bead-on-plate round robin: comparison of residual stress predictions and measurements. Int J Press Ves Pip 86(1):79–95CrossRefGoogle Scholar
  6. 6.
    Deng D, Zhang CH, Pu XW et al (2017) Influence of material model on prediction accuracy of welding residual stress in an austenitic stainless-steel multi-pass butt-welded joint. J Mater Eng Perform 26:1494–1505CrossRefGoogle Scholar
  7. 7.
    Muránsky O, Hamelin CJ, Smith MC et al (2012) The effect of plasticity theory on predicted residual stress fields in numerical weld analyses. Comp Mater Sci 54(1):125–134CrossRefGoogle Scholar
  8. 8.
    Muránsky O, Smith MC, Bendeich PJ et al (2012) Comprehensive numerical analysis of a three-pass bead-in-slot weld and its critical validation using neutron and synchrotron diffraction residual stress measurements. Int J Solids Struct 49(9):1045–1062CrossRefGoogle Scholar
  9. 9.
    Xu JJ, Gilles P, Duan YG et al (2012) Temperature and residual stress simulations of the NeT single-bead-on-plate specimen using SYSWELD. Int J Press Ves Pip 99–100(1):51–60CrossRefGoogle Scholar
  10. 10.
    Xu JJ, Chen JY, Duan Y et al (2017) Comparison of residual stress induced by TIG and LBW in girth weld of AISI 304 stainless steel pipes. J Mater Process Tech 248(1):178–184CrossRefGoogle Scholar
  11. 11.
    Xu JJ (2014) Effect of material hardening model on welding residual stresses of 316L stainless steel. Trans China Weld Inst 35(3):97–100Google Scholar
  12. 12.
    Fan YY, Xu JJ, Meng LC (2017) Welding softening character and numerical simulation of A6N01S-T5 aluminum alloy. Trans China Weld Inst 38(7):77–82Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Lichun Meng
    • 1
  • Xiaohong Sun
    • 1
  • Yongming Cheng
    • 1
  • Gongxiang Zhao
    • 2
  • Jijin Xu
    • 2
    Email author
  1. 1.CRRC Qingdao Sifang Co., Ltd.QingdaoChina
  2. 2.Key Lab of Shanghai Laser Manufacturing and Materials Modification, School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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