Improving prediction accuracy of thermal analysis for weld-based additive manufacturing by calibrating input parameters using IR imaging

  • Xingwang Bai
  • Haiou ZhangEmail author
  • Guilan Wang


In experiments, it is usually difficult to accurately determine simulation input parameters such as heat source parameters, material properties at high temperature, etc. The uncertainty of such input parameters is responsible for the large error of thermal simulation for weld-based additive manufacturing. In this paper, a new approach is presented to calibrate uncertain input parameters. The approach is based on the solution of the inverse heat conduction problem of small-scale five-layer deposition and the application of the infrared (IR) imaging technique. The calibration of heat source parameters involves a multivariate optimization search using the pattern search method, whereas the calibration of the combined radiation and convection model includes a number of one-dimensional searches using the Fibonacci search method. Based on an in-depth analysis of IR images, thermal characteristics such as mean layer temperature and cooling rate are selected as the comparison results and included in cost functions. Lastly, the validity of the approach is demonstrated by a simulation case of 15-layer deposition with calibrated input parameters. The comparison between the simulated and experimental results verifies the improved prediction accuracy.


Weld-based additive manufacturing Finite element analysis IR imaging Inverse analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Spencer JD, Dickens PM, Wykes CM (1998) Rapid prototyping of metal parts by three-dimensional welding. Proc Inst Mech Eng B J Eng Manuf 212(3):175–182CrossRefGoogle Scholar
  2. 2.
    Wang F, Williams S, Rush M (2011) Morphology investigation on direct current pulsed gas tungsten arc welded additive layer manufactured Ti6Al4V alloy. Int J Adv Manuf Technol 57(5–8):597–603CrossRefGoogle Scholar
  3. 3.
    Klingbeil NW, Beuth JL, Chin RK, Amon CH (2002) Residual stress-induced warping in direct metal solid freeform fabrication. Int J Mech Sci 44(1):57–77CrossRefzbMATHGoogle Scholar
  4. 4.
    Mughal MP, Fawad H, Mufti RA (2006) Three-dimensional finite-element modelling of deformation in weld-based rapid prototyping. Proc Inst Mech Eng C J Mech Eng Sci 220(6):875–885CrossRefGoogle Scholar
  5. 5.
    Zhao H, Zhang G, Yin Z, Wu L (2011) A 3D dynamic analysis of thermal behavior during single-pass multi-layer weld-based rapid prototyping. J Mater Process Technol 211(3):488–495CrossRefGoogle Scholar
  6. 6.
    Zhao H, Zhang G, Yin Z, Wu L (2012) Three-dimensional finite element analysis of thermal stress in single-pass multi-layer weld-based rapid prototyping. J Mater Process Technol 212(1):276–285CrossRefGoogle Scholar
  7. 7.
    Li P, Lu H (2011) Hybrid heat source model designing and parameter prediction on tandem submerged arc welding. Int J Adv Manuf Technol 62(5–8):577–585Google Scholar
  8. 8.
    Lindgren LE (2001) Finite element modeling and simulation of welding. Part 2: improved material modeling. J Therm Stress 24(3):195–231CrossRefGoogle Scholar
  9. 9.
    Azar AS, Ås SK, Akselsen OM (2012) Determination of welding heat source parameters from actual bead shape. Comp Mater Sci 54:176–182CrossRefGoogle Scholar
  10. 10.
    Heinze C, Schwenk C, Rethmeier M (2012) Effect of heat source configuration on the result quality of numerical calculation of welding-induced distortion. Simul Model Pract Theory 20(1):112–123CrossRefGoogle Scholar
  11. 11.
    Schwerdtfeger J, Singer RF, Körner C (2012) In situ flaw detection by IR-imaging during electron beam melting. Rapid Prototyp J 18(4):259–263CrossRefGoogle Scholar
  12. 12.
    Kannan T, Yoganandh J (2009) Effect of process parameters on clad bead geometry and its shape relationships of stainless steel claddings deposited by GMAW. Int J Adv Manuf Technol 47(9–12):1083–1095Google Scholar
  13. 13.
    Rao PS, Gupta OP, Murty SSN, Rao ABK (2009) Effect of process parameters and mathematical model for the prediction of bead geometry in pulsed GMA welding. Int J Adv Manuf Technol 45(5–6):496–505CrossRefGoogle Scholar
  14. 14.
    Xiong J, Zhang G, Hu J, Wu L (2012) Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J Intell Manuf. doi: 10.1007/s10845-012-0682-1 Google Scholar
  15. 15.
    Goldak J, Chakravarti A, Bibby M (1984) A new finite element model for welding heat sources. Metall Trans B 15B(6):299–305CrossRefGoogle Scholar
  16. 16.
    Abid M, Siddique M (2005) Numerical simulation to study the effect of tack welds and root gap on welding deformations and residual stresses of a pipe-flange joint. Int J Press Vessel Pip 82(11):860–871CrossRefGoogle Scholar
  17. 17.
    Ferro P, Porzner H, Tiziani A, Bonollo F (2006) The influence of phase transformations on residual stresses induced by the welding process—3D and 2D numerical models. Model Simul Mater Sci Eng 14(2):117–136CrossRefGoogle Scholar
  18. 18.
    Chamekh A, Bel Hadj Salah H, Hambli R (2008) Inverse technique identification of material parameters using finite element and neural network computation. Int J Adv Manuf Technol 44(1–2):173–179Google Scholar
  19. 19.
    Zhao HH, Zhang GJ, Yin ZQ, Wu L (2011) A 3D dynamic analysis of thermal behavior during single-pass multi-layer weld-based rapid prototyping. J Mater Process Technol 211(3):488–495CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations